wildlife topics for research papers

94 Wildlife Essay Topic Ideas & Examples

🏆 best wildlife topic ideas & essay examples, ⭐ good research topics about wildlife, 👍 simple & easy wildlife essay titles, ❓ research questions about wildlife.

• Wildlife Tourism Essay Tourism can lead to interference and destruction of the wildlife ecosystem, leading to decrease in the population of the animals and degradation of their habitats.
• Wildlife Management and Extinction Prevention in Australia This paper investigates the threats to wildlife in Australia and strategies for managing and preventing their extinction. In summary, this paper examines the threats to wildlife in Australia and outlines strategies for managing and preventing […]
• Wildlife Parks Visitor Management Issues Administrators of wildlife parks have to employ different strategies of visitor management to ensure that they have a balance of demand by visitors and the available regeneration capacity of the wildlife parks.
• Javan Rhinos: Wildlife Trading of Endangered Animals Out of the five rhino species, Javan rhinoceros is the most threatened species despite being in the ecosystem for millions of years, playing a crucial role in shaping the landscape by its feeding style.
• Wildlife Control in and Around Airports The main purpose of the paper is to describe possible ways to protect and control the airport area from wild animals and birds that are potentially dangerous to the safety of passengers and can disrupt […]
• Wildlife Management in Urban Areas The end result of reducing the number of predator and carnivores in a given ecological system will cause an imbalance that allows organisms in the lower levels of the food chain to multiply to the […]
• The Manas Wildlife Sanctuary A home to a great variety of wildlife and endangered species, the Manas Sanctuary is located in the Himalayan foothills, in the far eastern state of Assam.
• Oil Drilling in the Alaska Wildlife Refuge Therefore, drilling for oil in the Alaska Wildlife Refuge would be seen as an act that could potentially harm not only the wildlife and ecosystem in that location, but also affect the well-being of other […]
• Climate Crisis and Wildlife in Danger The structure of the presentation includes an explanation of the issue and reasons for the beagles’ rescue, followed by the time limit to find new homes for dogs and a chronology of facility inspections.
• The US Fish and Wildlife Service and the US Forrest Service Refuge Management Thus, the aim is to sustain natural resources with the purpose of providing people with the necessary benefits while ensuring the activities do not lead to the deterioration of the land.
• Wildlife Conservation and Food Safety for Human From the epidemiological investigation, the seafood market in Wuhan was termed as the cause of the outbreak and Coronavirus was identified as of bat origin.
• Immunization of the Wildlife Population Against Rabies The only way of reducing the number of casualties is by preventing the disease. The efficacy of the method is shown by significant achievements in the reduction of the number of rabies cases among the […]
• Human-Wildlife Conflict: Vehicle Collisions With Animals The issue of collisions between wildlife and motor vehicles is a major challenge in most countries owing to the unpredictability of the animals’ closing in correspondence to the vast sizes of the parks and lands […]
• Should the Arctic National Wild Life Refuge Be Opened to Oil Drilling? The Baloney Detection Kit used in the series of discussions provides the guidelines for the arguments presented. Wherever there is a need to justify an argument advanced for the debate or against the drilling, the […]
• How Global Warming Has an Effect on Wildlife? According to one of the most detailed ecological studies of climate change, global warming is already directly affecting the lives of animals and plants living in various habitats across the world.
• Trails of Wild Life Tourism The tourism of wild life should be looked in the way it is creating an impact on the ecological balance in the nature and also on the economy of the whole nation.
• Oil Development in Arctic National Wildlife Range This paper describes the issues based on the development policy of the Arctic National Wildlife Refuge and the efforts made by the government to conserve the ANWR.
• Hunting in Wildlife Refuges in California In addition, the lack of regulations and the prohibition of hunting in wildlife refuges in its entirety has led to the overpopulation of certain species and the introduction of imbalance to the ecosystem, with the […]
• Wildlife Controls Around Commercial Airports Managing the safety of the airports is one of the most important responsibilities of civil aviation authorities around the world. Security in the aviation sector is the factors often given priority because of the magnitude […]
• Wildlife in Art, Science and Public Attitudes In her opinion, Hirst’s approach to art that involves “taking things out of the world” to get to their essence is extremely contradictory and aims to oversimplify the concept of wilderness.
• Great Meadows National Wildlife Refuge’s Issues The article in question addresses the correlation between the value of property prices and the proximity of open spaces. The authors address two research questions, investigating the possibility of a correlation between the proximity of […]
• Relations of World Wildlife Fund for Nature and Media The purpose of this NGO is to safeguard nature and to stop the degradation of the planet’s environment and “to build a future in which humans live in harmony with nature”.
• Wildlife Forensic DNA Laboratory and Its Risks The mission of the Wildlife Forensic DNA Laboratory is to provide evidence to governmental and non-governmental organizations to ensure the protection of the wildlife in the country.
• Water Transportation Industry’s Impact on Wildlife It is possible to note that emissions and the use of ballast water can be seen as serious issues that pose hazards to maritime animals.
• Emerging Energy Development’ Impacts on Wildlife One of the major concerns involves the effect of energy development on wildlife and natural ecosystems. It is important to lessen the effects of energy development on wildlife and natural ecosystems.
• American National Park Service and Wildlife The law reads in part: “to conserve the scenery and the natural and historic objects and the wild life therein and to provide for the enjoyment of the same in such manner and by such […]
• Urban Wildlife Issues Actually, it is important to note that not all human developments are destructive; a focus toward taking care of or conserving animals in urban areas has promoted conservation and sustainability of environment and biodiversity.
• A Call for Conservation of Arctic National Wildlife Refuge Though economic benefits of such drilling are obvious, they do not outweigh the need to preserve the pristine nature of the area o the benefit of thousands of animal and plant species that depend on […]
• Learning During Wildlife Tours in Protected Areas: Towards a Better Understanding of the Nature of Social Relations in Guided Tours
• Wildlife-Based Recreation and Local Economic Development
• Location-Specific Modeling for Optimizing Wildlife Management on Crop Farms
• African Wildlife Policy: Protecting Wildlife Herbivores on Private Game Ranches
• Illegal Logging, Fishing, and Wildlife Trade
• Network Structure and Perceived Legitimacy in Collaborative Wildlife Management
• Protected Areas, Wildlife Conservation, and Local Welfare
• Habitat Conservation, Wildlife Extraction, and Agricultural Expansion
• The Transaction Costs Tradeoffs of Private and Public Wildlife Management
• Caring for Native Wildlife Securing Permit and Approval
• Evaluating Tax Policy Proposals for Funding Nongame Wildlife Programs
• Dealing With Wildlife Damage to Crops
• Clear Forest Cause Extinction of Wildlife
• Forensic Techniques for Wildlife Crime
• Bird and Wildlife Management at Airports
• Economic Benefits, Conservation and Wildlife Tourism
• Environmental Plans and Wildlife Management Programs
• The Current Issues Involving Wind Farms and Wildlife
• Ecological Fever: The Evolutionary History of Coronavirus in Human-Wildlife Relationships
• Opportunities for Transdisciplinary Science to Mitigate Biosecurity Risks From the Intersectionality of Illegal Wildlife Trade With Emerging Zoonotic Pathogens
• Mitigation Measures for Wildlife in Wind Energy Development
• Ecology and Wildlife Risk Evaluation Analysis
• Ethical Considerations for Wildlife Reintroductions and Rewilding
• Save Wildlife and Forest for Our Future Generations
• Spatial Data Analysis and Study of Wildlife Conservation
• Global Warming and Its Threat to the Future of Wildlife and Its Habitat
• Gabriela Cowperthwaite’s Blackfish: Treatment of the Sea World and Marine Wildlife
• Information and Wildlife Valuation: Experiments and Policy
• Arctic National Wildlife Refuge: Seasons of Life and Land
• Identifying and Assessing Potential Wildlife Habitat Corridors
• Regulating the Global Fisheries: The World Wildlife Fund, Unilever, and the Marine Stewardship Council
• Wildlife Gardening and Connectedness to Nature: Engaging the Unengaged
• Urban Sprawl: Impact Upon Wildlife
• Human Activities, Wildlife Corridors, and Laws and Policies
• Pollution and Its Effects on Wildlife
• Tourism, Poaching, and Wildlife Conservation: What Can Integrated Conservation and Development Projects Accomplish
• Wildlife-Based Tourism and Increased Tourist Support for Nature Conservation Financially and Otherwise
• Supporting Sustainable Livelihoods Through Wildlife Tourism
• Evolving Urban Wildlife Health Surveillance to Intelligence for Pest Mitigation and Monitoring
• Gray Lodge Wildlife Area: A Home for the Animals
• Can Local Communities Afford Full Control Over Wildlife Conservation?
• What Is the Biggest Threat to Wildlife Today?
• What Are the Major Causes of Loss of Wildlife?
• Should the Arctic National Wildlife Refuge Be Opened to Oil Drilling?
• How Does Hunting Affect Wildlife?
• What Are the Effects of Wildlife Depletion?
• What Is the Importance of Wildlife?
• What Human Activities Badly Affect Wildlife?
• What Will Happen if We Don’t Protect Wildlife?
• What Are the Top Ten Ways to Save Wildlife?
• What Are Man-Wildlife Conflicts?
• What Are the Five Major Impacts Humans Have on the Environment?
• How Killing Animals Affect the Wildlife Environment?
• How Can We Prevent Human-Wildlife Conflict?
• Where Is the Best Place to See Wildlife in the US?
• What US National Park Has the Most Wildlife?
• Does Florida Have a Lot of Wildlife?
• What Wildlife Is in Yellowstone?
• What Country Has the Most Exotic Wildlife?
• How Humans Are Affecting Wildlife?
• What Country Has the Best Wildlife?
• What Continent Has the Most Wildlife?
• What Is the Wildlife of Asia?
• Which Country in Asia Has the Most Wildlife?
• What Is the Most Common Wildlife in the Arctic?
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Wildlife Dissertation Topics

Published by Owen Ingram at December 29th, 2022 , Revised On August 11, 2023

Animals, plants, and microorganisms that can live in their natural habitat and are not domesticated or cultivated are considered wildlife. A wide range of animal and plant species are included in wildlife, including uncultivated mammals, reptiles, birds, and fish.

Numerous studies have been conducted in this area over the last couple of decades due to the continuously declining wildlife. Research on wildlife conservation, in particular, has received substantial funding. If you are thinking about the possible wildlife topics for writing a dissertation , our team has compiled many appealing wildlife dissertation topics that are sure to inspire you.

So without further ado, here is our selection of trending and focused wildlife thesis topics and ideas for your consideration whether you are an undergraduate, Master or PhD student.

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40 Excellent Wildlife Dissertation Topics

• The impact of avian migration patterns on illness transmission in seasonal host bird populations
• A study of the conservation efforts for the Himalayan snow leopard
• An investigation on how building railroads has affected the choice of habitat for moose in rural Canada
• Studying Wildlife Tours in Protected Areas: A Review of the Security Protocols & Procedures
• Optimizing Wildlife Management on Crop Farms using Site-Specific Modeling
• Protecting Wildlife Herbivores on Private Game Ranches in Africa
• A research project on avian ecology and protection in monsoon environments
• Researchers investigate the impact of shifting weather patterns on the migration patterns of Asian geese
• A review of the impact of selective annual hunting licenses on Pakistani markhor conservation
• A study of the successful rehabilitation of the declining markhor communities in northern Pakistan under communal ownership
• Structure of the Network and Perceived Legitimacy in Collaborative Wildlife Management
• Costs of the Transaction Private versus Public Wildlife Management Trade-offs
• Considering Tax Policy Ideas to Support Nongame Wildlife Programs
• A research project is looking at how beaver dams impact fish biodiversity
• How many other wildlife species are still undiscovered? Theory and proof
• A review of flagship species’ significance to conservation efforts
• A study of how politics affects the conservation of the African rhino. Are our concerns about doing business with China preventing us from saving rhinos?
• A study of how politics affects whale conservation. Does the imperative protect the whale trump our political worries about Japan?
• The results of aggressive initiatives for animal rights. How does it impact conservation efforts?
• Relationships between Humans and Wildlife: Coronavirus Evolution
• Possibilities for Interdisciplinary Science to Reduce Bio-security Risks from Illegal Wildlife Trade and Emerging Zoonotic Pathogens
• Opposition to animal testing. What progress has been made during the past 50 years?
• The impact of imprisonment on a grey wolf’s mating habits
• An investigation of the behavioural similarities and differences between domestic dogs and wolves kept in captivity
• Grey wolves’ responses to various confinement conditions focused on their mating habits
• The impact of the Fukushima nuclear disaster on local wildlife habitat and ecology
• The conservation efforts of commercial zoos
• The impact of industrial waste on the preservation of wildlife
• Global legislative impact of animal conservation
• The impact of climate change on the preservation of animals
• What Can Integrated Conservation and Development Projects Achieve in Tourism, Poaching, and Wildlife Conservation Areas?
• Increased tourist support for nature conservation, both financially and in other ways, including wildlife-based tourism
• Supporting Wildlife Tourism-Based Sustainable Livelihoods
• Urban Wildlife Health Surveillance Developing into Intelligence for Monitoring and Mitigation of Pests
• The Identification and Evaluation of Potential Wildlife Habitat Corridors
• What are some of the things that prevent the wildlife sector of the economy from growing?
• How can wildlife be improved so that people and various animals can species benefit?
• Why shouldn’t these animals be handled gently and with respect by everyone?
• What is the impact of tourists on the poor performance of wildlife sections in developing nations?
• Is it permissible for the government to use different types of trees and animals for scientific research?

