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🧬 The Felid Wiki — 3,000 research summaries
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150 summaries in Camera-Trap Methods
Research overview: the TEAM Network and tropical camera-trap monitoring
OverviewThe Tropical Ecology Assessment and Monitoring (TEAM) Network pioneered standardized, long-term camera-trap monitoring of terrestrial mammals and birds across multiple tropical forest sites worldwide.…
Research overview: the Wildlife Insights cloud platform for camera data
OverviewWildlife Insights is a cloud platform that lets researchers upload, manage, share, and analyze camera-trap data with integrated artificial-intelligence species identification, aiming to unify a fragme…
Research overview: the geographic and demographic closure assumption in capture-recapture
OverviewClosed-population capture-recapture models assume the population neither gains nor loses individuals during the survey, through births, deaths, immigration, or emigration, an assumption central to est…
Research overview: the science of trail-based versus random camera placement
OverviewWhere cameras are placed, on trails, roads, and game paths that funnel animal movement, or at random or systematic points representing the landscape, fundamentally shapes the data and the inferences i…
Research overview: time-to-event and space-to-event abundance estimators
OverviewTime-to-event, space-to-event, and instantaneous-sampling estimators are recognition-free methods that infer animal abundance from how long, or across how much sampled space, one waits until an animal…
Research overview: training-data bias and domain shift in camera-trap machine learning
OverviewMachine-learning classifiers trained on camera-trap images often perform poorly when applied to new locations, a failure rooted in training-data bias and domain shift, the mismatch between the backgro…
Research overview: transfer learning and generalization to new regions
OverviewTransfer learning, adapting models pretrained on large image collections to new camera-trap tasks, is central to making machine learning practical when a project has limited labeled data. This overvie…
Research overview: trap response, trap-shy and trap-happy behavior to cameras
OverviewAnimals may change their behavior toward cameras after first encountering them, becoming wary and avoiding the device (trap-shy) or, where lures are used, returning more often (trap-happy), and either…
Research overview: video, still imagery, and multimodal and thermal sensors
OverviewCamera traps can record still images, video, or both, and emerging deployments add thermal imaging and acoustic sensors, each modality offering different strengths for detecting and studying cats. Thi…
Research overview: white-flash versus infrared cameras and image quality trade-offs
OverviewCamera traps illuminate night scenes either with a bright white (xenon or LED) flash that yields full-color images or with infrared illumination that produces monochrome photos invisible to most anima…
Robust ecological analysis of camera trap data labelled by a machine learning model
PaperWhytock and colleagues confronted a practical worry: machine-learning species classifiers make errors, so can ecological conclusions drawn from automatically labeled images be trusted? Using African f…
Robin C. Whytock, Jedrzej Swiezewski, Joeri A. Zwerts, Tadeusz Bara-Slupski, Aurelie Flore Koumba Pambo, et al. · Methods in Ecology and Evolution · 2021
Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors
ReviewSteenweg and colleagues argued for treating camera traps as a globally coordinated biodiversity sensor network rather than isolated local tools. They reviewed how standardized protocols, shared data i…
Robin Steenweg, Mark Hebblewhite, Roland Kays, Jorge Ahumada, Jason T. Fisher, et al. · Frontiers in Ecology and the Environment · 2017
Snap happy: camera traps are an effective sampling tool compared with alternative methods
ReviewWearn and Glover-Kapfer evaluated camera traps against other terrestrial mammal survey methods, such as live trapping, sign surveys, and transects, assessing cost-effectiveness, species coverage, and…
Oliver R. Wearn, Paul Glover-Kapfer · Royal Society Open Science · 2019
Snapshot Serengeti: high-frequency annotated camera trap images of 40 mammalian species
PaperSwanson and colleagues published the Snapshot Serengeti dataset, one of the first large-scale, openly available, expertly verified camera-trap image collections, comprising millions of images from a d…
Alexandra Swanson, Margaret Kosmala, Chris Lintott, Robert Simpson, Arfon Smith, Craig Packer · Scientific Data · 2015
Spatial Capture-Recapture
ReviewThis comprehensive book by Royle, Chandler, Sollmann, and Gardner is the definitive monograph on spatially explicit capture-recapture, presenting the theory, models, and computational methods that und…
J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner · Academic Press · 2014
Spatially explicit maximum likelihood methods for capture-recapture studies
PaperBorchers and Efford provided the rigorous likelihood-based foundation for spatially explicit capture-recapture, formalizing how to estimate density when detectors such as camera stations have fixed lo…
