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Rewilding and restoring nature in a changing world

Contributed equally to this work with: Benis N. Egoh, Charity Nyelele

* E-mail: [email protected] , [email protected]

Affiliation Department of Earth System Science, University of California Irvine, Irvine, California, United States of America

¶ ‡ These authors also contributed equally to this work.

Affiliation Environmental Studies Department, University of California, Santa Cruz, California, United States of America

Affiliation UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, United Kingdom

Affiliation School of Geography, University of Leeds, Leeds, United Kingdom

Current address: Sussex Sustainability Research Programme, Brighton, United Kingdom

Affiliation School of Life Sciences, University of Sussex, Brighton, United Kingdom

• Benis N. Egoh, 
• Charity Nyelele, 
• Karen D. Holl, 
• James M. Bullock, 
• Steve Carver, 
• Christopher J. Sandom

Published: July 14, 2021

• https://doi.org/10.1371/journal.pone.0254249
• Reader Comments

Citation: Egoh BN, Nyelele C, Holl KD, Bullock JM, Carver S, Sandom CJ (2021) Rewilding and restoring nature in a changing world. PLoS ONE 16(7): e0254249. https://doi.org/10.1371/journal.pone.0254249

Editor: RunGuo Zang, Chinese Academy of Forestry, CHINA

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the paper.

Funding: NA

Competing interests: The authors have declared that no competing interests exist.

Increased anthropogenic pressure, invasive alien species and climate change, among other factors, continue to negatively impact and degrade the planet’s ecosystems and natural environment. As nature declines at alarming rates, the loss of biodiversity is not only a huge concern, but it also undermines the many ecological, social, human health and wellbeing benefits nature provides us. Numerous reports, including those from the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, https://www.ipbes.net/ ), have documented this unprecedented decline in nature across space and time. For example, the 2019 IPBES global assessment report on biodiversity and ecosystem services shows that 75% of the global land surface has been significantly altered, 66% of the ocean area is experiencing increasing cumulative impacts, and over 85% of wetland area has been lost (Brondizio et al. [ 1 ]). All the recent IPBES reports from global to regional scales and the Millennium Ecosystem Assessment of 2005 (Reid et al. [ 2 ]), point to one thing: the urgency for us to act to save nature and humankind. Ecological restoration has emerged as a powerful approach to combat degradation in land and water, mitigate climate change, and restore lost biodiversity and key ecosystem functions and services. In June this year (2021), the United Nations (UN) is launching the Decade on Ecosystem Restoration ( https://www.decadeonrestoration.org/ ), an ambitious program to trigger a global movement for restoring the world’s ecosystems. In line with that, PLOS ONE commissioned this Collection on Rewilding and Restoration. This is consistent with the year’s Earth Day theme, "Restore Our Earth”, which calls on everyone to be a part of the change and to focus on natural processes, emerging green technologies and innovative thinking that can restore the world’s ecosystems.

When PLOS ONE launched this Rewilding and Restoration collection, we were asked to identify exciting advances and emerging trends observed recently in the areas of rewilding and restoration. We highlight: 1) increasing recognition of the value of restoration in ecosystems worldwide, particularly in a time of rapid global environmental change; 2) understanding and incorporating benefits and beneficiaries in supporting and financing restoration initiatives; 3) exploring the theoretical underpinning for the importance of ‘megabiota’–the largest plants and animals–for driving biosphere scale processes such as ecosystem total biomass, resource flows and fertility; and 4) showcasing success stories on how rewilding nature in the developing world is reversing the impact of invasive species ( https://everyone.plos.org/2020/08/28/taking-a-walk-on-the-wild-side/ ). The broad range of publications in this Collection cover all these areas and much more, making it one of the most exciting collections on rewilding and restoring nature in recent times. The two main themes that emerge from the collection are related to restoration success stories (>40%) and best practices in restoration around the globe (>30%). The selected studies in this Collection, which cover six continents and at least 13 countries, were carried out in diverse settings and contexts, such as marine, fresh water and terrestrial habitats including forests and grasslands, rivers and coastal areas, woodlands, wetlands, and mountains (e.g., Sansupa et al. [ 3 ], Broughton et al. [ 4 ], Schulz et al. [ 5 ], Ndangalasi et al. [ 6 ]). Features of interest included in this Collection span from bacteria through large vertebrates (e.g., wild dogs, elephants) to ecosystems and their functions. These articles also showcase a range of methodological approaches from a series of small-scale field experiments (Wasson et al. [ 7 ]), wildlife tracking and remote sensing (Mata et al. [ 8 ]), and large-scale models to predict restoration outcomes (D’Acunto et al. [ 9 ]).

This rich collection from PLOS ONE addresses a range of related and interesting issues: 1) Different restoration approaches, from passive rewilding to active target driven restoration, are needed to achieve different restoration goals in different circumstances. 2) Nature is complex and context dependent and so diverse approaches to restoration will help ensure different taxonomic groups and ecosystem functions and services are supported. 3) Developing and recording best practice for different restoration approaches will greatly aid the achievement of restoration aims. 4) Measuring restoration success needs comprehensive, multi-dimensional, and quantifiable metrics to account for potentially complex trade-offs. 5) Arguments for restoration based on ecocentric and nature’s contribution to people both have merit and appeal to different audiences, but it should not be assumed goals derived from these different ways of thinking will be aligned. This is a diverse collection of restoration and rewilding research, and that diversity neatly reflects the diverse approaches and goals needed for restoration to be successful.

The articles in this issue discuss case studies that span a continuum of restoration interventions from removing anthropogenic disturbance and allowing the ecosystem to regenerate naturally (i.e., passive restoration or rewilding) to intensive interventions with ongoing management. For example, Broughton et al. [ 4 ] found that secondary woodlands in England that were adjacent to ancient woodlands recovered naturally over a period of a few decades. Díaz-García et al. [ 10 ] compared recovery of amphibians, ants, and dung beetles in naturally regenerating and actively planted tropical forests in Mexico; they found that passive and active restoration approaches were similarly effective in restoring species richness of all guilds, but that forest specialists were enhanced through active planting. In contrast, other studies show that intensive anthropogenic interventions such as transplanting corals (Ferse et al. [ 11 ]), or controlling invasive species and reintroducing fauna (Roberts et al. [ 12 ]) are necessary to facilitate recovery. The diversity of responses reported highlights the need to tailor restoration strategies to the local ecosystem type, the species of interest, and the level of prior disturbance.

Similarly, studies in this collection demonstrate complex interactions between wild and domestic herbivory, controls on grazing intensity and spatial ecological variables, making generalizations difficult and stressing the need for context-specific studies and understanding to guide management of disturbance regimes. One study in African savanna (Young et al. [ 13 ]) explores the impact of grazing on biodiversity and shows that plots protected from herbivory by large wild herbivores for the past 25 years have developed a rich diversity of woody vegetation species which could disappear upon rewilding depending on level of predation and associated behavioral patterns. However, they also show that individuals of the dominant tree species in this system, Acacia drepanolobium , greatly reduce their defense in the absence of browsers; hence the sudden arrival of these herbivores resulted in far greater elephant damage than for conspecifics in adjacent plots that had been continually exposed to herbivory. Similarly, Peacock et al. [ 14 ] suggests that cattle negatively impact regeneration of gallery forests in Bolivia and alter both the structure and composition of the shrub and ground layers with potential consequences for the diversity and abundance of wildlife. Previous studies including Hanke et al. [ 15 ] have shown increases in species diversity and ecological functioning with grazing. These results suggest that the impact of grazing on ecosystems, species and ecosystem functioning depends on the system, the grazing species, and their numbers, and overall carrying capacity.

Several best practices are highlighted in the Collection. Larson et al. [ 16 ] created a model to determine an “optimal maximum distance” that would maximize availability of native prairie seed in the midwestern United States (US) from commercial sources while minimizing the risk of novel invasive weeds via contamination. Pedrini et al. [ 17 ] test seed pretreatment methods to enhance vegetation establishment from direct seeding and illustrate how a range of life stage transitions including germination, emergence and survival of native grass species used in restoration programs can be improved by seed coating with salicylic acid. Roon et al. [ 18 ] used a before-after-control-impact experiment across three stream networks in the northwestern to provide guidance on riparian thinning to provide optimal stream habitat. These best practices are key in our ability to replicate in different places and achieving restoration success.

Determining the success of ongoing restoration efforts is crucial to assessing management actions but requires comprehensive, multi-dimensional, and quantifiable metrics and approaches consistent with restoration goals. Despite the plethora of restoration projects around the world, it is only now that we are beginning to understand whether the restoration goals have been met and what trade-offs exist (Mugwedi et al. [ 19 ]). The importance of measuring restoration outcomes against clearly specified goals and objectives cannot be overemphasized, as shown in a recent restoration study in China that aimed to improve carbon storage through tree planting but has severely depleted water resources (Zhao et al. [ 20 ]). Similarly, Valach et al. [ 21 ] show that productive wetlands restored for carbon sequestration quickly become net carbon dioxide (CO 2 ) sinks although the trade-offs need to be further assessed. In their study exploring restoration success in South Africa, del Río et al. [ 22 ] improve our understanding on how techniques such as remote sensing can be used to measure restoration success.

As shown in this Collection and in other studies, trade-offs in restoration efforts are not uncommon and ultimately, restoration is successful when we can achieve restoration goals while minimizing trade-offs. The successful stories from the restoration interventions across different habitats and species showcased in the Collection (e.g., Sansupa et al. [ 3 ], Roon et al. [ 18 ], Valach et al. [ 21 ], Bouley et al. [ 23 ]) are a valuable addition to the science needed to advocate for restoration as a pathway to the recovery of previously degraded, damaged, or destroyed ecosystems. Reporting successful restoration outcomes can help increase buy-in for further restoration projects and increase funding availability for such projects. However, such buy-in can only occur if stakeholders are interested in the set restoration goals. For example, the need for climate mitigation has been used to justify several restoration programs around the world (Alexander et al. [ 24 ], Griscom et al. [ 25 ]). In this Collection, Matzek et al. [ 26 ] ask whether including ecosystem services as a restoration goal will engage a different set of values and attitudes than biodiversity protection alone. They found that support for habitat restoration is generally based on ecocentric values and attitudes, but that positive associations between pro-environmental behavior and egoistic values emerge when emphasis is placed on ecosystem service outcomes. They emphasize the notion that the ecosystem services concept garners non-traditional backers and broadens the appeal of ecological restoration as it is seen as a means of improving human well-being. Nevertheless, several studies (Bullock et al. [ 27 ], Egoh et al. [ 28 ], Newton et al. [ 29 ]) have shown that there can be trade-offs between biodiversity and services during restoration and among different services, so restoration aims need to be clear rather than assuming win-win outcomes. Indeed, previous studies including Berry et al. [ 30 ] have suggested that a broad spectrum of perspectives on biodiversity conservation exist and should be used as arguments for conservation actions, from intrinsic to utilitarian values. In their analysis, the main differences between types of arguments appeared to result from the espousal of ecocentric or anthropocentric viewpoints, rather than from differences between the various stakeholder groups. This suggests that to promote restoration goals, a broad range of restoration goals are needed, including those that are more anthropocentric such as economic development.

While the positive impacts of ecological restoration on biodiversity are well established, less evidence is available regarding its impacts on economic development and employment. Although restoration efforts centered around economic development in Africa, such as the Working for Water Project and eThekwini forest restoration project in South Africa have generated strong support from government, not many such initiatives exist in other parts of the world (Mugwedi et al. [ 19 ]). In this collection, Newton et al. [ 29 ] examine the impacts of restoration on economic development and employment. They conclude that landscape-scale restoration or rewilding of agricultural land can potentially increase the contribution of farmland to economic development and employment, by increasing flows of multiple ecosystem services to the many economic sectors that depend on them. Indeed, restoration has contributed to the economy in many parts of the world leading to the framing of the term “restoration economy” or “green economy” which is now commonly used in the restoration literature (Bek et al. [ 31 ], Formosa et al. [ 32 ]). A recent report by Dasgupta [ 33 ] states that “our economies are embedded within Nature, not external to it”. While we are all looking forward to the UN Decade on Ecosystem Restoration launching this year, the uptake of restoration projects will depend on financing. Generating funds to support and sustain restoration projects is one of the biggest challenges facing restoration activities worldwide (FAO and Global Mechanism of the UNCCD [ 34 ]). The inclusion of a broad range of goals that span from biodiversity to anthropocentric goals such as those related to benefits of nature’s contribution to people to those that are purely development such as job creation may be the way forward.

• 1. Brondizio ES, Settele J, Díaz S, Ngo HT. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn: IPBES Secretariat; 2019.
• 2. Reid WV, Mooney HA, Cropper A, Capistrano D, Carpenter SR, Chopra K, et al. Ecosystems and human well-being-Synthesis: A report of the Millennium Ecosystem Assessment. Washington, DC: Island Press; 2005.
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• 33. Dasgupta P. The economics of biodiversity: the Dasgupta review. London: HM Treasury; 2021.
• 34. FAO and Global Mechanism of the UNCCD. Sustainable Financing for Forest and Landscape Restoration-Opportunities, Challenges and the Way Forward. Liagre L, Lara Almuedo P, Besacier C, Conigliaro M, editors. Rome: Food and Agricultural Organization; 2015.

• Systematic Map
• Open access
• Published: 27 April 2016

What are the effects of nature conservation on human well-being? A systematic map of empirical evidence from developing countries

• Madeleine C. McKinnon   ORCID: orcid.org/0000-0002-1208-290X 1 , 2 ,
• Samantha H. Cheng 3 , 4 ,
• Samuel Dupre 5 ,
• Janet Edmond 1 ,
• Ruth Garside 6 ,
• Louise Glew 7 ,
• Margaret B. Holland 5 ,
• Eliot Levine 8 ,
• Yuta J. Masuda 9 ,
• Daniel C. Miller 10 ,
• Isabella Oliveira 11 ,
• Justine Revenaz 12 ,
• Dilys Roe 13 ,
• Sierra Shamer 5 ,
• David Wilkie 14 ,
• Supin Wongbusarakum 15 , 16 &
• Emily Woodhouse 17  

Environmental Evidence volume  5 , Article number:  8 ( 2016 ) Cite this article

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A Systematic Review Protocol to this article was published on 05 August 2014

Global policy initiatives and international conservation organizations have sought to emphasize and strengthen the link between the conservation of natural ecosystems and human development. While many indices have been developed to measure various social outcomes to conservation interventions, the quantity and strength of evidence to support the effects, both positive and negative, of conservation on different dimensions of human well-being, remain unclear, dispersed and inconsistent.

We searched 11 academic citation databases, two search engines and 30 organisational websites for relevant articles using search terms tested with a library of 20 relevant articles. Key informants were contacted with requests for articles and possible sources of evidence. Articles were screened for relevance against predefined inclusion criteria at title, abstract and full text levels according to a published protocol. Included articles were coded using a questionnaire. A critical appraisal of eight systematic reviews was conducted to assess the reliability of methods and confidence in study findings. A visual matrix of the occurrence and extent of existing evidence was also produced.

A total of 1043 articles were included in the systematic map database. Included articles measured effects across eight nature conservation-related intervention and ten human well-being related outcome categories. Linkages between interventions and outcomes with high occurrence of evidence include resource management interventions, such as fisheries and forestry, and economic and material outcomes. Over 25 % of included articles examined linkages between protected areas and aspects of economic well-being. Fewer than 2 % of articles evaluated human health outcomes. Robust study designs were limited with less than 9 % of articles using quantitative approaches to evaluate causal effects of interventions. Over 700 articles occurred in forest biomes with less than 50 articles in deserts or mangroves, combined.

Conclusions

The evidence base is growing on conservation-human well-being linkages, but biases in the extent and robustness of articles on key linkages persist. Priorities for systematic review, include linkages between marine resource management and economic/material well-being outcomes; and protected areas and governance outcomes. Greater and more robust evidence is needed for many established interventions to better understand synergies and trade-offs between interventions, in particular those that are emerging or contested.

Registration CEE review 14-012

Across the globe, national governments are increasingly pursuing policies to secure biodiversity and natural ecosystems while ensuring economic prosperity and other aspects of human well-being including health, social relations and cultural values. In September 2015, the United Nations launched a set of 17 new Sustainable Development Goals to shape the international development agenda for the next 15 years [ 53 ]. In parallel to such policy shifts, several major international non-governmental organizations with a historical focus on nature conservation—including Birdlife International, Conservation International, The Nature Conservancy and Fauna & Flora International—now explicitly reference people in their mission and vision statements and aspire to achieving socially beneficial outcomes through their conservation efforts [ 24 ]. To achieve stated political and institutions goals, and to be able to monitor progress towards them, empirical data, relevant metrics, and monitoring systems are needed to quantify the linkages between specific conservation efforts and different aspects of human well-being [ 25 , 30 ].

While greater emphasis on the human dimensions of conservation efforts has undoubtedly occurred, evidence on the resulting socioeconomic outcomes is so far inconclusive. Over the years, conservation has been portrayed as both a win–win solution for poverty alleviation and sustainable development, and as a constraint on economic growth [ 52 ]. While several conservation projects and policies have achieved both conservation and development goals [ 2 , 3 ], conflicts and negative relationships between conservation and human well-being have also been highlighted [ 42 ], including loss of access rights [ 18 ], human-wildlife conflict [ 61 ], and evictions from protected areas [ 8 ]. Thus, increased monitoring of socioeconomic outcomes has thus been dually influenced by a need to demonstrate contributions to broader development goals, e.g., United States Agency for International Development’s Biodiversity Policy 2014, [ 54 ], World Bank Biodiversity road map [ 63 ], and by a genuine desire to “do no harm” and to ensure the longevity of natural ecosystems upon which vulnerable populations depend [ 64 ].

Diverse hypotheses exist about the explicit effects of conservation interventions. These might be related to measurable impacts, (e.g., economic and material well-being) as well as harder to quantify dimensions of well-being, (e.g., social cohesion, culture and freedom of political choice) [ 11 ]. For example, there are frequent claims that marine protected areas increase the food security of local fishers through the dual mechanisms of sustaining ecosystem services and the preferential reallocation of rights to fishing areas [ 17 ]. Similarly, community-based natural resource management is commonly linked to increased economic and material well-being, generated by commercial enterprises (e.g., eco-tourism or trophy hunting) that rely on the presence of charismatic species [ 34 ]. Education and awareness interventions (e.g., informational campaigns) are assumed to improve knowledge and skills that encourage more sustainable practices and behavior [ 15 ]. Alternatively, regulations restricting access might affect vulnerable groups dependent on natural resources for their livelihoods [ 56 ] or bring communities into conflict with wildlife populations managed under conservation arrangements [ 50 ]. These hypotheses have shaped conservation and development practices on the ground giving rise to integrated planning strategies (e.g., focus by USAID on Sustainable Landscapes models), an expansion of incentive-based conservation measures, such as Payments for Environmental Services (PES) [ 35 ], and socially-oriented approaches to conservation [ 10 ]. Over the past decade, conservation scholars and practitioners have developed conceptual frameworks for understanding and quantifying human well-being [ 31 , 32 , 66 ] and others that emphasis social effects from an ecosystem services perspective [ 48 ]. Fields outside of conservation have also started to express interest in conservation and nature. For instance, the public health field has recently called for a new discipline: planetary health [ 21 ], which emphasizes how unsustainable resource consumption and environmental degradation can setback decades of global health gains [ 58 ]. This interest aims to clarify the link between degrading natural systems and human health. In parallel, efforts have also been made to document the social impacts of conservation, and specific mechanisms by which these impacts are manifested [ 14 , 45 , 65 ], and synthesize empirical evidence on linkages between specific ecosystem services and aspects of poverty [ 40 , 47 ].

Data on the effects of conservation on human well-being is currently scattered across multiple sources, many of which are inaccessible to policy makers and other decision makers [ 37 ]. In the absence of a more comprehensive evidence base, anecdotal evidence is frequently used to support or refute particular positions or hypotheses. However, such evidence is highly variable and subject to differing interpretations, inhibiting the ability of decision makers to confirm linkages between human well-being outcomes and conservation interventions, or to understand the trade-offs and synergies between different interventions in meeting specific social targets. In response, a growing number of evidence syntheses have emerged on social impacts of conservation. Recent systematic reviews have focused on prominent interventions including protected areas [ 38 ], integrated water management [ 19 ], payment for environmental services [ 43 ], and community-based conservation (e.g., [ 7 , 9 , 44 ]). The benefits of a broader review of evidence include the ability to (1) reflect the scale at which strategic, investment, and political decisions for nature conservation are made by governments, international bodies, and non-governmental organizations; (2) avoid unsupported assumptions about the efficacy of widely applied interventions; (3) incorporate non-traditional or lesser known interventions and aspects of well-being; (4) capture multiple pathways and options by which conservation might affect well-being; and (5) place other specific reviews in broader context and highlight well-studied areas and potential research gaps or biases.

The recent launch of the UN Sustainable Development Goals underscores the need for greater and better evidence for identifying, monitoring and evaluating progress toward proposed targets [ 25 ]. While many indices and frameworks have been developed to document or measure various human well-being domains affected by conservation interventions, these have not been associated with critical assessments of extent and robustness of these assumed linkages between people and nature. To meet this important gap, we use systematic evidence mapping as a tool to identify, characterize and synthesize empirical research, documenting the impacts of nature conservation on human well-being. Systematic maps, also referred to as evidence gap maps or evidence maps, are thematic collections of primary research articles and systematic reviews within a sector [ 46 ]. The key output is a visual graphic that illustrates the distribution and occurrence of existing evidence using a categorical framework of policy-relevant interventions and outcomes. Our synthesis aims to shed light on areas of high and low occurrences of empirical research, existing biases in research efforts, and the robustness of current evaluation approaches. It is intended to be dynamic and ideally will be regularly updated to reflect new research findings and trends. The map will help researchers and policy makers rapidly locate and assess relevant scientific evidence to understand conservation interventions and human well-being outcomes are frequently evaluated.

The primary question of this systematic map was:

What is the extent and occurrence of empirical evidence documenting nature conservation impacts on human well-being in developing countries?

This question has the following components:

Population Human populations including individuals, households or communities within non-Organisation for Economic Cooperation and Development (OECD) countries

Intervention In-situ nature conservation interventions based upon the International Union for Conservation of Nature (IUCN) and Conservation Measures Partnership (CMP) typology of conservation actions [ 41 ]

Comparator Absence of intervention either between sites or groups, and/or over time

Outcome Positive or negative effects on multi-dimensional well-being status of human population

Study type Article empirically measuring effects of a program, activity or policy using observational or experimental data from primary or secondary sources

Secondary questions of this systematic map were:

What is the frequency and type of nature conservation interventions for which evidence are documented on human well-being outcomes?

What are the characteristics of documented evidence in terms of quantity, type of outcome measures, geographic location, and study design?

Where do gaps exist in the evidence base that represent research priorities?

What are promising areas for further synthesis?

This systematic mapping process was undertaken as part of an initiative led by the international conservation non-government organization, Conservation International, which was concerned with the extent and robustness of the evidence base, the pathways by which conservation affects human well-being, and the role of ecosystem services in mediating these relationships. In November 2013, a technical workshop of conservation, development and research experts convened to scope and review the search strategy and draft the systematic review protocol. The protocol was published in August 2014 [ 6 ]. Following preliminary screening, a further expert workshop organized by the Science for Nature and People Partnership, was convened in February 2015 to further refine categories for interventions and outcomes, coding of study design, and additional sources of evidence to be searched. The methods presented here are largely similar to those outlined in Bottrill et al. [ 6 ]. Adjustments from the original protocol are noted.

Search strategy

A comprehensive search of multiple electronic information sources attempted to capture an unbiased sample of literature, encompassing both published and grey literature. Searches were conducted from November 2014 to April 2015. Our mapping process followed the search strategy described in a protocol [ 6 ]. Different sources of information, e.g., online publication databases, search engines, topical databases and organisation websites, were searched to maximize the coverage of the search.

Search terms and languages

A search string comprising the following English search terms were used to query online bibliographic databases and internet search engines:

Intervention terms

(“conservation” OR “conserve” OR “conservancy” OR “protect*” OR “management” OR “awareness” OR “law*” OR “policy*” OR “reserve*” OR “govern*” OR “capacity-build*” OR “train*” OR “regulation” OR “payment for ecosystem services” OR “PES” OR “ecotourism” OR “sustainable use”) AND

Intervention adjacent terms

“marine” OR “freshwater” OR “coastal” OR “forest*” OR “ecosystem*” OR “species” OR “habitat*” OR “biodiversity” OR “sustainab*” OR “ecolog*” OR “integrated” OR “landscape” OR “seascape” OR “coral reef*” OR “natural resource*”) AND

Outcome terms

(“wellbeing” OR “well-being” OR “well being” OR “ecosystem service*” OR “nutrition” OR “skill*” OR “empower*” OR “clean water” OR “livelihood*” OR “(food) security” OR “resilience*” OR “vulnerability” OR “(social) capital” OR “attitude*” OR “perception*” OR “(human) health*” OR “human capital” OR “(traditional knowledge” or TEK) AND

Outcome adjacent terms

(“human*” OR “people” OR “person*” OR “community*” OR “household*” OR “fisher*” OR “collaborative”)

The search string was developed through a scoping exercise which examined relevant frameworks and search terms used from related systematic reviews and maps [ 7 , 38 , 40 , 43 , 44 ] and explored the effect of alternate terms, wildcards, and use of standardized Boolean search conventions commonly used in information systems and online databases.

Two peer-reviewed publication databases were searched: SciVerse’s Scopus and Thomson Reuters Web of Science, both of which cover natural and social sciences. A full description of the construction of the search string is documented [ 6 ].

We identified ‘grey’ literature (i.e., published and unpublished documents not available on online publication databases) in several ways. First, we searched a list of websites for relevant articles and systematic reviews and maps, in particular grey unpublished literature not documented in peer-reviewed journals (Additional file 1 : Appendix 1 Table S1). Given the limitations of search engines on specialist websites, we used an abridged set of search terms. Appendix 1 (Specialist search strategy) provides a description of revised search terms and search results from organization websites and specialist databases. Furthermore, a subset of academic thesis databases was searched (Additional file 1 : Appendix 1 Table S2) using a revised list of search terms. In addition, we contacted 50 key informants by email, representing a range of organizations, research institutes and geographic regions, with a request for relevant documents and/or journals, databases or websites where additional articles might be found. If no response was given following the first email, a second reminder email was sent.

We screened bibliographies of related systematic maps and reviews for relevant articles. In addition, if non-systematic reviews were identified then their bibliographies were searched for relevant articles meeting inclusion criteria. Due to the volume of articles identified and resource constraints, we did not conduct forward and backward screening of bibliographies of included articles.

Inclusion and exclusion criteria

Following compilation of search results from the various sources listed above, the screening process was implemented using an established set of inclusion and exclusion criteria to determine the relevance of articles. All criteria are required to be met for inclusion in the final dataset. Categories of interventions and outcomes are described in Tables  1 and 2 . Categories selected were based upon established frameworks such as the IUCN-Conservation Measures Partnership classification of Conservation Actions [ 41 ] and the Millennium Ecosystem Assessment (MEA 2005).

Inclusion criteria

Population(s)

The study focuses on the well-being of discrete individuals, households or communities, or nation states living in non-OECD countries

Intervention(s)

The study involves establishment, adoption, implementation or refinement of a program or policy that regulates, protects or manages biodiversity and natural ecosystems through in situ activities

The study measures or observes effects on one or more domains of human well-being categorized as follows: Economic Living Standards, Material Living Standards, Governance and Empowerment, Education and Capacity Building, Health, Subjective well-being, Security and Safety, Culture and Spirituality, Social Relations, Freedom of Choice and Action

Study type(s)

The study involves empirical measurement of direct or indirect effects of a policy or program

Systematic reviews and meta-analyses were marked and set aside separately for bibliographic searching

Exclusion criteria

The study focuses on OECD country(s)

The study comments on effects of undefined groups

The study documents or measures daily use or interaction by people with natural ecosystems and/or ecosystem goods or services rather than associated with a specific and discrete intervention

The study is focused on environmental regulatory measures and mitigation (e.g., air quality control, waste management, energy production) and ex situ conservation efforts (e.g., zoos, captive breeding, seed banks etc.)

The study does not empirically observe or measure human well-being outcome(s)

The study only focuses on biophysical outcomes of conservation or solely examines how status or trends in human well-being affect conservation outcomes

Theoretical articles or models

Commentary, editorials and narrative reviews

Following implementation of the search strategy, all titles and abstracts were uploaded into Excel and reviewed against the inclusion and exclusion criteria above. The title and abstract screening was by two researchers independently (MCM, SHC). If there was any doubt about the relevance of an article, it was retained for full text assessment. The assessors performed an initial screening of a random subset of 1000 titles in a pilot exercise to assess repeatability and consistency of selection criteria between assessors. Articles appearing to meet inclusion criteria from screening the title and abstract were obtained as full text, and further screened against the inclusion criteria by two reviewers independently to produce the final set of included articles.

Study coding strategy

Each included article was examined using a standard coding tool and supplementary codebook to extract and categorize data from each article. In our coding strategy, we did not distinguish between articles and studies, and treated all articles as single cases. While some articles discuss results from multiple studies, these were generally treated as reviews and excluded from our dataset. If we had counted individual studies within each article, the number of occurrences might be greater than those reported in this paper. Additionally, it is possible that a study could be included in multiple articles. However, post hoc separation of articles and individual studies within articles would require additional recoding.

The coding tool was piloted by two assessors (MCM, SHC) for a sample of 10 articles to ensure consistency. Results of piloting were compared. Due to the large volume of articles, double extraction by two assessors of all articles was not possible. The research team met regularly to discuss any ambiguous or unresolved articles. The initial extraction tool included in the protocol was adapted (see Additional file 2 : Appendix 2 coding tool for data extraction). A form for entering data in a consistent and efficient manner was developed in Google Forms, which was then automatically compiled into a spreadsheet.

The following broad categories of data were extracted:

Unique ID and assessor identification

Bibliographic information

Basic information about intervention

Basic information about study design, scale and location

Information about human well-being outcomes

Information about occurrence and type of conceptual framework

Summary information on main findings

Data on the robustness of study design were collated (as implemented within the article). Categories of design were adapted from Margoluis et al. [ 27 ]. Each article was coded in the systematic map at full text using four criteria: (1) type of data (quantitative, qualitative, mixed); (2) random assignment of a treatment group; (3) occurrence of comparison group or site; and (4) occurrence of comparison over continuous or interrupted/punctuated time series. This classification scheme was not intended to infer quality but rather categorize articles according to different designs with respect to levels of internal and external validity. We were specifically interested in the extent and occurrence of impact evaluations—systematic designs measuring the intended and unintended causal effects of conservation interventions on social and ecological conditions [ 28 ].

Data analysis and synthesis

A structured matrix of the distribution and frequencies of articles to document specific relationships, or linkages , between a range of interventions and outcomes was compiled. The matrix uses nested categories based upon a longer list of subcategories. Categories for describing intervention and outcome type were identified a priori (Tables  1 and 2 ) and form the basis of a structural matrix, the major output of the mapping process. Evidence on different outcomes (in rows) is mapped on to different categories of interventions (in columns). Each cell represents a linkage. The matrix represents the primary output of the systematic mapping process and allows an intuitive visual format for synthesizing data on specific articles and linkages.

Data extracted from each article was compiled into a database using the statistical programming language, R [ 39 ], to organize fields of data across many articles and enable rapid analysis. The database was used to generate descriptive statistics on key trends across and between articles, regions and linkages.

Coded data were sorted and compiled into an interrogable database using the packages ‘dplyr’ and ‘tidyr’ in R version 3.1.0 (R Development Core Team). A structural matrix of linkages between interventions and outcomes was visualized as a heat map using the package ggplot2 [ 59 ].

Quality assessment

Given the broad scope of a systematic map, individual articles were not appraised for quality, e.g., a detailed assessment of research design and study characteristics based upon study reliability and relevance [ 12 ]. Instead, appraisal was limited to assessing the confidence in the methods and findings of systematic reviews identified.

To assess the reliability of systematic reviews included within the evidence gap map, each review article was assessed according to a set of 14 criteria adapted by the International Initiative (3ie) [ 46 ] from the checklist developed by the SURE collaboration (The SURE collaboration [ 49 ]. These criteria assess the reliability of the methodology utilised by the systematic review in its search strategy, methods for critical appraisal of included articles, such as in criteria used to assess biases, and confidence in the interpretation of study findings. The checklist is a standardised critical appraisal tool, giving reviews an overall rating of high, medium or low in terms of the confidence with which their findings can be assured based upon methodological design.

Number and types of articles

Figure  1 illustrates the step-by-step results from the searching and screening strategies. Given the scope of this map, the search of online publication databases and additional sources yielded a large quantity of potential articles. Title and abstract screening significantly reduced the number of relevant articles. Full text assessment of articles further refined the list of included articles to a subset of 1043 articles for data extraction. A bibliography of included articles is listed in Additional file 3 : Appendix 3 in supplementary material (Additional file 3 : Appendix 3 Table S3. Bibliography of included articles). Excluded articles at the full text assessment stage are listed in Additional file 4 : Appendix 4 Table S4. Coded data for each individual article included in this study is listed in Additional file 5 : Appendix 5.

Flow diagram illustrating articles retrieved in initial search and articles included following subsequent screening and full text assessment. Diagram stages adapted from PRISM guidance (Moher et al. 2009)

Articles utilized a range of different comparators to examine effects of conservation interventions between sites and populations and over time periods. Almost a quarter of included articles were non-comparative (Fig.  2 ). About 12 % of articles compared effects of the intervention over time either using a baseline before the start of the intervention or other interrupted time series data.

Frequency of comparators used by included articles

The robustness of study designs is low with 9 % of articles applying quantitative methods to examine causal effects either before/after an intervention or a comparison group or site. Among these articles, 22 % percent used non-experimental, 73 % used quasi-experimental and 0.04 % used experimental methods to assign treatments to different groups or sites. Due to the size of the evidence base, data were not collated on qualitative approaches to impact evaluations such as stratified random sampling of interview subjects. Subsequent reference in this study to impact evaluations thus refer only to articles which quantitatively considered a counterfactual by which to compare effects and thus better attribute effects to an intervention (n = 67).

We found few articles prior to 1990 with a significant increase after 2002 (Fig.  3 ). The number of articles has increased exponentially since then. The number of impact evaluations on this topic has increased the past 10 years with the earliest article documented in 2002.