We recommend you pick more than one topic and conduct a little research on all of them. You can use the internet or your local library to gather sources that were created on issues similar to your selection.

If you do not find enough information on one topic, move to the next option. Researching multiple issues will help you collect enough data for various dissertation topics and choose the one you found the most information on. 

Take inspiration from our list of wildlife dissertation topics, and get started with your dissertation without any further delay. You can also order a professional dissertation writing service from our expert writers, so you focus on other areas of life. 

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How to find wildlife dissertation topics.

To discover wildlife dissertation topics:

• Research conservation challenges.
• Explore biodiversity hotspots.
• Analyze habitat or species concerns.
• Review scientific journals.
• Consult experts or professors.
• Select a topic aligning with your passion and field of study.

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Abstracts of Recently Published Papers on Wildlife and Habitat Conservation

Conservation Frontlines has selected a range of new scientific, peer-reviewed papers and thesis submissions. Scan the abstracts to get an overview. Links to the original papers are provided (check also additions to the CFL library for more recent material.)

The Rhinoceros Horn Trade Ban: Can Scenario Formulation help build Consensus amongst highly polarised South African Stakeholders? 2020. Jane Wiltshire. Doctoral Thesis submitted to The International School of Management, Paris, France. DOI: 10.13140/RG.2.2.16328.67841

Abstract: Many issues regarding wildlife trade are fiercely debated; often the various stakeholder groups have entrenched opposing positions which makes building consensus around the best solution/s extremely difficult. This is exacerbated in that stakeholders often come from entirely different disciplines and philosophical viewpoints.so that no common vocabulary or acceptable method of discussing the problem to reach a consensus exists. This study examines the use of a blend of two decision support methodologies, scenario formulation and a Delphi Study as part of a stakeholder analysis in building consensus in the debate on the legalisation of the international trade in rhino horn. The results gathered from the responses to two consecutive online questionnaires show the development of significant consensus over the process and performed far better in this regard than a traditional public debate. In addition, four decision scenarios – Fort Knox, Besieged, Arms Race and Golden Circle were crafted for wider use in public fora and a possible ‘Baptists and Bootlegger’ type of unwitting alliance between Animal Rights NGOs and Poachers, Middlemen and Criminal Syndicates was indicated.

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‘Intentional Genetic Manipulation’ as a conservation threat . 2019. Isa-Rita M. Russo, Sean Hoban, Paulette Bloomer, Antoinette Kotzé, Gernot Segelbacher, Ian Rushworth, Coral Birss & Michael W. Bruford. Conservation Genetics Resources volume 11, pages237–247(2019)

Abstract: Wildlife ranching including the hunting, collection, sales and husbandry of wild animals in captivity, is practiced worldwide and is advocated as an approach towards the conservation of wild species. While many authors have explored the biological impacts of intensive wild population management, primarily with respect to disease transmission (especially in ungulates and fish), the evolutionary and demographic effects of wildlife ranching have been examined less intensively. We discuss this issue through the case of intensive wildlife management in southern Africa. The genetic consequences of this global practice, with an emphasis on Africa, were addressed by a motion passed at the 2016 IUCN World Congress: “Management and regulation of intensive breeding and genetic manipulation of large mammals for commercial purposes.” Here, we highlight concerns regarding intensive breeding programs used to discover, enhance and propagate unusual physical traits, hereafter referred to as ‘Intentional Genetic Manipulation’. We highlight how ‘Intentional Genetic Manipulation’ potentially threatens the viability of native species and ecosystems, via genetic erosion, inbreeding, hybridization and unregulated translocation. Finally, we discuss the need for better policies in southern Africa and globally, regarding ‘Intentional Genetic Manipulation’, and the identification of key knowledge gaps.

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Impacts of hunting prohibitions on multidimensional well-being . 2020. Michael Strong & Julie A. Silva. Biological Conservation, Volume 243, March 2020, 108451.

Abstract: Prohibitions against wildlife hunting often have impoverishing outcomes for rural households. Previous research has emphasized the financial losses and attributed material deprivation as the motivation for illegal wildlife hunting. However, this narrow focus does not capture the values rural communities ascribe to hunting nor consider the broader outcomes hunting bans have on multidimensional well-being. In this study, we utilize Amartya Sen’s capability approach to gain a deeper understanding of hunting bans’ effects. Iterative content analysis of 435 interviews with respondents from three study sites located within or near protected areas in southern Africa revealed that individuals hunt for three primary reasons: to procure meat for household consumption, to manage human-wildlife conflict, and to generate revenue via commercial poaching. When detailing the impacts of hunting prohibitions, respondents overwhelmingly emphasized the instrumental value of hunting. They described significant material losses that are deeply intertwined with a broad range of non-material costs to well-being. The strongest objections to wildlife regulations centered on how they serve to humanize animals while de-humanizing people. Additionally, the non-material impacts of hunting bans exacerbated discontent with material losses arising from conservation. We find a need to critically examine the non-material losses of conservation given their potential to alienate rural communities, increase resistance, and undermine local residents’ voluntary participation in conservation efforts.

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The genetic legacy of 50 years of desert bighorn sheep translocations. 2018.  Joshua P. Jahner, Marjorie D. Matocq, Jason L. Malaney, Mike Cox4, Peregrine Wolff, Mitchell A. Gritts & Thomas L. Parchman. September 2018, Evolutionary Applications 12(2) DOI: 10.1111/eva.12708

Abstract: Conservation biologists have increasingly used translocations to mitigate population declines and restore locally extirpated populations. Genetic data can guide the selection of source populations for translocations and help evaluate restoration success. Bighorn sheep ( Ovis canadensis ) are a managed big game species that suffered widespread population extirpations across western North America throughout the early 1900’s. Subsequent translocation programs have successfully re-established many formally extirpated bighorn herds, but most of these programs pre-date genetically-informed management practices. The state of Nevada presents a particularly well-documented case of decline followed by restoration of extirpated herds. Desert bighorn sheep ( O. c. nelsoni ) populations declined to less than 3,000 individuals restricted to remnant herds in the Mojave Desert and a few locations in the Great Basin Desert. Beginning in 1968, the Nevada Department of Wildlife translocated ~2,000 individuals from remnant populations to restore previously extirpated areas, possibly establishing herds with mixed ancestries. Here we examined genetic diversity and structure among remnant herds and the genetic consequences of translocation from these herds using a genotyping-by-sequencing approach to genotype 17,095 loci in 303 desert bighorn sheep. We found a signal of population genetic structure among remnant Mojave Desert populations, even across geographically proximate mountain ranges. Further, we found evidence of a genetically distinct, potential relict herd from a previously hypothesized Great Basin lineage of desert bighorn sheep. The genetic structure of source herds was clearly reflected in translocated populations. In most cases, herds retained genetic evidence of multiple translocation events and subsequent admixture when founded from multiple remnant source herds. Our results add to a growing literature on how population genomic data can be used to guide and monitor restoration programs.

Characteristics that make trophy hunting of giant pandas inconceivable. 2020. Robert A. Montgomery, Madeline Carr, Charlie R. Booher, Abigail M. Pointer, Brendan M. Mitchell, Natalie Smith, Keegan Calnan, Georgina M. Montgomery, Mordecai Ogada & Daniel B. Kramer. Conservation Biology (April 2020). https://doi.org/10.1111/cobi.13458

Abstract: In November 1928, Theodore Jr. and Kermit Roosevelt led an expedition to China with the expressed purpose of being the first Westerners to kill the giant panda ( Ailuropoda melanoleuca ). The expedition lasted 8 months and resulted in the brothers shooting a giant panda in the mountains of Sichuan Province. Given the concurrent attention in the popular press describing this celebrated expedition, the giant panda was poised to be trophy hunted much like other large mammals around the world. Today, however, the killing of giant pandas, even for the generation of conservation revenue, is unthinkable for reasons related to the species itself and the context, in time and space, in which the species was popularized in the West. We found that the giant panda’s status as a conservation symbol, exceptional charisma and gentle disposition, rarity, value as a non-consumptive ecotourism attraction, and endemism are integral to the explanation of why the species is not trophy hunted. We compared these intrinsic and extrinsic characteristics with 20 of the most common trophy‐hunted mammals to determine whether the principles applying to giant pandas are generalizable to other species. Although certain characteristics of the 20 trophy‐hunted mammals aligned with the giant panda, many did not. Charisma, economic value, and endemism, in particular, were comparatively unique to the giant panda. Our analysis suggests that, at present, exceptional characteristics may be necessary for certain mammals to be excepted from trophy hunting. However, because discourse relating to the role of trophy hunting in supporting conservation outcomes is dynamic in both science and society, we suspect these valuations will also change in future.

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Towards a sustainable, participatory and inclusive wild meat sector. 2019. Lauren Coad (CIFOR / University of Sussex); John E. Fa (CIFOR / Manchester Metropolitan University); Nathalie Van Vliet (CIFOR); Katharine Abernethy (University of Stirling); Catalina Santamaria (SBSTTA-CBD), David Wilkie (Wildlife Conservation Society); Donna-Mareè Cawthorn (University of Salford); and Robert Nasi (CIFOR). Center for International Forestry Research (CIFOR), Bogor, Indonesia. DOI: 10.17528/cifor/007046. ISBN: 978-602-387-083-7

Summary: This in depth report summarizes available information on the scale and drivers of subsistence and commercial harvesting of wild terrestrial vertebrates for food in tropical and subtropical regions; emphasizes the contributions that wild meat makes to food security, human nutrition and well-being; and highlights the far-reaching impacts of over-exploitation on the long-term survival of species and the functioning of ecosystems. The report provides technical guidance to improve governance and sustainability of the resource by focusing on how to ensure that the supply of wild meat is sustainably managed upstream; how to reduce the consumption of wild meat especially the excessive demand in towns and cities; how joint approaches can be applied to solve the use of wild meat and finally on how to create an enabling environment for the sustainable management of wild meat. What emerges from this synthesis is that the governance of wild meat will ultimately depend on understanding and working with both local people and wider civil society, with approaches that focus solely on either ecological or socio-economic goals running the risk of failure in the long term.

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Humans, Livestock, and Lions in northwest Namibia. 2019. John Moore Heydinger. Dissertation submitted to the faculty of submitted to the faculty of the University of Minnesota in partial fulfillment of the requirements for the degree of Doctor of Philosophy.

Abstract: Humans, livestock, and lions have inhabited shared landscapes in northwest Namibia for hundreds of years. Currently, human-lion conflict (HLC) threatens pastoral livelihoods and the viability of the region’s desert-adapted lion population. In this dissertation I examine the history of human-livestock-lion relationships in the region. The goal is to create historically-informed solutions to HLC that are locally-inclusive. Drawing on archival, scientific, and governmental material, as well as social surveys and oral histories that I have performed, this is the first time that the disparate sources on human-livestock-lion relationships in northwest Namibia have been unified. While scholars of African environments have problematized interpretations of Africa’s environmental colonial and postcolonial past, this is the first work to examine human-predator relationships as a fulcrum for understanding colonial and postcolonial politics and the current challenges of conserving African lions. As a document informing ongoing conservation interventions, this is the first attempt to explicitly frame applied lion conservation activities within historical contexts, critically assessing livestock as mediators of human-lion interactions. I begin by showing how the precolonial and early-colonial experience of the region’s ovaHerero people was mediated through the control of livestock. I then examine how colonial era policies remade, and were aided by, the geography of predators. The effects of apartheid on the region’s wildlife showcase some of the important legacies of colonial-era policies. I then reveal the long history of human-lion interactions with particular emphasis on the transformative role of livestock. I then focus on the behavior and ecology of the desert-adapted lions, highlighting important contrasts with other lion populations and emphasizing how recent monitoring induced a paradigm shift. Finally, I center ongoing HLC within communal rangelands as experienced by pastoralists and suggest one way of reframing HLC that is founded in local perspectives.

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Sustainable Governance of Wildlife and Community-Based Natural Resource Management: From Economic Principles to Practical Governance . 2019. Brian Child. Routledge, London, UK. 1 edition (November 11, 2019) – Earthscan Studies in Natural Resource Management. Hardcover: 406 pages. ISBN-13: 978-0415793278; ISBN-10: 0415793270; eBook ISBN9781315211152.

