Camera trapping expands the view into global biodiversity and its change

Growing threats to biodiversity demand timely, detailed information on species occurrence, diversity and abundance at large scales. Camera traps (CTs), combined with computer vision models, provide an efficient method to survey species of certain taxa with high spatio-temporal resolution. We test the potential of CTs to close biodiversity knowledge gaps by comparing CT records of terrestrial mammals and birds from the recently released Wildlife Insights platform to publicly available occurrences from many observation types in the Global Biodiversity Information Facility. In locations with CTs, we found they sampled a greater number of days (mean = 133 versus 57 days) and documented additional species (mean increase of 1% of expected mammals). For species with CT data, we found CTs provided novel documentation of their ranges (93% of mammals and 48% of birds). Countries with the largest boost in data coverage were in the historically underrepresented southern hemisphere. Although embargoes increase data providers' willingness to share data, they cause a lag in data availability. Our work shows that the continued collection and mobilization of CT data, especially when combined with data sharing that supports attribution and privacy, has the potential to offer a critical lens into biodiversity. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.

[1]  H. Shibata,et al.  Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations , 2023, Philosophical Transactions of the Royal Society B.

[2]  M. O’Connor,et al.  A framework for the detection and attribution of biodiversity change , 2023, Philosophical Transactions of the Royal Society B.

[3]  N. Mandrak,et al.  Monitoring biodiversity loss in rapidly changing Afrotropical ecosystems: an emerging imperative for governance and research , 2023, Philosophical Transactions of the Royal Society B.

[4]  M. Londoño,et al.  Monitor biodiversity for action , 2022, Science.

[5]  C. Downs,et al.  Camera trap research in Africa: A systematic review to show trends in wildlife monitoring and its value as a research tool , 2022, Global Ecology and Conservation.

[6]  Patrick J. Wolff,et al.  Camera traps are an effective tool for monitoring insect–plant interactions , 2022, Ecology and evolution.

[7]  Mary T. Holden,et al.  Expert range maps of global mammal distributions harmonised to three taxonomic authorities , 2022, Journal of biogeography.

[8]  Christopher T. Rota,et al.  A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions , 2022, Journal of Agricultural, Biological and Environmental Statistics.

[9]  Silvia Zuffi,et al.  Perspectives in machine learning for wildlife conservation , 2021, Nature Communications.

[10]  W. Jetz,et al.  Global and national trends, gaps, and opportunities in documenting and monitoring species distributions , 2021, PLoS biology.

[11]  P. Teta,et al.  Illustrated Checklist of the Mammals of the World , 2021, Journal of Mammalogy.

[12]  Jennifer F. Moore,et al.  The potential and practice of arboreal camera trapping , 2021, Methods in Ecology and Evolution.

[13]  R. Kays,et al.  Carolina critters: a collection of camera trap data from wildlife surveys across North Carolina. , 2021, Ecology.

[14]  Christopher A. Lepczyk,et al.  SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States. , 2021, Ecology.

[15]  D. Jachowski,et al.  Shining the spotlight on small mammalian carnivores: Global status and threats , 2021 .

[16]  R. Swihart,et al.  Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation , 2021, Frontiers in Ecology and Evolution.

[17]  J. David Blount,et al.  Review: COVID-19 highlights the importance of camera traps for wildlife conservation research and management , 2021, Biological Conservation.

[18]  Komisi Penyelamatan,et al.  International Union for Conservation of Nature , 2021, Permanent Missions to the United Nations, No. 309.

[19]  Christopher P. Hansen,et al.  Does Use of Backyard Resources Explain the Abundance of Urban Wildlife? , 2020, Frontiers in Ecology and Evolution.

[20]  M. Obersteiner,et al.  Global priority areas for ecosystem restoration , 2020, Nature.

[21]  Jennifer L. Stenglein,et al.  Abundance estimation of unmarked animals based on camera‐trap data , 2020, Conservation Biology.

[22]  R. Bertrand,et al.  Species better track climate warming in the oceans than on land , 2020, Nature Ecology & Evolution.

[23]  H. Kühl,et al.  Drawn out of the shadows: Surveying secretive forest species with camera trap distance sampling , 2020, Journal of Applied Ecology.

[24]  W. Jetz,et al.  Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation , 2019, PLoS biology.

[25]  Diego Llusia,et al.  A roadmap for survey designs in terrestrial acoustic monitoring , 2019, Remote Sensing in Ecology and Conservation.

[26]  Mohammad Sadegh Norouzzadeh,et al.  A deep active learning system for species identification and counting in camera trap images , 2019, Methods in Ecology and Evolution.

[27]  J. Brodie,et al.  Pangolins in global camera trap data: Implications for ecological monitoring , 2019, Global Ecology and Conservation.

[28]  Walter Jetz,et al.  Wildlife Insights: A Platform to Maximize the Potential of Camera Trap and Other Passive Sensor Wildlife Data for the Planet , 2019, Environmental Conservation.

[29]  Walter Jetz,et al.  Essential biodiversity variables for mapping and monitoring species populations , 2019, Nature Ecology & Evolution.