Number of articles and impact evaluations at 5-year increments

Geographical representativeness of articles

The dataset of included articles represents a range of geographic regions (Fig.  4 a). The most studied regions, with over 200 articles, are Eastern Africa, Southern Asia, and Southeast Asia whereas North Africa and the Middle East had some of the fewest articles for their geographic extent. The five countries with the greatest number of all included articles are India, Nepal, China, Brazil and Tanzania (Fig.  4 b). In contrast, countries with the highest number of impact evaluations (n > 5) include China, Costa Rica, Brazil, Thailand and Tanzania (Fig.  4 c).

a Number of articles by region. b Geographical distribution of articles occurring in non-OECD countries. Darker countries indicate countries with higher occurrences of articles, lighter indicate lower occurrence. c Number of impact evaluations by country

Ecological coverage

Articles were distributed across a range of terrestrial and marine biomes (Fig.  5 ) with tropical moist broadleaf forests the most studied with more than 400 articles documented. Other relatively well-studied terrestrial biomes include tropical grasslands and savannas and montane grasslands. In marine biomes, tropical coral reefs were the most studied (n = 100). Relatively few articles were documented for freshwater biomes overall (n = 44) including freshwater floodplains and rivers, and lakes.

Number of articles by biome

Types of conservation interventions

Figure  6 a presents the distribution and extent of articles included according to ten broad intervention categories (Table  1 ). The occurrences of evidence, or the number of times a linkage between an intervention evaluated and outcomes measured is documented in our systematic map is also indicated. Multiple linkages might be documented within a single article.

a Number of articles by broad intervention category. Occurrences of evidence are indicated by numeric values . b Number of articles by intervention sub-category and grouped by broad categories. Occurrences of evidence are indicated by numeric values

Well-studied intervention categories, documented in over 300 articles, include Area protection, Land and Water management, Resource management, and interventions associated with Economic, livelihoods or other incentives. Many articles evaluated multiple interventions. Figure  6 b characterizes the distribution and extent of articles and occurrences of evidence by the adapted list of IUCN-CMP intervention types. These subcategories relate to specific types of activities, policies or programs within a broader category. The most frequently identified intervention subcategory was resource management/protection (n = 1153 occurrences).

Dimensions of human well-being studied

Figure  7 presents the distribution of articles identified by outcome category. Most articles measured more than one outcome with the average number of outcomes measured by article = 2.65 (±1.35SD).

Number of articles by human well-being outcome categories

Economic well-being was the most frequently documented outcome with over 700 articles including this as a measure. Over 400 articles measured outcomes associated with “Material well-being” and “Governance and empowerment”, respectively. Among outcome measures, few articles evaluated health effects of nature conservation interventions. Other types of outcomes based upon measuring well-being perceived by individuals, such as “Culture and Spirituality”, “Freedom of Choice and Action” and “Subjective well-being”, which may be more challenging to measure, were rarely documented.

Intersection of conservation interventions and human well-being outcomes

Figure  8 a maps the intersection of different conservation interventions and human well-being outcomes evaluated by articles included in our systematic map. Linkages with higher occurrences of evidence might be promising areas for further synthesis, such as with a systematic review or meta-analysis. Linkages with moderate or low occurrences of evidence, but which are priority topics for policy or program management by national governments, NGOs or conservation donors, might be promising areas for investment in research and additional impact evaluations. Figure  8 a provides an overview of the distribution and frequency of linkages across the framework of interventions and outcomes, and thus reflects the overall “systematic map” and major output for this study. Additional matrices (Figs.  8 b, 9 , 10 , 11 ) illustrate the diversity of ways that the data might be presented and patterns in evidence explored as discussed below.

a A systematic map on linkages between nature conservation and human well-being illustrated as a structural matrix of the distribution and frequency of occurrences of evidence Darker-shaded cells indicate higher occurrence of evidence with lower occurrence indicate by lighter cells indicating low. b Structural matrix illustrating the distribution and frequency of quantitative experimental, quasi-experimental, and non-experimental articles on linkages on nature conservation and human well-being

Structural matrix illustrating the distribution and frequency of occurrences of evidence from articles involving forest biomes

Structural matrix illustrating the distribution and frequency of occurrences of evidence from articles involving marine biomes

Structural matrix illustrating the distribution and frequency of occurrences of evidence from articles involving freshwater ecosystems

The linkage with the highest occurrence of evidence is “Resource Management and Economic well-being”. Other linkages with high occurrence of articles include “Area Protection and Economic well-being”, “Land and Water Management and Economic Well-being” and “Economic, livelihoods and other incentives and Economic well-being”. Interventions with relatively low occurrences of evidence include “Education” and “External Capacity-building”. Few articles have examined the health effects of any conservation intervention, effects on personal or community safety, or effects on culture and spirituality. Limited to no evidence was documented for the effects of conservation on individual or collective freedom of choice and action.

Figure  8 b illustrates the distribution and frequency of the subset of impact evaluations, i.e., those articles using a counterfactual comparison. Areas of high occurrence are closely aligned to the full set of articles with an emphasis on evaluating economic and material well-being outcomes from area protection, land and water management, resource management, and “Economic, livelihoods and other incentives”. On the other hand, linkages between conservation and “Governance and empowerment” outcomes have relatively high occurrences of overall evidence, with few evaluations using more robust study designs were documented. While the evidence from impact evaluations for health impacts of conservation is still relatively low, impact evaluations make up a higher proportion of overall evidence for this linkage than other linkages with many overall articles.

Figures  9 , 10 and 11 present a subset of articles that occurred in forest biomes (n = 733), marine biomes (n = 131), and freshwater biomes (n = 44), respectively. For forest biomes, the intersections between “Area protection and Economic living standards” and “Resource management and Economic living standards” have the highest level of occurrence.

In Fig.  10 , the subset of articles identified that occur in marine biomes are mapped according to interventions evaluated and outcomes measured. The linkage with the highest occurrence of evidence is “Resource management and Material well-being”. This likely reflects the emphasis on fisheries management in marine biomes. In parallel to Fig.  8 a, linkages associated with economic and material well-being outcomes and area protection and land and water management also are well-studied. In contrast with trends across the evidence base overall, this subset has a higher occurrence of evidence for linkages associated with subjective well-being and education.

Synthesis of systematic review findings

Eight systematic reviews were included (Table  3 ) in our study.

Figure  12 indicates the overlap between completed systematic reviews (n = 8, Table  3 ) and occurrence of evidence documented by our map. In general, recent review efforts converge with the distribution and frequency of existing evidence. Systematic reviews have targeted well-studied linkages associated with “Area Protection and Economic Well-being”, “Land and Water Management and Economic well-being” and “Resource Management and Economic well-being”. Linkages, with high occurrences of evidence, but which have yet to be the target of a systematic review include “Governance and empowerment and all HWB outcomes” and “Species Management and Economic well-being”.

Structural matrix illustrating the distribution of systematic reviews included in the systematic map and the level of confidence based upon reliability of review methodology. Numbers within the circles indicate the total number of systematic reviews on that particular linkage that fall within different levels of confidence

Among the eight systematic reviews, four were assessed as medium confidence based upon the 14 criteria developed by 3ie (Snilstveit et al. [ 46 ]. The remaining four had low confidence due to the absence of bias reduction in their screening strategies and a lack of meta-analysis. No reviews were rated as having high confidence. The four systematic reviews (SRs) with medium confidence encompass 19 cells (from a possible 80 cells total) or intervention-outcome linkages within our evidence gap across five intervention subcategories and four outcomes. We briefly summarize the main findings for the four SRs with medium confidence.

Pullin et al. [ 38 ] examined the impacts of terrestrial protected areas globally and human well-being. 79 impact evaluations were critically appraised for quantitative analysis with a further 34 qualitative articles to understand broader context. Despite being one of the most frequently applied conservation interventions, Pullin and authors find existing evidence remains disparate and fragmented. They conclude the existing evidence base is insufficient to draw conclusions about the scale of positive or negative impacts of protected area on human well-being. Impacts of protected areas were highly context dependent and the limited robustness and quantity of rigorous evidence restricted the authors’ ability to generalize policy recommendations based on current evidence.

Bowler et al. [ 7 ] examined the impacts of community forest management (CFM)—resource management in developing countries on global environmental benefits associated with securing carbon in existing forests and aspects of local livelihoods, including income, employment, income equality, social equity, food security and health. From among 42 included impact evaluations, some environmental benefits were observed, however evidence was insufficient to conclude effect of CFM on livelihoods.

In a related systematic review, Samii et al. [ 43 ] explored the current evidence base for decentralized forest management (DFM) on deforestation and poverty in low and middle income countries. Like Bowler et al. [ 7 ], this review sought to understand potential win–win outcomes from land management policies. In three quantitative articles identified on human well-being effects, DFM did boost forest or household income (for example, up to 35 % per capita expenditure in Ethiopia). Overall, the evidence base was limited in quality and quantity. Furthermore, the review found no impact evaluations which jointly measured deforestation and welfare effects.

With a similar scope to the systematic review on DFM, Samii et al. [ 44 ] conducted another systematic review on the effects of payments for environmental services (PES) on deforestation and poverty outcomes in low and middle-income countries. With 11 quantitative articles included, the evidence base was limited in quantity and quality. There were no randomized control trials or joint measurement of linked deforestation and welfare effects. In two articles examining poverty outcomes, income increased by 4 % in Mozambique and 14 % in China among participants. Findings suggest that PES does reduce deforestation, but impacts are modest and inefficient. Evidence from welfare impacts were inconclusive.

All of the four systematic reviews assessed focused on terrestrial ecosystems and primarily forest biomes. Broadly, the evidence base is insufficient to make generalizations about policy effectiveness or trade-offs between different options for improving human well-being through nature-based conservation.

Types of mechanisms for linking nature conservation and human well-being

In conjunction with characterizing the evidence base, we were interested in understanding the type and frequency of pathways and specific mechanisms by which conservation affects well-being and the extent to which these are empirically supported by existing evidence. As a first step to characterize these pathways, we identified articles with or without conceptual models. We define a conceptual model as a visual illustration of how interventions and specific activities are assumed to directly or indirectly connect the intermediate and long-term outcomes associated with changes in human well-being. These are also referred to as theory of change models or results chains [ 26 , 57 ]. During coding of included articles, occurrence of conceptual models was recorded when a visual graphic or diagram was included—other articles also included narrative description of models, but these individual qualitative descriptions were too varied and subjective to consistently characterize across the whole evidence base. Within the full evidence base, only 20 % of articles contain a visual conceptual model (n = 212 articles). Among these articles, a range of different models were used, including from established conceptual frameworks, to characterize linkages between natural conservation management and/or conservation and aspects of poverty or human well-being (Table  4 ).

The majority of articles developed bespoke, project-specific conceptual models, designed to reflect the specific operational or geographic context in which the program being evaluated occurs. The most commonly referenced conceptual model was the “Sustainable Livelihoods Framework” established by the UK Department for International Development [ 13 ].

This study has compiled the largest thematic synthesis to date of primary research articles documenting the impacts of nature conservation on human well-being outcomes in developing countries. This collection confirms recent and considerable research efforts on this topic across a vast array of linkages between conservation and socioeconomic outcomes. Well-studied relationships focus on established interventions, such as protected areas and community-based natural resource management, and economic and material aspects of well-being, such as income, employment and physical assets. Prominent gaps in the evidence base include the lack of evidence for interventions, such as education and species management, and measurement of important aspects of well-being, such as social relations between groups, that may be more difficult to quantify. The robustness of the evidence base overall is low with few articles applying robust quantitative methods. Where they exist, articles are focused on just a few linkages and geographic regions, indicating a substantial research bias. The volume of articles compiled by this mapping exercise indicates a broad scope and diversity of ongoing interest in this topic, but also required an enormous synthesis effort to comprehensively capture and compile these data. The scale and standardization of the research effort however demonstrates the value of systematic mapping in helping other researchers and practitioners more easily locate and assess existing evidence. The outputs of this study, a graphical map illustrating the extent and distribution of evidence occurring, confirms well-studied linkages, highlights knowledge gaps, and provides a tool for decision making by a range of stakeholders.

This study represents one of the first systematic maps for the environmental sector, and the largest to date. It therefore offers several general insights on the value and existing barriers of systematic mapping as a tool for supporting evidence-informed decision making. First, the scope and resonance of systematic maps is dependent upon clear and discrete typologies. Categories for interventions and outcomes should be policy relevant. Where possible, we aimed to utilize well-established typologies with broader currency to define and categorize different characteristics of the evidence base (e.g., IUCN-CMP Classification of Conservation Actions, [ 41 ]. However, there might be many competing frameworks to choose as we found with categorizing dimensions of human well-being. Standardized and consistent typologies for interventions and outcomes could help coordinate and target research efforts and inform policy about collective impacts [ 25 ]. Second, systematic maps enable other researchers to rapidly locate and assess the state of the evidence base. They illuminate well-studied linkages, confirm knowledge gaps and identify “known unknowns”. While searching, screening and coding of data requires extraordinary efforts by a small research team, maps save significant time for other researchers. Finally, maps currently provide a single snapshot of the existing evidence base, but could become more dynamic with periodic updating every 3–5 years. Recent improvements to synthesis approaches and new tools, such as use of reference management software (e.g., EPPI Reviewer) and text mining (e.g., TerMine), could automate and expedite stages of the mapping process to be more efficient, accurate and replicable in the future.

Limitations of our systematic map

The scope of our systematic map presented several limitations, which might be addressed in subsequent updates. First, while our search strategy was comprehensive, it was not exhaustive. Finite time and resources precluded additional searches of additional databases, forward and backward screening of the 1000+ included articles, and double assessment of the full dataset by two reviewers. Second, the search was limited to English language literature, although results from a search of Portuguese, Spanish and French language literature are forthcoming. Third, the map was focused on non-OECD countries which excluded research from 20+ developed countries. Expansion of the geographic scope to a global scale might allow interesting comparisons in interventions evaluated, outcomes measured and study designs used given variation in research capacity, economic prosperity and ecosystem health between developed and developing countries.

In addition to limitations to the scope of the search strategy, several caveats related to how data were synthesized and presented should be considered when interpreting results and using the systematic map for decision making. First, data extraction was intended to capture general characteristics for each article. This did not include assessment of the directionality or distribution of impacts observed by individual articles nor synthesis of average effect sizes for multiple articles as might be conducted as part of a more detailed systematic review or meta-analysis. Second, we extracted only limited information on the specific pathways and mechanisms by which conservation affects human well-being, directly or indirectly. In part this was due to the inconsistent and subjective nature of how these data were reported by articles as well as the volume of articles identified. Finally, high occurrence of evidence for a specific linkage or type of article does not equate to positive impact of an intervention on a particular outcome nor is evidence of higher levels of robustness. Our map gives an indication of robustness of the evidence, based on study design, but does not give a detailed quality appraisal of articles and how they deal with susceptibility to biases and heterogeneity of effects.

The extent and robustness of the evidence base was also affected by factors outside the design and scope of our study related to issues of accessibility, availability and bias in current research efforts. We were primarily limited to articles, documents and reports that were available electronically and distributed online. Books, monographs, and geographically discrete journals or those that targeting specialist groups, e.g., the Indian Forester, were less accessible from the library collections to which the research team subscribed. In addition, access to independent evaluations or reviews not published in peer-reviewed literature were dependent on commissioning organizations or researchers involved making these available and locatable electronically to the general public.

Gaps and biases in the evidence base

Beyond limitations in search strategy as discussed above, the current state of the evidence base is determined by gaps and biases in the distribution and extent of existing articles.

Limited or non-existent evidence, or gaps, on a specific linkage might be due to either a systemic bias in research efforts or rather to a lack of theory supporting a causal relationship between a specific intervention and outcome. The absence of evidence for some linkages might also indicate that a relationship is not plausible based upon existing theoretical thinking. Prominent linkages where we might have expected higher levels evidence to exist include articles measuring outcomes related to culture, security and safety, and human health. We posit several reasons for occurrence of these gaps. First, consistent time series data on more subjective outcomes, associated with dimensions such as culture, are rarely available at broad scales and often require primary data collection from individuals. Second, measurement of these outcome types involve lengthy timeframes, beyond the average program timeline, to observe demonstrable changes. Third, conservation might have a proximate or indirect effect on these types of outcomes, making it inherently more challenging methodologically to tease apart a specific interaction. Similarly, certain interventions associated with capacity building or empowerment within communities are often viewed as secondary activities intended to support other interventions, and thus might not be the target of monitoring. Fourth, the evidence base is skewed towards site level interventions in which direct, observable effects are more likely whereas larger more diffuse programs which potentially might have greater reach and impacts on well-being are more difficult to measure and thus less represented in the evidence base. Finally, the expertise required for analyzing linkages between many aspects of human well-being and conservation typically rest outside the realm of those working within conservation fields. Better understanding of health impacts for example would require knowledge on epidemiology, nutrition and health economics. Interdisciplinary collaboration is therefore essential when considering future research strategies to address these gaps.

Biases in research efforts have significant effect of the extent and distribution of existing evidence. Some biases, such as preferences for specific countries or biomes are well-documented, more broadly across the sector [ 20 , 51 ]. Others such as the types of outcomes measured, interventions evaluated and study designs used are more specific to the research question at hand. Determinants of these biases are numerous, but include historical trends, individual researcher focus, and data availability. The lack of robustness of study designs, or lack thereof, was one of the most prominent biases observed in the evidence base. This trend has been observed by other related reviews [ 38 , 43 , 44 ]. Applications of rigorous impact evaluation methods in conservation remains limited relative to efforts more broadly on conservation performance measurement [ 4 , 15 , 16 ]. Efforts to date have been concentrated in countries with political support, consistent longitudinal datasets, and focus on interventions involving rapid applications, e.g., protected areas or payment for environmental services [ 33 , 55 ].

Evaluations of conservation-related programs and policies have also focused first on biophysical outcomes with less attention to socioeconomic outcomes. Among the broader literature in environmental articles [ 1 ], recent reviews have observed few articles addressing joint effects between social and ecological outcomes [ 7 , 43 , 44 ]. A related bias observed in our study was the predominance of articles measuring specific aspects of well-being, e.g., economic and material. In many cases, these patterns may reflect the availability and accessibility of secondary quantitative datasets, e.g., USAID’s Demographic and Health Survey data, the World Bank’s Living Standards measurement surveys. There were few examples of articles measuring other important aspects of well-being, such as, culture and spirituality, freedom of choice and action. These aspects may be difficult to quantify but scales could be developed. They may be more suited for qualitative evaluation designs, e.g., stratified random sampling of household interviews, and thus require greater understanding of local contexts and data on tailored indicators collected from individual subjects. Better understanding of these dimensions may be particularly important given trade offs between financial and other outcomes, and because these may be distributed unequally across social strata, with the potential for widening social and health inequity [ 22 ].

Recommendations for conservation policy, practice and research

Interpretation of our results and their implications for conservation policy and practice are confined to findings from the included systematic reviews as these alone include critical appraisal of the direction and distribution of impacts between different interventions. Existing systematic reviews across this topic are targeted towards a subset of interventions (e.g., protected areas, community-based conservation and certification) and primarily in terrestrial biomes. Collectively, the reviews found conservation has both positive and negative effects on human well-being; yet benefits of specific interventions were inconclusive (e.g., community forest management, Bowler et al. [ 7 ]). A major implication is that existing evidence base is insufficient to determine the relative contribution of different interventions versus others to different aspects of well-being. As has been concluded by other recent reviews (e.g., [ 38 ]), the quantity and robustness of evidence needs to be dramatically increased to permit more concrete policy recommendations, and thus enable evidence-informed decision making. Our existing systematic map expands on these efforts by compiling a more complete range of interventions being applied across the sector and a more holistic overview of human well-being. This broad perspective helps to identify additional areas for further synthesis and critical frontiers for improved evaluation.

We recommend using this systematic map to support three follow-up actions: evidence synthesis, knowledge generation and theory development. Deciding which of these actions to take is dependent on occurrence and robustness of evidence across linkages identified in the evidence base. For linkages with high occurrence of evidence, further evidence synthesis using systematic reviews and, where possible, meta-analyses can provide information about directionality and distribution of impacts and in what contexts. For linkages with moderate occurrences of evidence and/or less robust evidence, we recommend implementing impact evaluations using robust study designs to boost internal and external validity. Where evidence is lacking or non-existent, exploration of underlying assumptions and existing theory is necessary. If a linkage is thought to be important, but no evidence exists, then it is important to examine whether a relationship between an intervention and an outcome is theoretically possible, and then to test this empirically with an impact evaluation. In the following sections, we discuss promising and priority questions related to each of these actions.

Promising and priority questions for synthesis

Our results suggest several areas in which evidence is sufficient for more detailed analysis and synthesis. The first relates to linkages between conservation and economic and material well-being. The high occurrence of evidence on these linkages confirms the continued predominance of economic constructs of poverty and development (see e.g., World Bank Group [ 62 ]).

Economic and material well-being have also been subject to a greater proportion of more rigorous impact evaluations and systematic reviews than other human well-being outcomes. Because these reviews vary in reliability (Fig.  10 ) and a number of new, robust articles have been undertaken since some of these reviews were published there is an opportunity to carry out additional syntheses on these linkages and expand their scope to marine and freshwater biomes. Synthesis of this evidence across intervention types opens up new possibilities for assessing the relative effectiveness of different (and emerging) strategies, such as market-based approaches, in realizing economic/material well-being goals, but also possible trade-offs with other aspects of well-being. Despite its value to theory, policy, and practice, there has been little to no comparative research of this kind to date. Such research is especially timely in the context of the Sustainable Development Goals and as the international community seeks the most effective means to reach the Aichi targets under the Convention on Biological Diversity.

The second area ripe for more detailed synthesis concerns governance and empowerment outcomes. There is sufficient evidence to examine links between these aspects of well-being and area and resource management. Though relatively few, there appears to be enough rigorous evaluations to explore this linkage. Exploration of the range of ways in which governance factors influence conservation human-well-being linkages is particularly pressing. Effective governance of natural resources might be a desired outcome of conservation policies and programs, but also a factor affecting the achievement of other social and ecological outcomes. There is a need, then, for synthesis of evidence on governance as an outcome. Specifically, those conservation programs that aim to target gaps or weaknesses in governance in their activities.

Promising and priority research questions

Further empirical evaluation is needed to document the magnitude and direction of particular conservation-well-being linkages, in particular for relationships commonly assumed in conceptual models, institutional strategies or global policy goals. Higher occurrence and more robust evidence on the contribution to sustainable development is an obvious priority given the recent launch of the Sustainable Development Goals. For example, surprisingly little evidence exists on the contribution of biodiversity conservation to Sustainable Development Goals 4 (Education), 5 (Gender Equality) 10 (Reduced Inequality), and 16 (Peace, Justice and Strong Institutions). The linkage between conservation and human health is an especially promising area for further research which might be informed by several ongoing initiatives such as the Health and Ecosystems: Analysis of Linkages (HEAL) collaboration ( http://www.wcs-heal.org ). While benefits of conserving wild populations for food provision and the flow of ecological processes upon which agriculture depends are promoted as part of ecosystem-based approaches [ 5 , 36 ], the map reveals health outcomes from conservation interventions, such as trends in nutrition and disease risk, are surprisingly understudied.

In addition to improving evidence on a broader range of human well-being outcomes, other promising areas for research involve expanding the scope of evaluations to target less studied interventions such as market forces and livelihood alternatives. Understanding effects of these incentive-based interventions is important given greater interest in market-based approaches among NGOs (e.g., ACDI/VOCA, WWF) and foundations (e.g., new strategies by the Gordon and Betty Moore Foundation) as well as new models for implementation involving public–private partnerships (e.g., USAID and the Walt Disney Corporation in Alto Mayo, Peru). Reliance on evidence solely from traditional interventions limits the range of options for those planning and investing in conservation, and also presents a potential risk by not reporting unintended or even negative outcomes from new, but increasingly popular, interventions.

How the map should be used

In this paper, we present the first systematic effort to map the evidence on the relationship between conservation interventions and human well-being. By synthesizing existing evidence into a single, searchable resource, the map becomes, in effect, a ‘treasure’ map, simultaneously revealing rich seams of evidence ripe for synthesis as well as under-explored topics for targeted research. The evidence map allows conservation scholars, policymakers and practitioners to mine the evidence base to support a range of decisions. In the first instance, the map provides a ‘potted’ reading list for particular interventions or outcome types, potentially saving considerable time and resources for anyone interested in this topic.

For scholars, the map highlights immediate research priorities as well as emergent properties of the evidence base for further analytical investigation, such as associations between individual intervention-outcome linkages, or internal (i.e., research design) and external (i.e., political, social, ecological or economic context) factors that shape evidence quantity or quantity.

For policymakers, the map places specific interventions into a broader context by highlighting possible intersections between conservation, sustainability and economic development. Development agencies such as the World Bank or USAID, therefore, might use the map to assess the extent to which conservation might present an alternative strategy to achieving poverty alleviation to compare with existing strategies.

For practitioners, the map offers a tool to support design, implementation and monitoring of conservation interventions at local, national or global scales. While the map does not provide sufficient information to determine which interventions are most effective in which contexts (further synthesis would be required), it does provide a range of options to choose from, what outcomes they are associated with, and where they have been applied before. This might help validate existing efforts, highlight new or non-traditional approaches, and improve program design and implementation. The map might also be used to inform and guide monitoring of conservation programs by highlighting relevant indicators and tested methods for tracking them. Existing evidence can provide useful information on types of data and methods for monitoring specific outcomes. The map can also inform allocation of monitoring efforts. For example, where evidence is currently lacking and therefore impacts are uncertain, it might be beneficial to direct monitoring to these areas to help manage potential risks.

Our ambition for this systematic map is to improve the evidence base and specifically to encourage generation of stronger and more rigorous evidence on key linkages. We must also be realistic that a complete evidence base might never be possible and decisions are made with imperfect knowledge. All linkages are not equally important and the value of the map is its ability to help decision makers weigh the value of evidence between different linkages between conservation and human well-being. A next step to build on this map is thus provide guidance on how the current evidence base matches to existing evidence needs, and thus which linkages are highest priorities for establishing stronger evidence.

The importance of identifying linkages between nature and people in all development and conservation domains, and the necessity of incorporating considerations of human well-being in conservation programs are now widely recognized. Yet, effective policy-making and informed decisions about how simultaneously to enhance human well-being and conserve nature depend on access to a robust and comprehensive evidence database. Furthermore, greater attention and research investment should be directed to improving evaluation study designs, increasing case studies that address intangible and subjective domains of human well-being that are evidence poor, and expanding research to include articles in data-poor geographies, biomes, and intervention categories. It is only with ongoing effort that sufficient evidence will be available to draw informed conclusions about the impacts of conservation on people and to effectively balance the economic, social and environmental dimensions of achieving the 17 Goals of the Agenda for Sustainable Development at the site and global levels.

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Authors’ contributions

MCM conceived of the idea for the systematic map. All authors provided conceptual and technical input on the scope of the systematic map and design of search strategy. MCM, SHC, DP, JE, MBH, IO, JR and SS implemented the search strategy, screening and coding of included articles. MCM and SHC conducted analysis and presentation of data. MCM and SHC wrote the manuscript and all authors provided critical review on a draft version. All authors read and approved the final manuscript.

Acknowledgements

We are grateful for constructive comments on aspects of the study scope, methodological approaches and data management from additional members of a Science for Nature and People working group on Evidence-based Conservation, in particular, Julien Brun, Rebecca Butterfield, Andrew Pullin, Mark Schildhauer, Birte Snilstveit and Will Turner. We thank key informants for providing additional sources of evidence. We are grateful for comments from three anonymous reviewers on an earlier version of this manuscript.

Competing interests

The authors declare that they have no competing interests.

This study was made possible by a grant from the Gordon and Betty Moore Foundation to Conservation International (Grant No. 3519). This research was conducted by the Evidence-based Conservation Working Group and financially supported in part by SNAP: Science for Nature and People, a collaboration of The Nature Conservancy, the Wildlife Conservation Society and the National Center for Ecological Analysis and Synthesis (NCEAS).

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13750_2016_58_moesm1_esm.docx.

Additional file 1: Appendix 1. Search strategy. Table S1. List of websites searched for relevant articles. Table S2. List of academic thesis databases searched for relevant articles.

Additional file 2: Appendix 2. Data extraction code book and questionnaire.

Additional file 3: appendix 3. included articles. table s3. list of included articles., additional file 4: appendix 4. excluded articles. table s4. list of excluded articles at full text assessment., additional file 5: appendix 5. coded data from systematic data for all included articles., rights and permissions.

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McKinnon, M.C., Cheng, S.H., Dupre, S. et al. What are the effects of nature conservation on human well-being? A systematic map of empirical evidence from developing countries. Environ Evid 5 , 8 (2016). https://doi.org/10.1186/s13750-016-0058-7

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Press Release

Study in nature: protecting the ocean delivers a comprehensive solution for climate, fishing and biodiversity.

Southern Line Islands

Photograph by Southern Line Islands

Groundbreaking global study is the first to map ocean areas that, if strongly protected, would help solve climate, food and biodiversity crises

London, UK (17 March 2021) —A new study published in the prestigious peer-reviewed scientific journal Nature today offers a combined solution to several of humanity’s most pressing challenges. It is the most comprehensive assessment to date of where strict ocean protection can contribute to a more abundant supply of healthy seafood and provide a cheap, natural solution to address climate change—in addition to protecting embattled species and habitats.

An international team of 26 authors identified specific areas that, if protected, would safeguard over 80% of the habitats for endangered marine species, and increase fishing catches by more than eight million metric tons. The study is also the first to quantify the potential release of carbon dioxide into the ocean from trawling, a widespread fishing practice—and finds that trawling is pumping hundreds of millions of tons of carbon dioxide into the ocean every year, a volume of emissions similar to those of aviation.

“Ocean life has been declining worldwide because of overfishing, habitat destruction and climate change. Yet only 7% of the ocean is currently under some kind of protection,” said Dr. Enric Sala, explorer in residence at the National Geographic Society and lead author of the study, Protecting the global ocean for biodiversity, food and climate .

“In this study, we’ve pioneered a new way to identify the places that—if strongly protected—will boost food production and safeguard marine life, all while reducing carbon emissions,” Dr. Sala said. “It’s clear that humanity and the economy will benefit from a healthier ocean. And we can realize those benefits quickly if countries work together to protect at least 30% of the ocean by 2030.”

To identify the priority areas, the authors—leading marine biologists, climate experts, and economists—analyzed the world’s unprotected ocean waters based on the degree to which they are threatened by human activities that can be reduced by marine protected areas (for example, overfishing and habitat destruction). They then developed an algorithm to identify those areas where protections would deliver the greatest benefits across the three complementary goals of biodiversity protection, seafood production and climate mitigation. They mapped these locations to create a practical “blueprint” that governments can use as they implement their commitments to protect nature.

The study does not provide a single map for ocean conservation, but it offers a first-in-kind framework for countries to decide which areas to protect depending on their national priorities. However, the analysis shows that 30% is the minimum amount of ocean that the world must protect in order to provide multiple benefits to humanity.

“There is no single best solution to save marine life and obtain these other benefits. The solution depends on what society—or a given country—cares about, and our study provides a new way to integrate these preferences and find effective conservation strategies,” said Dr. Juan S. Mayorga, a report co-author and a marine data scientist with the Environmental Market Solutions Lab at UC Santa Barbara and Pristine Seas at National Geographic Society.

The study comes ahead of the 15th Conference of the Parties to the United Nations Convention on Biological Diversity, which is expected to take place in Kunming, China in 2021. The meeting will bring together representatives of 190 countries to finalize an agreement to end the world’s biodiversity crisis. The goal of protecting 30% of the planet’s land and ocean by 2030 (the “30x30” target) is expected to be a pillar of the treaty. The study follows commitments by the United States, the United Kingdom, Canada, the European Commission and others to achieve this target on national and global scales.

Safeguarding Biodiversity

The report identifies highly diverse marine areas in which species and ecosystems face the greatest threats from human activities. Establishing marine protected areas (MPAs) with strict protection in those places would safeguard more than 80% of the ranges of endangered species, up from a current coverage of less than 2%.

The authors found that the priority locations are distributed throughout the ocean, with the vast majority of them contained within the 200-mile Exclusive Economic Zones of coastal nations.

The additional protection targets are located in the high seas—those waters governed by international law. These include the Mid-Atlantic Ridge (a massive underwater mountain range), the Mascarene Plateau in the Indian Ocean, the Nazca Ridge off the west coast of South America and the Southwest Indian Ridge, between Africa and Antarctica.

"Perhaps the most impressive and encouraging result is the enormous gain we can obtain for biodiversity conservation—if we carefully chose the location of strictly protected marine areas,” said Dr. David Mouillot, a report co-author and a professor at the Université de Montpellier in France. “One notable priority for conservation is Antarctica, which currently has little protection, but is projected to host many vulnerable species in a near future due to climate change."

Shoring up the Fishing Industry

The study finds that smartly placed marine protected areas (MPAs) that ban fishing would actually boost the production of fish—at a time when supplies of wild-caught fish are dwindling and demand is rising. In doing so, the study refutes a long-held view that ocean protection harms fisheries and opens up new opportunities to revive the industry just as it is suffering from a recession due to overfishing and the impacts of global warming.

“Some argue that closing areas to fishing hurts fishing interests. But the worst enemy of successful fisheries is overfishing—not protected areas,” Dr. Sala said.

The study finds that protecting the right places could increase the catch of seafood by over 8 million metric tons relative to business as usual.

“It’s simple: When overfishing and other damaging activities cease, marine life bounces back,” said Dr. Reniel Cabral, a report co-author and assistant researcher with the Bren School of Environmental Science & Management and Marine Science Institute at UC Santa Barbara. “After protections are put in place, the diversity and abundance of marine life increase over time, with measurable recovery occurring in as little as three years. Target species and large predators come back, and entire ecosystems are restored within MPAs. With time, the ocean can heal itself and again provide services to humankind.”

Soaking up Carbon

The study is the first to calculate the climate impacts of bottom trawling, a damaging fishing method used worldwide that drags heavy nets across the ocean floor. It finds that the amount of carbon dioxide released into the ocean from this practice is larger than most countries’ annual carbon emissions, and similar to annual carbon dioxide emissions from global aviation.

“The ocean floor is the world’s largest carbon storehouse. If we’re to succeed in stopping global warming, we must leave the carbon-rich seabed undisturbed. Yet every day, we are trawling the seafloor, depleting its biodiversity and mobilizing millennia-old carbon and thus exacerbating climate change. Our findings about the climate impacts of bottom trawling will make the activities on the ocean’s seabed hard to ignore in climate plans going forward,” said Dr. Trisha Atwood of Utah State University, a co-author of the paper.

The study finds that countries with the highest potential to contribute to climate change mitigation via protection of carbon stocks are those with large national waters and large industrial bottom trawl fisheries. It calculates that eliminating 90% of the present risk of carbon disturbance due to bottom trawling would require protecting only about 4% of the ocean , mostly within national waters.