Overview: This book develops the Sustainable Governance Approach and the principles of Community-Based Natural Resource Management (CBNRM). It provides practical examples of successes and failures in implementation, and lessons about the economics and governance of wild resources with global application. CBNRM emerged in the 1980s, encouraging greater local participation to conserve and manage natural and wild resources in the face of increasing encroachment by agricultural and other forms of land use development. This book describes the institutional history of wildlife and the empirical transformation of the wildlife sector on private and communal land, particularly in southern Africa, to develop an alternative paradigm for governing wild resources. With the twin goals of addressing poverty and resource degradation in the world’s extensive agriculturally marginal areas, the author conceptualizes this paradigm as the Sustainable Governance Approach, which integrates theories of proprietorship and rights, prices and economics, governance and scale, and adaptive learning. The author then discusses and defines CBNRM, a major subset of this approach. Interweaving theory and practice, he shows that the primary challenges facing CBNRM are the devolution of rights from the center to marginal communities and the governance of these rights by communities, a challenge which is seldom recognized or addressed. He focuses on this shortcoming, extending and operationalizing institutional theory, including Ostrom’s principles of collective action, within the context of cross-scale governance. Based on the author’s extensive experience this book will be key reading for students of natural resource management, sustainable land use, community forestry, conservation, and development. Providing practical but theoretically robust tools for implementing CBNRM it will also appeal to professionals and practitioners working in communities and in conservation and development.

Hunters as Citizen Scientists: Contributions to Biodiversity Monitoring in Europe. 2020. Cretois, Benjamin, John D. C. Linnell, Matthew Grainger, Erlend B. Nilsen, and Jan K. Rød. EcoEvoRxiv. March 10. doi:10.32942/osf.io/9f7k3

Abstract: 1. Monitoring biodiversity characteristics at large scales and with adequate resolution requires considerable effort and resources. Overall, there is clearly a huge scope for European hunters, a special and often overlooked group of citizen scientists, to contribute even more to biodiversity monitoring, especially because of their presence across the entire European landscape.

• Using the Essential Biodiversity Variables (EBVs) framework we reviewed the published and grey literature and contacted experts to provide a comprehensive overview of hunters’ contributions to biodiversity monitoring. We examined the methods used to collect data in hunter-based monitoring, the geographic and taxonomic extent of such contributions and the scientific output stemming from hunter-based monitoring data.
• Our study suggests that hunter-based monitoring is widely distributed across Europe and across taxa as 32 out of the 36 European countries included in our analysis involve hunters in the monitoring of at least one species group with ungulates and small game species groups which have the widest hunter-based monitoring coverage. We found that it is possible to infer characteristics on Genetic composition, Species population, Species traits and Community composition with data that are being routinely collected by hunters in at least some countries. The main types of data provided are hunting bags data, Biological samples including carcasses of shot animals and non-invasive samplings and observations for counts and indices.
• Hunters collect data on biodiversity in its key dimensions, collaborations between hunters and scientists are fruitful and should be considered a standard partnership for biodiversity conservation. To overcome the challenges in the use of hunters’ data, more rigorous protocols for sampling data should be implemented and improvements made in data integration methods.

Impacts of a trophy hunting ban on private land conservation in South African biodiversity hotspots . 2020. Kim Parker, Alta De Vos, Hayley S. Clements, Duan Biggs & Reinette Biggs. Conservation Science and Practice. 2020; e214. wileyonlinelibrary.com/journal/csp2 . https://doi.org/10.1111/csp2.214

Abstract: Private land conservation areas (PLCAs) have become critical for achieving global conservation goals, but we lack understanding of how and when these areas respond to global pressures and opportunities. In southern Africa, where many PLCAs rely on trophy hunting as an income-generating strategy, a potential ban on trophy hunting locally or abroad holds unknown consequences for the future conservation of these lands. In this study, we investigate the consequences of a potential trophy hunting ban in PLCAs in two biodiversity hotspots in South Africa’s Eastern and Western Cape provinces. We used semistructured interviews with PLCA managers and owners to elicit perceived impacts of an internationally imposed trophy hunting ban on conservation activities in PLCAs, and to probe alternative viable land uses. The majority of interviewees believed that both the economic viability of their PLCA and biodiversity would be lost following a hunting ban. Owners would primarily consider transitioning to ecotourism or livestock farming, but these options were constrained by the social-ecological context of their PLCA (e.g., competition with other PLCAs, ecological viability of farming). Our results suggest that a trophy hunting ban may have many unintended consequences for biodiversity conservation, national economies, and the livelihoods of PLCA owners and employees. Along with similar social-ecological studies in other areas and contexts, our work can inform policy decisions around global trophy hunting regulation.

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Michael K. Schwartz, Beth A. Hahn, Blake R. Hossack, Where the Wild Things Are: A Research Agenda for Studying the Wildlife-Wilderness Relationship, Journal of Forestry , Volume 114, Issue 3, May 2016, Pages 311–319, https://doi.org/10.5849/jof.15-070

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We explore the connection between US designated wilderness areas and wildlife with the goal of establishing a research agenda for better understanding this complex relationship. Our research agenda has two components. The first, “wildlife for wilderness,” considers the impact of wildlife on wilderness character. Whereas studies show that wildlife is important in both the perception and actual enhancement of wilderness character, the context and particulars of this relationship have not been evaluated. For instance, is knowing that a rare, native species is present in a wilderness area enough to increase perceptions of naturalness (an important wilderness quality)? Or does the public need to observe the species or its sign (e.g., tracks) for this benefit? The second part of our research agenda, “wilderness for wildlife,” considers the types of research needed to understand the impact of wilderness areas on wildlife and biodiversity conservation. Several studies show the effect of one area being designated wilderness on one wildlife species. Yet, there has been no research that examines how the networks of wilderness areas in the National Wilderness Preservation System (NWPS) are used by a species or a community of species. Furthermore, we found no studies that focused on how the NWPS affects ecological or trophic interactions among species. We hope that by providing a research agenda, we can spur multiple lines of research on the topic of wildlife and wilderness.

Management and Policy Implications: This article establishes a multiscale research agenda to help set the stage for research examining wildlife and wilderness. Our research agenda distinguishes the effects that wildlife has on wilderness character versus the impact that wilderness character has on wildlife populations, species, and communities. We consider both parts of this research agenda of equal importance. Understanding how wildlife contributes to wilderness character is essential to the legal mandate to preserve it. Managers are increasingly faced with decision tradeoffs in managing for both wildness and naturalness within wilderness through proposals such as assisted migration, wildlife reintroductions, and supplementations. Well-crafted social science can help with these policy decisions. The second prong of our research agenda examines how wilderness character affects wildlife. It encourages studies that go beyond the effect of one wilderness on one species. There has been increased perception in the policy and management arena that protection of one patch is inadequate for species protection and that management of the entire landscape matrix, across multiple jurisdictions and management plans, is critical for conservation. Our research agenda advocates research that understands the role of the network of wilderness areas in biodiversity conservation.

In hunting, the finding and killing of the game is after all but a part of the whole. The free, self-reliant, adventurous life, with its rugged and stalwart democracy; the wild surroundings, the grand beauty of the scenery, the chance to study the ways and habits of the woodland creatures—all these unite to give the career of the wilderness hunter its peculiar charm. The chase is among the best of all national pastimes; it cultivates that vigorous manliness for the lack of which in a nation, as in an individual, the possession of no other qualities can possibly atone.

This is a very happy and historic occasion for all who love the great American outdoors, and that, needless to say, includes me. The two bills that I am signing this morning are in the highest tradition of our heritage as conservators as well as users of America's bountiful natural endowments…. I believe the significance of this occasion goes far beyond these bills alone. In this century, Americans have wisely and have courageously kept a faithful trust to the conservation of our natural resources and beauty…. The wilderness bill preserves for our posterity, for all time to come, 9 million acres of this vast continent in their original and unchanging beauty and wonder.

Action has been taken to keep our air pure and our water safe and our food free from pesticides; to protect our wildlife ; to conserve our precious water resources.

Nothing in this Act shall be construed as affecting the jurisdiction or responsibilities of the several States with respect to wildlife and fish in the national forests.

There is no mention of the aim of protecting, conserving, or preserving wildlife in the Wilderness Act. Moreover, the Wilderness Act allows for actions with direct impacts on wildlife populations (e.g., hunting and fishing regulated by state agencies), as well as indirect impacts (e.g., continuance of livestock grazing in areas where it was already established before wilderness designation). Thus, the Wilderness Act cannot be thought of as a biodiversity, wildlife, or endangered species law as there is no language within the Wilderness Act that mandates wildlife or biodiversity protection. It took nearly 9 more years before the US Congress passed comprehensive legislation aimed to protect endangered species and ecosystems (i.e., Endangered Species Act of 1973 [ESA], although there had been prior individual acts protecting specific taxa or more limited in scope such as the Lacey Act of 1900, the Migratory Bird Conservation Act of 1929, the Bald Eagle Protection Act of 1940, the Marine Mammal Protection Act of 1972, or the Endangered Species Preservation Act of 1966).

A wilderness, in contrast with those areas where man and his own works dominate the landscape, is hereby recognized as an area where the earth and its community of life are untrammeled by man, where man himself is a visitor who does not remain. An area of wilderness is further defined to mean in this Act an area of undeveloped Federal land retaining its primeval character and influence, without permanent improvements or human habitation, which is protected and managed so as to preserve its natural conditions and which (1) generally appears to have been affected primarily by the forces of nature, with the imprint of man's work substantially unnoticeable; (2) has outstanding opportunities for solitude or a primitive and unconfined type of recreation; (3) has at least five thousand acres of land or is of sufficient size as to make practicable its preservation and use in an unimpaired condition; and (4) may also contain ecological, geological, or other features of scientific, educational, scenic, or historical value.

Wildlife and wildlife research are not central to the Wilderness Act, yet the topics of wildlife and wilderness are complexly intertwined and often understudied. With the 50th anniversary of the Wilderness Act celebrated in September 2014, we look toward a research agenda for the next 50 years of wilderness research. Although each wilderness will have its own unique matters to be addressed, we can broadly group questions into two categories, which form the basis of our wilderness-wildlife research agenda: How important is wildlife for wilderness character? and How does the existence and maintenance of wilderness character in an individual wilderness area or the network of many wilderness areas affect wildlife ( Figure 1 )? By “wilderness character” we mean the “combination of biophysical, experiential, and symbolic ideals that distinguishes wilderness from all other lands” defined by the qualities of (1) untrammeled, (2) natural, (3) undeveloped, and (4) opportunities for solitude or a primitive and unconfined type of recreation, as expressed in the Wilderness Act ( Landres 2004 , p. 9). Untrammeled is a word unique to the wilderness literature and can be defined as “unhindered and free from intentional actions of modern human control or manipulation” ( Landres et al. 2015 , p. 10–11), whereas “natural” is the quality that is “preserved when there are only indigenous species and natural ecological conditions and processes, and may be improved by controlling or removing nonindigenous species or by restoring ecological conditions.” ( Landres et al. 2015 , p. 11).

Schematic diagram showing the proposed research agenda for studying the wildlife-wilderness interaction.

It is often assumed that wildlife influences the perception of wilderness character. Certain indigenous wildlife species—such as grizzly bears ( Ursus arctos horribilis ), caribou ( Rangifer tarandus ), and gray wolves ( Canis lupus )—suggest wilderness ( Hendee and Mattson 2009 ). Senses of the natural qualities of wilderness character are often associated with encountering wildlife in wilderness. Yet, active management for wildlife in wilderness may degrade the qualities of wilderness character ( Knapp et al. 2001 , Landres et al. 2001 , 2015 ). Encountering a water capture device used to subsidize rare wildlife populations in arid environments may impact perceptions of wilderness by degrading the untrammeled and undeveloped qualities of wilderness character ( Wilderness Watch v. US Fish and Wildlife Service 2010).

In this research agenda, we consider a hierarchical approach for conducting studies to investigate the effect of wildlife on wilderness character, starting with the fundamental question, “How important is wildlife for wilderness character?” The Wilderness Act emphasizes not only the end goal (e.g., improving natural components by maintaining wildlife populations) but also the means of achieving this action (e.g., implementation methods). If wildlife is found to be only marginally important for the perception of naturalness then there is no need to degrade other wilderness qualities to enhance wildlife populations for the sake of maintaining wilderness character. Alternatively, if wildlife substantially increases the perception of naturalness, then there may be reasons to degrade other wilderness qualities to achieve this goal. Kammer (2013) suggests that restoring wildlife populations in wilderness is a commendable goal to achieve naturalness, but that this goal is secondary to the intent of the Act, which is to keep areas free from human control. Others have argued that the natural quality that wildlife brings to Wilderness is essential to maintaining wilderness character ( Wilderness Watch v. US Fish and Wildlife Service 2010) and is equal to managing for other qualities of wilderness character ( Landres et al. 2015 ; see Cole and Yung 2010 for this full debate). Understanding the relationship between wildlife and wilderness character should influence management standards and guidelines used to implement wilderness management (achieve naturalness) under the Wilderness Act. Without adequate research on this topic, wilderness managers must assume the importance of wildlife for enhancing wilderness character.