[30]  Oliver R. Wearn,et al.  Camera‐trapping version 3.0: current constraints and future priorities for development , 2019, Remote Sensing in Ecology and Conservation.

[31]  R. Sollmann.  A gentle introduction to camera-trap data analysis , 2018, African Journal of Ecology.

[32]  Kevin R. Crooks,et al.  Pairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance , 2018, Global Ecology and Conservation.

[33]  Paul M. Lukacs,et al.  Three novel methods to estimate abundance of unmarked animals using remote cameras , 2018, Ecosphere.

[34]  J. Wingfield,et al.  Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology , 2018, Science Advances.

[35]  Walter Jetz,et al.  Humboldt Core – toward a standardized capture of biological inventories for biodiversity monitoring, modeling and assessment , 2018 .

[36]  J. Andrew Royle,et al.  Spatial capture–recapture with partial identity: An application to camera traps , 2018 .

[37]  Brett R. Scheffers,et al.  Managing consequences of climate‐driven species redistribution requires integration of ecology, conservation and social science , 2018, Biological reviews of the Cambridge Philosophical Society.

[38]  Elizabeth Znidersic.  Camera Traps are an Effective Tool for Monitoring Lewin's Rail (Lewinia pectoralis brachipus) , 2017, Waterbirds.

[39]  S. T. Buckland,et al.  Distance sampling with camera traps , 2017 .

[40]  Louise McRae,et al.  Global biodiversity monitoring: From data sources to Essential Biodiversity Variables , 2017 .

[41]  Michael Wood,et al.  A review of camera trapping for conservation behaviour research , 2017 .

[42]  J. Fisher,et al.  Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: challenges and opportunities , 2017 .

[43]  P. Grandcolas,et al.  Taxonomic bias in biodiversity data and societal preferences , 2017, Scientific Reports.

[44]  Brett R. Scheffers,et al.  Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being , 2017, Science.

[45]  Timothy M. Perez,et al.  Most ‘global’ reviews of species’ responses to climate change are not truly global , 2017 .

[46]  J. Andrew Royle,et al.  Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors , 2017 .

[47]  Tavis D. Forrester,et al.  A multispecies occupancy model for two or more interacting species , 2016 .

[48]  Walter Jetz,et al.  Global priorities for an effective information basis of biodiversity distributions , 2015, Nature Communications.

[49]  Erin M. Bayne,et al.  REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes , 2015 .

[50]  W. Turner.  Sensing biodiversity , 2014, Science.

[51]  Jamie McCallum,et al.  Changing use of camera traps in mammalian field research: habitats, taxa and study types , 2013 .

[52]  A. Glen,et al.  Optimising Camera Traps for Monitoring Small Mammals , 2013, PloS one.

[53]  N. Pettorelli,et al.  Essential Biodiversity Variables , 2013, Science.

[54]  W. Jetz,et al.  The global diversity of birds in space and time , 2012, Nature.

[55]  Walter Jetz,et al.  Integrating biodiversity distribution knowledge: toward a global map of life. , 2012, Trends in ecology & evolution.

[56]  John Wieczorek,et al.  Darwin Core: An Evolving Community-Developed Biodiversity Data Standard , 2012, PloS one.

[57]  J. Nel.  Handbook of the Mammals of the World , 2010 .

[58]  J Andrew Royle,et al.  Bayesian inference in camera trapping studies for a class of spatial capture-recapture models. , 2009, Ecology.

[59]  Peter Arzberger,et al.  New Eyes on the World: Advanced Sensors for Ecology , 2009 .

[60]  K. Gaston,et al.  The sizes of species’ geographic ranges , 2009 .

[61]  Margaret F. Kinnaird,et al.  A picture is worth a thousand words: the application of camera trapping to the study of birds , 2008, Bird Conservation International.

[62]  Samuel T. Turvey,et al.  Estimating animal density using camera traps without the need for individual recognition , 2008 .

[63]  M. Tobler,et al.  An evaluation of camera traps for inventorying large‐ and medium‐sized terrestrial rainforest mammals , 2008 .

[64]  Walter Jetz,et al.  Species richness, hotspots, and the scale dependence of range maps in ecology and conservation , 2007, Proceedings of the National Academy of Sciences.

[65]  J. Andrew Royle.  N‐Mixture Models for Estimating Population Size from Spatially Replicated Counts , 2004, Biometrics.

[66]  J. Andrew Royle,et al.  ESTIMATING ABUNDANCE FROM REPEATED PRESENCE–ABSENCE DATA OR POINT COUNTS , 2003 .

[67]  J. Andrew Royle,et al.  ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE , 2002, Ecology.

[68]  Don E. Wilson,et al.  The Mammal Species of the World , 2009 .

[69]  J. Honacki,et al.  Mammal species of the world : a taxonomic and geographic reference , 1982 .

[70]  K..  ESTIMATING TIGER Panthera tigris POPULATIONS FROM CAMERA-TRAP DATA USING CAPTURE RECAPTURE MODELS , 2022 .