Closing a Gap

The study’s range of findings helps to close a gap in our knowledge about the impacts of ocean conservation, which to date had been understudied relative to land-based conservation.

“The ocean covers 70% of the earth—yet, until now, its importance for solving the challenges of our time has been overlooked,” said Dr. Boris Worm, a study co-author and Killam Research Professor at Dalhousie University in Halifax, Nova Scotia. “Smart ocean protection will help to provide cheap natural climate solutions, make seafood more abundant and safeguard imperiled marine species—all at the same time. The benefits are clear. If we want to solve the three most pressing challenges of our century—biodiversity loss, climate change and food shortages —we must protect our ocean.”

Additional Quotes from Supporters and Report Co-Authors

Zac Goldsmith, British Minister for Pacific and the Environment, UK

Kristen Rechberger, Founder & CEO, Dynamic Planet

Dr. William Chueng, Canada Research Chair and Professor, The University of British Columbia, Principal Investigator, Changing Ocean Research Unit, The University of British Columbia

Dr. Jennifer McGowan, Global Science, The Nature Conservancy & Center for Biodiversity and Global Change, Yale University

Dr. Alan Friedlander, Chief Scientist, Pristine Seas, National Geographic Society at the Hawai'i Institute of Marine Biology, University of Hawai'i

Dr. Ben Halpern, Director of the National Center for Ecological Analysis and Synthesis (NCEAS), UCSB

Dr. Whitney Goodell, Marine Ecologist, Pristine Seas, National Geographic Society

Dr. Lance Morgan, President and CEO, Marine Conservation Institute

Dr. Darcy Bradley, Co-Director of the Ocean and Fisheries Program at the Environmental Market Solutions Lab, UCSB

The study, Protecting the global ocean for biodiversity, food and climate , answers the question of which places in the ocean should we protect for nature and people. The authors developed a novel framework to produce a global map of places that, if protected from fishing and other damaging activities, will produce multiple benefits to people: safeguarding marine life, boosting seafood production and reducing carbon emissions. Twenty-six scientists and economists contributed to the study.

Study’s Topline Facts

• Ocean life has been declining worldwide because of overfishing, habitat destruction and climate change. Yet only 7% of the ocean is currently under some kind of protection.
• A smart plan of ocean protection will contribute to more abundant seafood and provide a cheap, natural solution to help solve climate change, alongside economic benefits.
• Humanity and the economy would benefit from a healthier ocean. Quicker benefits occur when countries work together to protect at least 30% of the ocean.
• Substantial increases in ocean protection could achieve triple benefits, not only protecting biodiversity, but also boosting fisheries’ productivity and securing marine carbon stocks.

Study’s Topline Findings

• The study is the first to calculate that the practice of bottom trawling the ocean floor is responsible for one gigaton of carbon emissions on average annually. This is equivalent to all emissions from aviation worldwide. It is, furthermore, greater than the annual emissions of all countries except China, the U.S., India, Russia and Japan.
• The study reveals that protecting strategic ocean areas could produce an additional 8 million tons of seafood.
• The study reveals that protecting more of the ocean--as long as the protected areas are strategically located--would reap significant benefits for climate, food and biodiversity.

Priority Areas for Triple Wins

• If society were to value marine biodiversity and food provisioning equally, and established marine protected areas based on these two priorities, the best conservation strategy would protect 45% of the ocean, delivering 71% of the possible biodiversity benefits, 92% of the food provisioning benefits and 29% of the carbon benefits.
• If no value were assigned to biodiversity, protecting 29% of the ocean would secure 8.3 million tons of extra seafood and 27% of carbon benefits. It would also still secure 35% of biodiversity benefits.
• Global-scale prioritization helps focus attention and resources on places that yield the largest possible benefits.
• A globally coordinated expansion of marine protected areas (MPAs) could achieve 90% of the maximum possible biodiversity benefit with less than half as much area as a protection strategy based solely on national priorities.
• EEZs are areas of the global ocean within 200 nautical miles off the coast of maritime countries that claim sole rights to the resources found within them. ( Source )

Priority Areas for Climate

• Eliminating 90% of the present risk of carbon disturbance due to bottom trawling would require protecting 3.6% of the ocean, mostly within EEZs.
• Priority areas for carbon are where important carbon stocks coincide with high anthropogenic threats, including Europe’s Atlantic coastal areas and productive upwelling areas.

Countries with the highest potential to contribute to climate change mitigation via protection of carbon stocks are those with large EEZs and large industrial bottom trawl fisheries.

Priority Areas for Biodiversity

• Through protection of specific areas, the average protection of endangered species could be increased from 1.5% to 82% and critically endangered species from 1.1% to and 87%.
• the Antarctic Peninsula
• the Mid-Atlantic Ridge
• the Mascarene Plateau
• the Nazca Ridge
• the Southwest Indian Ridge
• Despite climate change, about 80% of today’s priority areas for biodiversity will still be essential in 2050. In the future, however, some cooler waters will be more important protection priorities, whereas warmer waters will likely be too stressed by climate change to shelter as much biodiversity as they currently do. Specifically, some temperate regions and parts of the Arctic would rank as higher priorities for biodiversity conservation by 2050, whereas large areas in the high seas between the tropics and areas in the Southern Hemisphere would decrease in priority.

Priority Areas for Food Provision

• If we only cared about increasing the supply of seafood, strategically placed MPAs covering 28% of the ocean could increase food provisioning by 8.3 million metric tons.

The Campaign for Nature works with scientists, Indigenous Peoples, and a growing coalition of over 100 conservation organizations around the world who are calling on policymakers to commit to clear and ambitious targets to be agreed upon at the 15th Conference of the Parties to the Convention on Biological Diversity in Kunming, China in 2021 to protect at least 30% of the planet by 2030 and working with Indigenous leaders to ensure full respect for Indigenous rights.

Media Contact

The National Geographic Society is a global nonprofit organization that uses the power of science, exploration, education and storytelling to illuminate and protect the wonder of our world. Since 1888, National Geographic has pushed the boundaries of exploration, investing in bold people and transformative ideas, providing more than 15,000 grants for work across all seven continents, reaching 3 million students each year through education offerings, and engaging audiences around the globe through signature experiences, stories and content. To learn more, visit www.nationalgeographic.org or follow us on Instagram , LinkedIn, and Facebook .

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AbstractScientific evidence suggests that emotions affect actual human decision-making, particularly in highly emotionally situations such as human-wildlife interactions. In this study we assess the role of fear on preferences for wildlife conservation, using a discrete choice experiment. The sample was split into two treatment groups and a control. In the treatment groups the emotion of fear towards wildlife was manipulated using two different pictures of a wolf, one fearful and one reassuring, which were presented to respondents during the experiment. Results were different for the two treatments. The assurance treatment lead to higher preferences and willingness to pay for the wolf, compared to the fear treatment and the control, for several population sizes. On the other hand, the impact of the fear treatment was lower than expected and only significant for large populations of wolves, in excess of 50 specimen. Overall, the study suggests that emotional choices may represent a source of concern for the assessment of stable preferences. The impact of emotional choices is likely to be greater in situations where a wildlife-related topic is highly emphasized, positively or negatively, by social networks, mass media, and opinion leaders. When stated preferences towards wildlife are affected by the emotional state of fear due to contextual external stimuli, welfare analysis does not reflect stable individual preferences and may lead to sub-optimal conservation policies. Therefore, while more research is recommended for a more accurate assessment, it is advised to control the decision context during surveys for potential emotional choices.

Social Repercussion of Translocating a Jaguar in Brazil

The translocation of “problem-animals” is a common non-lethal strategy to deal with human-wildlife conflict. While processes of wildlife translocation have been widely documented, little is known about the social repercussions that take place once the capture and the return of a problem-animal to its natural habitat fail and it has to be permanently placed in captivity. We investigated how the public, an important stakeholder in wildlife conservation, perceived the translocation of a female jaguar to a wildlife captivity center. The objectives were to (1) assess the public's perceptions (e.g., attitudes, emotions, awareness) toward the jaguar and its translocation process, and (2) how these psychological constructs are related. We used the social media profiles of the three institutions involved in the process (one responsible for the jaguar rescues, one that supported its recovery, and the one responsible for the jaguar's final destination) and analyzed the comments left by their followers on posts related to the jaguar and the translocation itself during 25 days. A total of 287 comments were analyzed through coding, a categorizing strategy of qualitative analysis; 33 codes were identified. Results showed high admiration for the work done, positive attitudes and emotions, and concern toward the animal. Lack of awareness about the translocation process was high, with comments of curiosity toward the situation being one of the most commonly found. To a lesser extent, people felt sad for the jaguar not being able to return to the wild and criticized the need for translocation. Admiration for the work had a strong relation with gratitude and broader positive perceptions toward the jaguar's story. Criticism related to concern, which was also related to a need for more information and curiosity. Our findings suggest that the public who engaged with those institutions through their Instagram accounts were grateful for seeing the jaguar safe, but were not aware of the complexity of the operation nor about the nature of the conflict with farmers. The public can either reinforce a particular action or jeopardize an entire operation, depending on their perceptions of the matter. In the case of this jaguar, the public held a positive view; however, we acknowledge the limitations of our sample and recommend further analyses of social repercussions among people who are not followers of these organizations. Furthermore, we recommend engaging other stakeholders to fully understand the human dimensions of translocating this jaguar. Finally, for social acceptance, we highlight the importance of transparency and reliability of the organizations operating the translocation.

A Review of Human-Elephant Ecological Relations in the Malay Peninsula: Adaptations for Coexistence

Understanding the relationship between humans and elephants is of particular interest for reducing conflict and encouraging coexistence. This paper reviews the ecological relationship between humans and Asian elephants (Elephas maximus) in the rainforests of the Malay Peninsula, examining the extent of differentiation of spatio-temporal and trophic niches. We highlight the strategies that people and elephants use to partition an overlapping fundamental niche. When elephants are present, forest-dwelling people often build above-the-ground shelters; and when people are present, elephants avoid open areas during the day. People are able to access several foods that are out of reach of elephants or inedible; for example, people use water to leach poisons from tubers of wild yams, use blowpipes to kill arboreal game, and climb trees to access honey. We discuss how the transition to agriculture affected the human–elephant relationship by increasing the potential for competition. We conclude that the traditional foraging cultures of the Malay Peninsula are compatible with wildlife conservation.

Staff perceptions of COVID‐19 impacts on wildlife conservation at a zoological institution

Community-based tourism and local people's perceptions towards conservation.

Uganda is among the most bio-diverse countries and a competitive wildlife-based tourism destination in the world. Community-based tourism approach has been adopted in the country's conservation areas as a strategy to ensure that local communities benefit and support wildlife conservation. This chapter analyses local communities' perceptions of conservation and the benefits they get from tourism in Queen Elizabeth Conservation Area. The study reveals that local communities were concerned about loss of protected resources and support their conservation irrespective of the benefits they get from tourism in the conservation area. There is need to design conservation programmes that focus on local community-conservation-benefits nexus which take into consideration the perceived conservation values, strategies for benefit sharing and incorporation of indigenous knowledge systems.

KONSERVASI HUTAN PADA JURNAL BIOLOGI INDONESIA PERIODE 2010-2020: SEBUAH STUDI BIBLIOMETRIK

A bibliometric analysis was carried out on the Indonesian Biology Journal for the period 2010 – 2020, with the aim of knowing 1) the distribution of keywords to see the description of the research published in the Indonesian Biology Journal 2010-2020; 2) article classification; 3) distribution of articles by year; 4) distribution of articles by issue number; 5) authorship pattern; 6) the most prolific writer; 7) affiliations of authors who contribute to the Indonesian Biology Journal; 8) the type of document used as a reference in the Indonesian Biology Journal 2010-2020. The bibliometric method was used, and the data was taken from the Indonesian Biology Journal from 2010 to 2020, which was downloaded via the address https://e-journal.biologi.lipi.go.id/index.php/jurnal_biologi_indonesia. Furthermore, the analysis of the distribution of articles based on keywords, distribution of class numbers, distribution of articles by year, distribution of articles by number of publications, pattern of authorship, most productive authors, pattern of authorship affiliation was carried out. Based on the results and discussion, it can be concluded that during 2010-2020, 315 article titles have been published and there are 1,343 keywords. Of the 50 most keywords, the keyword Biodiversity often appears 21 times (1.56%) then Genetic variation and Wildlife conservation each 20 times (1.48%), then Animal population 18 times (1.34 %), followed by Plant conservation 17 times (1.19%) and Animal conservation 16 times (1.19%). Next is Feeds and Plant growth substances each with 15 (1.11%), then In vitro culture and Plant diversity each with 14 (1.04%). Next, Vegetation is 13 (0.90%), followed by Habitat conservation and Plant species, each with 11 (0.82%). On the order of 50 keywords Drought resistance, with a total of 4 (0.29%). The highest class is class 635 with a frequency of 35 (11.11%). Articles written by a single author (71 titles; 22.54%) and articles written by collaboration (244 titles; 77.46%). the least number of articles published is in 2020, which is 1 article title (3,17). For issue number 1 starting from volume 6 to volume 16, 164 article titles have been published (52.06%). As for number 2 with the same volume, there were 151 article titles (47.94%). The most prolific writer is Hellen Kurniati with 13 writings, followed by Wartika Rosa Farida with 12 writings and then Witjaksono with 11 writings. Then Andri Permata Sari, Niken Tunjung Murti Pratiwi, NLP. Indi Dharmayanti, Tri Muji Ermayanti with 10 each, followed by Didik Widyatmoko and Risa Indriani with 9 each, Atit Kanti and Yopi with 7 each and Dwi Astuti, Eko Sulistyadi, Ibnu Maryanto, Inna Puspa Ayu each. 6 posts. LIPI is the first institution that contributes the most articles, with a frequency of 260 times. It is known that 7,354 document titles are used as references and the journal is in the first order of cited documents, with 4,591 titles (62.42%).

Fauna diversity in the southern part of the Kon Ka Kinh National Park, Gia Lai province

Kon Ka Kinh National Park (KKK NP) is a priority zone for biodiversity protection in Vietnam as well as ASEAN. In order to survey the current fauna species diversity in the southern part of the KKK NP, we conducted camera trapping surveys in 2017, 2018, and 2019. 28 infrared camera traps were set up on elevations between 1041 to 1497 meters. In total, there were 360 days of survey using camera trap. As result, we recorded a total of 27 animal species of those, five species are listed in the IUCN Red List of Threatened Species (IUCN, 2020). The survey results showed a high richness of wildlife in the southern park region, and it also revealed human disturbance to wildlife in the park. The first-time camera trap was used for surveying wildlife diversity in the southern region of the KKK NP. Conducting camera trap surveys in the whole KKK NP is essential for monitoring and identifying priority areas for wildlife conservation in the national park.

The Growing Importance of Sustainable Wildlife Tourism in India and Involving Indian Youth in Promoting Wildlife Conservation

Federal funding and state wildlife conservation, human-wildlife conflict and community perceptions towards wildlife conservation in and around wof-washa natural state forest, ethiopia: a case study of human grivet monkey conflict.

Abstract Background: Human-wildlife conflict (HWC) is predicted to increase globally in the vicinity of protected areas and occurs in several different contexts and involves a range of animal taxonomic groups whose needs and requirements overlap with humans. Human-monkey conflict exists in different forms more in developing countries and ranks amongst the main threats to biodiversity conservation. Grivet monkeys (Cercopithecus aethiops aethiops) are any slender agile Old-World monkeys of the genus Cercopithecus. This study was conducted to investigate the status of human grivet monkey conflict and the attitude of local communities towards grivet monkey conservation in and around Wof-Washa Natural State Forest (WWNSF), Ethiopia from September 2017 to May 2018. Questionnaire survey (143) was used to study the human-grivet monkey conflict and its conservation status. Data were analyzed using descriptive statistics and the responses were compared using a nonparametric Pearson chi-square test. Results: Majority of respondents from both gender (male= 67.1%; female= 74.1%) were not supporting grivet monkey conservation due to its troublesome crop damaging effect. There was significant difference in respondents perceptions towards grivet monkey conservation based on distance of farmland from the forest (χ2= 12.7, df =4, P = 0.013). There was no significant difference in the techniques used by villagers to deter crop raiders (χ2= 14.73, df =15, P = 0.47). There was significant difference in respondents expectations on the mitigation measures to be taken by government (χ2= 40.01, df =15, P = 0.000). Based on the questionnaire result, 42.5 ± SD 8.68 of respondents in all villages elucidated that the causes of crop damage was habitat degradations.Conclusion: The encroachment of local communities in to the forest area and exploitation of resources that would be used by grivet monkey and enhanced crop damage by grivet monkey exacerbated the HGMC in the study area. As a result grivet monkeys have been killed relentlessly as a consequence of crop damage. This was due to negative energy developed in human perspective. Thus, awareness creation education program and feasible crop damage prevention techniques need to be implemented.

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The journals encourage publication of open opinions, forum papers, corrigenda, critical comments on a published paper and Author’s response to criticism.

Research misconduct may include: (a)  manipulating research materials, equipment or processes; (b) changing or omitting data or results such that the research is not accurately represented in the article; c) plagiarism. Research misconduct does not include honest error or differences of opinion. If misconduct is suspected, journal Editors will act in accordance with the relevant  COPE guidelines .

Plagiarism and duplicate publication policy A special case of misconduct is plagiarism, which is the appropriation of another person's ideas, processes, results or words without giving appropriate credit. Plagiarism is considered theft of intellectual property and manuscripts submitted to this journal which contain substantial unattributed textual copying from other papers will be immediately rejected. Editors are advised to check manuscripts for plagiarism via the iThenticate service by clicking on the "ïThenticate report" button. Journal providing a peer review in languages other than English (for example, Russian) may use other plagiarism checking services (for example, Antiplagiat).  Instances, when authors re-use large parts of their publications without providing a clear reference to the original source, are considered duplication of work. Slightly changed published works submitted in multiple journals is not acceptable practice either. In cases of plagiarism in an already published paper or duplicate publication, an announcement will be made on the journal publication page and a procedure of retraction will be triggered.

Responses to possible misconduct

All allegations of misconduct must be referred to the Editor-In-Chief. Upon the thorough examination, the Editor-In-Chief and deputy editors should conclude if the case concerns a possibility of misconduct. All allegations should be kept confidential and references to the matter in writing should be kept anonymous, whenever possible.

Should a comment on potential misconduct be submitted by the Reviewers or Editors, an explanation will be sought from the Authors. If it is satisfactory and the issue is the result of either a mistake or misunderstanding, the matter can be easily resolved. If not, the manuscript will be rejected or retracted and the Editors may impose a ban on that individual's publication in the journals for a certain period of time. In cases of published plagiarism or dual publication, an announcement will be made in both journals explaining the situation.

When allegations concern authors, the peer review and publication process for their submission will be halted until completion of the aforementioned process. The investigation will be carried out even if the authors withdraw the manuscript, and implementation of the responses below will be considered.

When allegations concern reviewers or editors, they will be replaced in the review process during the ongoing investigation of the matter. Editors or reviewers who are found to have engaged in scientific misconduct should be removed from further association with the journal, and this fact reported to their institution.

Retraction policies

Article retraction

According to the  COPE Retraction Guidelines  followed by this Journal, an article can be retracted because of the following reasons:

• Unreliable findings based on clear evidence of a misconduct (e.g. fraudulent use of the data) or honest error (e.g. miscalculation or experimental error).
• Redundant publication, e.g., findings that have previously been published elsewhere without proper cross-referencing, permission or justification.
• Plagiarism or other kind of unethical research.

Retraction procedure

• Retraction should happen after a careful consideration by the Journal editors of allegations coming from the editors, authors, or readers.
• The HTML version of the retracted article is removed (except for the article metadata) and on its place a retraction note is issued.
• The PDF of the retracted article is left on the website but clearly watermarked with the note "Retracted" on each page.
• In some rare cases (e.g., for legal reasons or health risk) the retracted article can be replaced with a new corrected version containing apparent link to the retracted original version and a retraction note with a history of the document.

Expression of concern

In other cases, the Journal editors should consider issuing an expression of concern, if evidence is available for:

• Inconclusive evidence of research or publication misconduct by the authors.
• Unreliable findings that are unreliable but the authors’ institution will not investigate the case.
• A belief that an investigation into alleged misconduct related to the publication either has not been, or would not be, fair and impartial or conclusive.
• An investigation is underway but a judgement will not be available for a considerable time.

Errata and Corrigenda

Pensoft journals largely follow the ICMJE guidelines for corrections and errata.

Admissible and insignificant errors in a published article that do not affect the article content or scientific integrity (e.g. typographic errors, broken links, wrong page numbers in the article headers etc.) can be corrected through publishing of an erratum. This happens through replacing the original PDF with the corrected one together with a correction notice on the Erratum Tab of the HTML version of the paper, detailing the errors and the changes implemented in the original PDF. The original PDF will be marked with a correction note and an indication to the corrected version of the erratum article. The original PDF will also be archived and made accessible via a link in the same Erratum Tab.

Authors are also encouraged to post comments and indicate typographical errors on their articles to the Comments tab of the HTML version of the article.

Corrigenda should be published in cases when significant errors are discovered in a published article. Usually, such errors affect the scientific integrity of the paper and could vary in scale. Reasons for publishing corrigenda may include changes in authorship, unintentional mistakes in published research findings and protocols, errors in labelling of tables and figures or others. In taxonomic journals, corrigenda are often needed in cases where the errors affect nomenclatural acts. Corrigenda are published as a separate publication and bear their own DOI. Examples of published corrigenda are available here .

The decision for issuing errata or corrigenda is with the editors after discussion with the authors.

COPE Compliance

This journal endorses the COPE (Committee on Publication Ethics) guidelines and will pursue cases of suspected research and publication misconduct (e.g. falsification, unethical experimentation, plagiarism, inappropriate image manipulation, redundant publication). For further information about COPE please see the website for COPE at http://www.publicationethics.org and journal's Publication Ethics and Malpractice Statement .

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Select Article Type

Nature Conservation considers the following categories of papers for publication:

• Original research articles
• Reviews as longer articles, offering a comprehensive overview, historical analysis and/or future perspectives of a topic
• Monographs and collection of papers with no limit in size, published as 'special issues'
• Short communications
• Letters and Forum papers
• Case studies
• Trend scanning papers
• Datasets and Data papers
• Book reviews

Nature Conservation shall consider for publication manuscripts related to the following topics:

Habitat and species conservation

• Conservation of genetic resources
• Evolution (Adaptation) and conservation
• Biodiversity monitoring
• Population dynamics, management, and harvesting
• Biogeography and macroecology
• Scaling in biodiversity conservation
• Restoration of habitats and ecosystems

Environmental ecology & conservation

• Climate change and nature conservation
• Land use change and nature conservation
• Conservation of ecosystem functions and ecosystem services
• Novel ecosystems and nature conservation
• Environmental impact assessment (EIA)

Societal aspects of nature conservation

• Governance and nature conservation
• Conservation policy
• Restoration policy
• Conservation law
• Economics of biodiversity and ecosystem use and management
• Access and benefit sharing
• Natural resource management and business studies
• Education and capacity building
• Public engagement/participation in nature conservation
• Science of nature conservation communication

Conservation in complex social-ecological systems

• Integrated solutions for multiple environmental challenges
• Conservation conflicts and their management
• Ecological expertise, expert systems, and evidence-based conservation     

Data publishing and ecological informatics

• Data and metadata management and environmental statistics
• Applications for data collection and data sharing
• Web-based tools
• Ecological modelling for conservation

Submission Guidelines

Submission procedure.

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Registration and login are required to submit items online and to check the status of current submissions.

Submission of manuscripts to this journal is possible only through the online submission module. We kindly request authors to consult the Focus and Scope section prior to submission. In order to submit a manuscript to the journal, authors are required to register with the journal and/or to login. Once logged in, you will find the online submission system either by clicking the " Submit manuscript " button.

The manuscript submission process is separated into the following steps:

• Step 1:  Specifying the manuscript type and completing the submission checklist
• Step 2:  Choosing the payment option and requesting optional services
• Step 3:  Typing in the author(s) names and affiliation, title, abstract, keywords, and other metadata
• Step 4:  Assigning classifications categories for your manuscript using hierarchical classification trees
• Step 5:  Completing the submission metadata by adding details about any supporting agencies, conflict of interest, ethical statement, comments to the editors
• Step 6:  Agreeing with the journal's Data Publishing Policy and specifying the availability of the data underpinning your article
• Step 7:  Uploading the submission file and the additional files (see below for details on how to prepare it)
• Step 8:  Confirming the automatically generated pdf review version of the article, and the metadata (or revising them, if needed)
• Step 9:  Uploading supplementary files (see below for details) and associated metadata
• Step 10:  Suggesting reviewers, final verification of the submitted files and confirmation

Stepwise guidance on new manuscript submission, with screenshots of the interface embedded, is available online in  this section of the User Manual .

Organizing Your Submission

Before starting your submission please make sure that your manuscript is formatted in accordance with the Author Guidelines.

Before attempting an online submission, please consider preparing the following file types:

1. Submission file

Review  version of the manuscript: a TEXT (MS WORD) file in either DOC, DOCX, RTF or ODT format. The total file size must be no larger than 80 MB . The system allows two options for the submission file upload:

it could contain all figures embedded at their respective places within the manuscript: 

Advantage: The review version of the manuscript will be more convenient for reading and understanding by the reviewers and editors. Likewise, if you opt to post your manuscript on ARPHA Preprints and this is allowed by the current journal’s policies, it will be better organised for the readers.

Drawback: Additional effort is needed to place and number the figures within the text.

it could contain the article text only, while the figures are added separately in the allowed formats (see below), so that the system can add them automatically to the PDF version that will be sent for review. The authors have the option to check and replace, if needed, the PDF review version generated at the first submission step:

Advantage: No additional effort is needed for placing and numbering the figures within the text.

Drawback: All figures will be placed at the end of the manuscript and the review version will be less convenient for reading and understanding by the reviewers and editors. The same concerns your preprint if you decide to post it on ARPHA Preprints and this is allowed by the current journal’s policies.

2. Additional files

High-resolution figures must be submitted during the same submission process as the additional files (Step 7) in one of the accepted file formats (see below). These may be compressed in order to reduce bandwidth during upload:

• Figures (each figure as an individual file in one of the following image file formats:  EPS, TIFF, JPEG, PNG, GIF, BMP, not larger than 20 MB each )
• Equations (each equation as an individual file in one of the above-mentioned image file formats)

Please note that the  maximum file size  that may be uploaded through our online submission system is  20 MB .

3. Supplementary files (appendices)

Large datasets or multimedia files, usually published as appendices in conventional print journals, should be uploaded as  supplementary files  complete with the associated metadata on the online submission form. Supplementary files should have their own legends.

Most file formats are accepted. Text-only appendices must be in DOC, DOCX, RTF, or ODF formats.

Should you have any technical problems in submitting a manuscript to this journal, please contact the  Editorial Office  at  [email protected] .

We encourage authors to send an inquiry to the respective Subject Editor prior to submitting a manuscript. The purpose of the presubmission inquiry is to solicit rapid initial feedback on the suitability of the manuscript for publication in this journal. Pre-submission inquiries may also be sent to the Editorial Office at  [email protected] .

Author Contributions

The journal is integrated with  Contributor Role Taxonomy  (CRediT), in order to recognise individual author input within a publication, thereby ensuring professional and ethical conduct, while avoiding authorship disputes, gift / ghost authorship and similar pressing issues in academic publishing.

During manuscript submission, the submitting author is strongly recommended to select a contributor role for each of co-author, using a list of 14 predefined roles, i.e. Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original draft, Writing - Review and Editing, Visualization, Supervision, Project administration, Funding Acquisition (see  more ). Once published, the article will be including the contributor role for all authors in the article metadata.

Prepare Your Manuscript

English language editing.

This journal has well-defined policies for English language editing. 

Authors are required to have their manuscripts written in fluent English or edited by a professional English language editor  BEFORE submission. Authors have to confirm by checking a tick box in the submission process that they have followed the above requirement:

"The text is edited by a professional English language editor, duly acknowledged in the manuscript. I am aware that non-edited manuscripts could be rejected prior to peer-review".

The submission process includes an option to request a professional linguistic editing at a price of EURO 15 per 1800 characters :

The authors are NOT obliged to use Journal's linguistic services, but they must ensure that their manuscripts have passed a proper linguistic editing before submission.

Title: The title should be in a sentence case (only scientific, geographic or person names should be with a first capital letter, i.e. Elater ferrugineus L., Germany, etc.), and should include an accurate, clear and concise description of the reported work, avoiding abbreviations. The titles of taxonomic papers should always include the upper rank taxa separated with comma in the following order, in brackets: (Phylum, Class, Order, Family). The Phylum and/or Class can be included optionally, depending on the community accepted practice in publishing on the respective phylum or class.

Authors and Affiliations: Provide the complete names of all authors, and their addresses for correspondence, including e.g., institutional affiliation (e.g. university, institute), location (street, boulevard), city, state/province (if applicable), and country. One of the authors should be designated as the corresponding author. It is the corresponding author's responsibility to ensure that the author list, and the individual contributions to the study are accurate and complete. If the article has been submitted on behalf of a consortium, all consortium members and their affiliations should be listed after the Acknowledgements section.

Abstract and Keywords: Please have your abstract and keywords ready for input into the submission module.

Body Text:  All papers should be in grammatically correct English. Authors are asked to certify that their manuscripts are written in fluent English or edited by a professional English language editor prior to submission. Use either British/Commonwealth or American English provided that the language is consistent within the paper. A manuscript must be written with precision, clarity, and economy. The voice - active or passive - and the tense used should be consistent throughout the manuscript. Avoid the use of parenthetical comments and italics or bold for emphasis. This journal discourages the use of quotation marks except for direct quotations, words defined by the author, and words used in unusual contexts. Short quotations should be embedded in the text and enclosed in double quotation marks ("). Long quotations should be on a separate line, italicized, but without quotation marks. Single quotation marks are to be used only for a quotation that occurs within another quotation.

Spacing, Fonts, Line and Page Numbering: 1.5 or double space all text and quotations, single space figure legends, tables, references, etc. Separate paragraphs with a blank line. Use a 12-point font (preferably Times New Roman or Arial). Please provide line numbers as well as page numbers.

Capitals: First capital letters should be used only in the beginning of a sentence, in proper names and in headings and subheadings, as well as to indicate tables, graphs and figure/s within the text. Software programmes should be written with capital letters (e.g., ANOVA, MANOVA, PAUP).

Italicization/Underlining: Scientific names of species and genera, long direct quotations and symbols for variables and constants (except for Greek letters), such as p, F, U, T, N, r, but not for SD (standard deviation), SE (standard error), DF (degrees of freedom) and NS (non significant) should be italicized. These symbols in illustrations and equations should be in italics to match the text. Italics should not be used for emphasis, and not in abbreviations such as e.g., i.e., et al., etc., cf. Underlining of any text is not acceptable.

Abbreviations: Abbreviations should be followed by ‘.' (full stop or period; for instance: i.e., e.g., cf., etc.). Note that you shouldn't add a full stop at the end of abbreviated words if the last letter of the abbreviation is the same as the last letter of the full word. For example, you should abbreviate "Eds", "Dr", "Mr" without full stop at the end. All measures, for instance, mm, cm, m, s, L, should be written without full stop.

On the use of dashes: (1) Hyphens are used to link words such as personal names, some prefixes and compound adjectives (the last of which vary depending on the style manual in use). (2) En-dash or en-rule (the length of an 'n') is used to link spans. In the context of our journal en-dash should be used to link numerals, sizes, dates and page numbers (e.g., 1977–1981; figs 5–7; pp. 237–258); geographic or name associations (Murray–Darling River; a Federal–State agreement); and character states combinations such as long–pubescent or red–purple. (3) Em-dash or em-rule (the length of an 'm') should be used rarely, only for introducing a subordinate clause in the text that is often used much as we use parentheses. In contrast to parentheses an em-dash can be used alone. En-dashes and em-dashes should not be spaced.

Footnotes: Avoid footnotes in the body text of the manuscript. It is always possible to incorporate the footnote into the main text by rewording the sentences, which greatly facilitates reading. Additionally, footnotes are not always handled well by the journal software, and their usage may cause a failure of submission. Footnotes are acceptable only below tables; instead of numbers, please use (in order): †, ‡, §, |, ¶, #, ††, ‡‡, §§, ||, ¶¶, ##.

Geographical coordinates: It is strongly recommended to list geographical coordinates as taken from GPS or online gazetteer, or georeferencer 

( http://wwold.gbif.org/prog/digit/Georeferencing ). Geographical coordinates must be listed in one of the following formats:

Definition: The locality consists of a point represented by coordinate information in the form of latitude and longitude. Information may be in the form of

Degrees, Minutes and Seconds (DMS),

Degrees and Decimal Minutes (DDM), or

Decimal Degrees (DD).

Records should also contain a hemisphere (E or W and N or S) or, with Decimal Degrees, minus (–) signs to indicate western and/or southern hemispheres.

Example 1: 36°31'21" N; 114°09'50"W (DMS)

Example 2: 36°31.46'N; 114°09.84'W (DDM)

Example 3: 36.5243°S; 114.1641°W (DD)

Example 4: −36.5243; −114.1641 (DD using minus signs to indicate southern and western hemispheres)

Note on accuracy: Because GPS units are very commonly used today to record latitude/longitude, many authors simply give the GPS readings for their localities. However, these readings are much too accurate. For example, a GPS unit might give the latitude in decimal seconds as 28°16'55.87"N. Since one second of latitude is about 30 m on the ground, the second figure after the decimal in 55.87 represents 30 cm, yet a typical handheld GPS unit is only accurate at best to a few metres.

We, therefore, recommend two ways to report GPS-based locations. If you give the GPS reading without rounding off, make sure you include an uncertainty figure as a context for the over-accurate GPS reading. We recommend the Darwin Core definition of uncertainty ( http://rs.tdwg.org/dwc/terms/index.htm#coordinateUncertaintyInMeters ):

"The horizontal distance (in meters) from the given decimalLatitude and decimalLongitude describing the smallest circle containing the whole of the Location."

If you only give the GPS reading, please round it off to an implied precision appropriate to the error in the measurement, or to the extent of the area sampled. We suggest rounding off

• to the nearest second in degree-minute-second format (28°16'56"N), which implies roughly ± 25-30 m at middle latitudes
• to four decimal places in decimal degree format (28.2822°N), which implies roughly ± 10-15 m at middle latitudes
• to two decimal places in decimal minute format (28°16.93'N), which implies roughly 15-20 m at middle latitudes

Altitude: Many GPS users simply record the elevation given by their GPS unit. However, GPS elevation is NOT the same as elevation above sea level. GPS units record the elevation above a mathematical model of the earth's surface. The difference between this elevation and elevation above sea level can be tens of metres. In any case, the accuracy of a GPS elevation is often the same as the usual accuracy in horizontal position, so a GPS elevation such as '753 m' is much too accurate and should be rounded off to 'ca 750 m'.