Public perceptions of wildlife in wilderness areas were evaluated during surveys conducting in 1994–1995 and 2000 ( Cordell et al. 2003 ). Respondents were asked to describe their perception of various wilderness benefits. In 1994–1995, “protection of wildlife habitat” ranked as the second of 14 most important benefits, but in 2000, it ranked third of 14 behind “protecting water quality” and “protecting air quality.” Despite dropping from second to third, there was an increase of 9.2% in respondents suggesting that wildlife habitat protection was “extremely” or ‘very important.” Similarly, “protection of endangered species” ranked fifth of 14 benefits in both time frames ( Cordell et al. 2003 ). Cordell et al. (2003) demonstrated that the public perceives a value of wilderness for wildlife, but this does not specifically address the question, “How important is wildlife for the public's perception of wilderness?” Watson et al. (2015) conducted a survey of wilderness visitors ( n = 635) to Sequoia and Kings Canyon National Parks, where they asked visitors to identify the relative importance of different characteristics in defining wilderness character. The survey characteristic “a place without non-native animals” ranked 15th of 19 in defining wilderness character. We believe research is vital to understanding how wildlife affects wilderness character.

Our initial question “How important is wildlife for wilderness character?” and the existing studies treat all wildlife encounters equally, but the range of possible answers is more nuanced. For instance, the perception of the natural quality of wilderness may vary with different types of wildlife, such as seeing a threatened, endangered, or socially important species, a charismatic or flagship species, or an invasive species. Viewing a native wolverine ( Gulo gulo ) in the John Muir Wilderness in California, where none have been seen for nearly a century ( Moriarty et al. 2009 ) may be perceived differently from seeing an introduced brook trout ( Salvelinus fontinalis ) or feral pig ( Sus scrofa ). Similarly, different user groups (e.g., hunters versus hikers) may respond differently to seeing Sierra Nevada bighorn sheep ( Ovis canadensis sierrae ), black bears ( Ursus americanus ), or moose ( Alces alces ) in a wilderness area. We would like to see the National Survey on Recreation and the Environment or a similar instrument ask more comprehensive questions about how different categories of wildlife are perceived by different user groups and how these various categories influence wilderness experiences.

Wildlife viewing is only one way to experience wildlife. There are many ways to be influenced by wildlife encounters in wilderness ( Figure 2 ), and future surveys could examine what specifically about wildlife influences wilderness experience. Wildlife experience ranges from simply knowing a species is present without witnessing it (left side of Figure 2 ) to knowing that wildlife is present as a functional component of the ecosystem (right side of Figure 2 ). For example, it may be enough for some wilderness users to know that lynx ( Lynx canadensis ) exist in the Bob Marshall Wilderness to increase their perception of the natural quality of wilderness character, whereas others may need to see tracks or the actual animal to receive benefits. Similarly, perceptions of the untrammeled and natural qualities of wilderness character may be heightened by seeing bands of native bighorn sheep ( Ovis canadensis ) in the Frank Church-River of No Return Wilderness, Idaho, whereas others may have similar perceptions of wilderness character from seeing only signs (e.g., tracks or feces). Future surveys could examine the specific influences of wildlife on the wilderness experience, which would provide managers with a target of how to preserve wilderness character. We extol surveys that ask the question “How important is wildlife for wilderness character?” However, more informative questions would be, “How important is wildlife for perceptions of the natural quality of wilderness character,” or “How important is wildlife for perceptions of the untrammeled quality of wilderness character?” The ideal would be a survey that could ascertain the relative importance of various wildlife experiences, by different user groups in influencing each quality of wilderness character.

Schematic diagram of a gradient of perception of a wildlife species in a wilderness area. The left portion of the bar represents the cases in which there is no direct human-wildlife interaction, whereas on the right are situations in which not only is wildlife playing a functional role in the ecosystem but also this interaction is observed.

A research agenda for understanding the role of wildlife in wilderness needs to investigate public attitudes about the range of acceptable wildlife management actions at large landscape scales to improve the natural quality of wilderness. Ecosystems within wilderness areas are not static, nor are wildlife populations. Wilderness is influenced by events occurring outside of individual wilderness areas and at temporal and spatial scales much larger than even the largest network of wilderness areas. Changes in regional fire regimes, elimination of source populations of wildlife outside of wilderness, spread of disease, climate change, and drought occur at spatial scales larger than a wilderness area. Yet these forces affect species compositions and demographics of wildlife within wilderness. Most notably, climate change can lead to changes in species compositions in wilderness due to idiosyncratic species responses to shifts in temperature or precipitation ( Thuiller et al. 2005 , Dobrowski et al. 2013 ). The reverse is also true: actions within wilderness areas may affect wildlife on surrounding lands both positively and negatively. What is the role of wilderness managers in offsetting large-scale, human-induced change?

Managers are also increasingly faced with decision tradeoffs in managing for both untrammeled and naturalness within wilderness through proposals such as assisted migration, predator control, wildlife reintroductions, and supplementations. Kammer (2013) suggests that managing with restraints and leaving areas “beyond humans' manipulative reach” takes primacy, but what happens when human-induced change is at the scale of the climate or ecosystem? Watson et al. (2015) explored the topic of managing effects beyond the scale of an individual wilderness. Wilderness visitors to Sequoia and Kings Canyon National Park supported the reintroduction of extirpated native species and the removal of nonnative species to support native species recovery but did not support potential intervention actions to mitigate the effects of climate change. A research agenda for the next 50 years would be incomplete without further examining this issue, which involves diverse scientific, legal, and ethical issues. There is an ongoing, robust debate about ecological restoration actions in wilderness related to species, habitats, and ecological processes (reviewed in Cole and Yung 2010 ). We would like to see more human dimensions-oriented studies to help inform this debate.

The second prong of our wildlife and wilderness research agenda considers how wilderness influences wildlife. Management for primeval character, the minimization of human activity, and the emphasis on maintaining natural conditions has often benefited fish and wildlife populations ( Kershner et al. 1997 , Mittermeier et al. 2003 , Hendee and Mattson 2009 ). For example, the reclamation of old logging roads can reduce human interactions and contact with wildlife, improving the survival of threatened species ( Cole et al. 1997 , McLellan et al. 1999 ). Yet the influence of wilderness character on wildlife is beyond the management of a single wilderness area. Research at multiple temporal and spatial scales is crucial and should include the effects not only of wilderness designation or improved wilderness character on a single species but also on how species interact in ecological communities.

A literature search revealed a paucity of published research on how federal wilderness designation or wilderness character affects species' population growth, vital rates (e.g., survival), or extinction risk. Searches of academic databases (e.g., Google Scholar, Web of Science, Journal of Wildlife Management , and Conservation Biology ) for the terms “wilderness” and “wildlife” or “fish” produced many published papers, but almost exclusively where the term “wilderness” is broadly defined and used as a descriptor of a landscape. Most papers did not differentiate between a federally designated wilderness that adheres to a strict set of laws and rules, and large remote areas that are descriptively called wilderness. Of the few studies that occurred in federally designated wilderness, most were autecological studies of one species in one wilderness (e.g., Koehler and Hornocker 1977 , Etchberger et al. 1989 , Papouchis et al. 2001 , Rominger et al. 2004 , Wasser et al. 2004 , Fraser et al. 2005 , Schoenecker et al. 2015 ). For example, Etchberger et al. (1989) found that human disturbance and the presence of habitat where fire had been excluded were responsible for a decrease in the range size of bighorn sheep from 79.5 to 17.0 mile 2 within Pusch Ridge Wilderness, Santa Catalina Mountains, Arizona. Other autecological studies described changes in animal behavior ( Walker and Marzluff 2015 ) with unknown implications for fitness (e.g., Titus and VanDruff 1981 ). In addition to studies that use wilderness as a general term or were limited to autecological examinations of species in a single wilderness, several studies speculated on the responses of wildlife to wilderness designation based first on principles of wildlife and conservation biology (e.g., Mech et al. 1988 ). These studies assume that the designation of an area as wilderness affords protection for conserving species. Yet, few studies have explicitly evaluated the role of wilderness in wildlife conservation.

We contend that it is important to collect data on wildlife in wilderness for several reasons. First, there are specific wildlife issues related to particular wilderness areas (e.g., Davidson and Knapp 2007 , Burger et al. 2012 ). Rominger et al. (2004) studied cause-specific mortality of translocated bighorn sheep in the Wheeler Peak Wilderness in northern New Mexico. They found that high mountain lion ( Puma concolor ) predation, encouraged by the encroachment of woody vegetation used for stalking cover and increased food subsidies from cattle operations, was the probable cause of the enhanced mortality. Equipped with this knowledge, wilderness managers can discuss the tradeoff between vegetation management, issuing of grazing permits, and the management goal to have bighorn sheep in the area.

Second, we cannot assume that biological, evolutionary, and geographic understandings from outside of wildernesses apply within wildernesses. The emphasis on unmanipulated landscapes may present different evolutionary pressures (called “unnatural selection”) to wildlife populations within wilderness compared with those outside these areas. Harvesting wildlife is allowed in most US federal wilderness areas but may be limited in some locations because of access or because they are part of a National Park that disallows hunting. Coltman et al. (2003) , Allendorf and Hard (2009) , and Douhard et al. (2015) showed that human harvest of desirable wild animals (e.g., large body size in fish or large horns and antlers in some mammals) can change gene frequencies responsible for the trait in the population. To be considered unnatural selection, it is important that this is genetic change and not plasticity or acclimation (i.e., change that is nongenetic and not heritable). For example, the extensive harvest of cod ( Gadus morhua ) off Georges Bank produced “fisheries-induced evolution,” where the age and size at maturity of cod dramatically declined in association with harvest pressure. This is due to a genetic change in the population (e.g., unnatural selection) as the trait has not shifted back after tighter fishing regulations and stock recovery ( Olsen et al. 2004 ). Similarly, Coltman et al. (2003) showed that the average horn length of bighorn sheep declined at Ram Mountain, Alberta, Canada, as a result of the heavy harvest of rams with genes that strongly contributed to horn growth. Wilderness areas can be remote and given their undeveloped mandate are often used less by sportsmen. National Parks, which do not allow hunting (with some notable exceptions) may also act as reservoirs where natural selection can dominate over unnatural selection. The difference may be that National Parks, which encourage development for the enjoyment of people, may be selecting for other behaviors or traits (e.g., reduced fear of humans or increased movement rates) ( Ciuti et al. 2012 ). By minimizing unnatural selection, wilderness can preserve the natural selection process that acts on wild populations and these genetic outcomes.

Last, it is not only evolutionary pressures that may be different in wilderness areas; habitat use and behavior of a species may be altered in wilderness. This may be especially true of species that are sensitive to development or degradation of other elements of wilderness character. Without directly studying how wildlife uses wilderness, we make assumptions about habitat use based on observed behavior and distributions outside of wilderness that may be incomplete ( McKelvey et al. 2008 , Schwartz et al. 2015 ).

Given that there are important reasons to study wildlife in wilderness, why is there such a paucity of studies measuring the role of wilderness in biodiversity and wildlife conservation? We believe this probably results from several contributing factors. The most obvious is that wilderness areas can be difficult to access ( Oelfke et al. 2000 ): systematic surveys are complicated by the lack of roads and other developed infrastructure (i.e., wilderness is defined as “an area of undeveloped federal land”). This leads to increased cost associated with accessing many wilderness locations to collect data. Second, the enacting wilderness legislation does not prioritize using wilderness areas as a scientific baseline for assessing change (see above). This means that wilderness managers may rank other activities over scientific data collection in a landscape not dominated by humans. Third, regardless of the size or accessibility of a wilderness, many research tools conflict with prohibited uses because equipment is mechanized, requires semipermanent or permanent installations, or degrades the untrammeled, natural, or undeveloped qualities of wilderness character ( Franklin 1987 , Landres et al. 2015 ). A common tool used to study habitat use of mammals is a radio or satellite collar placed on an individual animal. It is often perceived that capturing wildlife degrades the untrammeled quality of wilderness character, whereas the presence of the satellite collar on an animal degrades the undeveloped quality of wilderness character ( Landres et al. 2015 ). This can lead to tension between wildlife biologists trying to collect data and wilderness managers trying to preserve wilderness character by minimizing trammeling actions and effects on the natural quality of wilderness character ( Schwartz et al. 2011 ). This tension may hinder interest of wildlife researchers to work in wilderness. Schwartz et al. (2011) show that new nonintrusive and noninvasive research tools (e.g., noninvasive genetic sampling and stable isotope analysis) can substantially reduce conflict and make wildlife work in wilderness more feasible. Fourth, there is the perception that we can effectively extrapolate information on wildlife from outside of wilderness areas to inform decisions within wilderness. As we described above, there are biological reasons why habitat selection, behavior, and genetic composition may be different inside versus outside wilderness. Last, there is the untested hypothesis that wilderness buffers wildlife populations against declines; thus, research in these areas is less critical. This argument erroneously contends that active management is not allowed in wilderness; therefore, there is limited use of information obtained on wildlife in these areas.

Studies on the effect of wilderness on wildlife also may be limited because wilderness is not an ecological variable. Displaying the centroids of the congressionally reserved wilderness areas of the United States on the Omernik (1987) Level 1 and Level 3 Ecoregions of the contiguous United States demonstrates the range of ecosystems and landscapes represented by wildernesses ( Figures 3 and 4 ). Simply stated, not all wildernesses are the same; they are in different ecoregions ( Figure 3 ), have different landscape configurations ( Figure 4 ), and differ on a suite of ecological variables ( Dietz et al. 2015 ). Size, in terms of area and perimeter of wildernesses, vary as well ( Figure 4 ) and need to be considered along with other landscape shape metrics in the design of studies that consider wildlife in wilderness. Wilderness, while a legally and socially important construct, is not necessarily a biological or landscape characteristic meaningful to wildlife (see also Graber 1983 ). We encourage comprehensive studies that stratify by ecoregion, size, edge, and other landscape variables and then ask how wilderness protection acts on critical demographic or genetic parameters associated with a species. Given the large number of federally designated wilderness areas in the United States (762), this type of stratification is possible.

Centroids of the congressionally reserved wilderness areas of the United States plotted on Omernik (1987) Level 1 (left) and Level 3 (right) Ecoregions of the contiguous United States. This figure illustrates the range of Level 1 and Level 3 Ecoregions represented by wilderness areas.

Plot of the distribution of wilderness areas by size (top) and edge or perimeter (bottom). (Data are from www.wilderness.net ; last accessed Sept. 1, 2014).

To advance wilderness-wildlife research, we recommend studies conducted on multiple spatial and ecological scales. We encourage the wildlife biology community to go beyond one wildlife species-one area questions and ask how networks of wildernesses are used by a species (a one-to-many relationship). We also encourage questions on how networks of wilderness areas are being used by a community of species, studying the species themselves, the community, and the interspecific interactions (many-to-many relationship) ( Figure 1 ). In our review, we found no studies that focused on how the NWPS or any portion of this network affects biological diversity, nor have we found studies on how the NWPS affects ecological interactions among trophic levels. Rizzari et al. (2015) identified different trophic interactions among species within marine protected areas versus outside of these areas where fishing is allowed. We encourage similar studies within versus outside of wilderness areas.

We explored our own data to demonstrate the kinds of questions that can be asked with existing information once placed in a wilderness context. Copeland et al. (2010) showed that wolverines, a rare mustelid in the contiguous United States, are dependent on snow for denning. Thus, their distribution can best be predicted by where snow is present in the spring (April 24–May 15). Spring snow not only predicts locations year-round but also gene flow of wolverines across a large space ( Squires et al. 2007 , Schwartz et al. 2009 , Parks et al. 2013 ). In the Rocky Mountains, 28% of wolverine habitat, as mapped by the spring snow association, is in federally designated wilderness areas. Climate change is predicted to reduce spring snow cover and thus the distribution of wolverines ( McKelvey et al. 2011 ). Considering a multiple wilderness-to-single species relationship, we can ask how important will the NWPS (i.e., a network of wilderness areas) become for wolverines in the future given climate change? Using the McKelvey et al. (2011) climate predictions, we project that by 2045, 35% of wolverine habitat in the Rocky Mountains will be in wilderness, and by 2085, 45% of wolverine habitat will be in wilderness. If we include areas used for dispersal (e.g., Schwartz et al. 2009 ), the NWPS becomes even more important for wolverine persistence in the contiguous United States, providing important climate refugia for this species over time ( Figure 5 ).

The increasing importance of wilderness habitat to wolverine in the contiguous United States given the climate change projections detailed in McKelvey et al. (2011) for the contemporary time period (historical reconstruction), 2045, and 2085. The area in blue is correlated to wolverine den detections and snow present in the spring. Wilderness areas are shown in red. Areas where wolverine detections overlap with wilderness areas are shown in dark red.

We urge wildlife biologists to work with biogeographers to understand the gaps in wildlife habitat protection in the United States. The amount of wilderness area protected per ecoregion compared with the total area of the ecoregion indicates that not all areas are equally represented ( Dietz et al. 2015 , Aycrigg et al. 2016 ). Proportionally more northwestern forested mountains are protected than even North American deserts, despite the large total area of deserts in the NWPS ( Figure 6 ). Similarly, eastern temperate ecoregions are widespread, but proportionally underrepresented as an ecotype in the NWPS. This may translate to vulnerability of entire suites of wildlife species not adequately represented or protected by wilderness areas. Recent analyses that have included other types of land protection have identified similar gaps in biodiversity conservation protection ( Jenkins et al. 2015 ). We recommend that wildlife researchers explore the impact of the NWPS as a whole in conserving wildlife and biodiversity, especially in light of climate change and other large human-driven stressors.

The sum of wilderness area per ecoregion compared with the total area of the ecoregion (using Omernik 1987 Level 1 Ecoregions). The cluster of points in the lower left quadrant of this figure are the Northern Forests, Tropical Wet, Temperate Sierras, Southern Semiarid Highlands, Taiga, and Mediterranean California Ecoregions. N.A. Deserts is an abbreviation for North American Deserts and NW Forested Mountains is an abbreviation for Northwestern Forested Mountains.

Last, our wildlife-wilderness research agenda encourages going beyond correlation and conducting studies to understand the mechanistic relationship between qualities of wilderness character and wildlife. We recommend studies on how the qualities of wilderness character (natural, solitude, undeveloped, and untrammeled) affect wildlife or wildlife interactions. For instance, there are qualities of wilderness character that directly influence the ecology of a species and can be quantified. That is, there may be common mechanisms that impact both wilderness character and wildlife simultaneously, such as the presence of roads. To find these common variables, we can begin by examining a relationship between the components of wilderness character and a population's growth rate or fitness components (e.g., survival of juveniles or number of offspring produced in a lifetime). Is the population response (either growth rate or fitness) relatively impervious to development in a wilderness area and sensitive to naturalness ( Figure 7 )? Imagine, for example, populations of mountain yellow-legged frogs ( Rana muscosa ) in the Sierra Nevada of California. It is well established that human use in and near streams can negatively affect the survival and recruitment of eggs and larvae, as well as impact the survival of adults ( US Fish and Wildlife Service 2012 ). Similarly, nonnative trout introductions into previously fishless lakes have drastically reduced the geographic range of many amphibian species ( Knapp et al. 2007 ). If we evaluated population growth rate relative to an index of naturalness and an index of development for multiple wilderness areas, we can determine whether species are responding more to one type of wilderness character versus another ( Figure 7 ). This approach provides an understanding of mechanisms needed for biodiversity and wildlife conservation in association with wilderness protection.

Schematic diagram showing possible research avenues that go beyond examining the impact of one wilderness area on one population of wildlife for one species. This diagram shows a hypothetical relationship between an index of two components of wilderness character (naturalness and developed) and either a population's growth rate or a fitness measure (e.g., survival of juveniles or number of offspring produced in a lifetime). In this hypothetical example, the population response (either growth rate or fitness) is relatively impervious to levels of development in a wilderness area, whereas it is sensitive to an index of naturalness.

We wrote this article surrounding the 50th anniversary of the Wilderness Act with the hopes of establishing a multiscale research agenda to help set the stage for the wilderness-wildlife research for the next 50 years. Our research agenda distinguishes the effects that perceptions of wildlife have on wilderness character versus the impact that wilderness character has on wildlife populations, species, and communities. Both parts of this research agenda are of equal importance in our estimation. Understanding how perceptions of wildlife contribute to wilderness character is essential to the legal mandate to preserve it. Not all wildernesses started out as pristine, untouched landscapes, but rather many have been recently designated and are only now beginning to be dominated by dynamic, natural processes. Restoring naturalness may mean facilitating the recovery of threatened or endangered species. Even just the known presence of rare species can increase the perception of the natural quality of wilderness quality. We support rigorous social science studies that investigate how different types of human-wildlife experiences influence wilderness character.

Our research agenda also examines how wilderness character affects wildlife by encouraging studies that go beyond the effect of one wilderness on one wildlife species. We hope that future studies examine how the NWPS, a network of wilderness areas, influences one species or an entire ecological community. Over the past decades, there has been increased perception in the wildlife and conservation biology arena that protection of one patch is often inadequate for protection of one species and that management of the entire landscape matrix, across multiple jurisdictions and management plans, is critical for conservation ( Bailey 2007 ). Our research agenda supports this concept and pushes research that explores wilderness in the context of global biodiversity conservation.

Acknowledgments: We thank the organizers of the National Wilderness Conference for hosting the Wildlife and Wilderness symposium from which this article was created. We thank Sean Parks for his work on wolverine modeling and James Tricker for his guidance on wilderness representation by ecosystem. We also thank Susan Fox for encouraging us to work on this topic and Peter Landres for providing comments on an early draft of this manuscript. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.

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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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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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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.

Author information

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

Authors and Affiliations

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

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Computer Science Department, University of Münster, Münster, Germany

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

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Computer Science Department, University of Bristol, Bristol, UK

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

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

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

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

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

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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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Thesis: Manipulation of food quality and quantity by black brant geese Advisor: Jeff Black

Jeanne Hammond , 2008

Thesis: Nest predation at the Cosumnes River Preserve: Is an introduced predator, the black rat (Rattus rattus), limiting songbird breeding productivity? Advisor: Matthew Johnson

Amy Roberts , 2008

Thesis: Bat use of redwood basal hollows with increasing isolation in contiguous, remnant, and legacy redwood forest stands. Advisor: Matthew Johnson

Dominic Bachman , 2008

Thesis: Aleutian Cackling Goose Habitat Management [co-advised with Dr. Jeff Black]. Advisor: Matthew Johnson

Emily Bjerre  , 2007

Thesis: Optimal grit : investigating grit acquisition and site use by black brant Advisor: Jeff Black

Eric Wood , 2007

Thesis: Predictive modeling of focal bird species in Central Sierra Nevada Foothill woodlands. Advisor: Matthew Johnson

Rebecca Green , 2007

Thesis: Distribution of forest carnivores and evaluation of habitat models for American Marten in Sequoia and Kings Canyon National Parks. Advisor: Matthew Johnson

Amy Leist , 2007

Thesis: The importance of fruit to Swainson’s thrushes, Catharus ustulatus, at stopover sites during fall migration: A field test of plasma metabolite analysis. Advisor: Matthew Johnson

Jherime Kellermann , 2007

Thesis: Assessing an economic incentive for bird-friendly coffee cultivation in Jamaica, West Indies. Advisor: Matthew Johnson

Jim Tietz , 2006

Thesis: Stopover ecology and habitat selection in fall migrant Swainson’s Thrushes (Catharus ustulatus) along the northern California coast. Advisor: Matthew Johnson

Chris Tonra , 2006

Thesis: Hatching Synchrony in Brown-Headed Cowbirds: The influence of host density, chick gender, and habitat. Advisor: Matthew Johnson

Anne Mini  , 2005

Thesis:  Energetic expenditure of Aleutian Canada geese experiencing different management regimes. Advisor: Jeff Black

Jeff Moore  , 2003

Thesis: Distribution of spring staging black brant Branta bernicla nigricans in relation to feeding opportunities on south Humboldt Bay, California Advisor: Jeff Black

Ken Griggs  , 2003

Thesis:  Parental investment in Western Canada geese. Advisor: Jeff Black

Derek Lee  , 2002

Thesis:  Spring stopover of black brant geese at Humboldt Bay, CA. Advisor: Jeff Black

John Quinn  , 2000

Thesis:  Relationship between red-breasted geese and peregrine falcons during the breeding season, Oxford University, England. Advisor: Jeff Black

Friederike Woog , 1999

Thesis:  Dominance and dispersal of Hawaiian geese, University of Hohenheim, Stuttgart, Germany. Advisor: Jeff Black

Glynn Young , 1995

Thesis: The systematic position of Meller's Duck: a behavioural approach, University of Kent, England. Advisor: Jeff Black

Friederike Woog , 1993

Thesis:  Ecology of Hawaiian geese in habitats of Haleakala National Park, Maui, University of Hohenheim, Stuttgart, Germany. Advisor: Jeff Black

Sharmila Choudhury  , 1992

Thesis:  Mate choice in barnacle geese, Oxford University. Advisor: Jeff Black

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wildlife conservation Recently Published Documents

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How much Fear? Exploring the Role of Integral Emotions on Stated Preferences for Wildlife Conservation

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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Trends in Wildlife Research: A Bibliometric Approach

• First Online: 26 April 2016

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• Beatriz Arroyo 7 ,
• Rafael Mateo 7 &
• Jesús T. García 7  

Part of the book series: Wildlife Research Monographs ((WIREMO,volume 1))

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“Wildlife” is a word that has different meanings for different people and in different contexts. In fact, many people use it with an unconscious attachment to a particular meaning, not necessarily aware of it being used differently by other people. According to the Oxford Advanced Learner’s Dictionary, wildlife means “ the native fauna ( and sometimes flora ) of a region ”. In many cases, however, this “native fauna” is, consciously or unconsciously, limited to vertebrate species, and it sometimes excludes fish (as implicitly implied in the names of the “Fish and Wildlife” societies and services in the US). Conversely, fish (at least fresh-water fish) is considered as “wildlife” in many countries, as they are part of the same ecosystems and their management is analogous. Likewise, butterflies and other invertebrates are usually included in “wildlife inventories” at least in the UK. Wildlife is also used as a term for “ undomesticated animals living in the wild ” (American Heritage Dictionary) or “ animals and plants that grow independently of people , usually in natural conditions ” (Cambridge Advance Learner’s Dictionary & Thesaurus). Here, the emphasis is put in the “untamed” quality of species considered as wildlife. Traditionally, “wildlife” includes all game species in the US, as hunting represents, in the social discourse there, a way to approach wilderness (Good 1997). Indeed, according to the Webster’s Dictionary, wildlife means “ wild animals , especially those hunted for food or sport ”. On the other hand, game species are, at least in Europe, intensively managed, so they do not “grow independently of people”, and some voices claim that, in these circumstances, they are livestock rather than wildlife (Díaz et al. 2009). In some European languages, there are different words for game species and non-game species, and only the latter include some reference to “wild” in the non-English term (e.g. faune sauvage vs gibier in French, or fauna silvestre vs fauna cinegética or caza in Spanish). The recent change of name of the “Game Conservancy Trust” in the UK to the “Game and Wildlife Conservancy Trust” somehow also confronts both terms, as if they were, if not antonyms, at least dissimilar or complementary.