We strongly recommend the use of Example 2 (the DDM format). The other three are also possible but will be recalculated to DDM during the process of online mapping from the HTML version of the paper.

The only restriction on format is in creating a KML (Keyhole Markup Language) file. KML latitudes and longitudes must be in the DD format shown above in Example 4.

Please also consider submitting a table of localities with your manuscript, either as a spreadsheet or in CSV text format. By doing so you will make your specimen localities much more easily available for use in biodiversity databases and geospatial investigations. The geospatial table will be put online as supplementary material for your paper. A minimum table will have three fields: species (or subspecies) name, latitude and longitude. A full table will have the same data for each specimen lot as appears in the text of your paper. Please check latitude/longitude carefully for each entry.

Units: Use the International System of Units ( SI ) for measurements. Consult Standard Practice for Use of the International System of Units (ASTM Standard E−380−93) for guidance on unit conversions, style, and usage.

Statistics: Use leading zeroes with all numbers, including probability values (e.g., P < 0.001). For every significant F−statistic reported, provide two df values (numerator and denominator). Whenever possible, indicate the year and version of the statistical software used.

Web (HTML) links: Authors are encouraged to include links to other Internet resources in their article. This is especially encouraged in the reference section. When inserting a reference to a web-page, please include the http:// portion of the web address.

Supplementary files: Larger datasets can be uploaded separately as Supplementary Files. Tabular data provided as supplementary files can be uploaded as an Excel spreadsheet (.xls), as an OpenOffice spreadsheets (.ods) or comma separated values file (.csv). As with all uploaded files, please use the standard file extensions.

Headings and subheadings: Main headings: The body text should be subdivided into different sections with appropriate headings. Where possible, the following standard headings should be used: Introduction, Methods, Results, Discussion, Conclusions, Acknowledgements, References. These headings need to be in bold font on a separate line and start with a first capital letter. Please do not number headings or subheadings.

Introduction − The motivation or purpose of your research should appear in the Introduction, where you state the questions you sought to answer, and then provide some of the historical basis for those questions.

Methods − Provide sufficient information to allow someone to repeat your work. A clear description of your experimental design, sampling procedures, and statistical procedures is especially important in papers describing field studies, simulations, or experiments. If you list a product (e.g., animal food, analytical device), supply the name and location of the manufacturer. Give the model number for equipment used. Supply complete citations, including author (or editor), title, year, publisher, and version number, for computer software mentioned in your article.

Results − Results should be stated concisely and without interpretation.

Discussion − Focus on the rigorously supported aspects of your study. Carefully differentiate the results of your study from data obtained from other sources. Interpret your results, relate them to the results of previous research, and discuss the implications of your results or interpretations. Point out results that do not support speculations or the findings of previous research, or that are counter-intuitive. You may choose to include a Speculation subsection in which you pursue new ideas suggested by your research, compare and contrast your research with findings from other systems or other disciplines, pose new questions that are suggested by the results of your study, and suggest ways of answering these new questions.

Conclusion −This should state clearly the main conclusions of the research and give a clear explanation of their importance and relevance. Summary illustrations may be included.

References − The list of References should be included after the final section of the main article body. A blank line should be inserted between single-spaced entries in the list. Authors are requested to include links to online sources of articles, whenever possible!

Where possible, the standard headings should be used in the order given above. Additional headings and modifications are permissible.

Subordinate headings: Subordinate headings (e.g. Field study and Simulation model or Counts, Measurements and Molecular analysis ), should be left-justified, italicized, and in a regular sentence case. All subordinate headings should be on a separate line.

Citations and References

Citations within the text: Before submitting the manuscript, please check each citation in the text against the References and vice-versa to ensure that they match exactly.

Citations in the text should be formatted as follows:

One author: Smith (1990) or (Smith 1990)

Note: The citations format depends on the way it is incorporated within the article’s text:

• According to Smith (1990), these findings…
• These findings have been first reported in the beginning of the nineties (Smith 1990).

Two authors: Brock and Gunderson (2001) or (Brock and Gunderson 2001)

Note: When choosing between formats refer back to examples above.

Three or more authors: Smith et al. (1998) or (Smith et al. 1998)

When citing more than one source , in-text citations should be ordered by the year of publication, starting with the earliest one:

(Smith et al. 1998, 2000, 2016; Brock and Gunderson 2001; Felt 2006).

Note: When you have a few citations from the same author but from different years (such as the case with Smith et al. above), the first year is taken into consideration when ordering the sources (in this case 1998, which is why Smith et al. come first in the list).

When having two or more fully identical citations (this can happen when you have more than one reference with exactly the same authors and years for one or two authors, or the same first author and year for author teams of three or more), the references are distinguished by adding the letters 'a', 'b', 'c', etc. after the years and this marking is followed in the in-text citations, respectively:

(Reyes-Velasco et al. 2018a, 2018b)

Authorship references for species should include a "," between author and year:

Brianmyia stuckenbergi Woodley, 2012.

References: It is important to format the references properly, because all references will be linked electronically as completely as possible to the papers cited. It is desirable to add a DOI (digital object identifier) number for either the full-text or title and abstract of the article as an addition to traditional volume and page numbers. If a DOI is lacking, it is recommended to add a link to any online source of an article.

List all authors cited in the References. For multiauthored papers, give all author names in full; the abbreviation "et al." is only allowed in the text. All journal titles should be spelled out completely and should not be italicized. Ensure that the References are complete and arranged according to name and year of publication. Personal communications and submitted manuscripts should be listed as unpublished results in the text and not listed in the References section.

Please use the following style for the reference list (or download the Pensoft EndNote style ): here . It is   also available in Zotero, when searched by journal name or by "Pensoft Journals".

Published Papers: Polaszek A, Alonso-Zarazaga M, Bouchet P, Brothers DJ, Evenhuis NL, Krell FT, Lyal CHC, Minelli A, Pyle RL, Robinson N, Thompson FC, van Tol J (2005) ZooBank: The open-access register for zoological taxonomy: Technical Discussion Paper. Bulletin of Zoological Nomenclature 62: 210–220.

Accepted Papers: Same as above, but ''in press'' appears instead of the year in parentheses.

Electronic Journal Articles: Mallet J, Willmott K (2002) Taxonomy: Renaissance or Tower of Babel? Trends in Ecology and Evolution 18(2): 57–59.  https://doi.org/10.1016/S0169-5347(02)00061-7

Paper within conference proceedings: Orr AG (2006) Odonata in Bornean tropical rain forest formations: Diversity, endemicity and applications for conservation management. In: Cordero Rivera A (Ed.) Forest and Dragonflies. Fourth WDA International Symposium of Odonatology, Pontevedra (Spain), July 2005. Pensoft Publishers, Sofia-Moscow, 51–78.

Book chapters: Mayr E (2000) The biological species concept. In: Wheeler QD, Meier R (Eds) Species Concepts and Phylogenetic Theory: A Debate. Columbia University Press, New York, 17–29.

Books: Goix N, Klimaszewski J (2007) Catalogue of Aleocharine Rove Beetles of Canada and Alaska. Pensoft Publishers, Sofia-Moscow, 166 pp.

Book with institutional author: International Commission on Zoological Nomenclature (1999) International code of zoological nomenclature. Fourth Edition. The International Trust for Zoological Nomenclature, London.

PhD thesis: Dalebout ML (2002) Species identity, genetic diversity and molecular systematic relationships among the Ziphiidae (beaked whales). PhD Thesis, University of Auckland, Auckland, New Zealand.

Link/URL: BBC News: Island leopard deemed new species http://news.bbc.co.uk/

Citations of Public Resource Databases: It is highly recommended all appropriate datasets, images, and information to be deposited in public resources. Please provide the relevant accession numbers (and version numbers, if appropriate). Accession numbers should be provided in parentheses after the entity on first use. Examples of such databases include, but are not limited to:

• ZooBank ( www.zoobank.org )
• Morphbank ( www.morphbank.net )
• Genbank ( www.ncbi.nlm.nih.gov/Genbank )
• BOLD ( www.barcodinglife.org )

Providing accession numbers to data records stored in global data aggregators allows us to link your article to established databases, thus integrating it with a broader collection of scientific information. Please hyperlink all accession numbers through the text or list them directly after the References in the online submission manuscript.

All journal titles should be spelled out completely and should NOT be italicized.

Provide the publisher's name and location when you cite symposia or conference proceedings; distinguish between the conference date and the publication date if both are given. Do not list abstracts or unpublished material in the References. They should be quoted in the text as personal observations, personal communications, or unpublished data, specifying the exact source, with date if possible. When possible, include URLs for articles available online through library subscription or individual journal subscription, or through large international archives, indexes and aggregators, e.g., PubMedCentral, Scopus, CAB Abstracts, etc. URLs for pdf articles that are posted on personal websites only should be avoided.

Authors are encouraged to cite in the References list the publications of the original descriptions of the taxa treated in their manuscript.

Ordering references:  All references should be ordered alphabetically by author name (but see below).

If the references have the same first author and a varying number of co-authors, the ordering should be based on the number of co-authors starting with the lowest; a ll articles with the same first author and  two or more co-authors (thus cited as et al. in the text) should be listed chronologically,  as follows:

Smith J (2018) Article Title. Journal Name 1: 1–10. https://doi.org/10.3897

Smith J, Gunderson A (2017) Article Title. Journal Name 1: 10–20. https://doi.org/10.3897  

Smith J, Gunderson A, Brock B (2011) Article Title. Journal Name 1: 20–30. https://doi.org/10.3897

Smith J, Brock B, Gutierrez R,  Gunderson A (2013) Article Title. Journal Name 1: 15–30. https://doi.org/10.3897

Smith J, Brock B,  Gunderson A (2015) Article Title. Journal Name 1: 10–30. https://doi.org/10.3897

If both the first author and year of publication match within the categories above, the references are distinguished by adding the letters 'a', 'b', 'c', etc. after the year of publication and this marking is followed in the in-text citations, respectively.

Illustrations, Figures and Tables

Figures and illustrations are accepted in the following image file formats:

• EPS (preferred format for diagrams)
• TIFF (at least 300dpi resolution, with LZW compression)
• PNG (preferred format for photos or images)
• JPEG (preferred format for photos or images)

Vector files in any of the following formats EPS, SVG  or PDF are requested for phylogenetic trees and cladograms.

The journal is printed in A4 paper size with the maximum printing area of 167 mm × 242 mm. Whenever possible, individual figures should be prepared as composite figures.

Should you have any problems in providing the figures in one of the above formats, or in reducing the file below 20 MB , please contact the Editorial Office at [email protected]

Figure legends: All figures should be referenced consecutively in the manuscript; legends should be listed consecutively immediately after the References. For each figure, the following information should be provided: Figure number (in sequence, using Arabic numerals − i.e. Figure 1, 2, 3 etc.); short title of figure (maximum 15 words); detailed legend, up to 300 words.

Illustrations of measurable morphological traits should bear mute scale bars, whose real size is to be given in the figure captions.

Please note that it is the responsibility of the author(s) to obtain permission from the copyright holder to reproduce figures or tables that have previously been published elsewhere.

Figure citations in the text should always be with Capital "F" and En-dash for ranges. One figure with a full stop, figures without.

Example: Fig. 1, Figs 1–3, Fig. 2A–E.

Citations of figures from other publications should always be Lower Case (fig. / figs). When two subsequent figures or parts are cited (for instance figures 1 and 2 or A and B), a comma should be used.

Example:   Figs 1, 2 and Fig. 1A, B.

Parts belong to one figure.

Example: Fig. 1A, B and Fig. 2A-E.

On the use of Google Maps All uses of  Google Maps  and  Google   Earth Content must provide attribution to Google , according to Google Maps/Earth Additional Terms of Service  (see also  Permission Guidelines for Google Maps and Google Earth ). The attribution should be visible on each map in the form, for example: "Map data 2019 (C) Google".

Tables: Each table should be numbered in sequence using Arabic numerals (i.e. Table 1, 2, 3 etc.). Tables should also have a title that summarizes the whole table, maximum 15 words. Detailed legends may then follow, but should be concise.

Small tables can be embedded within the text, in portrait format (note that tables on a landscape page must be reformatted onto a portrait page or submitted as additional files). These will be typeset and displayed in the final published form of the article. Such tables should be formatted using the 'Table object' in a word processing program to ensure that columns of data are kept aligned when the file is sent electronically for review. Do not use tabs to format tables or separate text. All columns and rows should be visible, please make sure that borders of each cell display as black lines. Colour and shading should not be used; neither should commas be used to indicate decimal values. Please use a full stop to denote decimal values (i.e., 0.007 cm, 0.7 mm).

Larger datasets can be uploaded separately as Supplementary Files. Tabular data provided as supplementary files can be uploaded as an Excel spreadsheet (.xls), as an OpenOffice spreadsheets (.ods) or comma-separated values file (.csv). As with all uploaded files, please use the standard file extensions.

Materials and Methods

In line with responsible and reproducible research, as well as FAIR ( Findability, Accessibility, Interoperability and Reusability)  data principles, we highly recommend that authors describe in detail and deposit their science methods and laboratory protocols in the open access repository protocols.io .

Once deposited on protocols.io , protocols and methods will be issued a unique digital object identifier (DOI), which could be then used to link a manuscript to the relevant deposited protocol. By doing this, authors could allow for editors and peers to access the protocol when reviewing the submission to significantly expedite the process.

Furthermore, an author could open up his/her protocol to the public at the click of a button as soon as their article is published.

Stepwise instructions:

• Prepare a detailed protocol via protocols.io .
• Click Get DOI to assign a persistent identifier to your protocol.
• Add the DOI link to the Methods section of your manuscript prior to submitting it for peer review.
• Click Publish to make your protocol openly accessible as soon as your article is published (optional).
• Update your protocols anytime.

Authorship of AI

Supplementary files.

Online publishing allows an author to provide datasets, tables, video files, or other information as supplementary information, greatly increasing the impact of the submission. Uploading of such files is possible in Step 9 of the submission process.

The maximum file size for each Supplementary File is 20 MB.

The Supplementary Files will not be displayed in the printed version of the article but will exist as linkable supplementary downloadable files in the online version.

While submitting a supplementary file the following information should be completed:

• File format (including name and a URL of an appropriate viewer if format is unusual)
• Title of data
• Description of data

All supplementary files should be referenced explicitly by file name within the body of the article, e.g. 'See supplementary file 1: Movie 1" for the original data used to perform this analysis.

Ideally, the supplementary files should not be platform-specific, and should be viewable using free or widely available tools. Suitable file formats are:

For supplementary documentation:

• PDF (Adobe Acrobat)

For animations:

• SWF (Shockwave Flash)

For movies:

• MOV (QuickTime)

For datasets:

• XLS (Excel spreadsheet)
• CSV (Comma separated values)
• ODS (OpenOffice spreadsheets)

As for images, file names should be given in the standard file extensions. This is especially important for Macintosh users, since the Mac OS does not enforce the use of standard file extensions. Please also make sure that each additional file is a single table, figure or movie (please do not upload linked worksheets or PDF files larger than one sheet).

The journal is integrated with the ARPHA Preprints platform, thereby allowing authors to post their pre-review manuscript as a preprint by simply checking the relevant box while completing the submission of their manuscript.

Due to the integration, the authors are not required to re-format or submit any additional files, as the system uses the manuscript to automatically generate a preprint. Subject to a basic editorial screening, the preprint will be posted on ARPHA Preprints within a few days after the manuscript’s submission.

When submitting their manuscripts and requesting a preprint publication authors must keep in mind that preprints are preliminary versions of works accessible electronically in advance of publication of the final version. They are not issued for the purposes of botanical, mycological or zoological nomenclature and  are not effectively/validly published in the meaning of the Codes . Therefore, papers containing or dealing with nomenclatural novelties (new names) or other nomenclatural acts (designations of type, choices of priority between names, choices between orthographic variants, or choices of gender of names)   will NOT be posted as preprints .

Explore the Benefits of posting a preprint or visit ARPHA’s blog to learn more about ARPHA Preprints .

Find more about how to submit your preprint in the ARPHA Manual .

Processing Your Manuscript

Revising your article.

Authors must submit the revised version of the manuscript using Track Changes/Comments tools of Word so that the Subject Editor can see the corrections and additions.

Authors must address all critiques of the referees in a response letter to the editor and submit it along with the revised manuscript through the online editorial system. In case a response letter is not submitted by the authors, the editor has the right to reject the manuscript without further evaluation. When resubmitting a manuscript that has been previously rejected with resubmission encouraged, authors must include the response letter to the article text file, and the pdf review version, so that it gets to the Subject Editor and the reviewers during the peer review.

When submitting corrections to proofs (during the layout stage), authors  must  upload the latest proof (in PDF format) containing their revisions as track changes.

Post-acceptance Procedure

Concise copyediting instructions.

The copyediting instructions below represent a concise summary of the journal's formatting requirements. The instructions are intended for use by the authors during preparation of the final revised versions of their manuscripts, technical editors, copy editors and typesetters.  

Author names

• Omit titles, degrees, etc.
• Provide ORCID if available

Affiliation

(Department,) Institution, City, Country

Article title

Title of article: Subtitle of article

• Title: Sentence case
• Colon between title and subtitle (if any)
• No footnotes
• No bold (use when needed sub-/superscript, and/or italics only for the terms in Latin)
• Higher taxa within the title should be separated with commas and not with a semicolon

Running head

• A short version of title up to 50 characters (including spaces); normally the short title should have been suggested by the authors and checked for clarity by the copy editor
• No references to tables, figures, etc., no footnotes
• If citations unavoidable: Complete citations, allowing unambiguous identification of cited publication!
• Should be written consistently in either third or first person
• Note: The abstract has to be a stand-alone entity, to present a really well written and concise summary of the article! A special care for copy editors to check!
• Designations of nomenclatural novelties should be in bold and spelled in the way suggested ( sp. nov., gen. nov., comb. nov. )

Keywords (up to 8 words)

keyword a, keyword b, keyword n

• Do not repeat words from the title
• Listed in alphabetical order and separated by commas
• Lowercase letters, except proper names
• No bold font
• Without any punctuation marks after last keyword
• Table 2. Table caption text.
• Numbered consecutively with Arabic numerals
• Heading for every column (including the leftmost!)
• No shading of cells, rows, columns; no colored fonts
• No horizontal or vertical lines in table body
• Same number of decimal places for same statistics (usually within same column)
• Text formatting in the cell without paragraph and line break
• Table must be in an editable format (.docx, .xlsx, etc., not as images)
• Caption and footnotes as texts (not as part of a table)
• Figure 6. Figure caption text.
• Figure 1. Figure general caption text. A part caption text B part caption text N part caption text.
• If abbreviations are used, these are placed after the parts with a colon, i.e.: Abbreviations: xxxx
• If there are scale bars on the figure parts, reference to them is last and in the format: Scale bars: 20 μm ( D, N, O, Q ); 50 μm ( F, K ); 10 μm ( G, P ); 5 μm ( H ); 100 μm ( M ).
• High quality (at least 300 dpi)
• Text sharp and readable (e.g., no overlap of text and graphical elements like lines)
• White or transparent background
• No image border
• Caption as text (not as part of the image)

Capitalization

• Article title: Sentence case
• Running head: Sentence case
• For separated titles (usually H1-H3): Sentence case
• For paragraph titles (usually H4): Sentence case
• Table captions: Sentence case
• Sentence case or lower case (check for consistency only!)
• Figure captions: Sentence case
• Fig. 4; Figs 1, 2; Table 2; Appendix 1
• In text body: Titles of articles, book chapters, books, tests
• In references: Sentence case

Equations and statistical symbols

• standard typeface for Greek letters, sub-/superscripts, and abbreviations that are not variables
• italic typeface for all other statistical symbols
• Space before and after equal/inequality signs
• Same number of decimal places for decimal values
• Use leading zeros before a decimal fraction including for statistical values pertaining to probability
• Abbreviations e.g., i.e., et al., etc., cf., vs.
• Greek letter e.g., α, β, γ, δ, ε, σ, φ, χ, ω
• Scientific names of taxa of species and genera (authorities in regular font, not in italics)
• Long direct quotations
• Symbols for variables and constants, such as p , F , U , T , N , r , but not for SD (standard deviation), SE (standard error), DF (degrees of freedom), and NS (non significant). These symbols in illustrations and equations should be in italics to match the text.
• Do not use italics for emphasis
• No underlining
• Subheadings, sections and subsections
• Table 1. Table caption text.
• In systematic sections for specimen designation such us: holotype , paratype, syntype , lectotype , isotype , etc.
• NHML Natural History Museum, London
• MW Naturhistorisches Museum, Vienna
• EL length of elytra
• EW maximum width of elytra
• TL total length (PL+EL)
• In species descriptions – designation of main anatomical structures followed by a colon mark, i.e. Head: …, Thorax: …, Legs: …, Abdomen: … , etc., in this case these should be followed by a section describing other anatomical organs and structures attached to these.
• COUNTRY • specimens [e.g. 1 ♂, size ]; geographic/locality data [from largest to smallest]; coordinates; altitude/elevation/depth [using alt./m a.s.l. etc.]; date [format: 16 Jan. 1998]; collector [followed by "leg."]; other collecting data [e.g. micro habitat/host/method of collecting]; barcodes/identifiers [e.g. GenBank: MG779236]; institution code and specimen code [e.g. CBF 06023]. For Example: Holotype: CHINA • ♀; Sichuan, Kangding; 30.04°N, 101.57°E; 15.VI.2017; Yanzhou Zhang leg.; Hyp-2018-06, original number ZYZ-2017-28. Paratypes: CHINA • 1♀1♂; Sichuan, Kangding; 29.VI.2017; Yanzhou Zhang leg.; Hyp-2018-01, Hyp-2018-02, original number ZYZ-2017-08 • 1♀; Sichuan: Kangding; 2.VIII.2017; Yanzhou Zhang leg.; Hyp-2018-03, original number ZYZ-2017-20 • 1♂, Sichuan: Kangding; 29.VI.2017; Yanzhou Zhang leg.; Hyp-2018-08, original number ZYZ-2017-029.
• Punctuation: A bullet point "•" (unicode: 2022) is used to signify the beginning of a material citation. Within each citation, the different fields are delimited by a semicolon. A single field can be composed of several elements, which are separated by commas (e.g. the details region, area, town and street for the ‘locality’ field). Semicolons should not be used elsewhere in a material citation.
• Repetitive data: Authors can indicate repetitive data with indications such as " same data as for holotype ", "same data as for preceding", " same locality ", " ibid ", etc. as long as the same method and wording are used consistently throughout the paper.
• ‘Missing’ elements: It is not necessary to include information such as "no date" or "no locality data"; just list the elements that are available.
• see more details here
• Avoid quotation marks except for direct quotations, words defined by the author, and words used in unusual contexts.
• Short quotations should be embedded in the text and enclosed in double quotation marks ("). Long quotations should be on a separate line, italicized, but without quotation marks.
• Single quotation marks are to be used only for a quotation that occurs within another quotation.
• Consistent use of (-, –, —).
• In contrast to parentheses an em-dash can be used alone.
• link words such as personal names, some prefixes and compound adjectives (the last of which vary depending on the style manual in use)
• link spans.
• link numerals, sizes, dates and page numbers (e.g., 1977–1981; figs 5–7; pp. 237–258 )
• geographic or name associations (e.g., Murray–Darling River; a Federal–State agreement )
• character states combinations (e.g., long–pubescent or red–purple ).
• only for introducing a subordinate clause in the text that is often used much as we use parentheses.

Section hierarchy

• No more than 4 levels, from hierarchical level 1 (H1) to hierarchical level 4 (H4)
• Unambiguous hierarchy levels
• No numbering of hierarchical levels

Section titles

Mandatory statements.

• The author has no funding to report.
• The authors have no funding to report.
• The author has declared that no competing interests exist.
• The authors have declared that no competing interests exist.
• The author has no support to report.
• The authors have no support to report.
• Data Resources (mandatory for empirical articles)

Geographical coordinates

One of the following formats should be used:

• 36°31'21"N; 114°09'50"W
• 36°31.46'N; 114°09.84'W
• 36.5243°S; 114.1641°W
• −36.5243; −114.1641 (using minus to indicate southern and western hemispheres)

In-Text Citations

• Jackson and Miller (2012) found out that...
• A recent study (Jackson and Miller 2012) confirmed that...
• Jackson et al. (2012) found out that...
• A recent study (Jackson et al. 2012) confirmed that...
• Jackson and Miller (2012, 2015) found out that...
• Recent studies (Jackson et al. 2012, 2015) confirmed that...
• (Smith et al. 1998, 2000, 2016; Brock and Gunderson 2001; Felt 2006)
• Jackson 2008a, 2008b
• Jackson and Miller 2014a, 2014b
• Reyes-Velasco et al. 2018a, 2018b
• Jackson and Miller (2012: 120–121) found out that
• A recent study (Jackson and Miller 2012: 120) confirmed that
• Fig. 1A–D
• Figs 1–3
• Figs 1A, B, 3F, G, 7A
• Tables 1, 2
• Tables 1–3
• Appendices 1, 2
• Appendices 1–4
• All figures, tables, etc., from other sources should be written with small letters i.e.: see fig. 2 in Author (Year) ...
• de Albuquerque PRA
• Different authors separated by comma
• Year in brackets; no comma or full stop after it
• No italics (except for Latin terms)

Published papers:

Polaszek A, Alonso-Zarazaga M, Bouchet P, Brothers DJ, Evenhuis NL, Krell FT, Lyal CHC, Minelli A, Pyle RL, Robinson N, Thompson FC, van Tol J (2005) ZooBank: The open-access register for zoological taxonomy: Technical Discussion Paper. Bulletin of Zoological Nomenclature 62: 210–220.

Accepted papers:

Same as above, but ''in press'' appears instead of the year in parentheses.

Electronic journal articles:

Mallet J, Willmott K (2002) Taxonomy: Renaissance or Tower of Babel? Trends in Ecology and Evolution 18(2): 57–59. https://doi.org/10.1016/S0169-5347(02)00061-7

Paper within conference proceedings:

Orr AG (2006) Odonata in Bornean tropical rain forest formations: Diversity, endemicity and applications for conservation management. In: Cordero Rivera A (Ed.) Forest and Dragonflies. Fourth WDA International Symposium of Odonatology, Pontevedra (Spain), July 2005. Pensoft Publishers, Sofia-Moscow, 51–78.

Book chapters:

Mayr E (2000) The biological species concept. In: Wheeler QD, Meier R (Eds) Species concepts and phylogenetic theory: A debate. Columbia University Press, New York, 17–29.

Goix N, Klimaszewski J (2007) Catalogue of Aleocharine Rove Beetles of Canada and Alaska. Pensoft Publishers, Sofia-Moscow, 166 pp.

Book with institutional author:

ICZN [International Commission on Zoological Nomenclature] (1999) International code of zoological nomenclature. Fourth Edition. The International Trust for Zoological Nomenclature,  London.

PhD thesis:

Dalebout ML (2002) Species identity, genetic diversity and molecular systematic relationships among the Ziphiidae (beaked whales). PhD Thesis, University of Auckland, Auckland, ## pp.

BBC News (2012) Island leopard deemed new species http://news.bbc.co.uk/ [Accessed on dd.mm.yyyy]

Data Publishing Guidelines

We strongly encourage and support various strategies and methods for data publication. The preferable way is to store data in internationally recognised data repositories and link back to the data set(s) in the respective article. Data can also be published as supplementary files to the articles, however this should be an exception rather than a rule (see How to publish data ). The key to discover, use and cite your data is to include the data references in the reference lists of the articles and  always  include the DOIs of the data sets, when available, in the citation record . You may read more about this in  How to cite data  section of the article below. A good example of concise data citation guidelines using DOIs is also available on the  GBIF website  and on other data repositories.

Darwin Core-structured species occurrence records and observations (primary biodiversity data) should be published with GBIF using either the Integrated Publishing Toolkit (IPT) (for which Pensoft maintains an instance, in case such is not available to the authors).  Alternatively, DwC data could also be published in trusted and community-recognised repositories (for example, Atlas of Living Australia, Symbiota,  Arctos or others), however deposition at GBIF should always have a priority over the alternatives. In case a dataset is deposited in more than one repository, the data paper should link to the dataset which is actually described, again with GBIF having a priority over the others.

Authors who want to publish species occurrence data as supplementary files only or through generic repositories (e.g. Zenodo, Dryad), instead of submitting these to GBIF, should justify their decision to do so in a letter to the editors.

For biodiversity and biodiversity-related data the reader may consult the  Strategies and guidelines for scholarly publishing of biodiversity data   (Penev et al. 2017,   Research Ideas and Outcomes 3: e12431.  https://doi.org/10.3897/rio.3.e12431 ).   For reader's convenience, we list here the hyperlinked table of contents of these extensive guidelines:

• Introduction
• What is a Dataset
• Why Publish Data
• How to Publish Data
• How to Cite Data
• General Policies for Biodiversity Data
• Data Publishing Licenses
• General Information
• Taxonomic Data  
• Species-by-Occurrence and Sample-Based Data
• Phylogenies
• Gene Sequence
• Protein Sequence
• Other Omics
• Various Data Types
• Biodiversity Literature
• Data Published within Supplementary Information Files
• Import of Darwin Core Specimen Records into Manuscripts
• Data Published in Data Papers
• Data Papers Describing Primary Biodiversity Data
• Data Papers Describing Ecological and Environmental Data
• Data Papers Describing Genomic Data
• Software Description Papers
• Quality of the Manuscript
• Quality of the Data
• Consistency between Manuscript and Data

The core of the data publishing project of Pensoft is the concept of "Data Paper" developed in a cooperation with the Global Biodiversity Information Facility (GBIF). Data Papers are peer-reviewed scholarly publications that describe the published datasets and provide an opportunity to data authors to receive the academic credit for their efforts. Currently, Pensoft offers the opportunity to publish Data Papers describing occurrence data and checklists, Barcode-of-Life genome data and biodiversity-related software tools, such as interactive keys and others.

Examples of data papers

ZooKeys: Antarctic, Sub-Antarctic and cold temperate echinoid database A dataset from bottom trawl survey around Taiwan Project Description: DNA Barcodes of Bird Species in the National Museum of Natural History, Smithsonian Institution, USA Literature based species occurrence data of birds of northeast India MOSCHweb — a matrix-based interactive key to the genera of the Palaearctic Tachinidae (Insecta, Diptera) Amundsen Sea Mollusca from the BIOPEARL II expedition Iberian Odonata distribution: data of the BOS Arthropod Collection (University of Oviedo, Spain FORMIDABEL: The Belgian Ants Database Circumpolar dataset of sequenced specimens of Promachocrinus kerguelensis (Echinodermata, Crinoidea)

PhytoKeys: Florabank1: a grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region) Database of Vascular Plants of Canada (VASCAN): a community contributed taxonomic checklist of all vascular plants of Canada, Saint Pierre and Miquelon, and Greenland Herbarium of Vascular Plants Collection of the University of Extremadura (Spain)

Nature Conservation: Antarctic macrobenthic communities: A compilation of circumpolar information

Press releases on data papers New incentive for biodiversity data publishing Dat a publishing policies and guidelines for biodiversity data by Pensoft First database-derived 'data paper' published in journal A new type of data papers designed to publish online interactive keys Data paper describes Antarctic biodiversity data gathered by 90 expeditions since 1956 Unique information on Belgian ants compiled and published through FORMIDABEL data paper Database simplifies finding Canadian plant names and distribution A synthesis of the 36451 specimens from the UNEX Herbarium in a new data paper

Data Quality Checklist and Recommendations

INTRODUCTION

An empowering aspect of digital data is that they can be merged, reformatted and reused for new, imaginative uses that are more than the sum of their parts. However, this is only possible if data are well curated. To help authors avoid some common mistakes we have created this document to highlight those aspects of data that should be checked before publication.

By "mistakes" we do not mean errors of fact, although these should also be avoided! It is possible to have entirely correct digital data that are low-quality because they are badly structured or formatted, and, therefore, hard or impossible to move from one digital application to another. The next reader of your digital data is likely to be a computer program, not a human. It is essential that your data are structured and formatted so that they are easily processed by that program, and by other programs in the pipeline between you and the next human user of your data.

The following list of recommendations will help you maximise the re-usability of your digital data. Each represents a test carried out by Pensoft when auditing a digital dataset at the request of an author. Following the list, we provide explanations and examples of each recommendation.

Authors are encouraged to perform these checks themselves prior to data publication. For text data, a good text editor ( https://en.wikipedia.org/wiki/List_of_text_editors ) can be used to find and correct most problems. Spreadsheets usually have some functions for text checking functions, e.g. the "TRIM" function that removes unneeded whitespace from a data item. The most powerful text-checking tools are on the command line, and the website "A Data Cleaner's Cookbook" ( https://www.datafix.com.au/cookbook/ ) is recommended for authors who can use a BASH shell.

When auditing datasets for authors, Pensoft does not check taxonomic or bibliographic details for correctness, but we will do basic geochecks upon request, e.g. test to see if the stated locality is actually at or near the stated latitude/longitude. We also recommend checking that fields do not show "domain schizophrenia", i.e. fields misused to containing data of more than one type.

Proofreading data takes at least as much time and skill as proofreading text. Just as with text, mistakes easily creep into data files unless the files are carefully checked. To avoid the embarrassment of publishing data with such mistakes, we strongly recommend that you take the time to run these basic tests on your data.

• The dataset is UTF-8 encoded
• The only characters used that are not numbers, letters or standard punctuation, are tabs and whitespaces
• Each character has only one encoding in the dataset
• No line breaks within data items
• No field-separating character within data items (tab-separated data preferred)
• No "?" or replacement characters in place of valid characters
• No Windows carriage returns
• No leading, trailing, duplicated or unnecessary whitespaces in individual data items
• No broken records, i.e. records with too few or too many fields
• No blank records
• No duplicate records (as defined by context)
• No empty fields
• No evident truncation of data items
• No unmatched braces within data items
• No data items with values that are evidently invalid or inappropriate for the given field
• Repeated data items are consistently formatted
• Standard data items such as dates and latitude/longitude are consistently formatted
• No evident disagreement between fields
• No unexpectedly missing data

RECOMMENDATIONS

Characters  

Computer programs do not "read" characters like "A" and "4". Instead, they read strings of 0's and 1's and interpret these strings as characters according to an encoding scheme. The most universal encoding scheme is called UTF-8 and is based on the character set called Unicode. Text data should always be shared with UTF-8 encoding, as errors can be generated when non-UTF-8 encodings (such as Windows-1252) are read by a program expecting UTF-8, and vice-versa. (See also below, on replacement characters).  