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Arroyo, B., Mateo, R., García, J.T. (2016). Trends in Wildlife Research: A Bibliometric Approach. In: Mateo, R., Arroyo, B., Garcia, J. (eds) Current Trends in Wildlife Research. Wildlife Research Monographs, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-27912-1_1

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Literature of Wildlife

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Introduction

Wildlife literature is part of the larger scientific literature and is composed of applied research in wildlife and basic research in related disciplines. Scientific literature is the principal medium for communicating the results of scientific research and represents a permanent record of the collective achievements of the scientific community. This scientific knowledge base is composed of the individual "end products" of scientific research and continues to expand as new research builds on earlier research.

Scientific literature is divided into two basic categories - "primary" and "secondary". Publications that report the results of original scientific research constitute the "primary" literature and include journal papers, conference papers, monographic series, technical reports, theses, and dissertations. The "primary" literature is eventually compacted into "secondary" sources which synthesize and condense what is known on specific topics. These include reviews, monographs, textbooks, treatises, handbooks, and manuals.

Availability of scientific literature varies depending upon its publication format. Some formats are widely available, e.g., journal papers, while others have limited distribution and are difficult to identify and acquire. This "gray literature" commonly includes technical reports, theses, and dissertations.

Scientific Research/Publication Cycle

The following chart illustrates common steps involved in the scientific research process and the publication sequence of "primary" and "secondary" literature.

Wildlife Serials

Wildlife serials can be grouped into the following three categories:

• Journals -  regularly  issued publications that contain papers reporting the results of scholarly research in the discipline
• Magazines and Newsletters - contain popular reports on developments in the discipline
• Monographic series -  irregularly  issued publications that, in most cases, contain the results of scholarly research

Copies of papers contained in serials that are not available in the  Library can be requested through the Library's  Interlibrary Loan Service .

Since most wildlife indexes and "Reference Cited" lists abbreviate serial titles the following sources can help you find the unabbreviated serial title:

• Articles and Databases: Wildlife  databases - click "More Details" for available abbreviation lists
• Journal Title Abbreviations  lists both general sources and more specific sources in the sciences

For a more expanded distinction between journals and magazines see  Journals - Scholarly or Popular?

Journals . The research paper published in a scientific journal represents the most important "primary" source of information for the wildlife scientist and manager. Papers published in journals generally go through a "peer review" process before acceptance and publication. Presently there are over 25,000 peer reviewed scientific journals that being published. Seventy-five percent of the wildlife research literature is published in this format.

Databases listed in  Articles and Databases: Wildlife  can be used to find individual research papers by author, subject, taxonomic category, habitat, time period, chemical substance, or geographic area. In addition many journal publisher websites now maintain a searchable database of articles that have been published in their journals.

The following list contains many of the print and online fulltext journals available through the HSU Library which publish research of interest to wildlife scientists and managers. Check the  Journal and Newspaper Finder  for specific holdings and call number and for other titles that are not on this list.

Magazines and Newsletters . Articles appearing in these publications tend to be popular in format and scope. They may contain news and perspectives of professional societies and environmental organizations, report on research published in scholarly journals, report on environmental problems and new political initiatives, or contain articles aimed at the layperson.

African Wildlife  Alaska Fish & Game  American Birds  Arctic Birds: Newsletter of International Breeding Conditions Survey   Arizona Wildlife Views  Audubon  Birdscapes: News from the International Habitat Conservation Partnerships  (US Fish & Wildlife Service) (print copy also available in Docs I 49:100/4)  Black Lechwe  Bokmakierie  British Birds  California Biodiversity News   Colorado Outdoors  Conservationist  Endangered Species Bulletin (US Fish & Wildlife Service) (see also  Issues and Articles Online )  Field and Stream  Fish & Wildlife News  (US Fish & Wildlife Service)  Game Bird Breeders and Conservationists Gazette  High Country News Illahee: Journal for the Northwest Environment  International Wildlife  Living Bird  Louisiana Conservationist  Missouri Conservationist  Montana Outdoors  Natural History  Naturalist  Nature Conservancy Magazine  Nebraskalands  New Mexico Wildlife  Outdoor News Bulletin  (print copy available in SK 351.08)  Oregon Wildlife  Outdoor California  Outdoor Life  Outdoor News Bulletin  Washington Wildlife  Wyoming Wildlife  Zoonooz

Monographic Series.  While the results of most wildlife research are published in journals, perhaps 10% of the research is published in individual issues of monographic series. Longer contributions resulting from scientific research are often published in this format. Monographic series typically have the following characteristics:

• They are published by government agencies, major universities or professional organizations.
• Individual issues are collectively published in a continuing series which has a distinctive name. Typical names include  Bulletin ,  Special Report ,  Special Paper ,  Technical Report , and  Technical Paper .
• Individual issues in the series are consecutively numbered, e.g. Technical Paper No. 36.
• Each issue has a distinctive author and title.
• There is no regular publication schedule in contrast to a journal.
• Individual issues contain the completed results of a single research project.
• Individual issues range from several pages to several hundred pages.

A typical example is:

Wheeler, W.E., R.C. Gatti, & G.A. Bartlett.(a) 1984.  Duck Breeding, Ecology and Harvest Characteristics on Grand River Marsh Wildlife Area .(b) Wisconsin Department of Natural Resources(c) Technical Bulletin(d) No. 145(e). where a=individual author; b=individual title; c=series author; d=series title; e=series number

To locate monographic series in the HSU Library you need to consult the following sources:

• For federal and California State agency series use the catalogs and indexes listed in  Natural Resources Agency Government Documents and Reports . They are are physically located in the Documents Collection.
• For all other monographic series use the library catalog or the  Journal and Newspaper Finder . The key is to look for the series of which an individual issue is a part. You must look under either the series title ( Technical Bulletin  in the above example)  or  the sponsoring organization ( Wisconsin Department of Natural Resources  in the above example). In the above example there is  no  listing under the author "Wheeler..."" or the title "Duck Breeding..." since these are the author and title of the individual issue. The catalog entry will note each number held by the Library in a particular series, e.g., #1-25, 26-30, 35-

As with individual journal papers databases included in  Articles and Databases: Wildlife  also can be used to identify research published in this format.

The following monographic series of interest to wildlife are found in the regular bookstacks of the Library.

• American Ornithologists' Union.  Ornithological Monographs
• Special Report
• Technical Report
• Occasional Paper
• Progress Notes
• Report Series
• Division Report
• Outdoor Facts: Game Information Leaflet
• Technical Publication
• Field Museum of Natural History.  Fieldiana. Zoology
• Finnish Game & Fisheries Research Institute. Game Division.  Finnish Game Research
• Idaho Department of Fish and Game.  Wildlife Bulletin
• Biological Notes
• U.S. Pacific Flyway Study Committee.  Pacific Flyway Waterfowl Report
• University of Alaska.  Biological Papers
• University of California.  University of California Publications in Zoology
• Miscellaneous Publications
• Occasional Publications
• University of Michigan. Museum of Zoology
• Occasional Papers
• Utah Division of Wildlife Resources.  Publications
• Wildlife Society.  Wildlife Monographs
• Wildlife Tust.  Wildfowl
• Wisconsin. Department of Natural Resources.  Technical Bulletin

Theses and Dissertations

The outcome of graduate study conducted at universities is commonly a master's thesis or doctoral dissertation. In addition to the formal thesis or dissertation, research results are often communicated in other "primary" literature formats, such as the journal paper.

You can find and acquire 1) CPH masters theses; and 2) theses and dissertations produced at other universities that are available in other libraries and on the Internet. In addition the following are specialized directories and databases to theses and dissertations in wildlife:

• Bibliography of Wildlife Theses  (SK 353 M66) Lists U.S. and Canadian theses and dissertations completed between 1900 and 1968.
• Wildlife and Ecology Studies Worldwide  (HSU users only) Iindexes U.S. dissertations and theses in wildlife.

Conference Papers

Papers presented at national and international conferences, symposia, and workshops are another source of "primary" scientific information in wildlife. For many conferences the presented papers are eventually published in a "proceedings" or "transactions" volume. Papers with no published proceedings may be refined and reworked for formal publication in a journal. Proceedings available are listed in the library catalog under both author (generally the name of the conference, individual editor or sponsoring organization) and title.

Some databases included in  Articles and Databases: Wildlife  provide subject, taxonomic, geographic, and author access to individual conference papers.

Following are some of the regularly recurring wildlife conferences received by the Library. Check the library catalog for call numbers and specific holdings. In addition there are many other one-time specialty conferences listed in the catalog.

• Conference on Wetlands Restoration and Creation.  Proceedings
• Desert Bighorn Council.  Transactions
• Eastern Wildlife Damage Control Conference.  Proceedings
• Federal-Provincial Wildlife Conference.  Transactions
• International Association of Fish and Wildlife Agencies.  Proceedings
• International Conference on Wildlife Biotelemetry.  Proceedings
• International Conference on Bear Research & Management.  Bears: Their Biology & Management
• International Congress of Game Biologists.  Transactions
• International Ornithological Congress.  Proceedings
• International Symposium on Biotelemetry.  Proceedings
• International Waterfowl Symposium.  Proceedings
• Interstate Antelope Conference.  Transactions
• National Quail Symposium.  Proceedings
• National Wild Turkey Symposium.  Proceedings
• North American Prairie Conference.  Proceedings
• North American Wildlife and Natural Resources Conference.  Transactions
• Northern Wild Sheep and Goat Council.  Proceedings
• Southeastern Association of Fish and Wildlife Agencies.  Proceedings
• Vertebrate Pest Conference.  Proceedings
• Western Association of Fish and Wildlife Agencies.  Proceedings
• Western Black Bear Workshop.  Proceedings
• Western Foundation of Vertebrate Zoology.  Proceedings
• Western States and Provinces Elk Workshop.  Proceedings
• Wildlife Society. Western Section.  Transactions  (continues Cal-Neva Wildlife)
• World Conference on Birds of Prey and Owls.  Proceedings

Monographs (Books)

Monographs generally are not part of the "primary" literature of science, but rather are "secondary" sources of information. They may be either scholarly contributions or popularizations on specific topics. Through scholarly monographs the "primary" literature on specific topics is condensed, summarized or reviewed. Most include references back to the "primary" literature. They may take the format of textbooks, treatises, taxonomic works, or a multitude of reference works, such as encyclopedias or handbooks. 

• << Previous: Wildlife Subject Directories/ Portals/Gateways
• Next: Journal of Wildlife Management Citation Style >>

Frontiers | Science News

• Science News

Research Topics

Biodiversity loss: three research topics revealing threats and solutions.

The planet is demanding a reset in our interactions with nature. Protecting and restoring biodiversity is no longer optional because when nature suffers, so do we .

According to United Nations data, "current negative trends in biodiversity and ecosystems will undermine progress towards 80% of the assessed targets of eight Sustainable Development Goals."

As a result, the theme for this year’s International Day for Biological Diversity is ’Be part of the Plan’, a call to action for everyone to support the implementation of the Kunming-Montreal Global Biodiversity Framework , also known as the Biodiversity Plan.

In light of the crucial role of biodiversity to the health of our planet, we have listed three of our most impactful Research Topics on the causes and consequences of biodiversity loss.

All articles are openly available to view and download.

1 | Aquatic One Health—The Intersection of Marine Wildlife Health, Public Health, and Our Oceans

33,400 views | 10 articles

This Research Topic provides insights into marine wildlife and aquaculture disease processes, conditions, and health issues. It also demonstrates the potential to influence public health within the One Health framework.

The interrelatedness of environmental, animal, and public wellbeing form the basis of the 'One Health' concept, a framework to guide research and conservation efforts by studying not only animal health in isolation, but also in the context of public and environmental health.

Humankind's past and present use of ocean ecosystems as waste sinks has had significant, wide-ranging, and negative effects on marine life and human health, making this topic highly relevant to the mission of biodiversity preservation.

View Research Topic

2 | Ethnofood Chemistry: Bioactive Components in Unexploited Foods from Centres of Biodiversity

45,000 views | 11 articles

A Research Topic looking at ethno plant foods from centers of biodiversity -Africa, Asia and Australia, North, and Central America, South America, Europe, and Central Asia- with bioactive components of nutritional and health value.

Ethnofoods—traditional foods—originate from the heritage and culture of an ethnic group that uses their knowledge of local plants and animal sources. They are also unexploited and underutilized by the wider community worldwide.

This topic highlights the importance of incorporating ethno-plant foods into nutrition intervention programs globally to combat hidden hunger and provide nutrition and food security. Furthermore, it contributes to demonstrating the possibility of developing sustainable food systems.

3 | Community Series in the Wildlife Gut Microbiome and Its Implication for Conservation Biology, Volume II

53,100 views | 21 articles

This Research Topic dives into the potential connection between gut microbiome and conservation biology. Microbiome studies can increase our understanding of non-native species invasion, host response to pathogens and chemical contamination, and host ability to tolerate climate change.

The animal gut microbiota can be beneficial in many ways, including dietary supplementation, host immune function, and behavior. The microbiomes of animals affect host fitness, population characteristics such as demography, and health status, as well as adaptability. For example, the fitness effects of gut microbiomes on wild animals may have important implications for the conservation and management of species.

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Protecting wildlife begins with understanding how best to counter wildlife crimes

Senior Research Assistant, Canadian Centre for Evidence-Based Conservation, Carleton University

Senior Research Scientist, Canadian Centre for Evidence-Based Conservation, Carleton University

Disclosure statement

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

Carleton University provides funding as a member of The Conversation CA.

Carleton University provides funding as a member of The Conversation CA-FR.

View all partners

Global biodiversity is declining , and human activities are mainly to blame.

Indeed, 96 per cent of the world’s total remaining mammalian biomass — the combined weight, or mass, of mammal organic life — consists of either humans or our domesticated animals .

Every day across the world, conservation organizations, community members, conservation scientists and law enforcement authorities work tirelessly to counter this biodiversity decline. These actions can take the form of community-based patrols or enforcing regulations, such as in the case of preventing illegal harvest or patrolling efforts to deter or arrest poachers .

At the more extreme end, law enforcement officials and investigative journalists have even worked to break-up a global ring of individuals who paid to take part in the torture, and eventual murder, of baby monkeys .

These actions are broadly called counter-wildlife crime interventions .

Read more: Why the British Columbia Conservation Officer Service should be designated as a provincial police service

Given the rapidly narrowing window to reverse dramatic biodiversity declines around the world, and the finite resources available to conduct conservation activities, it is important to know what types of conservation interventions work and which don’t work.

Our work at the Canadian Centre for Evidence-Based Conservation (CEBC) — in collaboration with staff from the United States Fish & Wildlife Service (USFWS) and colleagues with experience in wildlife crime and conservation — uses a mixture of evidence synthesis and “ systematic mapping ” to provide these vital insights.

Our work used a systematic mapping approach to summarize current research addressing the effectiveness of counter-wildlife crime interventions for conserving African, Asian and Latin American wildlife directly threatened by exploitation.

The effectiveness of interventions was viewed in terms of whether they could be linked to biological recovery (such as in increased abundance or biomass) or to threat reduction outcomes (such as fewer poaching incidents). Below we share our findings.

Where are counter-wildlife crime actions taking place?

From our synthesis of 530 studies, we found that most (81 per cent) concerned Africa and Asia, with relatively fewer (13 per cent) in Latin America. This geographical imbalance may be due, in part, to a language bias on our part, as we only considered English language articles, and not Spanish ones.

However, other studies have also noted a lack of funding and data for counter-wildlife crime investigations and interventions in Latin America .

In addition, most studies focused on the most popular and charismatic species, such as African and Asian elephants (16 per cent) and wild cats (14 per cent), followed by turtles and tortoises (11 per cent).

Evaluating interventions

Put simply, the effectiveness of most counter-wildlife crime interventions have not been rigorously evaluated.

We found that around 90 per cent of studies evaluating counter-wildlife crime interventions only measured outcomes after an intervention was implemented. This is realistic, considering the way conservation operates in the real-world with funding often providing for a short time frame to operate. However, it is also largely ineffective in determining a causal relationship.

Read more: Pangolins in Africa: expert unpacks why millions have been traded illegally and what can be done about it

We found several knowledge gaps that would benefit from more attention and research.

More efforts are required to understand the effectiveness of counter-wildlife crime interventions in Latin America. Additionally, we found that current research on the topic is lacking for plants, birds, and reptile species.

Moreover, research into the effectiveness of interventions that aim to protect wildlife before they are exploited, rather than interventions aimed at detecting or disrupting illegal wildlife trade, are sorely needed.

Finally, there are critical gaps in our knowledge on the outcomes of counter-wildlife crime efforts at the population and species level (for example, ultimate conservation targets such as wildlife abundance and biomass).

Why is this research needed?

Our work highlights where current research efforts have been focused. We also show where we need to direct future research attention. The bottom line is that we need to improve testing of what conservation tools are most effective.

Ask yourself, would you swallow a pill if you knew that medicine hadn’t been clinically tested for safety and effectiveness? Probably not! And why should wildlife conservation be any different?

Our findings force us to confront some difficult questions about the assumptions made when investing in a counter-wildlife crime intervention. Chief among these is just how unreliable the evidence is that routinely applied interventions actually work. That is not to say that counter-wildlife crime interventions don’t work, but rather that we’re working off rules of thumb instead of evidence, which risks us investing in ineffective interventions.

Jen Miller, a program officer with the USFWS’ Combating Wildlife Trafficking Program and a co-author on the study , said to the Canadian Centre for Evidence-Based Conservation:

“These findings are invaluable feedback to donor agencies like USFWS that contribute to projects combating wildlife trafficking. This flashing red light of alarm could lead us to a transformational moment. This isn’t just a call for more research — it’s a wake-up call to roll out a different model of conservation, where we implement interventions while simultaneously testing their effectiveness.”

Our research suggests it’s time we start rigorously testing our conservation tools to ensure we’re responsibly applying solutions that protect wildlife, people and the planet we all call home.

• Climate change
• Conservation
• Biodiversity
• Biodiversity loss
• anti-poaching
• Wildlife Crime

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Protecting wildlife begins with understanding how best to counter wildlife crimes

by Lisa Kelly and Trina Rytwinski, The Conversation

Global biodiversity is declining , and human activities are mainly to blame.

Indeed, 96% of the world's total remaining mammalian biomass—the combined weight, or mass, of mammal organic life—consists of either humans or our domesticated animals .

Every day across the world, conservation organizations, community members , conservation scientists and law enforcement authorities work tirelessly to counter this biodiversity decline. These actions can take the form of community-based patrols or enforcing regulations, such as in the case of preventing illegal harvest or patrolling efforts to deter or arrest poachers .

At the more extreme end, law enforcement officials and investigative journalists have even worked to break-up a global ring of individuals who paid to take part in the torture, and eventual murder, of baby monkeys .

These actions are broadly called counter-wildlife crime interventions .

Given the rapidly narrowing window to reverse dramatic biodiversity declines around the world, and the finite resources available to conduct conservation activities , it is important to know what types of conservation interventions work and which don't work.

Our work at the Canadian Center for Evidence-Based Conservation (CEBC)—in collaboration with staff from the United States Fish & Wildlife Service (USFWS) and colleagues with experience in wildlife crime and conservation—uses a mixture of evidence synthesis and " systematic mapping " to provide these vital insights.

Our work used a systematic mapping approach to summarize current research addressing the effectiveness of counter-wildlife crime interventions for conserving African, Asian and Latin American wildlife directly threatened by exploitation.

The effectiveness of interventions was viewed in terms of whether they could be linked to biological recovery (such as in increased abundance or biomass) or to threat reduction outcomes (such as fewer poaching incidents). Below we share our findings.

Where are counter-wildlife crime actions taking place?

From our synthesis of 530 studies, we found that most (81%) concerned Africa and Asia, with relatively fewer (13%) in Latin America. This geographical imbalance may be due, in part, to a language bias on our part, as we only considered English language articles, and not Spanish ones.

However, other studies have also noted a lack of funding and data for counter-wildlife crime investigations and interventions in Latin America .

In addition, most studies focused on the most popular and charismatic species, such as African and Asian elephants (16%) and wild cats (14%), followed by turtles and tortoises (11%).

Evaluating interventions

Put simply, the effectiveness of most counter-wildlife crime interventions have not been rigorously evaluated.

We found that around 90% of studies evaluating counter-wildlife crime interventions only measured outcomes after an intervention was implemented. This is realistic, considering the way conservation operates in the real-world with funding often providing for a short time frame to operate. However, it is also largely ineffective in determining a causal relationship.

We found several knowledge gaps that would benefit from more attention and research.

More efforts are required to understand the effectiveness of counter-wildlife crime interventions in Latin America. Additionally, we found that current research on the topic is lacking for plants, birds, and reptile species.

Moreover, research into the effectiveness of interventions that aim to protect wildlife before they are exploited, rather than interventions aimed at detecting or disrupting illegal wildlife trade, are sorely needed.

Finally, there are critical gaps in our knowledge on the outcomes of counter-wildlife crime efforts at the population and species level (for example, ultimate conservation targets such as wildlife abundance and biomass).

Why is this research needed?

Our work highlights where current research efforts have been focused. We also show where we need to direct future research attention. The bottom line is that we need to improve testing of what conservation tools are most effective.

Ask yourself, would you swallow a pill if you knew that medicine hadn't been clinically tested for safety and effectiveness? Probably not! And why should wildlife conservation be any different?

Our findings force us to confront some difficult questions about the assumptions made when investing in a counter-wildlife crime intervention. Chief among these is just how unreliable the evidence is that routinely applied interventions actually work. That is not to say that counter-wildlife crime interventions don't work, but rather that we're working off rules of thumb instead of evidence, which risks us investing in ineffective interventions.

Jen Miller, a program officer with the USFWS' Combating Wildlife Trafficking Program and a co-author on the study , said to the Canadian Center for Evidence-Based Conservation:

"These findings are invaluable feedback to donor agencies like USFWS that contribute to projects combating wildlife trafficking. This flashing red light of alarm could lead us to a transformational moment. This isn't just a call for more research—it's a wake-up call to roll out a different model of conservation, where we implement interventions while simultaneously testing their effectiveness."

Our research suggests it's time we start rigorously testing our conservation tools to ensure we're responsibly applying solutions that protect wildlife, people and the planet we all call home.

Provided by The Conversation

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The Macroeconomic Impact of Climate Change: Global vs. Local Temperature

This paper estimates that the macroeconomic damages from climate change are six times larger than previously thought. We exploit natural variability in global temperature and rely on time-series variation. A 1°C increase in global temperature leads to a 12% decline in world GDP. Global temperature shocks correlate much more strongly with extreme climatic events than the country-level temperature shocks commonly used in the panel literature, explaining why our estimate is substantially larger. We use our reduced-form evidence to estimate structural damage functions in a standard neoclassical growth model. Our results imply a Social Cost of Carbon of $1,056 per ton of carbon dioxide. A business-as-usual warming scenario leads to a present value welfare loss of 31%. Both are multiple orders of magnitude above previous estimates and imply that unilateral decarbonization policy is cost-effective for large countries such as the United States.

Adrien Bilal gratefully acknowledges support from the Chae Family Economics Research Fund at Harvard University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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Mentioned in the News

More from nber.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

Researchers investigate mercury concentrations in fish and wildlife on a global scale

A paper, titled " Global Mercury Concentrations in Biota: Their Use as a Basis for a Global Biomonitoring Framework " and published in the journal Ecotoxicology , describes for the first time currently available mercury data for fish and wildlife on a global scale. Data from the peer-reviewed literature, compiled in the Global Biotic Mercury Synthesis (GBMS) database of the Biodiversity Research Institute (BRI), will help inform worldwide efforts to reduce the impact of mercury pollution on people and the environment.

"An important provision of the Minamata Convention on Mercury is to monitor and evaluate the effectiveness of the adopted measures and their implementation," says David Evers, Ph.D., BRI's executive director and chief scientist, and lead author on the paper.

"During the negotiations for the Convention, we recognized that capturing mercury data into a central location was key to evaluating how effective the treaty could be. We knew that a global database that provided a standardized and comprehensive platform for understanding mercury concentrations at spatial and temporal scales would be critical for establishing a biomonitoring framework that would be needed to track mercury concentrations in biota worldwide."

Since 2013, BRI has been developing the GBMS database, which currently contains more than 588,000 data points from individual organisms sampled from more than 4,100 locations in a total of 139 countries. The paper represents the work of 32 researchers collecting data over the course of more than a decade from 1,700 peer-reviewed scientific published papers.

"The breadth and depth of this endeavor cannot be overstated," says Luis Fernandez, Ph.D., executive director of the Center for Amazonian Scientific Innovation (CINCIA), research professor of biology at Wake Forest University, and co-author on the paper.