• The only characters used that are not numbers, letters or standard punctuation are tabs and whitespaces

Unusual characters sometimes appear in datasets, especially when databases have been merged. These "control" or "gremlin" characters are sometimes invisible when data are viewed within a particular application (such as a spreadsheet or a database browser) but can usually be revealed when the data are displayed in a text editor. Examples include vertical tab, soft hyphen, non-breaking space and various ASCII control characters ( https://en.wikipedia.org/wiki/Control_character ).

We have seen individual datasets in which the degree symbol (°) is represented in three different ways, and in which a single quotation mark (') is also represented as a prime symbol, a right single quotation mark and a grave accent. Always use one form of each character, and preferably the simplest form, e.g. plain quotes rather than curly quotes.

Spreadsheet and database programs often allow users to have more than one line of text within a data item, separated by linebreaks or carriage returns. When these records are processed, many computer programs understand the embedded linebreak as the end of a record, so that the record is processed as several incomplete records:

item A  itemB1          itemC

               itemB2

itemA           itemB1

itemB2          itemC

Data are most often compiled in table form, with a particular character used to separate one field ("column") from the next. Depending on the computer program used, the field-separating character might be a comma (CSV files), a tab (TSV files), a semicolon, a pipe (|) etc.

Well-structured data keeps the field-separating character out of data items, to avoid confusion in processing. Because commas are commonly present within data items, and because not all programs understand how to process CSVs, we recommend using tabs as field-separating characters (and avoiding tabs within data items!): https://en.wikipedia.org/wiki/Tab-separated_values .

When text data are moved between different character encodings, certain characters can be lost because the receiving program does not understand what the sending program is referring to. In most cases, the lost character is then represented by a question mark, as in "Duméril" becoming "Dum?ril", or by a replacement character, usually a dark polygon with a white question mark inside.

It is important to check for these replacements before publishing data, especially if you converted your data to UTF-8 encoding from another encoding.

On UNIX, Linux and Mac computers, a linebreak is built with just one character, the UNIX linefeed '\n' ('LF'). On Windows computers, a linebreak is created using two characters, one after the other: '\r\n' ('CRLF'), where '\r' is called a 'carriage return' ('CR'). Carriage returns are not necessary in digital data and can cause problems in data processing on non-Windows computers. Check the documentation of the program in which you are compiling data to learn how to remove Windows carriage returns.

Like "control" and "gremlin" characters, whitespaces are invisible and we pay little attention to them when reading a line of text. Computer programs, however, see whitespaces as characters with the same importance as "A" and "4". For this reason, the following four lines are different and should be edited to make them the same:

Aus bus (Smith, 1900)

   Aus bus (Smith, 1900)

Aus bus (Smith,   1900)

Aus  bus   (Smith, 1900  )

  Records

If a data table contains records with, for example, 25 fields, then every record in the table should have exactly 25 data items, even if those items are empty. Records with too few fields are often the result of a linebreak or field separator within a data item (see above). Records with too many fields also sometimes appear when part of a record has been moved in a spreadsheet past the end of the table.

Blank records contribute nothing to a data table because they contain no information, and a tidy data table has no blank lines. Note, however, that a computer program looking for blank lines may not find what looks to a human like a blank line, because the "blank" line actually contains invisible tabs or whitespaces.

It can be difficult to find duplicate records in some datasets, but our experience is that they are not uncommon. One cause of duplicates is database software assigning a unique ID number to the same line of data more than once. Context will determine whether one record is a duplicate of another, and data compilers are best qualified to look for them.

  Fields

  Fields containing no data items do not add anything to the information content of a dataset and should be omitted.

•   No evident truncation of data items

The end of a data item is sometimes cut off, for example when a data item with 55 characters is entered into a database field with a 50-character maximum limit. Truncated data items should be repaired when found, e.g.

Smith & Jones in Smith, Jones and Bro

repaired to :

Smith & Jones in Smith, Jones and Brown, 1974

These are surprisingly common in datasets and are either data entry errors or truncations, e.g.

Smith, A. (1900 A new species of Aus. Zool. Anz. 23: 660-667.

5 km W of Traralgon (Vic

For example, a field labelled "Year" and containing years should not contain the data item "3 males".

•   Repeated data items are consistently formatted

The same data item should not vary in format within a single dataset, e.g.

Smith, A. (1900) A new species of Aus. Zool. Anz. 23: 660-667.

Smith, A. 1900. A new species of Aus. Zoologischer Anzeiger 23: 660-667.

Smith, A. (1900) A new species of Aus. Zool. Anz. 23, 660-667, pl. ix.

Data compilers have a number of choices when formatting standard data items, but whichever format is chosen, it should be used consistently. A single date field should not, for example, have dates represented as 2005-05-17, May 19, 2005 and 23.v.2005.

If there are fields which contain linked information then these fields should be checked to ensure that they do not conflict with each other. For example, the year or an observation cannot be after the year it was published.  Examples:

Year            Citation

1968            Smith, A. (1966) Polychaete anatomy. Academic Press, New York; 396 pp.

Genus           Subgenus

Aus             Bus (Aus)

This is a rare issue in datasets that have been audited, but occasionally occurs. An example is the Darwin Core "verbatimLocality" field for a record containing a full latitude and longitude, but with the "decimalLatitude" and "decimalLongitude" fields blank.

• Spelling of Darwin Core terms

Darwin Core terms are usually considered case sensitive, therefore you should use their correct spelling ( http://rs.tdwg.org/dwc/ ).

We thank Dr. Robert Mesibov for preparing the Data Quality Checklist draft and Dr. Quentin Groom for reviewing it.

Dryad Repository Submissions

This journal is integrated with the Dryad Digital Repository to make data publication simple and easy for authors. There is a $150 Data Publishing Charge for Dryad submissions, payable via the Dryad website.  For more information, please see their FAQ .

For Editors and Reviewers

Guidelines for editors, how to access a manuscript.

Manuscripts can be accessed after login

• Login is possible after registration at the journal's website. Our Editorial Office will register all first-time editors and reviewers. New users will receive an automated notification with a request to confirm registration and account information, and options for setting a password, email alerts and other features.   Note:  All users can use their registration details to login in all three (Book, E-Book and the respective Journal) platforms of  www.pensoft.net . Note:  Please remember that you may have registered with two or more different email addresses, that is why you may have more than one valid account at  www.pensoft.net . We advise using only one email address, hence one password associated with it, for all your operations at  www.pensoft.net . We highly recommend  that in case the user has two or more different accounts, to merge these through the user's profile.

Note:  The users can at any time change the initially set password and correct personal details using their user's profile menu (by clicking on the user's name in the upper right corner of the screen appearing after login).

If you have forgotten your password, please use the function  Forgot your password? or write to request it from  [email protected] .

There are two ways to access a manuscript

After login, please go to the respective journal’s web page and click on My Tasks button in the upper right corner of the screen. This way, you will be able to see all manuscripts you are responsible for as an author or reviewer or editor.

Note: The manuscripts are grouped by categories, e.g., In Review (no.), In layout (no.), Published (no.), and Archived (no.) etc. The number in brackets after each category shows the number of manuscripts that were assigned to you.

Click on the active manuscript link provided in the email notification you have received from the online editorial system. The link will lead you directly to the manuscript.

General Responsibilities of Editors

Subject, or Associate, editors in Pensoft’s journals carry the main responsibility for the scientific quality of the papers. They take the final decision on a manuscript’s acceptance or rejection and their names are listed as Academic Editor  in the header of each published article.

The editorial process is facilitated through an online editorial system and a set of email notifications. The online editorial system informs the Subject Editor about any change in the status of a manuscript from submission to publication.

The online editorial system is designed to save time and effort for Subject Editors in checking the status of the manuscripts. There is no need for editors to visit the journal’s website to keep track on the manuscript they are responsible for. The online system will inform the Subject Editor when an invited reviewer has accepted or declined to review. The email notifications contain stepwise instructions what action is needed at each stage, as well as a link to the respective manuscript (accessible by clicking on the link in the email notification or after login – see How to Access a Manuscript ).

Subject Editors are not expected to provide a thorough linguistic editing or copyediting of a manuscript, but rather focus on its scientific quality and overall style, which should correspond to good practices in clear and concise academic writing. It is the author’s responsibility to submit the manuscript in linguistically and grammatically correct English. The Subject Editor should not hesitate to recommend either Reject , or Reject, but resubmission encouraged  PRIOR to the peer-review process, in cases when a manuscript is scientifically poor and/or does not conform to journal’s style, and/or is written in poor English (see Note under point 1 below how to reject a manuscript prior to peer review). 

Editors-in-Chief, Managing Editors or their deputies are allowed to publish a limited proportion of papers per year co-authored by them, after considering some extra precautions to avoid an impression of impropriety, endogeny, conflicts of interest and ensure that the editorial decision-making process is transparent and fair.

It often happens that even carefully written manuscripts may contain small errors in orthography or stylistics. We shall be thankful if editors spot such errors during the reading process and correct them.

Stepwise Description of the Editorial Process

• Once a manuscript is submitted, the Managing Editor (or the Editor-in-Chief) briefly checks if the manuscript conforms with the journal's Focus, Scope, Policies and style requirements and decides whether it is potentially suitable for publication and can be processed for review, or rejected immediately, or returned to the author for improvement and re-submission. Note: There are two ways to reject/return a manuscript prior to review process: - Through the buttons Reject  or Return to the author for correction  in the Editorial tab. Please note, however, that the buttons will be made active only after a justification for the rejection or return is provided in the text field. - Through an email to the Editorial office explaining the reason for rejection or return. The manuscript will be then rejected/returned through the online editorial system and the respective notification email will be sent from the Editorial Office.
• At this stage, the Managing Editor (or the Editor-in-Chief) can also check the manuscript for plagiarism via the iThenticate service by clicking on the "ïTehnticate report" button. Journals providing a peer review in languages other than English (for example Russian) may use other plagiarsim checking services (for example Antiplagiat).
• When a manuscript is suitable, the Managing Editor (or the Editor-in-Chief) assigns it to the Subject Editor responsible for the respective topic (e.g., science branch or taxon). The Subject Editor receives a notification email on the assignment. Note: The link to the respective manuscript is available in the editorial assignment email and all consequent reminder emails. The manuscript is accessible by clicking on the link in the email notifications, or via the user's dashboard after login. Please see How to Access a Manuscript above in case you have any difficulties.
• The assigned Subject Editor next reads the manuscript to decide whether it is potentially suitable for publication and can be processed for review, or rejected immediately, or returned to the author for improvement and re-submission. Reasons for rejection can be a low scientific quality, non-conformance to the journal’s style/policies, and/or linguistically or grammatically poor English language. Note: There are two ways to reject a manuscript prior to review process: -  Through the buttons  Reject or Reject, but resubmission encouraged  in the Editorial tab. Please note, however, that the buttons become active only after a justification for the rejection is provided in the text field.  -  Through an email to the Editorial office explaining the reason for rejection. The manuscript will be then rejected/returned through the online editorial system and the respective notification email will be sent from the Editorial Office.
• In case the manuscript is acceptable for peer review, the Subject Editor has to invite reviewers by clicking on the Invite reviewers  link. The Subject Editor can select from a list of reviewers, starting with the ones suggested by the authors during the submission process, and followed by the reviewers who are already listed in the database, or add new reviewers.
• Once reviewers are chosen, the Subject Editor has to click the Invite reviewers green button at the end of the page which will generate email templates with review invitations. It is highly recommended that the Subject Editor adds some personal words above the standard email text of the review invitation.
• In case a reviewer is absent from our users' data base, the Subject Editor can add his/her name and email through the Add new reviewer  link, which will appear once the search field reveal no results. It is possible that the needed reviewer has already been registered in the Pensoft database either as customer or author/reviewer of another journal. If this is the case, then his/her name, affiliation and other metadata will automatically appear once the e-mail field is populated in the Create user online form.
• The Subject Editor receives a notification email when the Reviewer agrees or declines to review. The Subject Editor takes care to appoint additional reviewers in case some of the invited reviewers decline.
• Once all Reviewers submit their reviews, the Subject Editor receives an email notification, inviting him/her to consider Reviewers' opinions, read through the manuscript and take a decision through the Proceed  button. Note: Editorial comments can be added in the online editorial form; comments and corrections are expected to be added also in the manuscript file (either on the PDF version or in the text file), that should be uploaded during finalization of the editorial decision process. 
• At this stage, the editor should take a decision either to (1) accept the manuscript, (2) reject it, (3) recommend Major or Minor Revisions or reject it, or open a second review round. In case the manuscript is not rejected, but recommended for Minor Revision, Major Revision, or Acceptance, the author is expected to submit a revised version within a certain period of time (and the Subject Editor will be notified by email about that). Note 1: Authors must submit revised versions as a text file using Track Changes/Comments tools of Word so that the Subject Editor can see their corrections/additions. Authors must reply to the essential critiques and comments of reviewers separately through the online editorial system. Note 2: During the second, or next, review round, the Subject Editor may decide to ask reviewers to evaluate the revised version of the manuscript. He/she may also make a decision based on the author’s responses and the revised version of the manuscript without asking additional Reviewers' support.
• After acceptance, the manuscript will go to proofreading and layout . The Subject Editor will be notified by email when the final proof is uploaded on the journal’s website. The Subject Editor is expected to look at the proofs and notify the Editorial Office through email in case the proofs need improvement.
• The Subject Editor may always access information on the manuscripts which have been edited by him/her through the menu My Tasks –>  Subject Editor  on the journal’s web page – In Review (no.), In Edit (no.), Published (no.), and Archived (no.). The number in brackets after each category shows the number of manuscripts that were assigned.

Editorial Decision

In this journal, the final decision on acceptance or rejection of a manuscript lies with the Editor-in-Chief. The Editor-in-Chief’s decision is to be made after the Subject Editor provides a final recommendation on acceptance or rejection of a manuscript. The workflow is explained stepwise below.  

• Once submitted by the author, a manuscript is sent to a Subject Editor, who will have the choice to either accept, reject or request further minor/major revision through as many review iterations as needed.

After peer review, the manuscript is returned to the author. After the author revises his/her paper, the Subject Editor is once again presented with the same set of options. The procedure is repeated until the Subject Editor decides to either accept or reject the manuscript.

As soon as the Subject Editor makes a decision to either accept or reject the manuscript, the Editor-in-Chief is informed via email about this recommendation.

In his/her turn, the Editor-in-Chief can either accept, reject or request further minor/major revision, regardless of the Subject Editor’s recommendation.

Should the manuscript be sent back to the author for revision, the revised version will be returned to the Subject Editor and will undergo the same procedure until the paper is either accepted or rejected for publication by the Editor-in-Chief.

Editors’ and Reviewers’ Workload Stats

While selecting a Reviewer or a Subject Editor to assign to a manuscript, Editors can access the current and past workload for the person they are considering.

By clicking on the user’s name, an Editor sees how many editorial or review tasks the person is currently assigned with, as well as a record of the user’s previous performance across all ARPHA-hosted journals (i.e. number of accepted and declined editorial and review assignments, as well as the titles of the corresponding journals).

The feature is meant to facilitate and expedite the editorial process by discouraging assignment of tasks to overburdened or inactive users.

Find how to Manage Subject editor assignments and Invite Reviewers in the ARPHA Manual.

Review Quality Rating

Subject Editors should evaluate each review submitted to a manuscript they are handling by using a 5-star rating system. The average score is visible for Subject editors who consider the user as a Reviewer. The feature is meant to expedite the editorial process by aiding Subject Editors in the selection of the most suitable reviewers.

Find how to Rate a peer review in the ARPHA Manual.

Guidelines for Reviewers

Pensoft journals support the open science approach in the peer review and publication process. We encourage our reviewers to open their identity to the authors and consider supporting the peer review oaths, which tend to be short declarations that reviewers make at the start of their written comments, typically dictating the terms by which they will conduct their reviews (see Aleksic et al. 2015, doi:  10.12688/f1000research.5686.2  for more details): Principles of the open peer-review oath

• Principle 1: I will sign my name to my review
• Principle 2: I will review with integrity
• Principle 3: I will treat the review as a discourse with you; in particular, I will provide constructive criticism
• Principle 4: I will be an ambassador for the practice of open science
• Login is possible after registration at the journal's website. Our Editorial Office will register all first-time editors and reviewers. New users will receive an automated notification with a request to confirm registration and account information, and options for setting a password, email alerts and other features.   Note:  All users can use their registration details to login in all three (Book, E-Book and the respective Journal) platforms of  www.pensoft.net . Note:  Please remember that you may have registered with two or more different email addresses, that is why you may have more than one valid account at  www.pensoft.net . We advise using only one email address, hence one password associated with it, for all your operations at  www.pensoft.net . We highly recommend  that, in case the user has two or more different accounts, to merge these through user's profile.   Note: Users can at any time change the initially set password and correct personal details using their user's profile menu (by clicking on the user's name in the upper right corner of the screen appearing after login).

After login, please go to the respective journal’s web page and click on My Tasks button in the upper right corner of the screen. This way, you will be able to see all manuscripts you are responsible for as Author or Reviewer or Subject Editor.

Note: The manuscripts are grouped by categories, e.g., In Review (no.), In layout (no.), Published (no.), and Archived (no.) etc. The number in brackets after each category shows the number of manuscripts assigned to you.

General Responsibilities of Reviewers

This journal uses a single-blind peer review process. The reviewers are encouraged to disclose their identity, if they wish so. The peer review and editorial process is facilitated through an online editorial system and a set of email notifications. The online editorial system sends the Reviewer a review request, initiated by the Subject Editor or the Editorial Office. The online system will also inform about delays in the reviewing and will confirm a successful review submission. The email notifications contain stepwise instructions about the actions needed at each stage along with the link to the respective manuscript (accessible only after login – see section  How to Access a Manuscript ).

Reviewers are not expected to provide a thorough linguistic editing or copyediting of a manuscript, but rather focus on its scientific quality and overall style, which should correspond to the good practices in clear and concise academic writing. If Reviewers recognize that a manuscript requires linguistic edits, we shall be grateful for them to inform both the Author and the Subject Editor in the report. It is the Author’s responsibility to submit the manuscript in linguistically and grammatically correct English.

It often happens that even carefully written manuscripts may contain small errors in orthography or stylistics. We shall be thankful if Reviewers spot such errors during the reading process and correct them.

The manuscripts will generally be reviewed by two or three experts with the aim of reaching a first decision as soon as possible. Reviewers do not need to sign their reports, but are welcome to do so. They are also asked to declare any conflicts of interest.

Reviewers are asked whether the manuscript is scientifically sound and coherent, how interesting it is and whether the quality of the writing is acceptable. Where possible, the final decision is made on the basis of the peer reviews. In cases of strong disagreement between the reports or between the authors and peer reviewers, the editor can assess these according to his/her expertise or seek advice from a member of the journal's Editorial Board.

The ultimate responsibility for editorial decisions lies with the respective Subject Editor and/or, in some journals, with the Editor-in-Chief. All appeals should be directed to the Editor-in-Chief, who may decide to seek advice from the Subject Editors or the Editorial Board.

During a second review round, reviewers may be asked to evaluate the revised version against their recommendations submitted during the first review round.

Reviewers are kindly asked to be polite and constructive in their reports. Reports that may be insulting or uninformative will be rescinded.

Reviewers are asked to start their report with a very brief summary of the reviewed paper. This will help the editor and the authors see whether the reviewer correctly understood the paper or whether a report might be based on misunderstanding.

Furthermore, reviewers are also asked to comment on originality, structure and previous research:

Originality:  Is the paper sufficiently novel and does it contribute to a better understanding of the topic under scrutiny? Is the work rather confirmatory and repetitive?

Structure:  Is the introduction clear and concise? Does it place the work into the context that is necessary for a reader to comprehend aims, hypotheses tested, experimental design or methods? Are Material and Methods clearly described and sufficiently explained? Are reasons given when choosing one method over another one from a set of comparable methods? Are the results clearly, but concisely described? Do they relate to the topic outlined in the introduction? Do they follow a logical sequence? Does the discussion place the paper in scientific context and go a step beyond the current scientific knowledge on the basis of the results? Are competing hypotheses or theories reasonably related to each other and properly discussed? Do the conclusions seem reasonable?

Previous research:  Is previous research adequately incorporated into the paper? Are references complete, necessary and accurate? Is there any sign that substantial parts of the paper are copies of other works?

Stepwise Description of the Peer Review Process

This journal uses a single-blind peer review process. Notwithstanding with that, the Reviewers are encouraged to disclose their identities, if they wish to do so. 

The Reviewer receives a review request generated by the Subject Editor or the Editorial Office and is expected to either agree to provide a review, or decline, through pressing the  Will do the review  or  Unable to do the review  link in the online editorial system. In case the Reviewer agrees to review the manuscript, he/she should submit the review within a certain time frame, which may vary in the different journals. Note:  The link to the respective manuscript is available in the review request email and all consequent reminder emails. The manuscript is accessible by clicking on the link in the email notification, or after login. Please look at the section  How to Access a Manuscript  above in case you have any difficulties.

The review should be submitted through the Proceed  button. The review should consist of:

• a simple online questionnaire to be answered by ticking either Yes , No , or N/A;
• comments addressed to the Author and the Subject Editor in the online form;
• associated files (corrected/commented manuscript file, review submitted in a separate text file, etc.), if any.

Note:  Reviewers can insert corrections and comments in the manuscript review version (PDF) and/or in the manuscript text file (usually Microsoft Word, rarely Open Office file). When working in the PDF, please use either the Text Edits or the Sticky Notes tools (available through the menu Tools -> Comments & Markup of the Acrobat Reader). When editing in Microsoft Word please use the Track Changes / Comments tools. Note: Associated files should be submitted at the end of the review process by clicking on the Browse  button, then selecting the respective file on your computer, and then pressing the Upload  button. A Reviewer may upload as many files to support his/her review as needed.

The Reviewer may decide to stay anonymous or open his/her identity by ticking the Show my name to the author(s)  box at the bottom of the reviewer’s form. Please be aware that your identity might be revealed in the comments or in Track Changes corrections of the Microsoft Word or PDF file you correct. Therefore, please make sure that you delete your name and initials in the Options section of your Word or PDF processor if you want to remain anonymous.

In addition to the above, by checking a box at the bottom of the submission form, reviewers may opt for making their contribution public in the event that the article is accepted and published. The reviewer's name, affiliation and email address will be displayed next to those of the Academic editor (or Subject editor) on the article webpage.

The review process is completed by selecting a recommendation from five options: (1)  Reject;  (2)  Reject, but resubmission encouraged ; (3)  Major Revision ; (4)  Minor Revision ; (5)  Accept . The system will ask for one more confirmation of the selected recommendation before submission. The submitted review cannot be changed after submission. Note:  Reasons for rejection can be a low scientific quality, non-conformance to the journal’s style/policies, and/or grammatically poor English language. Note:  It is also possible for review and associated files (e.g., a corrected manuscript file) to be sent as attached files to the email of the Editorial Office. We strongly recommend  avoiding this option, and instead uploading reviews through the online editorial management system.

Once a Reviewer submits a review of a manuscript, he/she receives an acknowledgement email from the journal.

The submission of the review is also automatically reported to Clarivate  - Web of Science Reviewer Recognition Service (formerly Publons). Reviewers are asked to confirm whether they want their reviews to be recorded on Clarivate.

When all Reviewers have submitted their reviews, the Subject Editor makes a decision to either accept, reject or request further minor/major revision.

After the Subject Editor's decision, the manuscript is sent back to the author for comments and further revision. The Author needs to submit a revised version in due time.

Reviewers are notified via email when the revised version of a manuscript that they have reviewed is submitted by the Author. They receive a link to the revised version along with the editorial decision and all reviews of the manuscript. Reviewers are also provided with a feedback form should they have any comments on the revised version. 

When an article is published, all Reviewers who have provided a review for the respective manuscript receive an email acknowledgment. In the email, there is a link to view/download the published article.

The Reviewer may always access information on the manuscripts that are being / have been reviewed by him/her through the menu My Tasks  –> Reviewer  on the journal’s web page – In Review (no.), In Edit (no.), Published (no.), and Archived (no.). The number in brackets after each category shows the number of manuscripts that have been assigned to you.

Benefits for Editors and Reviewers

This journal does not exclude editors from publishing papers in the journal (co-)authored by them. However, this is only possible for a limited proportion of papers per year, with some extra precautions and procedures to avoid an impression of impropriety, endogeny or conflicts of interest, and to ensure the editorial decision-making process is transparent and fair. For more information please consult the Policies page on the journal's website.

Pensoft editors and reviewers are entitled to a set of benefits in appreciation for their contribution to the quality of the works we publish. Please make sure to apply for your discount prior to the manuscript submission.

* When an individual qualifies for multiple discounts Pensoft will use the largest that applies.

Special issues & Topical collections

Special issues and Topical collections are collections of articles grouped together by a shared topic or interest group, such as an emerging area of research, proceedings of a conference, outcomes of a research project or a Festschrift volume.

Article collections aim to aid the dissemination and outreach of multiple research outcomes and also bring together research teams from around the globe working on similar topics, thus increasing the opportunities for collaboration, sharing and re-use of research. Article collections bring credit, increased discoverability, visibility and recognition to both their collection editors and participating authors.

Special issues are available only in journals published in consequent issues within an yearly volume and are subject to a submission deadline set at the time the call for papers is issued. The publication date of a Special issue is also pre-scheduled and all papers are published simultaneously on the same date as a separately numbered issue within the yearly journal's volume. This means that the Special issue will be published only when all articles are ready to be published. 

Topical collections can be opened in any journal hosted on ARPHA and can be permanent or made subject to a submission deadline. It is only up to the Collection editor(s) to decide whether and when the collection is to be closed for submission (given a timely public announcement is provided). The articles are published on a rolling basis, as soon as they are ready for publication, and can be part of different journal issues, published across many years.

Article collections are managed by a Collection editor and associated Guest editors. To pitch a Special issue or a Topical collection, either contact the Editor-in-Chief or submit an Open an article collection proposal form. Before pitching a Special issue or a Topical collection, please make yourself aware of the specificity of the focus, scope and policies of the journal and the associated responsibilities and benefits for you as a Collection editor.

How It Works

The following guidelines apply for both Special issues and Topical collections in the ARPHA journals.

Article collections can be opened in any of the ARPHA-hosted journals. It is subject to the journal's policy, however, to offer this feature or not. 

Special issues are available only in the journals that publish consecutively numbered issues within a yearly volume, while topical collections are available to all ARPHA-hosted journals, depending on their policies.

Collections may have subcollections, for example, topical subcollections. A subcollection cannot be managed separately from the parent collection, except in the case of conference proceedings submission workflows available at some ARPHA-hosted journals.

Opening and managing a collection

The article collections are managed by a Collection editor and Guest editors. The Collection editor is responsible for approving or declining manuscripts submitted to the article collection; assigning a Guest editor to each manuscript for handling the peer review process; and managing the collection on the journal’s website (e.g. change the collection’s description or the order of the papers). The Collection editor has the full rights of a Guest editor and can also handle manuscripts.

Before pitching a collection, assure that you are ready to appoint other Guest editors, if necessary. The Collection editor and the Guest editors are also expected to commission an initial set of manuscripts to be submitted soon after the opening of the collection.

Submit the Article collection proposal form or contact the Editor-in-Chief via email. The collections should fully comply with the journal’s focus, scope and editorial policies.

Online proposals are forwarded to the Editor-in-Chief and to the journal’s editorial office for approval. The editorial office checks and confirms the guest editors' credentials.

Upon approval of the proposal, the journal’s editorial office will set up the collection on the journal’s website.

Open collections will be promoted through the journal’s website and social media in collaboration with the Collection and Guest editors. 

Editors of Special issues need to assure that the minimum volume of articles is met within the set deadline and that, if necessary, a deadline extension is announced well in advance.

Editors of Topical collections with no set submission deadlines need to inform the journal’s editorial office if they wish to close the collection for submissions in a timely manner. 

Authors opt for assigning their manuscript to a collection during submission. In case the manuscript is declined from the collection, it undergoes the regular evaluation and peer review process at the journal.

Once the manuscript passes the initial pre-review screening by the Editor-in-Chief and the journal's editorial office, it is forwarded to the Collection editor to either approve or decline it for the collection. The Collection editor is notified about each new submission to the collection via email sent by the system.

After reading the paper, the Collection editor can:

accept it in the collection and assign it to a Guest editor.

decline it from the collection and send it back to the journal's editorial office.

Once a manuscript is assigned to a Guest editor, he or she takes on the responsibility to invite reviewers and provide an editorial decision for revision, rejection or acceptance of the manuscript, based on the reviews and personal evaluation. Papers submitted by the guest editor(s) must be handled under an independent review process and make up no more than 25% of the collection's total.

The editorial decisions are automatically forwarded to the authors by the system.

The guest editors are overseen by the journal's Editor-in-Chief and/or dedicated board members, and may intervene in the editorial process. Depending on the journal’s policy, the journal’s Editor-in-Chief might need to approve the Guest editor’s final decision before the manuscript is accepted for publication.

Benefits of Editing a Collection

The main advantages to open and edit an article collection can be summarised as follows:

Credit and recognition for the Collection and Guest editors who take care to organise and manage the article collection.

Facilitates discoverability and usability of topically related studies, which in turn benefits both authors and readers.

Increases the visibility of related papers, even when papers might otherwise lack in viewership. 

Prompts simultaneous citation of multiple articles related to a certain subject.

Facilitates citation and referencing of the whole issue as a complete entity.

To show our gratitude to the collection editors, we are also providing a free publication to collection editors in the collection they edit and manage.

Editor’s Responsibilities

By proposing an article collection (Special issue or Topical collection), you agree to act as a Collection editor, whose main responsibilities are:

Working with the editorial office to set up the article collection on the journal’s website.

Appoint Guest editors for the article collection.

Approve or decline each manuscript submitted to the article collection.

Assign a Guest editor for each manuscript submitted to the article collection.

Assure that the article collections complies with any relevant requirements, as set up by the journal and the agreement (if any).

Inform the journal’s editorial office about any changes or issues concerning the management of the collection in due time.

You will also be granted the user rights of a Guest editor necessary to handle manuscripts in the system (i.e. assign reviewers and provide an editorial decision on the acceptance/rejection of the manuscript). 

The responsibilities of a Guest editor are:

Handling the peer review of the manuscripts they have been assigned to.

Making an editorial decision for revision, acceptance or rejection of the manuscripts they have been assigned to, based on the reviews provided and personal evaluation.

Taking into consideration the recommendations of the journal’s Editor-in-Chief.

The journal adheres to the " Best practices for guest edited collections " by the Committee on Publication Ethics (COPE). The collection editors should first familiarise themselves with those guidelines before applying for or starting working on a guest edited article collection.

For more information about the editorial workflow, visit How it works ?

Article Processing Charges

Publisher's statement.

A key policy and strategic aim of Pensoft is to provide high-quality and inclusive publishing services at highly competitive and affordable Article Processing Charges (APCs) or for free through its diamond open access journals. See Pensoft’s journal portfolio here .

In order to ensure long-term sustainability of the journals and cover the cost of the associated in-house publishing services, our journals require Article Processing Charges (APCs). These charges apply only after a submitted manuscript is accepted for publication, and may be partially or fully covered by institutional funds to reduce financial burdens on authors of research.

Pensoft strongly supports measures that ensure an inclusive and FAIR publishing environment, which in turn prompts quality, sustainability and reasonable pricing in scholarly publishing. You can find more about the publisher’s view on quality, transparency, openness and equity in scholarly publishing in Pensoft’s official statement , prompted by the publication of the European Union’s Conclusions on high-quality, transparent, open and equitable scholarly publishing . 

In compliance with the Plan S requirements , Pensoft provides a breakdown of the APC following the  guidelines by the Fair Open Access Alliance (FOAA) . The report on the journal’s APC is submitted on a yearly basis to the  Journal Comparison Service by Coalition S and the detailed breakdown is available to the participating funding institutions on the platform.

Authors who are unable to pay their APCs for several reasons, should consult the Journal’s Discounts and Waivers page, use the diamond open-access journals (free to publish and free to read) hosted on Pensoft’s ARPHA Publishing Platform , or contact the journal’s Editor-in-Chief directly. 

Core Charges

Core services included in our Article Processing Charges:

• Online submission and editorial management system, professional peer review and editorial assistance.
• Personal attitude, technical support and fast reply to any inquiry coming from authors, editors or reviewers.
• Automated email notification and alert system to save you time from tracking the progress of your manuscript.
• Automated registration of peer reviews at Clarivate (formerly Publons).
• Copy-editing, technical editing, typesetting and proofreading services.
• Publication in 3 digital formats: semantically enhanced HTML, PDF and machine-readable JATS XML.
• Rapid publication process, normally within 1-2 weeks time after a manuscript is accepted for publication.
• Full-color (no extra-charges for color), high-resolution hardcopy of reprints or whole issues.
• Advanced data publishing workflows. 
• Semantic Web enhancements to the article text. 
• Markup and visualization of all biological taxon names and taxon treatments in your work, if present.
• Immediate free access to the article on the day of publication.
• Copyright retained by the authors, articles distributed under the Creative Commons Attribution (CC-BY) 4.0 license.
• Active dissemination and promotion through social bookmarking tools and social media.
• Automated email acknowledgements to editors and reviewers upon publication.
• Automated alert service through email and RSS on the day of publication. 
• Registration of all new taxa in ZooBank, IPNI, MycoBank or Index Fungorum (where relevant).
• Export and display of taxon treatments to Encyclopedia of Life (EOL), Plazi, Species-ID, Globalnames, and other aggregators (where relevant).
• Immediate distribution of your publication to scientific databases, indices and search engines (Web of Science, Scopus, Google Scholar, CAB Abstracts, DOAJ Content and others).
• Archiving in international repositories (PubMedCentral, CLOCKSS, Zenodo).
• Bibliography search and discovery tool. 
• Citation export in various formats.
• Cited-by records statistics and display.
• Article- and sub-article-level metrics (Altmetric, Dimensions, number of downloads separately for the PDF, XML and HTML, usage stats for figures, tables and supplementary files).

Innovative papers and reviews of special importance for science are to be priced by agreement.

Please note that the above prices do not include VAT (Value Added Tax). VAT is applicable only for VAT NON-registered customers based within the European Union. To avoid charging VAT, the EU companies or persons should provide their VAT registration numbers validated with the EU taxation database ( https://ec.europa.eu/taxation_customs/vies/ ).

Article collections enable conference organizers or project coordinators to publish a number of articles under a common theme and editorship. Depending on the number of articles to be included, Pensoft offers discounts on APCs as described in the table below.