"Because of the extensive number of individuals, populations, species, and families of biota adversely impacted by methylmercury, researchers can now identify locations in the world where the most sensitive ecosystems overlap with important biodiversity areas and which species are at most risk. This information is critical in the race to prevent loss of biodiversity."

Major findings

• Mercury contamination is ubiquitous in global marine and freshwater.
• Mercury concentrations in freshwater and marine fish, sea turtles, birds, and marine mammals vary greatly by species—best indicators for mercury biomonitoring are identified for all continents and ocean basins.
• Based on over 1,700 references and nearly 600,000 data points, mercury body burdens regularly exceed adverse effect levels across freshwater and marine fish, birds, and marine mammals.
• Data gaps of biotic mercury data for freshwater and terrestrial ecosystems are largest in Africa, Asia, and Australia; for marine ecosystems, Southern Hemisphere Ocean basins have the fewest data.
• Many potential food items, especially certain fish and marine mammal species, often contain methylmercury concentrations that exceed safe levels for human.
• Marine fish of interest and greatest risk to human health include the larger species of tuna, billfish, sharks, barracuda, and some grouper and mackerel species.
• A global meta-analysis demonstrates that over two-thirds of tuna, billfish, shark, and toothed marine mammal species exceed food safety levels for people; geographic areas of concern for healthy fish consumption include the Mediterranean Sea and freshwater systems in South America and parts of North America.
• It is feasible to generate cost-efficient and reliable biomonitoring approaches at geographic scales of interest that can be integrated with existing local and regional mercury biomonitoring networks.
• There is an urgency to monitor and assess the influence of methylmercury on biota because of the potential adverse impacts to biological diversity.

Article 17 of the Minamata Convention on Mercury provides that Parties facilitate the exchange of information including scientific, technical, economic, and legal information concerning mercury and mercury compounds, including toxicological, ecotoxicological and safety information.

"BRI's global mercury database is expected to provide scientific backing in planning and taking stock of national and international measures to control mercury supply and trade, reduce the use, emissions and releases of mercury, raise public awareness, and build the necessary institutional capacity," says Eisaku Toda, senior program officer, Secretariat of the Minamata Convention on Mercury.

In collaboration with the Minamata Convention, BRI has been working to build the GBMS database through studies across the world and shares this information online. "Ultimately, understanding the biotic response to methylmercury availability in the environment for all biomes and key biota over this next decade is vital for evaluating the effectiveness of the Minamata Convention," says Evers.

More information: David C. Evers et al, Global mercury concentrations in biota: their use as a basis for a global biomonitoring framework, Ecotoxicology (2024). DOI: 10.1007/s10646-024-02747-x

Provided by Biodiversity Research Institute (BRI)

Office of Conservation Investment

The Wildlife and Sportfish Restoration Program is now the Office of Conservation Investment. The Office of Conservation Investment will continue to build on our heritage started in 1937 while creating an identity that is more holistic and inclusive of the work we support. Conservation is by its very nature an investment in the future. Under the new program name, we will continue to support our fellow Service programs, and work with states, tribal nations, landowners, nongovernmental organizations, and industry to benefit fish and wildlife and the habitats they call home. We look forward to serving our partners with the same commitment and standards while embracing a new identity to lead us into the future.

Our Projects and Initiatives

The Office of Conservation Investment and our partners have fostered conservation under a variety of program names since our beginning more than 80 years ago. Our foundation is built on successful partnerships established through Wildlife Restoration and Sport Fish Restoration grants. These grants are still going strong and are a cornerstone of our work. Today, the Office of Conservation Investment responsibilities have expanded, and other grants have been entrusted to us to manage. Collectively we provide grant administration and technical assistance for thousands of individual grants annually that support the operation and maintenance of wildlife management areas for hunting and wildlife viewing, support the operation and maintenance of areas to launch boats, foster imperiled and endangered species conservation, restore and enhance coastal wetlands, aid Tribal partners with wildlife and habitat stewardship, provide hunting and aquatic education courses for millions, and much more. The Office of Conservation Investment also supports other Service efforts in the administration of additional grant programs. Learn more about the grants we support through funding, oversight and administration, and technical support from the links below:

• Hunter Education
• Aquatic Education
• Boating Access
• Boating Infrastructure
• Clean Vessel Act
• Endangered Species
• Great Lakes Recovery Initiative
• Highlands Conservation Act
• Multistate Conservation
• National Coastal Wetlands Conservation 
• State Wildlife Grants
• Tribal Wildlife Grants
• The National Survey of Fishing, Hunting, & Wildlife-Associated Recreation
• Theodore Roosevelt Genius Prize and Advisory Council

Latest Stories and Topics

Projects and Research

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We do not guarantee that the websites we link to comply with Section 508 (Accessibility Requirements) of the Rehabilitation Act. Links also do not constitute endorsement, recommendation, or favoring by the U.S. Fish and Wildlife Service.

How to Find Research Topics to Write About

So, you’ve got a research paper due, and the dread sets in – what to research about? We’ve all experienced that frustrating moment when finding a topic feels about as simple as finding a needle in a haystack.

But hold on! Before you check is essay pro legit enough to find someone to write your paper for you (tempting, I know!), take a deep breath. Finding a killer research topic doesn’t have to take weeks. It can actually be the spark that ignites your curiosity and leads you down a fascinating rabbit hole of discovery. 

The key is knowing where to look for inspiration and how to narrow down those endless possibilities into a topic that’s manageable, meaningful, and maybe even a little bit exciting.

So, let’s come up with unconventional strategies, look for hidden sources of inspiration, and give you the tools to choose a topic that will give your research paper a head-start.

Source: https://www.pexels.com/photo/photo-of-a-woman-thinking-941555/ 

Look Beyond the Textbook

Sure, your course material is a good starting point for finding a research topic, but don’t let it be your only source of inspiration. Look around you – the world is full of fascinating questions just waiting to be explored. 

What current events spark your interest? What social issues keep you up at night? Maybe there’s a scientific breakthrough that’s left you wanting to know more. 

Don’t be afraid to let your curiosity guide you. Some of the most engaging research papers are born out of genuine interest and a desire to learn more about the world.

Another often-overlooked source of inspiration on what to research is your own life experiences. Have you ever faced a personal challenge or overcome an obstacle that could be relevant to others? Maybe you have a unique cultural background or a hobby that could be the basis for an intriguing research question. 

Don’t undermine the potential of your own story to spark a meaningful topic.

Digg Deeper With Unconventional Sources

Now, let’s move beyond the obvious. While academic journals and textbooks are important resources, they’re not the only game in town. 

Explore documentaries, podcasts, TED Talks, and even social media for potential research topic ideas. These sources often present complex issues in a more accessible way, making them a great way to spark your interest and notice new perspectives.

If you’re feeling stuck, try branching out into different fields of study. Maybe a sociology paper on the impact of social media on mental health or a history paper on the role of music in social movements could pique your interest. 

Remember, research is about making connections, so don’t be afraid to get a little interdisciplinary with your topic choices.

Source: https://www.pexels.com/photo/box-with-brain-inscription-on-head-of-anonymous-woman-7203727/

Find Out What Makes a Good Research Paper Topic

So, you’ve got a whole bunch of potential research article topics swirling around in your head. Now what? It’s time to narrow it down and find the perfect fit. 

A good topic should be several things:

• Interesting. You’re going to be spending a lot of time on this topic, so make sure it’s something you actually care about!
• Manageable. Choose a topic that’s narrow enough to be thoroughly researched within the scope of your assignment. Avoid topics that are too general or too niche.
• Relevant. Make sure your topic is in line with your course or field of study. If you’re unsure, talk to your professor for guidance.
• Original. While it’s okay to build on existing research, try to gauge a new angle or perspective on your topic.

If you still feel you can’t come up with the right topic, don’t hesitate to seek out help. Talk to your professor or librarian, or even consider consulting with one of the best coursework writing services to brainstorm ideas and get feedback on your choices. 

Choosing a topic for a research paper is just the first step in the process. The real fun begins when you start diving into the research and getting new insights.

The Bottom Line

The journey of finding a research topic doesn’t have to be a dreaded chore. It can be an exciting opportunity to expand your knowledge and bring out new passions. Remember, there are no “easy research topics.” The best topics are the ones that ignite your curiosity and challenge you to think critically about the world around you.

So, feel free to get a little creative with your research topic choices. Whether you’re exploring a current event, a personal experience, or a complex social issue, the most important thing is to choose a topic that inspires you and makes you eager to dive into the research.

Learning how to find a research topic is an essential skill for any college student, and with the right approach, it can even be enjoyable! So, put on your explorer hat, embrace your curiosity, and let your research process begin.

Cold-blooded killer: Study finds climate change is driving deadly cold waves, harming wildlife

While most climate studies focus on heating, surprising new research finds sudden cold events can be devastating, by matthew rozsa.

Bull sharks (Carcharhinus leucas) are large, voracious predators. Measuring anywhere from 7 to 11 feet from tail to snout, with dark gray skin and a white underbelly, bull sharks eat everything from fishes and dolphins to other sharks and even the occasional human . It is for that last reason that it takes a lot of gumption to attach electronic tags to wild bull sharks — yet that is precisely what scientists did in a recent study  for the journal Nature.

Their findings proved, tragically, that there is a newly-discovered way in which climate change makes life more difficult for both bull sharks and other undersea life.

"This shows the potential impacts of increased cold events, an understudied aspect of climate change research."

Specifically, the study found that climate change is making both more frequent and more intense a phenomenon known as extremely cold upwelling events. Those refer to when pockets of cold water replacer warm water at the surface after being brought there by strong winds and ocean currents. Led by researcher Nicolas Lubitz from James Cook University in Queensland, Australia, the scientists learned that the sharks chose shallower water during upwelling events because they needed to avoid the newly-colder water.

In a related study, the researchers also analyzed 41 years of sea surface temperature data and 33 years of wind records to learn about the history of upwelling events in the Indian Ocean’s Agulhas Current and the East Australian Current — particularly when they had a large death toll in animals like sharks, squid and manta rays.

The researchers concluded that the cold upwelling events turn lethal depending on how fast they cause the temperature to drop. Additionally, when an event lasts for a long time such as multiple days, marine animals like fish species and turtles are more likely to die or suffer serious health problems from issues like hypothermia. An increasing number of killer cold upwellings might also cause a sort of "bait and switch" to occur in certain ecosystems, with species that reside in the subtropics expanding their territorial range while those closer to the poles suffer an increased risk of dying from the cold. Finally, these events will almost certainly impact humans, particularly those who rely on the fishing industry for survival.

“We’re seeing changes in how often the upwelling occurs, how intense it is, which might impact the fishing communities in these areas,” Lubitz told CNN . “It’s really an economic thing as well as the biodiversity thing.”

More broadly, the study reveals that climate change is not only about the planet warming, but in a seeming paradox can also trigger catastrophic cooling events.

"This shows the potential impacts of increased cold events, an understudied aspect of climate change research, and highlights the complexities of climate change effects on marine ecosystems," the authors write.

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"Studies of climate change and ocean temperatures usually focus on heating rather than cooling."

Studies of climate change and ocean temperatures usually focus on heating rather than cooling. For example, a recent report by the Great Barrier Reef Marine Park Authority found that more than half of 1,000 reefs analyzed out of more than 2,900 in total suffered from significant levels of bleaching, with a mere quarter being unaffected. Coral bleaching occurs when coral become stressed due to high temperatures or lack of nutrients and  expel the algae that live symbiotically within it . This is the fifth mass bleaching event to occur on that reef in the last eight years.

Climate change is causing ocean temperatures to spike in unprecedented ways , with temperature records shattered every single day in 2023.

In a similar development, in 2022 the journal Advances in Atmospheric Sciences published a study  measuring ocean temperatures by studying ocean heat content (OHC), a catch-all term referring to the saltiness (or salinity), different layers of temperatures and other factors that culminate to determine global oceanic temperatures. The group found that as of that year, 2022 had been the hottest in the historical record, surpassing the previous maximum record set in 2021. The authors speculated that the increase in OHC could have factored into extreme weather events like the increased number of wildfires and floods.

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In the future, if the oceans warm too much, it could further destabilize the  Atlantic meridional overturning circulation (AMOC) , a conveyor belt of ocean currents essential to the fishing industry and maintaining stable weather. AMOC has already shown signs of weakening, and if it is drastically altered, the result could be even more extreme weather changes.

In short, climate change has a complex relationship with ocean temperatures, sometimes raising them to dangerous levels and on other occasions causing them to drop precipitously for ocean animals that depend on stability. At least one of those species, however, was able to shed light on the terrible toll being taken for the humans causing the problem: sharks.

“That was really the key in this study in that we could see when the sharks migrate,” Lubitz told CNN. “We could see how the temperature profiles change, and how the sharks were swimming shallower when they were in upwelling areas because they were trying to avoid the colder water from the depths.”

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Matthew Rozsa is a staff writer at Salon. He received a Master's Degree in History from Rutgers-Newark in 2012 and was awarded a science journalism fellowship from the Metcalf Institute in 2022.

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