We are happy to discuss alternative arrangements if there is a better way to suit your needs for n article collection. Please do not hesitate to contact us or to submit your proposal through the article collection application form .

Discounts and Waivers

Please note that the discounts and waivers policy below  is applicable for all manuscripts submitted after  1st of January 2024 .

Authors can apply for a discount or a waiver during manuscript submission if they comply with the conditions listed below. The journal will not consider requests made during the review process or after acceptance. Formal letters to the editors will not be considered outside the application process during manuscript submission. The waiver system will be managed by administrative staff not involved in decisions regarding article acceptance. We ask authors not to discuss any issues concerning payment with editors.

• Scientists working privately, not affiliated with an institution.
• Graduate and PhD students if they are first authors of a manuscript. 
• Scientists affiliated with institutions located in Research4Life Group B countries ( https://www.research4life.org/access/eligibility/#groupb ) if they are lead or corresponding authors of a manuscript. In cases of multiple affiliations, all institutions should be located in eligible countries. 
• Discounts are also offered to our editors and reviewers, for more information see here . 
• Special discounts can be requested by the authors of extensive review papers and monographs.
• Retired scientists who are editors or active reviewers for this journal (1-3 reviews provided in the year before the manuscript submission). 
• Scientists affiliated with institutions located in Research4Life Group A countries ( https://www.research4life.org/access/eligibility/#groupa ), if they are lead or corresponding authors of a manuscript. In cases of multiple affiliations, all institutions should be located in eligible countries.

The journal offers also various institutional programs and membership plans  to support Open Access scientific publishing. To be eligible, the author must be a corresponding author affiliated with the institution or agency.

Discounts and waivers do not accumulate.

Please note that the discounts and waivers policy below  is applicable for all manuscripts submitted before  1st of January 2024 .

Authors can apply for discount or waiver during manuscript submission if they comply with the conditions listed below. The journal will not consider requests made during the review process or after acceptance. Formal letters to the editors will not be considered outside the application process during manuscript submission.

• Scientists living and working in lower middle-income countries ( http://data.worldbank.org/income-level/lower-middle-income ) if they are sole authors of a manuscript, or authors' research is funded primarily (50% or more of the work contained within the article) by an institution or organization from the eligible countries. 
• Discounts are also offered to our editors and reviewers, for more information see  here . 
• Scientists living and working in low-income countries ( http://data.worldbank.org/income-level/low-income ), if they are sole authors of a manuscript, or authors' research is funded primarily (50% or more of the work contained within the article) by an institution or organization from the eligible countries.

The journal offers also various  institutional programs and membership plans  to support Open Access scientific publishing. To be eligible, the author must be a corresponding author affiliated with the institution or agency.

Nature Conservation is published in identical print (high-resolution, full-color) and online (PDF) versions.

Printed versions of this journal may be ordered in parts or subscribed for. To subscribe please contact us by writing to  [email protected] .

Please include the full delivery address and indicate your preferred payment method. Please contact us if you need a quotation or proforma invoice.

Separate issues or reprints (high-resolution, full-color) can be ordered using the "Order now" button available under each issue or article on the journal's website.

Prices are given in EURO and are exclusive of postage and handling. Payment in USD is also possible according to the exchange rate on the day of payment.

IMPORTANT: Our prices do not include VAT. Orders from countries outside the European Union (EU) or from VAT-registered EU customers will be processed VAT-free. VAT (20%) will be added ONLY to NOT VAT-registered customers based in the European Union.

Prices of full-color, high-resolution printed version (separate article and complete issues)

Additional Services (Optional)

*This service can be discounted or waived for articles of outstanding importance for the science and society. **Pensoft reviewers do not usually have time to check through large data files included with manuscripts. If you would like us to have your data files checked, we offer the services of Pensoft editor Dr Bob Mesibov, who is also a data auditor. Suitable data files for checking would be large tables of occurrence records or of genetic data. These can be checked for duplicate and broken records, misuse of fields, disagreements between fields, character encoding problems and incorrect or inconsistent formatting. Georeferencing can also be checked, on request. Please note that this service does not apply to taxonomic, nomenclatural or bibliographic details in data files.

Institutional and Other Membership Plans

Our plans provide additional flexibility and affordability for institutions, research groups, consortia, conference organizers and other larger research teams and organizations. Affiliated authors can publish in any Pensoft journal by using a streamlined payment interface. Pensoft’s plans are a great way to support open access publishing, while also simplifying budgeting, invoicing, and author reimbursement procedures. We offer three plans to choose from, however, if they do not quite suit your needs, we would be happy to discuss alternative arrangements with you. Please do not hesitate to contact us for a preliminary conversation about our plans!

Please find more details about each individual plan below. If you would like to recommend Pensoft’s plans to your institution you can fill out this simple form or contact us at [email protected] and we will forward your recommendation with some additional information.

Annual Memberships

Annual memberships allow institutions to plan their publishing expenses in the beginning of the fiscal year by providing unlimited publishing in all Pensoft journals in exchange for a flat annual payment. The cost of membership depends on the total publishing output capacity of the institution and its historical publishing pattern in Pensoft journals. We will adjust the cost of your membership annually.

Pre-Paid Plans

Pre-paid plans allow institutions and / or research groups to deposit a certain amount of funds with Pensoft and make them available to affiliated researchers for covering Article Processing Charges (APCs)  in any Pensoft journal . Member institutions decide whether to cover APCs in full or share the expenses with the authors. Depending on the amount members are prepared to commit, Pensoft is offering a discount on APCs per the table below. Additional funds can be added to an account at any point in time within the calendar year of purchasing the plan, while leftover funds are preserved until spent.

Direct Billing

The direct billing plan allows institutions to reduce the complexity of billing and reimbursements. It consolidates all Pensoft invoices for articles authored by researchers affiliated with an institution into a single monthly bill that is sent directly to the institution.

Annual Awards

To recognise the research impact of our authors and the invaluable support of our editors, while also encouraging further valuable and extensive contributions to science, each January, we will be awarding the first authors 1 of the three most cited 3-year-old 2 articles , according to Web of Science, and the three editors 3 with the most editorial tasks completed in the last calendar year. 

Each awardee will receive a voucher for one free publication in the journal eligible for a submission made during the next two years. 

One waiver is valid for one manuscript of standard size (up to 20 print pages), where additional pages will be charged according to our regular article processing charges. In case awarded authors do not intend to use their vouchers, they are welcome to pass the waiver to any of their co-authors.

_______________ 1 In case the third place is shared by multiple papers, we are awarding the most recently published one in recognition of its addressing a particularly ‘hot’ topic in the field. 2 We assume that this is the minimum period for an article to accumulate a prominent amount of citations. For example, in 2023, we are awarding articles published in 2020. 3 If multiple editors share the third place in the ranking, the award is given to the one with the highest all-time record at the journal.

2023 Awards

At the end of 2023, the Nature Conservation team awards the lead authors of the publications:

• D'Cruze N , Assou D, Coulthard E, Norrey J, Megson D, Macdonald DW, Harrington LA, Ronfot D, Segniagbeto GH, Auliya M (2020) Snake oil and pangolin scales: insights into wild animal use at "Marché des Fétiches" traditional medicine market , Togo. Nature Conservation 39: 45-71.  https://doi.org/10.3897/natureconservation.39.47879
• Harrington LA , Green J, Muinde P, Macdonald DW, Auliya M, D'Cruze N (2020) Snakes and ladders: A review of ball python production in West Africa for the global pet market . Nature Conservation 41: 1-24.  https://doi.org/10.3897/natureconservation.41.51270
• Auliya M , Hofmann S, Segniagbeto GH, Assou D, Ronfot D, Astrin JJ, Forat S, Koffivi K. Ketoh G, D’Cruze N (2020) The first genetic assessment of wild and farmed ball pythons (Reptilia, Serpentes, Pythonidae) in southern Togo . Nature Conservation 38: 37-59.  https://doi.org/10.3897/natureconservation.38.49478

Awards from Nature Conservation also receive the three most active editors in 2023:

• Franco Andreone
• Mark Auliya

Science Communication

Our journal and the PR team at Pensoft invites authors to contribute to the communication and promotion of their published research, thereby increasing the visibility, outreach and impact of their work. 

Authors are welcome to notify us whenever their institution is working on a promotional campaign about their work published in our journal. We are always happy to reshare and/or repost (where appropriate). 

You can contact our PR team at [email protected] to discuss the communication and promotion of your research.

Tailored PR Campaign

(Paid service*)

We encourage authors, who feel that their work is of particular interest to the wider audience, to email us with a press release draft** (see template and guidelines ), outlining the key findings from the study and their public impact. Then, the PR team will work with them to finalise the announcement that will be:

• Issued on the global science news service Eurekalert!
• Sent out to our media contacts from the world’s top-tier news outlets
• Posted on ARPHA’s or Pensoft’s blog
• Shared on social media via suitable ARPHA-managed accounts

Following the distribution of the press announcement, our team will be tracking the publicity across news media, blogs and social networks, in order to report back to the author(s), and reshare any prominent media content.

Request our Tailored PR campaign service by selecting it while completing your submission form and you will be contacted once your manuscript is accepted for publication. Alternatively, contact our PR team  ( [email protected] ), preferably upon the acceptance of your manuscript.

* The Tailored PR campaign is an extra service charged at EUR 150 . However, we would consider discounts and even full waivers for studies of particular interest for the society.

**  Please note that our PR team reserves the right to edit your press release at their discretion. No press announcements will be issued until we receive the author’s final approval to do so. The service is only available for studies published within the past 3 months.

Guest Blog Post

(Free service)

Authors are strongly encouraged to promote their work and its impact on society to the audience beyond their immediate public of fellow scientists by means of storytelling in plain language. Ideally, such guest blog posts will be:

• Written from the author’s own point of view, using conversational tone;
• Written in fluent English;
• Presenting some curious background information, in order to place the discovery in context;
• Including attractive non-copyright imagery.

Request our Guest blog post service by contacting the PR department ( [email protected] ), regardless of the status of your submission, as there are no time constraints for guest blog post publication. Particularly encouraged are follow-up contributions telling the story of, for example, a research paper that has led to an important policy to be set in place; or an article that has met remarkable attention or reactions in the public sphere.

Following the necessary final touches to the guest blog post by the PR team, the contribution will be:

• Shared on social media via multiple and relevant ARPHA-managed accounts

Please note that the PR team reserves the right to refuse publication of a guest blog post on the occasion that it is provided in poor English, uses considerable amount of jargon or does not abide by basic ethical standards. Our PR team reserves the right to request changes to the text related to formatting or language. No blog posts will be issued until we receive the author’s final approval to do so.

Find past guest blog posts on Pensoft’s blog here . 

Video Podcast

To efficiently increase the outreach of their research, authors are suggested to prepare a video contribution (i.e. elevator video pitch, video abstract or topical video), where they present their work to an audience beyond their immediate public of fellow scientists by means of visual storytelling.

To do so, they are expected to send us a short (up to 02’00’’) video clip, presenting their study in a nutshell, in order to spark the viewer’s further interest in their findings and work, as well as the research topic as a whole. Ideally, such contribution will be:

• filmed in high quality, preferably with .mp4 file extension with the H.264 video codec;
• directed from the author’s own point of view, using conversational tone and minimal jargon;
• presented in fluent English or featuring English subtitles;
• accompanied by a transcript in English;
• accompanied by a short text introduction for the purposes of a blog post.

Request our Guest video contribution service by contacting the PR department ( [email protected] ), regardless of the status of your submission, since there are no time constraints for guest blog post publication.

Following the necessary final touches to the guest blog post, the contribution will be:

• Shared on Pensoft’s YouTube channel ;
• Posted on ARPHA’s or Pensoft’s blog;
• Shared on social media via multiple and relevant ARPHA-managed accounts. 

Please note that the PR team reserves the right to refuse distribution of a guest contribution on the occasion that it is provided in poor English, uses considerable amount of jargon or does not abide by basic ethical standards.

Custom Social Media Content

To help increase the visibility and outreach of their research, authors are welcome to suggest custom social media content to be distributed via suitable Pensoft- and ARPHA-managed social media accounts.

Social media posts are expected to:

• Be limited to two short sentences or 280 characters (including links);
• Be written in a conversational tone;
• Contain minimal jargon;
• Include the DOI link of the article;
• Not duplicate the title or abstract of the article;
• Include attractive non-copyright imagery;
• Possibly include up to 10 social media accounts, e.g. co-authors (Twitter only), affiliations, funding bodies etc. relevant to the study.

Request our Custom social media content service by contacting our PR department ( [email protected] ).

Please note that our PR team reserves the right to edit your text at their discretion.

Media Center

Follow Nature Conservation on Twitter and Facebook .

Learn about some of the most notable research published in Nature Conservation on Pensoft's blog.

See top news stories from around the globe, mentioning research published in Nature Conservation, in National Geographic , The Conversation , Chinese Global TV Network (CGTN),  Asian Scientist , World Economic Forum , Business Standard  and The Science Times .

Boost the reach of your paper(s) to a larger audience by making the most of Pensoft's science communication services .

Download journal presentation slides as a PowerPoint or PDF file.

Download journal leaflet .

Download journal logo .

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Journal Info

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• Open access
• Published: 10 May 2024

Navigating an unpredictable environment: the moderating role of perceived environmental unpredictability in the effectiveness of ecological resource scarcity information on pro-environmental behavior

• Dian Gu 1 , 2 &
• Jiang Jiang 3  

BMC Psychology volume  12 , Article number:  261 ( 2024 ) Cite this article

135 Accesses

6 Altmetric

Metrics details

The global issue of ecological resource scarcity, worsened by climate change, necessitates effective methods to promote resource conservation. One commonly used approach is presenting ecological resource scarcity information. However, the effectiveness of this method remains uncertain, particularly in an unpredictable world. This research aims to examine the role of perceived environmental unpredictability in moderating the impact of ecological resource scarcity information on pro-environmental behavior (PEB).

We conducted three studies to test our hypothesis on moderation. Study 1 ( N  = 256) measured perceived general environmental unpredictability, perceived resource scarcity and daily PEB frequencies in a cross-sectional survey. Study 2 ( N  = 107) took it a step further by manipulating resource scarcity. Importantly, to increase ecological validity, Study 3 ( N  = 135) manipulated the information on both ecological resource scarcity and nature-related environmental unpredictability, and measured real water and paper consumption using a newly developed washing-hands paradigm.

In Study 1, we discovered that perceived resource scarcity positively predicted PEB, but only when individuals perceive the environment as less unpredictable (interaction effect: 95% CI  = [-0.09, -0.01], Δ R 2  = 0.018). Furthermore, by manipulating scarcity information, Study 2 revealed that only for individuals with lower levels of environmental unpredictability presenting ecological resource scarcity information could decrease forest resource consumption intention (interaction effect: 95% CI  = [-0.025, -0.031], Δ R 2  = .04). Moreover, Study 3 found that the negative effect of water resource scarcity information on actual water and (interaction effect: 95%CI = [3.037, 22.097], η p 2  = .050) paper saving behaviors (interaction effect: 95%CI = [0.021, 0.275], η p 2  = .040), as well as hypothetical forest resource consumption (interaction effect: 95%CI = [-0.053, 0.849], η p 2  = .023) emerged only for people who receiving weaker environmental unpredictability information.

Across three studies, we provide evidence to support the moderation hypothesis that environmental unpredictability weakens the positive effect of ecological resource scarcity information on PEB, offering important theoretical and practical implications on the optimal use of resource scarcity to enhance PEB.

Peer Review reports

Introduction

Ecological resource scarcity, such as water and energy, poses significant challenges in our current times. The reduction of renewable freshwater resources per capita by 55% from 1993 to 2014 emphasizes the urgency of addressing this issue [ 1 ]. According to the World Economic Forum (2019), water shortages remain a top concern for policymakers and business leaders worldwide. In response to resource scarcity, various entities, including governments, water utilities, and community-based organizations, have employed different strategies to promote resource conservation [ 2 ]. One of the most common approaches is to raise problem awareness by conveying information about resource scarcity [ 2 ]. For example, the fact that billions of people lack access to safe water is utilized in the World Water Day campaign in 2023 to encourage more people to take action. Additionally, the Hong Kong SAR Government’s “Let’s Save 10L Water 2.0” campaign emphasizes the importance of conserving water by highlighting the limited availability of this resource.

Despite these efforts, it is important to recognize the complexity and interconnectedness of the world we live in, which makes predicting future environmental conditions challenging. Unforeseen events such as pathogen prevalence, natural disasters, wars, and financial crises illustrate the dynamic nature of our environment. In such an unpredictable world, can simply providing information about ecological resource scarcity lead to a significant increase in pro-environmental behaviors?

In the current research, we aimed to explore whether ecological resource scarcity information could promote pro-environmental behaviors effectively in the unpredictable world. We argued that ecological resource scarcity information is not necessarily useful in promoting pro-environmental behaviors and proposed that environmental unpredictability is a vital factor weakening the effect of ecological resource scarcity on resource consumption.

Uncertain association between ecological resource scarcity information and pro-environmental behaviors

Based on the information-motivation-behavioral skills (IMB) model, individuals are more likely to change their behavior when they are informed about a problem, along with being motivated to act and have skills to act [ 3 ]. In the environmental protection domain, there is a general lack of problem awareness about ecological resource scarcity [ 4 , 5 ]. This lack of awareness hinders individuals from engaging in pro-environmental behaviors (PEB), which refers to the actions that enhance the quality of the environment, regardless of the intent behind them [ 6 ]. Resource conservation campaigns often focus on resource scarcity information to encourage PEB [ 7 ]. In some empirical studies, the resource scarcity information was found to be effective. For example, individuals living in regions that experience drought have a higher tendency to make behavioral changes to conserve water [ 8 , 9 ]. People who perceived stronger ecological resource scarcity reported higher resource-saving behavioral frequencies [ 10 ], and indicated a higher frequency of PEB [ 11 ]. And water scarcity information was linked to a significant decrease in water use [ 12 , 13 , 14 ].

However, we identified some conflicting evidence. Information about resource scarcity is often not sufficient to reduce resource consumption in intervention [ 15 ], and the effectiveness of awareness campaigns is unclear [ 16 ]. For example, presenting the information about water resource scarcity only was evaluated as ineffective to promote water-saving behaviors by lay people [ 10 ]. Energy scarcity information was not strong enough to affect attitudes, intentions, and behaviors toward electricity energy saving [ 17 ]. Moreover, resource scarcity information failed to modify resource consumption behaviors in experimental settings [ 2 , 18 ].

The uncertain relationship between resource scarcity and PEB can be understood through an evolutionary psychological approach. According to the life history theory, individuals may adopt various strategies for allocating resources [ 19 , 20 , 21 , 22 , 23 ]. Those who choose a slow life history strategy prioritize long-term benefits and future planning, which leads them to behave in an environmentally friendly manner for the sake of future generations. On the other hand, individuals adopting a fast life history strategy prioritize immediate gains over long-term consequences [ 24 ], resulting in less PEB.

This theory, combined with empirical evidence, suggest that the impact of resource scarcity on PEB may vary depending on the situation, implying that promoting pro-environmental actions may require considering factors beyond simply informing individuals about scarcity. If PEB is seen as an investment in the environment, people engaging in PEB expect long-term benefits from it. However, the environment does not always provide consistent long-term benefits, particularly in today’s unpredictable world. When the expected advantages of environmental protection become uncertain, individuals may prioritize immediate gains, exploit natural resources, and reduce their commitment to PEB. This study hence focuses on the situational factor related to the unpredictable environment, testing its importance in influencing individuals’ PEB under resource scarcity.

Moderating role of environmental unpredictability

Environmental unpredictability is defined as the level of spatial–temporal variation in environmental harshness [ 24 ]. Past empirical studies measured environmental unpredictability in diverse ways [ 25 ]. In the current research, we tried to capture both individual-related and nature-related environmental unpredictability in temporal or spatial dimensions. Individual-related environmental unpredictability is mostly indicated by residential changes, and changes in parental financial status for children [ 19 , 24 , 26 ]. It shows whether the structure of an environment, such as the social or economic environment in which one lives, changes over time. Nature-related environmental unpredictability focuses on the pattern of variation that makes environments unpredictable, such as unpredictability of weather and the unpredictability of natural disasters [ 25 ].

Based on the life history theory, the environment plays a crucial role in shaping individuals’ life history strategies [ 19 , 20 , 21 , 22 , 23 ]. In predictable environments individuals are more likely to adopt a slow life-history strategy, while highly unpredictable environments promote a fast life-history strategy [ 24 ]. Importantly, environmental unpredictability during childhood can influence short-sighted tendencies [ 27 , 28 , 29 , 30 ], and this effect can also be observed in adulthood [ 31 ]. In an unpredictable environment, individuals prioritize immediate desires over future needs because investing in long-term environmental protection may not yield future benefits. This has implications for PEB, as present efforts on environmental protection may not be effective in improving resource scarcity in the future when the environment is unpredictable.

There are two aspects that illustrate the expectation that PEB efforts may not pay off in unpredictable environments. Firstly, in an unpredictable environment, there is a flow of uncontrollable information, which makes it challenging for individuals to maintain strong beliefs that their actions can bring about positive outcomes, such as improving resource scarcity [ 32 ]. According to the theories of reasoned action and planned behavior, the impact of awareness of the problem on behavior is greater when individuals perceive a higher level of control over their actions [ 33 ]. Hence, environmental unpredictability not only reduces the perceived personal control but also creates a barrier between scarcity awareness and PEB.

Secondly, in unpredictable environments, individuals are more likely to fear free riders, which further hinders behavioral change towards environmental protection under resource scarcity. When deciding whether to take action to protect the environment, people consider whether others will cooperate. However, in unpredictable environments, the likelihood of others investing in PEB becomes uncertain as well, which induces a heightened fear of free riders. For instance, experimental games have shown that individuals behave less cooperatively and invest fewer public goods when the probability of benefiting from them is uncertain [ 34 ]. Moreover, studies have demonstrated that individuals are less likely to prioritize the interests of others over their own when environmental unpredictability is primed [ 31 , 35 ]. Due to the fear that others will not take action in an unpredictable environment, individual efforts to protect the environment may appear less effective in solving the issue of resource scarcity.

Taken together, stronger environmental unpredictability is associated with a fast life-history strategy characterized by low self-efficacy and high fear of free riders, which ultimately leads to less PEB performance in the face of resource scarcity. Both multilevel and individual-level studies have indicated that psychological traits similar to the fast life history strategy weaken the association between environmental problem awareness and actual PEB [ 10 , 36 ]. Besides, some indirect evidence revealed that resource scarcity and environmental unpredictability could lead to some psychological outcomes that go against promoting PEB. Specifically, poorer childhood and economic uncertainty jointly increase the present orientation and decrease the sense of control [ 37 , 38 ]. A strong present orientation and low sense of control discourage people from taking actions to save resources [ 39 ]. With the above in mind, the following moderation hypothesis was proposed:

Hypothesis: Environmental unpredictability will moderate the effect of ecological resource scarcity on PEB. Specifically, ecological resource scarcity information would play a less effective role in promoting PEB when environmental unpredictability is stronger.

Current research

In the current research, we conducted three studies to test our hypothesis on moderation. In Study 1, we examined whether perceived general environmental unpredictability would moderate the relationship between perceived resource scarcity and daily PEB frequencies. Study 2 took it a step further by manipulating resource scarcity to test whether the positive effect of ecological resource scarcity information on forest resource consumption intention would be weakened by individual-related environmental unpredictability, specifically the frequency of residential changes. Importantly, to increase ecological validity, Study 3 manipulated the information on both ecological resource scarcity and nature-related environmental unpredictability, and measured real water and paper consumption using a newly developed washing-hands paradigm.

To examine the moderating effect of environmental unpredictability on the relationship between ecological resource scarcity and daily PEB frequency, we conducted a cross-sectional survey for Study 1. We hypothesised that ecological resource scarcity would predict higher frequencies of daily PEB for individuals who perceived the environment as predictable. However, we expected this positive association to diminish for individuals who perceived high levels of environmental unpredictability.

Participants

To ensure sufficient statistical power (80% power, α = .05) to detect a small-to-medium-sized effect for our moderation hypothesis, based on previous research in the same domain [ 10 ], we estimated that a sample size of 256 participants would be required using G*Power 3.1 [ 40 ]. Participants were recruited from a Chinese online survey platform ( www.wjx.cn ) and received monetary compensation for their participation. The survey platform utilized a voluntary opt-in panel, inviting users to complete the questionnaire. A total of 263 participants from China completed the survey. It is important to note that data collection was planned to conclude once 256 observations were collected within a three-week period.

The average age of the participants was 32.21 ± 7.11 years (ranging from 18 to 66 years), with 44.1% of them being male ( N  = 116). In terms of educational attainment, 1.9% held a middle-school degree or below, 1.9% had a high school degree, 8.7% held a junior college degree, 79.8% had a bachelor’s degree, and 7.6% had a master’s degree or higher. The average annual family income was 23.65 ± 21.04 ten thousand yuan.

Procedure and measures

To address the potential influence of priming participants’ perceived resource scarcity through items expressing the seriousness of resource scarcity [ 11 , 41 , 42 ], we carefully structured the data collection process. Firstly, we measured the dependent variable, PEB frequencies. Following this, participants completed the measure of perceived environmental unpredictability, and subsequently rated their perceived ecological resource scarcity. Additionally, to account for potential bias in self-reported PEB due to social desirability [ 43 ], we included a measurement of social desirability as a control variable. Finally, participants provided their demographic information, including age, gender, educational attainment, and annual personal income.

Perceived resource scarcity

The measurement of perceived ecological resource scarcity, consisting of 5 items, was adapted from a previous study conducted by Gu and her colleagues [ 10 ] (Cronbach’s α in the current study is 0.79). Participants were asked to indicate their level of agreement with statements such as “There are not enough resources for everyone in the place where I live” and “In the place where I live, I have already noticed some signs of resource scarcity.” Each item was rated on a 7-point Likert scale, ranging from 1 ( strongly disagree ) to 7 ( strongly agree ). The mean score of the entire scale was computed. Higher scores on this scale indicated higher levels of perceived ecological resource scarcity.

Perceived general environmental unpredictability

The item “For me, the environment we live in is unpredictable” developed by Reynolds and McCrea [ 44 ], was used to measure how participants perceived the general unpredictability of their environment. Participants rated this item on a 7-point Likert scale, ranging from 1 ( strongly disagree ) to 7 ( strongly agree ). Higher score indicated stronger perceived unpredictability.

Daily PEB frequency

Participants were asked to rate the frequency of PEB in their daily lives on a scale from 1 ( never ) to 5 ( always ). They were presented with six common resource conservation actions and asked to consider their behaviors in the year prior to the survey. The items are “do not turn the tap to the maximum when using water”, “switch off the lights when you leave”, “set the air conditioner’s temperature to 26–28 degrees centigrade in summer”, “buy and use energy-efficient appliances”, “avoid using disposable tableware whenever possible”. These six PEB were then converted into a PEB frequency scale, and a mean score was calculated for each participant. Higher scores indicated a higher frequency of PEB. Although the Cronbach’s α for the PEB scale was relatively low at .50, we decided to keep the measure because the items were face-valid. It is worth noting that removing any of the items did not improve the Cronbach’s alpha. Consistent with findings from previous studies, different types of PEB were not completely consistent [ 45 , 46 ]. And importantly, using the common score derived from the six items did not significantly alter the results.

Social desirability

Social desirability was measured using the liar subscale of the Eysenck Personality Questionnaire (EPQ) [ 47 ]. This subscale consists of 12 items, with participants answering each question with a “ Yes ” or “ No ” response. A code of 1 was assigned to “ Yes ” and 0 to “ No ”. Higher scores on this subscale indicated a stronger tendency towards social desirability. The measure demonstrated good internal consistency with a Cronbach’s α of .75.

Correlation analyses

Prior to conducting hypothesis testing, all variables exhibited normal distributions, as indicated by skewness values ranging from -0.89 to + 0.05 and kurtosis values ranging from -0.72 to + 0.77. We computed Pearson’s correlation coefficients to explore the relations among the studied variables (see Table  1 for descriptive statistics and intercorrelation coefficients). We found a marginally significant positive relationship between perceived ecological resource scarcity and PEB frequency ( r  = 0.12, p  = .058). And there was no correlation between environmental unpredictability and PEB frequency ( r  = -0.07, p  = .29). Importantly, as expected, social desirability was positively associated with PEB ( r  = 0.30, p  < .001), indicating that it should be controlled for in subsequent analyses.

Moderation analyses

To examine the impact of environmental unpredictability on the relationship between perceived ecological resource scarcity and PEB, we used the PROCESS macro for SPSS [ 48 ]. Controlling for social desirability, we found a significant interaction effect between perceived ecological resource scarcity and environmental unpredictability ( b  = -0.05, SE  = 0.02, t  = -2.26, p  = .025, 95% CI  = [-0.09, -0.01], Δ R 2  = 0.018). To further understand this interaction, we conducted a floodlight analysis [ 49 ]. The results showed that perceived ecological resource scarcity was positively and significantly associated with PEB when environmental unpredictability was below 4.41 ( b  = 0.07, SE  = 0.03, t  = 1.97, p  = .05, 95% CI  = [0.000, 0.136]), but not when it was above 4.41.

Additionally, we performed a simple slope analysis to examine the relationship between perceived ecological resource scarcity and PEB for individuals with different levels of perceived environmental unpredictability with social desirability controlled (see Fig.  1 ). The results indicated that perceived ecological resource scarcity positively predicted PEB for individuals with lower levels of environmental unpredictability (-1 SD ), b  = 0.13, SE  = 0.05, t  = 2.83, p  = .005, 95% CI  = [0.039, 0.219]. However, this relationship was not significant for individuals with higher levels of environmental unpredictability (+ 1 SD ), ( b  = -0.02, SE  = 0.05, t  = -0.35, p  = 0.73, 95% CI  = [-0.114, 0.079]).

The effect of resource scarcity on PEB at different levels of environmental unpredictability (Study 1)

Furthermore, controlling for demographic variables did not significantly change the results of moderation analysis. In summary, individuals who perceived the environment as more predictable were more likely to engage in PEB when facing ecological resource scarcity.

Brief discussion

Study 1 identified a moderating effect of environmental unpredictability on associations between perceived ecological resource scarcity and daily PEB. Individuals who perceived the environment as less unpredictable were more likely to adopt environmentally friendly ways to respond to ecological resource scarcity. However, it is important to consider the potential influence of responding to the PEB items on participants’ perceptions of ecological resource scarcity. The act of responding to these items may have directed participants’ attention towards environmental issues, potentially leading to an implicit increase in their perceived ecological resource scarcity. Therefore, it is not possible to infer the direction of the causal relationship between perceived ecological resource scarcity and PEB frequencies solely from correlational data. In addition, using a single item for measuring environmental unpredictability may raise concerns about the comprehensiveness of measurement. To address these limitations, we conducted Study 2, where we manipulated perceived ecological resource scarcity in order to demonstrate its causal effect, and further explore the moderating effect of environmental unpredictability by using another measurement.

Furthermore, it is important to note that the observed moderation effect size was small, which could be attributed to the fact that we measured various types of PEB in this study. According to the Goal System Theory, PEB can be motivated by multiple goals. In the context of resource scarcity, individuals who perceive the environment as more predictable are more likely to prioritize environmental protection for the benefit of future generations, especially if they themselves also stand to gain [ 50 ]. For instance, engaging in electricity-saving behaviors not only benefits the environment in the long run but also reduces personal electricity bills. In other words, personal benefits may matter. In our subsequent studies, we will focus on examining PEB that does not involve salient personal benefits in order to highlight the moderating effect of environmental unpredictability.

In Study 2, we sought to replicate the moderating effect of environmental unpredictability on the link between ecological resource scarcity and PEB by manipulating resource scarcity information. We proposed that receiving ecological resource scarcity information would increase PEB intention for individuals with lower levels of environmental unpredictability but that the effect would disappear for individuals with higher levels of environmental unpredictability.

To test our moderation hypothesis, we determined that a sample size of 107 would be necessary to achieve 80% power (α = .05) in order to detect a small-to-medium-size effect ( f 2  = .075) based on previous research [ 10 ] using G*Power 3.1 [ 40 ]. We established the rule for ending data collection prior to gathering data, stipulating that the survey link would be closed after obtaining more than 150 observations. Ultimately, we recruited 155 Chinese adults who completed an anonymous online questionnaire and all of these responses were valid.

The participants had an average age of 32.91 ± 10.10 years (range = 18–59 years) and 41.90% of them were males ( N  = 65). In terms of educational attainment, 9.70% held a high school degree, 16.1% held a junior college degree, 66.6% held a bachelor’s degree, and 13.5% held a master’s degree or higher. The average annual personal income was 11.18 ± 44.58 ten thousand yuan.

In the present study, participants reported their demographic information first. Then, environmental unpredictability was measured. Next, participants were randomly assigned to one of two experimental conditions to read a news article, where exposure to the information of resource scarcity (vs. control condition) was the manipulated factor. Finally, PEB intention was measured using a forest management task.

Manipulation of ecological resource scarcity information

Participants were assigned at random to read one of two news articles. The articles were created specifically to manipulate perceptions of ecological resource scarcity. In the scarcity group ( n  = 77), participants read an article titled “Interpretation of China’s Resources through Big Data: Invisible Resource Scarcity in China”, which highlighted the severity of natural resource scarcity in China. In the control group ( n  = 78), participants read an article of similar length that aimed to evoke similar levels of negative arousal. This article was titled “Interpretation of Sleep through Big Data: Invisible Sleeping Problems in China” and discussed sleep issues in China. To ensure the credibility of the mock news articles, participants were informed that the articles were sourced from The People’s Daily , a reputable Chinese newspaper.

Immediately after reading their respective article, participants rated their perception of ecological resource scarcity using a 7-point Likert scale ranging from “ strongly disagree ” (1) to “ strongly agree ” (7). The item presented was: “Currently, I believe that we live in an environment where natural resources are extremely scarce.” Besides, participants also responded to one item on their mood at the moment for the manipulation check on a 7-point Likert scale (1 = “ very negative ” to 7 = “ very positive ”).

• Environmental unpredictability

At the individual level, environmental unpredictability is mostly indicated by residential changes [ 24 , 25 ]. The frequency of residential changes showed whether the structure of an environment one lives in changes over time, which is the important aspect of environmental unpredictability. Therefore, Study 2 used the frequency individuals moved in the past to represent their environmental unpredictability. Higher score indicates stronger environmental unpredictability ( M  = 3.59, SD  = 2.17, Min  = 0, Max  = 11). The variable showed approximately normal distribution, with skewness = 0.64 and kurtosis = 0.65. Hence, the raw data of moving frequency are used for analysis.

PEB intention

A forest management task was used to measure PEB intention, specifically in relation to forest resource conservation intention [ 51 ]. Participants were asked to imagine that they were the owner of a timber company and must compete with three other companies to harvest timber in the same forest. They need to cut down as many trees as possible for their companies to profit and thrive. However, the rapid deforestation could lead to forest destruction. Then, participants were asked to answer one question about deforestation rate on a 7-point Likert scale, ranging from 1 ( very slow ) to 7 ( very fast ), which asked, ‘How fast do you want your company to cut down trees?’ Additionally, they were asked one question about forest resource consumption, ranging from 1 to 100 acres, which asked, ‘How many acres of trees do you expect your company to cut down?’. Give that both questions indicate greedy for forest resources, the average of participants’ reversed standardized scores on the two questions was computed to represent PEB intention. Higher scores indicate stronger forest resource conservation intention. We also treated the two items separately to test our hypothesis, which can be found in the Additional file 1 .

Manipulation checks

The manipulation of resource scarcity information was successful. Specifically, participants in the scarcity condition ( M  = 5.17, SD  = 1.25) compared to those in the control condition ( M  = 4.55, SD  = 1.56), reported higher levels of awareness on ecological resource scarcity, t (153) = 2.72, p  = .007, 95%CI = [0.169, 1.066], d  = 0.44. Furthermore, there was no difference of mood between the two conditions ( M scarcity  = 5.06, SD scarcity  = 1.19; M control  = 4.92, SD control  = 1.23), t (153) = 0.73, p  > .05, 95%CI = [-0.526, 0.242].

Hypothesis test

To test for the moderating effect of environmental unpredictability, we regressed the forest resource conservation intention on ecological resource scarcity information (dummy coded: 1 =  scarcity condition, 0 =  control condition), environmental unpredictability and their interaction by employing the PROCESS macro (Model 1, 5000 bootstrap samples) for SPSS [ 48 ]. The results showed a significant main effect of ecological resource scarcity information ( b  = 0.63, SE  = 0.23, t  = 2.78, p  = .006, 95% CI  = [0.183, 1.078]). And there was no main effect of environmental unpredictability ( b  = 0.04, SE  = .03, t  = 1.23, p  > .05, 95% CI  = [-0.026, 0.109]).

Results showed a significant interaction effect ( b  = -0.14, SE  = 0.06, t  = -2.54, p  = .012, 95% CI  = [-0.025, -0.031], Δ R 2  = .04), meaning that the effect of ecological resource scarcity information on forest resource conservation intention was moderated by environmental unpredictability. Specifically, for individuals with lower levels of environmental unpredictability (below 1 SD ), participants in the scarcity condition exhibited stronger forest resource conservation intention relative to those in the control condition, b  = 0.43, SE  = 0.16, t  = 2.63, p  = .0095, 95% CI = [0.107, 0.755]. In contrast, for individuals with higher levels of environmental unpredictability (above 1 SD ), the ecological resource scarcity manipulation had no effect on forest resource conservation intention, b  = -0.17, SE  = 0.17, t  = -1.04, p  > .05, 95% CI = [-0.512, 0.158] (see Fig.  2 ).

The effect of resource scarcity × environmental unpredictability on forest resource conservation intention (Study 2)

Besides, a floodlight analysis was performed to decompose the interaction [ 49 ]. It revealed that ecological resource scarcity manipulation increased forest resource conservation intention for any value of environmental unpredictability less than 2.78 ( b  = 0.24, SE  = 0.12, t  = 1.98, p  = .05, 95% CI = [0.000, 0.487]), but not for any value greater than 2.78. More importantly, the above findings did not significantly differ after controlling for demographic variables.

Study 2 replicated results of Study 1 and identified that environmental unpredictability weakened the positive effect of ecological resource scarcity information on resource conservation. Presenting ecological resource scarcity information could effectively increase forest conservation intention, particularly for individuals who move less frequently, indicating lower levels of environmental unpredictability.

However, the results of Study 2 were limited in several aspects. First, environmental unpredictability can be caused either by individuals themselves, such as frequent relocation, or by nature, such as unforeseen natural disasters. The present study focused on individual-related environmental unpredictability only. Secondly, the measurement of resource conservation intention instead of actual behaviors may have restricted the ecological validity of the findings. Thirdly, it is possible that the moderation effect was underestimated. In the forest management task, the psychological experience of forest resource scarcity may have been primed in both conditions, as participants were informed about the need to compete with other companies for limited forest resources. Consequently, participants’ decisions may have been heavily influenced by the forest management scenario.

Based on above discussions of Study 2, in Study 3, actual PEB was measured to increase ecological validity, and nature-caused environmental unpredictability was focused to improve generalizability. In addition, hypothetical forest resource conservation was also measured to replicate findings of Study 2. We proposed that receiving ecological resource scarcity information would increase actual resource conservation and forest resource conservation intention under predictable environmental conditions but that this effect would disappear under unpredictable environmental conditions.

We conducted a power analysis through G*Power 3.1 with the moderating effect size in Study 2, which suggested that a sample size of 135 would be required to achieve 80% power ( α  = .05) [ 40 ]. A total of 142 college students in Beijing, China was recruited to participate in the experiment in exchange for monetary compensation. Six participants who failed to finish all experimental tasks were excluded from data analysis. It is worth noting that the rule for terminating data collection was decided before data collection began: the experiment was terminated when more than 135 observations were collected in two weeks.

The average age of the participants was 21.87 ± 2.67 years (range = 17–29 years), and 75.00% of them were female ( N  = 102). The average annual household income was 12.37 ± 17.10 thousand yuan .

Research design and procedure

A 2 (water resource scarcity vs. control) × 2 (unpredictable vs. predictable environment) between-subject design was used.

Before arriving at the lab, participants were asked to fill out their demographic information in an online survey. Upon arrival at the lab, participants were randomly assigned into one of four groups to read a newspaper. These newspapers were designed to be looked like real Beijing Daily newspapers. In each type of newspaper, there were two pieces of news. One was designed to manipulate the water resource scarcity information, and another was designed to manipulate environmental unpredictability information. Then, actual water and paper consumption data was recorded in a washing-hands paradigm. Finally, forest resource consumption intention was measured.

Manipulation of water resource scarcity information

Similar to Study 2, in the scarcity condition ( n  = 67), the news article described the seriousness of water resource scarcity in Beijing. While, in the control condition ( n  = 69), the news article described Beijing residents’ sleep problems. After reading the article, participants responded to 1 item on perceived ecological resource scarcity on a 7-point Likert scale (1 = “ strongly disagree ” to 7 = “ strongly agree ”), which was adapted from new ecological paradigm scale (NEP): “The earth has plenty of natural resources if we just learn how to develop them” [ 52 ].

Manipulation of environmental unpredictability information

In the unpredictable condition ( n  = 68), the news article was titled “Natural Disasters are Unpredictable and Difficult to Prevent: 9.578 million People were Affected by Various Natural Disasters in January”. The news conveyed the information that natural disasters happened frequently, which caused many people to be affected in January, and there was no way to predict and prevent disasters. By contrast, in the predictable condition ( n  = 68), the news stated that even though natural disasters are frequent in China and many people were affected, now some devices can help predict and prevent disasters. The title was “Predication and Prevention of the Occurrence of Natural Disasters is Possible: 9.578 million People were Affected by Various Natural Disasters in January”.

Manipulation check items were rated right after reading the news article. Participants responded to 2 items about perceived unpredictability on a 7-point Likert scale (1 = “ strongly disagree ” to 7 = “ strongly agree ”): “The environment where I live is unstable”, and “The environment where I live is unpredictable”. The average score of the two items was computed such that a higher score indicated stronger perceived unpredictability.

Actual water and paper resource consumption

To cover up our real purpose, participants were told that the research was attempting to study palms, so that we would collect their fingerprints in the study. In the washing-hands paradigm, participants were asked to use the inkpad and leave their fingerprints on a sheet of white paper to study their palms. After that, they had to wash their hands in the lab. The amount of water and paper they used was recorded.

To measure the water consumption, the experimenter placed one measuring cylinder under the washbasin, and the measuring cylinder was linked to the washbasin’s outlet pipe. Importantly, participants could not see the cylinder. To measure their paper consumption, a bag of paper was placed on the washbasin for the participants to use. Besides, to exclude the experimenter effect, participants washed their hands without experimenter observation. Importantly, participants did not know that their behaviors were recorded, and participants were not aware of the real purpose of the study (see Fig.  3 ). All of the participants were debriefed at the end of the study.

Set-up of washing-hands paradigm

Considering that water and paper consumption for washing ink from hands might be affected by palm size, we recorded the palm area for each participant based on their fingerprints. Then, actual resource consumption was represented by average water consumption and average paper consumption, calculated by water or paper consumption divided by palm area.

Hypothetical forest resource conservation

Same as Study 2, the forest management task was used. After reading the scenario, participants were asked to answer the question, “How many acres of trees do you expect your company to cut down?”, ranging from 1 to 100 acres. A higher score on the measurement indicates a lower intention for forest resource conservation.

Perceived resource scarcity was significantly greater in the scarcity condition ( n  = 67, M  = 5.61, SD  = 1.19) than that in the control condition ( n  = 69, M  = 5.13, SD  = 1.38), t (134) = 2.17, p  = .032, 95%CI = [0.043, 0.920], d  = 0.37. Perceived unpredictability was also significantly greater in the unpredictable condition ( n  = 68, M  = 5.13, SD  = 1.28) compared to the predictable condition ( n  = 68, M  = 4.55, SD  = 1.39), t (134) = 2.51, p  = .013, 95%CI = [0.121, 1.026], d  = 0.43. Overall, the manipulations were successful and valid.

To examine the interaction effect between water resource scarcity and environmental unpredictability on resource conservation, two-factor MANOVAs were conducted.

Concerning the average water consumption, gender, age, household income, and cleanliness habits are included as control variables. The findings revealed that the main effect of scarcity was significant ( F (1,128) = 5.44, p  = 0.021, 95%CI = [-14.168, -0.673], η p 2  = .041), and the main effect of environmental unpredictability was not significant ( F (1,128) = 0.23, p  > .05, 95%CI = [-7.437, 5.984]). As expected, the interaction was significant ( F (1,128) = 6.81, p  = .01, 95%CI = [3.037, 22.097], η p 2  = .050). Then, simple effect analysis revealed that under the predictable condition, the average water consumption was significantly less under the scarcity condition ( M  = 25.29, SD  = 13.87) than under the control condition ( M  = 37.13, SD  = 13.91), F (1,128) = 12.25, p  < .001, 95%CI = [-18.535, -5.146], η p 2  = .087. However, under the unpredictable condition, there was no significant difference between the scarcity condition ( M  = 32.71, SD  = 13.93) and control condition ( M  = 31.98, SD  = 13.90), F (1,128) = 0.05, p  > .05, 95%CI = [-5.984, 7.437] (see Fig.  4 ).

Average water consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

Moreover, the results in average paper consumption showed a similar pattern. Main effects of scarcity ( F (1,128) = 0.42, p  > .05, 95%CI = [-0.137, 0.042]) and environmental unpredictability ( F (1,128) = 0.70, p  > .05, 95%CI = [-0.143, 0.036]) were not significant. A significant interaction effect was detected, F (1,128) = 5.30, p  = .023, 95%CI = [0.021, 0.275], η p 2  = .040. As predicted, in the predictable condition, paper consumption in the scarcity condition ( M  = 0.28, SD  = 0.18) was significantly less than in the control condition ( M  = 0.38, SD  = 0.18), F (1,128) = 4.39, p  = .038, 95%CI = [-0.184, -0.005], η p 2  = .033. No significant difference in paper consumption were observed between scarcity condition ( M  = 0.33, SD  = 0.19) and control condition ( M  = 0.28, SD  = 0.19) in unpredictable condition, F (1,128) = 1.39, p  > .05, 95%CI = [-0.036, 0.143] (see Fig.  5 ).

Average paper consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

More importantly, the above findings did not significantly differ without control variables in data analysis, and also did not significantly differ using the raw scores of water and paper consumption. Detailed results can be found in the Additional file 1 .

Hypothetical forest resource consumption was log transformed as it showed non-normal distribution. The findings showed that the main effects of scarcity ( F (1,130) = 1.800, p  > .05, 95%CI = [-0.363, 0.271]) and environmental unpredictability ( F (1,130) = 2.189, p  > .05, 95%CI = [-0.688, 0.049]) were not significant. A marginally significant interaction effect was detected, F (1, 130) = 3.04, p  = .084, 95%CI = [-0.053, 0.849], η p 2  = .023. As predicted, in the predictable condition, forest resource consumption in the scarcity condition ( M raw  = 30.76, SD raw  = 14.97) was significantly less than the control condition ( M raw  = 40.74, SD raw  = 25.41), F (1,130) = 4.71, p  = .032, 95%CI = [-0.672, -0.031], η p 2  = .035. No significant difference of forest resource consumption was observed between scarcity condition ( M raw  = 42.15, SD raw  = 20.41) and control condition ( M raw  = 44.00, SD raw  = 28.17) in unpredictable condition, F (1,130) = 0.08, p  > .05, 95%CI = [-0.027, 0.363] (see Fig.  6 ).

Forest resource consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

As expected, Study 3 replicated the findings of the previous two studies. We identified a moderating effect of nature-caused environmental unpredictability on ecological resource scarcity information’s effect on actual PEB. Specifically, individuals who received lower levels of environmental unpredictability information exhibited more water-saving and paper-saving behaviors, and were inclined to harvest fewer forest resources in the face of water scarcity. Interestingly, even though our manipulation focused solely on water scarcity, both paper consumption and forest resource consumption were affected as well, despite their lack of direct association with water. These results highlight the robust influence of resource scarcity information and environmental unpredictability on PEB, thereby enhancing the ecological validity of our findings.

General discussion

Focusing on the global issue of environmental unpredictability, the current research explored when does showing resource scarcity information promote PEB. In Study 1, a cross-sectional study, we discovered that resource scarcity information effectively enhances PEB, but only when individuals perceive the environment as less unpredictable. Furthermore, by manipulating scarcity information, Study 2 revealed that only for individuals with lower levels of environmental unpredictability could presenting ecological resource scarcity information decrease forest resource consumption intention. Moreover, an experiment with high ecological validity was conducted in Study 3 and found that the negative effect of water resource scarcity information on actual water and paper saving behaviors, as well as hypothetical forest resource consumption emerged only for people who receiving weaker environmental unpredictability information.

Theoretical contribution and practical implication

Environmental unpredictability is an important concept in life history theory. Numerous studies have verified that childhood environmental unpredictability plays a crucial role in shaping life history strategies [ 27 , 28 , 30 , 37 , 53 ]. However, little is known about how adulthood environmental unpredictability functions. The current research provided preliminary evidence that unpredictability in adulthood can also function in shaping behaviors. Adulthood unpredictability, including both individual- and nature-related environmental unpredictability, demotivates individuals to sacrifice present interests for future environmental benefits when facing scarcity.

Some psychological factors, including those discussed earlier (such as short-sighted tendency, fear of free riders, and perceived lack of control), as well as self-interest and competitive orientation, can serve as potential mechanisms underlying the moderating effect of environmental unpredictability. Self-interest and competitive orientation are important ways for individuals to survive in a harsh environment. Individuals may adopt a competitive orientation to obtain more benefits for themselves to survive during periods of scarcity. In addition, they may also seek to weaken others’ interests. These factors have been identified as “Stone Age” psychological biases leading to environment destruction [ 54 ]. To better respond to ecological resource scarcity, the current research demonstrated the importance of creating a predictable and peaceful world by removing the psychological barriers to mitigate ecological resource scarcity.

The IMB model provides a comprehensive framework advancing resource conservation research and intervention implements [ 3 ]. Even though the IMB model captures three vital components, information, motivation, and behavioral skills on behavior change, the psychological barriers caused by environmental unpredictability were ignored. As illustrated in a recent meta-analysis [ 15 ], compared with the control group, of the 38 interventions including IMB components, water use was reduced by only 5.9% in average with a small effect size, and the magnitude of effect varied widely in different interventions. According to the findings in the current research, levels of environmental unpredictability may be the underlying reason for the varied efficacy. Therefore, to best strengthen reducing resource consumption interventions based on the IMB model, it’s necessary to take environmental unpredictability into consideration.

Importantly, the current research developed a new paradigm, washing-hands paradigm, to measure actual resource consumption in the lab. As illustrated in previous studies, there are gaps between self-reported behaviors, and objective behaviors [ 43 ]. However, over 80% of recent studies only relied on self-reported data [ 55 ]. The washing-hands paradigm sets up a situation to capture actual water and paper resource consumption data. Importantly, the confounding variables can be controlled in the paradigm, such as habits, individual difference on palm size, and social desirability. This paradigm can help to establish causality and improve ecological validity of lab experiments, advancing resource conservation research.

The current research also provides some vital practical implications for both policy makers and environmental organizations. Our data suggested that creating a predictable environment can help promote resource conservation when facing ecological resource scarcity information. Governments should try to eliminate unpredictable factors. However, some unpredictable factors are difficult to address, such as natural disasters and virus spread. In such conditions, individual-level practices appear to be more important. For countries with a predictable environment, the strategy of the reminders of the ecological resource scarcity information is effective. However, for countries with an unpredictable environment, governments and organizations can consider using public media to decrease residents’ perceived unpredictability. Moreover, inspired by our Study 2, emphasizing predictable environmental information when reminding residents of scarcity should be encouraged. Environmental organizations should provide information that the environment is predictable when calling for resource conservation to respond to scarcity.

Limitations and future directions

The current research faces the limitation that the measurement in the correlation study is restricted due to the use of only one item to measure the moderator, and the alpha level of the PEB measure is low. For future studies, one aspect to consider is the exploration potential mechanisms of the moderation hypothesis. The current research did not delve into psychological mechanisms. It is suggested that future research could investigate underlying potential mechanisms of the moderation hypothesis to enrich the framework. Another related issue pertains to the IMB model. In the current research, we mainly focused on the effectiveness of scarcity information component but didn’t include motivation and behavioral skills components. It’s worthy for future research to test if creating a predictable environment can still strengthen the effect of IMB intervention. Besides, there are various types of resource conservation behaviors that individuals can engage in. Importantly, different behaviors are not necessarily highly relevant. For example, factors predicting shutting down electronics at night could not predict upgrading to energy-efficient appliances because these behaviors may cluster into distinct dimensions [ 56 , 57 ]. In the current research, we may not be able to generalize our findings to other types of behaviors. Thus, future research is encouraged to investigate whether the moderation hypothesis can be verified in other types of resource conservation behaviors.

Across three studies, we provided evidence to support the moderation hypothesis that environmental unpredictability weakens the positive effect of ecological resource scarcity information on PEB. Moving forward, it would be valuable to delve deeper into the underlying mechanisms, examine the moderation effect across various types of PEB, and investigate its potential application in PEB interventions.

Availability of data and materials

No datasets were generated or analysed during the current study.

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The authors acknowledge the financial support from the National Natural Science Foundation of China (31871126), Chongqing Normal University (23XWB043) and Social Science Fund of Chongqing, China (2023BS076).

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Gu, D., Jiang, J. Navigating an unpredictable environment: the moderating role of perceived environmental unpredictability in the effectiveness of ecological resource scarcity information on pro-environmental behavior. BMC Psychol 12 , 261 (2024). https://doi.org/10.1186/s40359-024-01762-1

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Climate change is most prominent threat to pollinators

A paper published in the CABI Reviews journal has found that climate change is the most prominent threat to pollinators -- such as bumblebees, wasps, and butterflies -- who are essential for biodiversity conservation, crop yields and food security.

The research, which is entitled 'What are the main reasons for the world-wide decline in pollinator populations?', suggests that many of the threats to pollinators result from human activities.

Pollinator populations are declining worldwide and 85% of flowering plant species and 87 of the leading global crops rely on pollinators for seed production. The decline of pollinators seriously impacts biodiversity conservation, reduces crop yield, and threatens food security.

Risk of extinction

According to The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), approximately 16% of vertebrate pollinators, such as birds and bats, and 40% of invertebrate pollinators, such as bees and butterflies, are at risk of extinction.

Dr Johanne Brunet and Dr Fabiana Fragoso, authors of the review, argue that efforts to control the various factors that negatively impact pollinators must continue given the dire consequences.

They stress that understanding the drivers of pollinator decline can guide the development of strategies and action plans to protect and conserve pollinators and the essential ecosystem services they provide.

Dr Brunet said, "This review introduces the diversity of pollinators, addresses the main drivers of pollinator decline, and presents strategies to reduce their negative impacts.

"We discuss how managed bees negatively affect wild bee species, and examine the impact of habitat loss, pesticide use, pests and pathogens, pollution, and climate change on pollinator decline. Connections between humans and pollinator decline are also addressed."

Changes in water and temperature

The researchers believe that the changes in water and temperature associated with climate change can lower the quantity and quality of resources available to pollinators, decrease the survival of larvae or adults, and modify suitable habitats.

Meanwhile, pollinators, they argue, are negatively impacted by human actions including habitat loss and degradation, the application of agrochemicals, climate change, and pollution.

The researchers say, that in the absence of pollinators, the human diet will shift towards a preponderance of wheat, rice, oat, and corn, as these are wind-pollinated crops. Crops that reproduce vegetatively, such as bananas, will be maintained.

Dr Fragoso said, "Widespread use of sustainable practices in agriculture, and further development of integrated pollinator management strategies, eco-friendly strategies including reduction of pesticide use, will help preserve pollinators.

"Potential adverse effects of managed bees on the local wild bee populations must be mitigated. Non-lethal collection methods should be developed and adopted globally in response to the increasing need for base-line pollinator data collection."

Holistic approach to pollinator conservation

The researchers conclude by advising that adopting a more holistic approach to pollinator conservation, with management strategies that integrate natural habitats and agricultural systems, together with managed and wild bees, should become a priority worldwide.

"Measures must keep being implemented to reduce climate change and prevent its serious negative impacts on pollinators. Climate change has the most diverse negative impacts on pollinators and is the threat most difficult to control," said Dr Brunet. "However, its consequences threaten food security and world stability, thus efforts to control it must be prioritized at a global scale."

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Materials provided by CABI . Original written by Wayne Coles. Note: Content may be edited for style and length.

Journal Reference :

• Johanne Brunet, Fabiana P. Fragoso. What are the main reasons for the worldwide decline in pollinator populations? CABI Reviews , 2024; DOI: 10.1079/cabireviews.2024.0016

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• Published: 09 February 2022

Perspectives in machine learning for wildlife conservation

• Devis Tuia   ORCID: orcid.org/0000-0003-0374-2459 1   na1 ,
• Benjamin Kellenberger 1   na1 ,
• Sara Beery 2   na1 ,
• Blair R. Costelloe   ORCID: orcid.org/0000-0001-5291-788X 3 , 4 , 5   na1 ,
• Silvia Zuffi   ORCID: orcid.org/0000-0003-1358-0828 6 ,
• Benjamin Risse   ORCID: orcid.org/0000-0001-5691-4029 7 ,
• Alexander Mathis   ORCID: orcid.org/0000-0002-3777-2202 8 ,
• Mackenzie W. Mathis   ORCID: orcid.org/0000-0001-7368-4456 8 ,
• Frank van Langevelde   ORCID: orcid.org/0000-0001-8870-0797 9 ,
• Tilo Burghardt 10 ,
• Roland Kays   ORCID: orcid.org/0000-0002-2947-6665 11 , 12 ,
• Holger Klinck 13 ,
• Martin Wikelski   ORCID: orcid.org/0000-0002-9790-7025 3 , 4 ,
• Iain D. Couzin   ORCID: orcid.org/0000-0001-8556-4558 3 , 4 , 5 ,
• Grant van Horn 13 ,
• Margaret C. Crofoot 3 , 4 , 5 ,
• Charles V. Stewart 14 &
• Tanya Berger-Wolf   ORCID: orcid.org/0000-0001-7610-1412 15 , 16  

Nature Communications volume  13 , Article number:  792 ( 2022 ) Cite this article

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• Computer science
• Conservation biology

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

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Technology to accelerate ecology and conservation research.

Animal diversity is declining at an unprecedented rate 1 . This loss comprises not only genetic, but also ecological and behavioral diversity, and is currently not well understood: out of more than 120,000 species monitored by the IUCN Red List of Threatened Species, up to 17,000 have a ‘Data deficient’ status 2 . We urgently need tools for rapid assessment of wildlife diversity and population dynamics at large scale and high spatiotemporal resolution, from individual animals to global densities. In this Perspective, we aim to build bridges across ecology and machine learning to highlight how relevant advances in technology can be leveraged to rise to this urgent challenge in animal conservation.

How are animals currently monitored? Conventionally, management and conservation of animal species are based on data collection carried out by human field workers who count animals, observe their behavior, and/or patrol natural reserves. Such efforts are time-consuming, labor-intensive, and expensive 3 . They can also result in biased datasets due to challenges in controlling for observer subjectivity and assuring high inter-observer reliability, and often unavoidable responses of animals to observer presence 4 , 5 . Human presence in the field also poses risks to wildlife 6 , 7 , their habitats 8 , and humans themselves: as an example, many wildlife and conservation operations are performed from aircraft and plane crashes are the primary cause of mortality for wildlife biologists 9 . Finally, the physical and cognitive limitations of humans unavoidably constrain the number of individual animals that can be observed simultaneously, the temporal resolution and complexity of data that can be collected, and the extent of physical area that can be effectively monitored 10 , 11 .

These limitations considerably hamper our understanding of geographic ranges, population densities, and community diversity globally, as well as our ability to assess the consequences of their decline. For example, humans conducting counts of seabird colonies 12 and bats emerging from cave roosts 13 tend to significantly underestimate the number of individuals present. Furthermore, population estimates based on extrapolation from a small number of point counts have large uncertainties and can fail to capture the spatiotemporal variation in ecological relationships, resulting in erroneous predictions or extrapolations 14 . Failure to monitor animal populations impedes rapid and effective management actions 3 . For example, insufficient monitoring, due in part to the difficulty and cost of collecting the necessary data, has been identified as a major challenge in evaluating the impact of primate conservation actions 15 and can lead to the continuation of practices that are harmful to endangered species 16 . Similarly, poaching prevention requires intensive monitoring of vast protected areas, a major challenge with existing technology. Protected area managers invest heavily in illegal intrusion prevention and the detection of poachers. Despite this, rangers often arrive too late to prevent wildlife crime from occurring 17 . In short, while a rich tradition of human-based data collection provides the basis for much of our understanding of where species are found, how they live, and why they interact, modern challenges in wildlife ecology and conservation are highlighting the limitations of these methods.

Recent advances in sensor technologies are drastically increasing data collection capacity by reducing costs and expanding coverage relative to conventional methods (see the section “New sensors expand available data types for animal ecology”, below), thereby opening new avenues for ecological studies at scale (Fig.  1 ) 18 . Many previously inaccessible areas of conservation interest can now be studied through the use of high-resolution remote sensing 19 , and large amounts of data are being collected non-invasively by digital devices such as camera traps 20 , consumer cameras 21 , and acoustic sensors 22 . New on-animal bio-loggers, including miniaturized tracking tags 23 , 24 and sensor arrays featuring accelerometers, audiologgers, cameras, and other monitoring devices document the movement and behavior of animals in unprecedented detail 25 , enabling researchers to track individuals across hemispheres and over their entire lifetimes at high temporal resolution and thereby revolutionizing the study of animal movement (Fig.  1 c) and migrations.

a The BirdNET algorithm 61 was used to detect Carolina wren vocalizations in more than 35,000 h of passive acoustic monitoring data from Ithaca, New York, allowing researchers to document the gradual recovery of the population following a harsh winter season in 2015. b Machine-learning algorithms were used to analyze movement of savannah herbivores fitted with bio-logging devices in order to identify human threats. The method can localize human intruders to within 500 m, suggesting `sentinel animals' may be a useful tool in the fight against wildlife poaching 148 . c TRex, a new image-based tracking software, can track the movement and posture of hundreds of individually-recognized animals in real-time. Here the software has been used to visualize the formation of trails in a termite colony 149 . d , e Pose estimation software, such as DeepPoseKit ( d ) 75 and DeepLabCut ( e ) 74 , 142 allows researchers to track the body position of individual animals from video imagery, including drone footage, and estimate 3D postures in the wild. Panels b , c , and d are reproduced under CC BY 4.0 licenses. Panels b and d are cropped versions of the originals; the legend for panel b has been rewritten and reorganized. Panel e is reproduced with permission from Joska et al. 142 .

There is a mismatch between the ever-growing volume of raw measures (videos, images, audio recordings) acquired for ecological studies and our ability to process and analyze this multi-source data to derive conclusive ecological insights rapidly and at scale. Effectively, ecology has entered the age of big data and is increasingly reliant on sensors, advanced methodologies, and computational resources 26 . Central challenges to efficient data analysis are the sheer volume of data generated by modern collection methods and the heterogeneous nature of many ecological datasets, which preclude the use of simple automated analysis techniques 26 . Crowdsourcing platforms like eMammal ( emammal.si.edu ), Agouti ( agouti.eu ), and Zooniverse ( www.zooniverse.org ) function as collaborative portals to collect data from different projects and provide tools to volunteers to annotate images, e.g., with species labels of the individuals therein. Such platforms drastically reduce the cost of data processing (e.g., ref. 27 reports a reduction of seventy thousand dollars), but the rapid increase in the volume and velocity of data collection is making such approaches unsustainable. For example, in August 2021 the platform Agouti hosted 31 million images, of which only 1.5 million were annotated. This is mostly due to the manual nature of the current annotation tool, which requires human review of every image. In other words, methods for automatic cataloging, searching, and converting data into relevant information are urgently needed and have the potential to broaden and enhance animal ecology and wildlife conservation in scale and accuracy, address prevalent challenges, and pave the way forward towards new, integrated research directives.

Machine learning (ML, see glossary in Supplementary Table  1 ) deals with learning patterns from data 28 . Presented with large quantities of inputs (e.g., images) and corresponding expected outcomes, or labels (e.g., the species depicted in each image), a supervised ML algorithm learns a mathematical function leading to the correct outcome prediction when confronted with new, unseen inputs. When the expected outcomes are absent, the (this time unsupervised) ML algorithm will use solely the inputs to extract groups of data points corresponding to typical patterns in the data. ML has emerged as a promising means of connecting the dots between big data and actionable ecological insights 29 and is an increasingly popular approach in ecology 30 , 31 . A significant share of this success can be attributed to deep learning (DL 32 ), a family of highly versatile ML models based on artificial neural networks that have shown superior performance across the majority of ML use cases (see Table  1 and Supplementary Table  2 ). Significant error reduction of ML and DL with respect to traditional generalized regression models has been reported routinely for species richness and diversity estimation 33 , 34 . Likewise, detection and counting pipelines moved from rough rule of thumb extrapolations from visual counts in national parks to ML-based methods with high detection rates. Initially, these methods proposed many false positives which required further human review 35 , but recent methods have been shown to maintain high detection rates with significantly fewer false positives 36 . As an example, large mammal detection in the Kuzikus reserve in 2014 was improved significantly by improving the detection methodologies, from a recall rate of 20% 35 to 80% 37 (for a common 75% precision rate). Finally, studies involving human operators demonstrated that ML enabled massive speedups in complex tasks such as individual and species recognition 38 , 39 and large-scale tasks such as animal detection in drone surveys 40 . Recent advances in ML methodology could accelerate and enhance various stages of the traditional ecological research pipeline (see Fig.  2 ), from targeted data acquisition to image retrieval and semi-automated population surveys. As an example, the initiative Wildlife Insights 41 is now processing millions of camera trap images automatically (17 million in August 2021), providing wildlife conservation scientists and practitioners with the data necessary to study animal abundances, diversity, and behavior. Besides pure acceleration, use of ML also massively reduces analysis costs, with reduction factors estimated between 2 and 10 42 .

Traditional ecological research pipeline (colored text and boxes) and contributions of ML to the different stages discussed in this paper (black text).

A growing body of literature promotes the use of ML in various ecological subfields by educating domain experts about ML approaches 29 , 43 , 44 , their utility in capitalizing on big data 26 , 45 , and, more recently, their potential for ecological inference (e.g., understanding the processes underlying ecological patterns, rather than only predicting the patterns themselves) 46 , 47 . Clearly, there is a growing interest in applying ML approaches to problems in animal ecology and conservation. We believe that the challenging nature of ecological data, compounded by the size of the datasets generated by novel sensors and the ever-increasing complexity of state-of-the-art ML methods, favor a collaborative approach that harnesses the expertise of both the ML and animal ecology communities, rather than an application of off-the-shelf ML methodologies to ecological challenges. Hence, the relation between ecology and ML should not be unidirectional: integrating ecological domain knowledge into ML methods is essential to designing models that are accurate in the way they describe animal life. As demonstrated by the rising field of hybrid environmental algorithms (leveraging both DL and bio-physical models 48 , 49 ) and, more broadly, by theory-guided data science 50 , such hybrid models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models. To reach this goal of an integrated science of ecology and ML, both communities need to work together to develop specialized datasets, tools, and knowledge. With this objective in mind, we review recent efforts at the interface of the two disciplines, present success stories of such symbiosis in animal ecology and wildlife conservation, and sketch an agenda for the future of the field.

New sensors expand available data types for animal ecology

Sensor data provide a variety of perspectives to observe wildlife, monitor populations, and understand behavior. They allow the field to scale studies in space, time, and across the taxonomic tree and, thanks to open science projects (Table  2 ), to share data across parks, geographies, and the globe 51 . Sensors generate diverse data types, including imagery, soundscapes, and positional data (Fig.  3 ). They can be mobile or static, and can be deployed to collect information on individuals or species of interest (e.g., bio-loggers, drones), monitor activity in a particular location (e.g., camera traps and acoustic sensors), or document changes in habitats or landscapes over time (satellites, drones). Finally, they can also be opportunistic, as in the case of community science. Below, we discuss the different categories of sensors and the opportunities they open for ML-based wildlife research.

Studies frequently combine data from multiple sensors at the same geographic location, or data from multiple locations to achieve deeper ecological insights. Sentinel-2 (ESA) satellite image courtesy of the U.S. Geological Survey.

Stationary sensors

Stationary sensors provide close-range continuous monitoring over long time scales. Sensors can be image-based (e.g., camera traps) or signal-based (e.g., sound recorders). Their high level of temporal resolution allows for detailed analysis, including presence/absence, individual identification, behavior analysis, and predator-prey interaction. However, because of their stationary nature, their data is highly spatiotemporally correlated. Based on where and when in the world the sensor is placed, there is a limited number of species that can be captured. Furthermore, many animals are highly habitual and territorial, leading to very strong correlations between data taken days or even weeks apart from a single sensor 52 .

Camera traps are among the most used sensors in recent ML-based animal ecology papers, with more than a million cameras already used to monitor biodiversity worldwide 20 . Camera traps are inexpensive, easy to install, and provide high-resolution image sequences of the animals that trigger them, sufficient to specify the species, sex, age, health, behavior, and predator-prey interactions. Coupled with population models, camera-trap data has also been used to estimate species occurrence, richness, distribution, and density 20 . But the popularity of camera traps also creates challenges relative to the quantity of images and the need for manual annotation of the collections: software tools easing the annotation process are appearing (see, e.g., AIDE in Table  1 ) and many ecologists have already incorporated open-source ML approaches for filtering out blank images (such as the Microsoft AI4Earth MegaDetector 36 , see Table  1 ) into their camera trap workflows 52 , 53 , 54 . However, problems related to lack of generality across geographies, day/night acquisition, or sensors are still major obstacles to production-ready accurate systems 55 . The increased scale of available data due to de-siloing efforts from organizations like Wildlife Insights ( www.wildlifeinsights.org ) and LILA.science ( www.lila.science ) will help increase ML accuracy and robustness across regions and taxa.

Bioacoustic sensors are an alternative to image-based systems, using microphones and hydrophones to study vocal animals and their habitats 56 . Networks of static bioacoustic sensors, used for passive acoustic monitoring (PAM), are increasingly applied to address conservation issues in terrestrial 57 , aquatic 58 , and marine 59 ecosystems. Compared to camera traps, PAM is mostly unaffected by light and weather conditions (some factors like wind still play a role), senses the environment omnidirectionally, and tends to be cost-effective when data needs to be collected at large spatiotemporal scales with high resolution 60 . While ML has been extensively applied to camera trap images, its application to long-term PAM datasets is still in its infancy and the first DL-based studies are only starting to appear (see Fig.  1 a, ref. 61 ). Significant challenges remain when utilizing PAM. First and foremost among these challenges is the size of data acquired. Given the often continuous and high-frequency acquisition rates, datasets often exceed the terabyte scale. Handling and analyzing these datasets efficiently requires access to advanced computing infrastructure and solutions. Second, the inherent complexity of soundscapes requires noise-robust algorithms that generalize well and can separate and identify many animal sounds of interest from confounding natural and anthropogenic signals in a wide variety of acoustic environments 62 . The third challenge is the lack of large and diverse labeled datasets. As for camera trap images, species- or region-specific characteristics (e.g., regional dialects 63 ) affect algorithm performance. Robust, large-scale datasets have begun to be curated for some animal groups (e.g., www.macaulaylibrary.org and www.xeno-canto.org for birds), but for many animal groups as well as relevant biological and non-biological confounding signals, such data is still nonexistent.

Remote sensing

Collecting data on free-ranging wildlife has been restricted traditionally by the limits of manual data collection (e.g., extrapolating transect counts), but have increased greatly through the automation of remote sensing 35 . Using remote sensing, i.e., sensors mounted on moving platforms such as drones, aircraft, or satellites—or attached to the animals themselves—allows us to monitor large areas and track animal movement over time.

On-animal sensors are the most common remote sensing devices deployed in animal ecology 10 . They are primarily used to acquire movement trajectories (i.e., GPS data) of animals, which can then be classified into activity types that relate to the behavior of individuals or social groups 10 , 64 . Secondary sensors, such as microphones, video cameras, heart rate monitors, and accelerometers, allow researchers to capture environmental, physiological, and behavioral data concurrently with movement data 65 . However, power supply and data storage and transmission limitations of bio-logging devices are driving efforts to optimize sampling protocols or pre-process data in order to conserve these resources and prolong the life of the devices. For example, on-board processing solutions can use data from low-cost sensors to identify behaviors of interest and engage resource-intensive sensors only when these behaviors are being performed 66 . Other on-board algorithms classify raw data into behavioral states to reduce the volume of data to be transmitted 67 . Various supervised ML methods have shown their potential in automating behavior analysis from accelerometer data 68 , 69 , identifying behavioral state from trajectories 70 , and predicting animal movement 71 .

Unmanned aerial vehicles (UAVs) or drones for low-altitude image-based approaches, have been highlighted as a promising technology for animal conservation 72 , 73 . Recent studies have shown the promise of UAVs and deep learning for posture tracking 74 , 75 , 76 , semi-automatic detection of large mammals 42 , 77 , birds 78 , and, in low-altitude flight, even identification of individuals 79 . Drones are agile platforms that can be deployed rapidly—theoretically on demand—and with limited cost. Thus, they are ideal for local population monitoring. Lower altitude flights in particular can provide oblique view points that partially mitigate occlusion by vegetation. The reduced costs and operation risks of UAVs further make them an increasingly viable alternative to low-flying manned aircraft.

Common multi-rotor UAV models are built using inexpensive hardware and consumer-level cameras, and only require a trained pilot with flight permissions to perform the survey. To remove the need for a trained pilot, fully autonomous UAV platforms are also being investigated 79 . However, multi-rotor drone-based surveys remain limited in the spatial footprint that can be covered, mostly because of battery limitations (which become even more stringent in cold climates like Antarctica) and local legislation. Combustion-driven fixed wing UAVs flying at high altitudes and airplane-based acquisitions can overcome some of these limitations, but are significantly more costly and preclude close approaches for visual measurements of animals. Finally, using drones also has a risk of modifying the behavior of the animals. A recent study 80 showed that flying at lower altitudes (e.g., lower than 150 m) can have a significant impact on group and individual behavior of mammals, although the severity of wildlife disturbance from drone deployments will depend heavily on the focal species, the equipment used, and characteristics of the drone flight (such as approach speed and altitude) 81 —this is a rapidly changing field and advances that will limit noise are likely to come. More research to quantify and qualify such impacts in different ecosystems is timely and urgent, to avoid both biased conclusions and increased levels of animal stress.

Satellite data is used to widen the spatial footprint and reduce invasive impact on behavior. Public programs such as Landsat and Sentinel provide free and open imagery at medium resolution (between 10 and 30 m per pixel), which, though usually not sufficient for direct wildlife observations, can be useful for studying their habitats 34 , 82 . Meanwhile, commercial very high resolution (less than one meter per pixel) imagery is narrowing the gap between UAV acquisitions and large-scale footprinting with satellites. Remote sensing has a long tradition of application of ML algorithms. Thanks to the raster nature of the data, remote sensing has fully adopted the new DL methods 83 , which are nowadays entering most fields of application that exploit satellite data 49 . In animal ecology, studies focused on large animals such as whales 84 or elephants 85 attempt direct detection of the animals on very high-resolution images, increasingly with DL. When focusing on smaller-bodied species, studies resort to aerial surveys to increase resolution in order to directly visualize the animals or focus on the detection of proxies instead of the detection of the animal itself (e.g., the detection of penguin droppings to locate colonies 86 ). More research is currently required to really harness the power of remote sensing data, which lies, besides the large footprint and image resolution, in the availability of image bands beyond the visible spectrum. These extra bands are highly appreciated in plant ecology 87 and multi- and hyperspectral DL approaches 88 are yet to be deployed in animal ecology, where they could help advancing the characterization of habitats.

Community science for crowd-sourcing data

An alternative to traditional sensor networks (static or remote) is to engage community members as wildlife data collectors and processors 89 , 90 . In this case, community participants (often volunteers) work to collect the data and/or create the labels necessary to train ML models. Models trained this way can then be used to bring image recognition tasks to larger scale and complexity, from filtering out images without animals in camera trap sequences to identifying species or even individuals. Several annotation projects based on community science have appeared recently (Table  2 ). For simple tasks like animal detection, community science effort can be open to the public, while for more complex ones such as identifying bird species with subtle appearance differences (“fine-grained classification”, also see the glossary), communities of experts are needed to provide accurate labels. A particularly interesting case is Wildbook (see Box  1 and Table  1 ), which routinely screens videos from social media platforms with computer vision models to identify individuals; community members (in this case video posters) are then queried in case of missing or uncertain information. Recent research shows that ML models trained on community data can perform as well as annotators 91 . However, it is prudent to note that the viability of community science services may be limited depending on the task and that oftentimes substantial efforts are required to verify volunteer-annotated data. This is due to annotator errors, including misdetected or mislabeled animals due to annotator fatigue or insufficient knowledge about the annotation task, as well as systematic errors from adversarial annotators. Another form of community science is the use of images acquired by volunteers: in this case, volunteers replace camera traps or UAVs and provide the raw data used to train the ML model. Although this approach sacrifices control over image acquisitions and is likewise prone to inducing significant noise to datasets, for example through low-quality imagery, it provides a substantial increase in the number of images and the chances of photographing species or single individuals in different regions, poses, and viewing angles. Community science efforts also increase public engagement in science and conservation. The Great Grevy’s Rally, a community science-based wildlife census effort occurring every 2 years in Kenya 92 , is a successful demonstration of the power of community science-based wildlife monitoring.

Box 1 Wildbook: successes at the interface between community science and deep learning

Wildbook, a project of the non-profit Wild Me, is an open-source software platform that blends structured wildlife research with artificial intelligence, community science, and computer vision to speed population analysis and develop new insights to help conservation (Fig.  4 ). Wildbook supports collaborative mark-recapture, molecular ecology, and social ecology studies, especially where community science and artificial intelligence can help scale-up projects. The image analysis of Wildbook can start with images from any source—scientists, camera traps, drones, community scientists, or social media—and use ML and computer vision to detect multiple animals in the images 100 to not only classify their species, but identify individual animals applying a suite of different algorithms 101 , 147 . Wildbook provides a technical solution for wildlife research and management projects for non-invasive individual animal tracking, population censusing, behavioral and social population studies, community engagement in science, and building a collaborative research network for global species. There are currently Wildbooks for over 50 species, from sea dragons to zebras, spanning the entire planet. More than 80 scientific publications have been enabled by Wildbook. Wildbook data has become the basis for the IUCN Red List global population numbers for several species, and supported the change in conservation status for whale sharks from “vulnerable” to “endangered”. Wildbook’s technology also enabled the Great Grevy’s Rally, the first-ever full species census for the endangered Grevy’s zebra in Kenya, using photographs captured by the public. Hosted for the first time in January 2016, it has become a regular event, held every other year. Hundreds of people, from school children and park rangers, to Nairobi families and international tourists, embark on a mission to photograph Grevy’s zebras across its range in Kenya, capturing ~50,000 images over the 2-day event. With the ability to identify individual animals in those images, Wildbook can enable an accurate population census and track population trends over time. The Great Grevy’s Rally has become the foundation of the Kenya Wildlife Service’s Grevy’s zebra endangered species management policy and generates the official IUCN Red List population numbers for the species. Wildbook’s AI enables science, conservation, and global public engagement by bringing communities together and working in partnership to provide solutions that people trust.

Wildbook allows scientists and wildlife managers to leverage the power of communities and ML to monitor wildlife populations. Images of target species are collected via research projects, community science events (e.g., the Great Grevy’s Rally; see text), or by scraping social media platforms using Wildbook AI tools. Wildbook software uses computer vision technology to process the images, yielding species and individual identities for the photographed animals. This information is stored in databases on Wildbook data management servers. The data and biological insights generated by Wildbook facilitates exchange of expertise between biologists, data scientists, and stakeholder communities around the world.

Machine learning to scale-up and automate animal ecology and conservation research

The sensor data described in the previous section has the potential to unlock ecological understanding on a scale difficult to imagine in the recent past. But to do so, it must be interpreted and converted to usable information for ecological research. For example, such conversion can take the form of abundance mapping, individual animal re-identification, herd tracking, or digital reconstruction (three-dimensional, phenotypical) of the environment the animals live in. The measures yielded by this conversion, reviewed in this section, are also sometimes referred to as animal biometrics 93 . Interestingly, the tasks involved in the different approaches show similarities with traditional tasks in ML and computer vision (e.g., detection, localization, identification, pose estimation), for which we provide a matching example in animal ecology in Fig.  5 .

Imagery can be used to capture a range of behavioral and ecological data, which can be processed into usable information with ML tools. Aerial imagery (from drones, or satellites for large species) can be used to localize animals and track their movements over time and model the 3D structure of landscapes using photogrammetry. Posture estimation tools allow researchers to estimate animal postures, which can then be used to infer behaviors using clustering algorithms. Finally, computer vision techniques allow for the identification and re-identification of known individuals across encounters.

Wildlife detection and species-level classification

Conservation efforts of endangered species require knowledge on how many individuals of the species in question are present in a study area. Such estimations are conventionally realized with statistical occurrence models that are informed by sample-based species observations. It is these observations where imaging sensors (camera traps, UAVs, etc.), paired with ML models that detect and count individuals in the imagery, can provide the most significant input. Early works used classical supervised ML algorithms (algorithms needing a set of human-labeled annotations to learn, see Supplementary Table  2 ): these algorithms were used to make the connection between a set of characteristics of interest extracted from the image (visual descriptors, e.g., color histograms, spectral indices, etc., also see the glossary) and the annotation itself (presence of an animal, species, etc.) 35 , 94 . Particularly in camera trap imagery, foreground (animal) segmentation is occasionally performed as a pre-processing step to discard image parts that are potentially confusing for a classifier. These approaches, albeit good in performance, suffer from two limitations: first, the visual descriptors need to be specifically tailored to the problem and dataset at hand. This not only requires significant engineering efforts, but also bears the risk of the model becoming too specific to the particular dataset and external conditions (e.g., camera type, background foliage amount, and movement type) at hand. Second, computational restrictions in these models limit the number of training examples, which is likely to have detrimental effects on variations in data (temporal, seasonal, etc.), thus reducing the generalization capabilities to new sensor deployments or regions. For these reasons, detecting and classifying animal species with DL for the purpose of population estimates is becoming increasingly popular for images 52 , 53 , acoustic spectrograms 95 , and videos 96 . Models performing accurately and robustly on specific classes (e.g., the MegaDetector - see Box  2  - or AIDE; see Table  1 ) are now being used routinely and integrated within open systems supporting ecologists performing both labeling and detection, respectively counting of their image databases. Issues related to dependence of the models performance to specific training locations are still at the core of current developments 52 , an issue known in ML as “domain adaptation” or “generalization”.

Box 2 AI for Wildlife Conservation in Practice: the MegaDetector

One highly-successful example of open source AI for wildlife conservation is the Microsoft AI for Earth MegaDetector 36 (Fig.  6 ). This generic, global-scale human, animal, and vehicle detection model works off-the-shelf for most camera trap data, and the publicly-hosted MegaDetector API has been integrated into the wildlife monitoring workflows of over 30 organizations worldwide, including the Wildlife Conservation Society , San Diego Zoo Global , and Island Conservation . We would like to highlight two MegaDetector use cases, via Wildlife Protection Solutions (WPS) and the Idaho Department of Fish and Game (IDFG). WPS use the MegaDetector API in real-time to detect threats to wildlife in the form of unauthorized humans or vehicles in protected areas. WPS connect camera traps to the cloud via cellular networks, upload photos, run them through the MegaDetector via the public API, and return real-time alerts to protected area managers. They have over 400 connected cameras deployed in 18 different countries, and that number is growing rapidly. WPS used the MegaDetector to analyze over 900K images last year alone, which comes out to 2.5K images per day. They help protected areas detect and respond to threats as they occur, and detect at least one real threat per week across their camera network.

Idaho is required to maintain a stable population of protected wolves. IDFG relies heavily on camera traps to estimate and monitor this wolf population, and needs to process the data collected each year before the start of the next season in order to make informed policy changes or conservation interventions. They collected 11 million camera trap images from their wolf cameras last year, and with the MegaDetector integrated into their data processing and analysis pipeline, they were able to fully automate the analysis of 9.5 million of those images, using model confidence to help direct human labeling effort to images containing animals of interest. Using the Megadetector halved their labeling costs, and allowed IDFG to label all data before the start of the next monitoring season, whereas manual labeling previously resulted in a lag of ~5 years from image collection to completion of labeling. The scale and speed of analysis required in both cases would not be possible without such an AI-based solution.

The near-universal need of all camera trap projects to efficiently filter empty images and localize humans, animals, and vehicles in camera trap data, combined with the robustness to geographic, hardware, and species variability the MegaDetector provides due to its large, diverse training set makes it a useful, practical tool for many conservation applications out of the box. The work done by the Microsoft AI for Earth team to provide assistance running the model via hands-on engineering assistance, open-source tools, and a public API have made the MegaDetector accessible to ecologists and a part of the ecological research workflow for over 30 organizations worldwide.

Individual re-identification

Another important biometric is animal identity. The standard for identification of animal species and identity is DNA profiling 97 , which can be difficult to scale to large, distributed populations 54 , 93 . As an alternative to gene-based identification, manual tagging can be used to keep track of individual animals 10 , 93 . Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification 54 , 98 , 99 , 100 . The aim is to learn identity-bearing features from the appearance of animals. Identifying individuals from images is even more challenging than species recognition, since the distinctive body patterns of individuals might be subtle or not be sufficiently visible due to occlusion, motion blur, or overhead viewpoint in the case of aerial imagery. Yet, conventional 101 and more recently DL-based 38 , 54 , 102 methods have reached strong performance for specific taxa, especially across small populations. Some species have individually-unique coat or skin markings that assist with re-identification: for example, accuracy exceeded 90% in a study of 92 tigers across 8000 video clips 103 . However, effective re-identification is also possible in the absence of patterned markings: a study of a small group of 23 chimpanzees in Guinea applied facial recognition techniques to a multi-year dataset comprising 1 million images and achieved >90% accuracy 38 . This study compared the DL model to manual re-identification by humans: where humans achieved identification accuracy between 20% (novices) and 42% (experts) with an annotation time between 1 and 2 h, the DL model achieved an identification accuracy of 84% in a matter of seconds. The particular challenges for animal (re-)identification in wild populations are related to the difficulty of manual curation, larger populations, changes in appearance (e.g., due to scars, growth), few sightings per individual, and the frequent addition of new individuals that may enter the system due to birth or immigration, therefore creating an “open-set” problem 104 wherein the model must deal with “classes” (individuals) unseen during training. The methods must have the ability to identify not only animals that have been seen just once or twice but also recognize new, previously unseen animals, as well as adjust decisions that have been made in the past, reconciling different views and biological stages of an animal.

Animal synthesis and reconstruction

3D shape recovery and pose estimation of animals can provide valuable, non-invasive insights on wild species in their natural environment. The 3D shape of an individual can be related to its health, age, or reproductive status; the 3D pose of the body can provide finer information with respect to posture attributes and allows, for instance, kinematic as well as behavioral analyses. For pose estimation, marker-less methods based on DL have tremendously improved over the last years and already impacted biology 105 . Various user-friendly toolboxes are available to extract the 2D posture of animals from videos (Fig.  1 d, e), while the user can define which body parts should be estimated (reviewed in ref. 76 ). Extracting a dense set of body surface points is also possible, as elegantly shown in ref. 106 , where the DensePose technique originally developed for humans was extended to chimpanzees. The reconstruction of the 3D shape and pose of animals from images often follows a model-based paradigm, where a 3D model of the animal is fit to visual data. Recent work defines the SMAL (Skinned Multi-Animal Linear) model, a 3D articulated shape model for a set of quadruped families 107 . Biggs et al. built on this work for 3D shape and motion of dogs from video 108 and for recovery of dog shape and pose across many different breeds 109 . In ref. 110 , the SMAL model has been used in a DL approach to predict 3D shape and pose of the Grevy’s zebra from images. 3D shape models have been recently defined also for birds 111 . Image-based 3D pose and shape estimation methods provide rich information about individuals but require, in addition to accurate shape models, prior knowledge about the animal’s 3D motion.

Reconstructing the environment

Wildlife behavior and conservation cannot be dissociated from the environment animals evolve and live in. Studies have shown that animal observations like trajectories highly benefit from additional cues included in the environmental context 112 . Satellite remote sensing has become an integral part to study animal habitats, biological diversity, and spatiotemporal changes of abiotic conditions 113 , since it allows to map quantities like land cover, soil moisture, or temperature at scale. Reconstructing the 3D shape of the environment has also become central in behavior studies: for example, 3D reconstructions of kill sites for lions in South Africa revealed novel insights into the predator-prey relationships and their connection to ecosystem stability and functioning 114 , while 3D spatial reconstructions shed light on the impact of forest structures on bat behavior 115 . Such spatial reconstructions of the environment can either be extracted by using dedicated sensors such as LiDAR 116 or can be reconstructed from multiple images, either by stitching the images into a unified two-dimensional panorama (e.g., mosaicking 117 ) or by computing the three-dimensional environment from partially overlapping images (e.g., structure from motion 118 or simultaneous localization and mapping 119 ). All these approaches have strongly benefited from recent ML advancements 120 , but have seldom been applied for wildlife conservation purposes, where they could greatly help when dealing with images acquired by moving or swarms of sensors 121 . However, applying these techniques to natural wildlife imagery is not trivial. For example, unconstrained continuous video recordings at potentially high frame-rates will result in large image sets which require efficient image processing 117 . Moreover, ambiguous environmental appearances and structural errors such as drift accumulate over time and therefore decrease the reconstruction quality 118 . Last but not least, a variety of inappropriate camera motions or environmental geometries can result in so-called critical configurations which cannot be resolved by the existing optimization schemes 122 . As a consequence, cues from additional external sensors are usually integrated to achieve satisfactory environmental reconstructions from video data 123 .

Modeling species diversity, richness, and interactions

Analyses of biodiversity, represented by such measures as species abundance and richness, are foundational to much ecological research and many conservation initiatives. Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography 124 , 125 . Non-parametric ML techniques like Random Forest 126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes 127 , 128 , spiders 129 , and small mammals 130 . Tree-based techniques have also been used to predict species interactions: for example, regression trees significantly outperformed classical generalized linear models in predicting plant-pollinator interactions 33 . Tree-based methods are well-suited to these tasks because they perform explicit feature ranking (and thus feature selection) and are able to model nonlinear relationships between covariates and species distribution. More recently, graph regression techniques were deployed to reconstruct species interaction networks in a community of European birds with promising results, including better causality estimates of the relations in the graph 131 .

Attention points and opportunities

Machine and deep learning are becoming necessary accelerators for wildlife research and conservation actions in natural reserves. We have discussed success stories of the application of approaches from ML into ecology and highlighted the major technical challenges ahead. In this section, we want to present a series of “attention points" that highlight new opportunities between the two disciplines.

What can we focus on now?

State-of-the-art ML models are now being applied to many tasks in animal ecology and wildlife conservation. However, while an out-of-the-box application of existing open tools is tempting, there are a number of points and potential pitfalls that must be carefully considered to ensure responsible use of these approaches.

Inherent model biases and generalization . Most ecological datasets suffer from some degree of geographic bias. For example, many open imagery repositories such as Artportalen.se , Naturgucker.de , and Waarneming.nl collect images from specific regions, and most contributions on iNaturalist 132 (see Table  2 ) come from the Northern hemisphere. Such biases need to be understood, acknowledged, and communicated to avoid incorrect usage of methods or models that by design may only be accurate in a specific geographic region. Biases are not limited to the geographical provenance of images: the type of sensors used (RGB vs . infrared or thermal), the species they depict, and the imbalance in the number of individuals observed per species 55 , 132 must also be considered when training or using models to avoid potentially catastrophic drop-offs in accuracy, and transparency around the training data and the intended model usage is a necessity 133 .

Curating and publishing well-annotated benchmark datasets without doing harm . The long-term advancement of the field will ultimately require the curation of large, diverse, accurately labeled, and publicly available datasets for ecological tasks with defined evaluation metrics and maintained code repositories. However, opening up existing datasets (and especially when using private-owned images acquired by non-professionals as in ref. 92 ) is both a necessary and difficult challenge for the near future. Fostering a culture of individual and cross-institutional data sharing in ecology will allow ML approaches to improve in robustness and accuracy. Furthermore, proper credit has to be given to the data collectors, for example through appropriate data attribution and digital object identifiers (DOIs) for datasets 133 .

Understanding the ethical risks involved . Computer scientists must also be aware of the ethical and environmental risks of publishing certain types of datasets. It is important to understand the limits of open data sharing in animal conservation in nature parks. In some cases, it is imperative that the privacy of the data be preserved, for instance to avoid giving poachers access to locations of animals in near-real-time 134 . Security of rangers themselves is also at stake; for example, the flight path of drones might be backtracked to reveal their location.

Standards of quality control are urgently needed . Accountability for open models needs to be better understood. The estimations of models remain approximations and need to be treated as such: population counts without uncertainty estimation can lead to erroneous and potentially devastating conclusions. Increased quality control on the adequacy of a model to a new scientific question or study area is important and can be achieved by close cooperation between model developers (who have the ability to design, calibrate, and run the models at their best) and practitioners (who have the domain and local knowledge). Without such quality control measures, relying on model-based results is risky and could have difficult-to-evaluate impacts on research in animal ecology, as incorrect results hidden in a suboptimally trained model will become more and more difficult to detect. Computer scientists must be aware that errors by their models can lead to erroneous decisions on site that can be catastrophic for the population they are trying to preserve or for the populations that live at the border of human/wildlife conflicts.

Environmental and financial costs of machine learning . ML is not free. Training and running models with millions of parameters on large volumes of data requires powerful, somewhat specialized hardware. Purchasing prices of such machines alone are often prohibitively high especially for budget-constrained conservation organizations; programming, running, and maintenance costs further add to the bill. Although cloud computing services exist that forgo the need of hardware management, they likewise pose per-resource costs that quickly scale to several thousands of dollars per month for a single virtual machine. Besides monetary costs, ML also uses significant amounts of energy: recently, it has been estimated that large, state-of-the-art models for understanding natural language emit as much carbon as several cars in their entire lifetime 135 . Even though the models currently used in animal ecology are far from such a carbon footprint, environmental costs of AI are often disregarded, as energy consumption of large calculations is still considered an endless resource (assuming that the money to pay for it is available). We believe this is a mistake, since disregarding environmental costs of ML models equals exchanging one source environmental harm (loss and biodiversity) for another (increase of emissions and energy consumption). Particular care needs to be paid to designing models that are not oversized and that can be trained efficiently. Smaller models are not only less expensive to train and use, their lighter computational costs allow them to be run on smaller devices, opening opportunities for real-time ML “on the edge”—i.e., within the sensors themselves.

What’s new: vast scientific opportunities lie ahead

In the previous sections, we describe the advances in research at the interface of ML, animal ecology, and wildlife conservation. The maturity of the various detection, identification, and recognition tools opens a series of interesting perspectives for genuinely novel approaches that could push the boundaries towards true integration of the disciplines involved.

Involving domain knowledge from the start . The ML and DL fields have focused mainly on black box models that learn correlations from data directly, and domain knowledge has been repeatedly ignored in favor of generic approaches that could fit to any kind of dataset. Such universality of ML is now strongly questioned and the inductive bias of traditional DL models is challenged by new approaches that bridge domain knowledge, fundamental laws, and data science. This “hybrid models” paradigm 48 , 50 is one of the most exciting avenues in modern ML and promises real collaboration between domains of application and ML, especially when coupled with algorithmic designs that allow interpretation and understanding of the visual cues that are being used 136 . This line of interdisciplinary research is small but growing, with several studies published in recent years. A representative one is Context R-CNN 52 for animal detection and species classification, which leverages the prior knowledge that backgrounds in camera trap imagery exhibit little variation over time and that camera traps acquire data with low sampling frequency and occasional dropouts. By integrating image features over long time spans (up to a month), the model is able to increase mean species identification precision in the Snapshot Serengeti dataset 137 by 17.9%. In another example 138 , the hierarchical structure of taxonomies, as well as locational priors, are leveraged to constrain plant species classification from iNaturalist in Switzerland, leading to improvements of state-of-the-art models of about 5%. Similarly ref.  139 , incorporate knowledge about the distribution of species as well as photographer biases into a DL model for species classification in images and report accuracy improvements of up to 12% in iNaturalist over a baseline without such priors. Finally ref.  140 , used expert knowledge of park rangers to augment sparse and noisy records of poaching activity, thereby improving predictions of poaching occurrence and enabling more efficient use of limited patrol resources in a Chinese nature reserve. These approaches challenge the dogma of ML models learning exclusively from data and achieve more efficient model learning (since base knowledge is available from the start and does not have to be re-learnt) and enhanced plausibility of the solutions (because the solution space can be constrained to a range of ecologically plausible outcomes).

Laboratories as development spaces . In recent years, modern ML has rapidly changed laboratory-based non-invasive observation of animals 76 , 105 . Neuroscience studies in particular have embraced novel tools for motion tracking, pose estimation (Fig.  1 d, e), and behavioral classification (e.g., ref. 141 ). The high level of control (e.g., of lighting conditions, sensor calibration, and environment) afforded by laboratory settings facilitated the rapid development of such tools, many of which are now being adopted for use in field studies of free-moving animals in complex natural environments 75 , 142 . In addition, algorithmic insights gained in the lab can be transferred back into the wild—studies on short videos or camera traps can leverage lab-generated data that is arguably less diverse, but easier to control. This opens interesting research opportunities for the adaptation of lab-generated simulation to real-world conditions, similar to what has been observed in the field of image synthesis for self driving 143 and robotics 144 in the last decade. Thus, laboratories rightly serve as the ultimate development space for such in-the-wild applications.

Towards a new generation of biodiversity models . Statistical models for species richness and diversity are routinely used to estimate abundances and study species co-occurrence and interactions. Recently, DL methods have also started to be employed to model species’ ecological niches 82 , 145 , facilitated by the development of machine-learning-ready datasets such as GeoLifeCLEF. GeoLifeCLEF curated a dataset of 1.9 million iNaturalist observations from North America and France depicting over 31,000 species, together with environmental predictors (land cover, altitude, climatic data, etc.), and asked users to predict a ranked list of likely species per geospatial grid cell. The task is complex: only positive counts are provided, no absence data are available, and predictions are counted as correct if the ground truth species is among the 30 predicted with highest confidence. This challenging task remains an open challenge—the winners of the 2021 edition achieved only an approximate 26% top-30 accuracy.

A recent review of species distribution modeling aimed at ML practitioners 146 provides an accessible entry point for those interested in tackling the challenges in this complex, exciting field. Open challenges include increasing the scale of joint models geospatially, temporally, and taxonomically, building methods that can leverage multiple data types despite bias from non-uniform sampling strategies, incorporating ecological knowledge such as species dispersal and community composition, and expanding methods for the evaluation of these models.

Finally, we wish to re-emphasize that the vision described here cannot be achieved without interdisciplinary thinking: for all these exciting opportunities, processing big ecological data is necessitating analytical techniques of such complexity that no single ecologist can be expected to have all the technical expertise (plus domain knowledge) required to carry out groundbreaking studies 65 . Cross-disciplinary collaborations are undeniably a critical component of ecological and conservation research in the modern era. Mutual understanding of the field-specific vocabularies, of the fields’ expectations, and of the implications and consequences of research ethics are within reach, but require open dialogs between communities, as well as cross-domain training of new generations.

Conclusions

Animal ecology and wildlife conservation need to make sense of large and ever-increasing streams of data to provide accurate estimations of populations, understand animal behavior and fight against poaching and loss of biodiversity. Machine and deep learning (ML; DL) bring the promise of being the right tools to scale local studies to a global understanding of the animal world.

In this Perspective , we presented a series of success stories at the interface of ML and animal ecology. We highlighted a number of performance improvements that were observed when adopting solutions based on ML and new generation sensors. Although often spectacular, such improvements require ever-closer cooperation between ecologists and ML specialists, since recent approaches are more complex than ever and require strict quality control and detailed design knowledge. We observe that skillful applications of state-of-the-art ML concepts for animal ecology now exist, thanks to corporate (e.g., Wildlife Insights) and research (AIDE, MegaDetector, DeepLabCut) efforts, but that there is still much room (and need) for genuinely new concepts pushed by interdisciplinary research, in particular towards hybrid models and new habitat distribution models at scale.

Inspired by these observations, we provided our perspective on the missing links between animal ecology and ML via a series of attention points, recommendations, and vision on future exciting research avenues. We strongly incite the two communities to work hand-in-hand to find digital, scalable solutions that will elucidate the loss of biodiversity and its drivers and lead to global actions to preserve nature. Computer scientists have yet to integrate ecological knowledge such as underlying biological processes into ML models, and the lack of transparency of current DL models has so far been a major obstacle to incorporating ML into ecological research. However, an interdisciplinary community of computer scientists and ecologists is emerging, which we hope will tackle this technological and societal challenge together.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

We thank Mike Costelloe for assistance with figure design and execution. S.B. would like to thank the Microsoft AI for Earth initiative, the Idaho Department of Fish and Game, and Wildlife Protection Solutions for insightful discussions and providing data for figures. M.C.C. and T.B.W. were supported by the National Science Foundation (IIS 1514174 & IOS 1250895). M.C.C. received additional support from a Packard Foundation Fellowship (2016-65130), and the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research. C.V.S. and T.B.W. were supported by the US National Science Foundation (Awards 1453555 and 1550853). S.B. was supported by the National Science Foundation Grant No. 1745301 and the Caltech Resnick Sustainability Institute. I.D.C. acknowledges support from the ONR (N00014-19-1-2556), and I.D.C., B.R.C., M.W., and M.C.C. from, the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy-EXC 2117-422037984. M.W.M. is the Bertarelli Foundation Chair of Integrative Neuroscience. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

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These authors contributed equally: Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe.

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School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Devis Tuia & Benjamin Kellenberger

Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, USA

Max Planck Institute of Animal Behavior, Radolfzell, Germany

Blair R. Costelloe, Martin Wikelski, Iain D. Couzin & Margaret C. Crofoot

Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany

Department of Biology, University of Konstanz, Konstanz, Germany

Blair R. Costelloe, Iain D. Couzin & Margaret C. Crofoot

Institute for Applied Mathematics and Information Technologies, IMATI-CNR, Pavia, Italy

Silvia Zuffi

Computer Science Department, University of Münster, Münster, Germany

Benjamin Risse

School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Alexander Mathis & Mackenzie W. Mathis

Environmental Sciences Group, Wageningen University, Wageningen, Netherlands

Frank van Langevelde

Computer Science Department, University of Bristol, Bristol, UK

Tilo Burghardt

Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA

Roland Kays

North Carolina Museum of Natural Sciences, Raleigh, NC, USA

Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA

Holger Klinck & Grant van Horn

Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA

Charles V. Stewart

Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA

Tanya Berger-Wolf

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D.T. coordinated the writing team; D.T., B.K., S.B., and B.C. structured and organized the paper with equal contributions; all authors wrote the text; B.C. created the figures.

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Tuia, D., Kellenberger, B., Beery, S. et al. Perspectives in machine learning for wildlife conservation. Nat Commun 13 , 792 (2022). https://doi.org/10.1038/s41467-022-27980-y

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