David L. Borchers, Murray G. Efford · Biometrics · 2008
Spatially explicit models for inference about density in unmarked or partially marked populations
PaperChandler and Royle developed spatial count models that estimate density from spatially correlated counts of unmarked individuals, extending spatially explicit capture-recapture to populations where no…
Richard B. Chandler, J. Andrew Royle · Annals of Applied Statistics · 2013
Statistical inference from capture data on closed animal populations
PaperOtis, Burnham, White, and Anderson produced the foundational monograph on closed-population capture-recapture, defining the suite of models that allow for variation in capture probability over time, b…
David L. Otis, Kenneth P. Burnham, Gary C. White, David R. Anderson · Wildlife Monographs · 1978
The use of camera traps for estimating jaguar abundance and density using capture/recapture analysis
PaperSilver and colleagues adapted the tiger camera-trap framework to jaguars, whose unique rosette patterns serve as individual marks. Working in Belize and across Latin American sites, they detailed prac…
Scott C. Silver, Linde E. T. Ostro, Laura K. Marsh, Leonardo Maffei, Andrew J. Noss, et al. · Oryx · 2004
Three critical factors affecting automated image species recognition performance for camera traps
PaperSchneider and colleagues systematically investigated factors that determine how well automated species-recognition models perform on camera-trap images, identifying training-set size, the number of sp…
Stefan Schneider, Saul Greenberg, Graham W. Taylor, Stefan C. Kremer · Ecology and Evolution · 2020
Three novel methods to estimate abundance of unmarked animals using remote cameras
PaperMoeller, Lukacs, and Horne introduced a family of estimators, the space-to-event, time-to-event, and instantaneous-sampling models, to estimate abundance of unmarked animals from camera-trap data with…
Anna K. Moeller, Paul M. Lukacs, Jon S. Horne · Ecosphere · 2018
Tigers and their prey: predicting carnivore densities from prey abundance
PaperUsing camera-trap density estimates of tigers alongside line-transect surveys of ungulate prey across many Asian sites, Karanth and colleagues tested whether predator density is governed by prey avail…
K. Ullas Karanth, James D. Nichols, N. Samba Kumar, William A. Link, James E. Hines · Proceedings of the National Academy of Sciences · 2004
Tigers need cover: multi-scale occupancy study of the big cat in Sumatran forest and plantation landscapes
PaperSunarto and colleagues used multi-scale occupancy modeling, informed by camera-trap and field detections, to investigate how Sumatran tigers respond to forest cover and human-modified landscapes inclu…
Sunarto, Marcella J. Kelly, Karmila Parakkasi, Sybille Klenzendorf, Erin Septayuda, Harry Kurniawan · PLOS ONE · 2012
Towards automatic wild animal monitoring: identification of animal species in camera-trap images using deep CNNs
PaperGomez Villa and colleagues were among the early adopters of very deep convolutional neural networks for camera-trap species identification, testing architectures on challenging real-world images from…
Alexander Gomez Villa, Augusto Salazar, Francisco Vargas · Ecological Informatics · 2017
Trap configuration and spacing influences parameter estimates in spatial capture-recapture models
PaperSun, Fuller, and Royle examined through simulation how detector spacing and array geometry affect bias and precision in spatially explicit capture-recapture estimates. They demonstrated that when dete…
Catherine C. Sun, Angela K. Fuller, J. Andrew Royle · PLOS ONE · 2014
Use of camera-trapping to estimate puma density and influencing factors in central Brazil
PaperNegroes and colleagues estimated puma density in the central Brazilian Cerrado using camera traps, confronting the difficulty of individually identifying pumas from their relatively unmarked coats by…
Nuno Negroes, Peter Sarmento, Joao Cruz, Catarina Eira, Erica Revilla, et al. · Journal of Wildlife Management · 2010
Volunteer-run cameras as distributed sensors for macrosystem mammal research
PaperMcShea and colleagues described the eMammal model, in which trained volunteers operate camera traps across broad regions and contribute expertly verified images to a centralized, standardized data rep…
William J. McShea, Tavis Forrester, Robert Costello, Zhihai He, Roland Kays · Landscape Ecology · 2016
Which camera trap type and how many do I need? A review of camera features and study designs
ReviewRovero, Zimmermann, Berzi, and Meek provided a practical review answering the questions newcomers most often ask: what kind of camera to buy and how many to deploy. They compared camera features such…
Francesco Rovero, Fridolin Zimmermann, Duccio Berzi, Paul Meek · Hystrix, the Italian Journal of Mammalogy · 2013
Wildlife Insights: a platform to maximize the potential of camera trap and passive sensor data
PaperAhumada and colleagues described Wildlife Insights, a cloud-based platform built by a consortium of conservation organizations and technology partners to store, manage, share, and analyze camera-trap…
Jorge A. Ahumada, Eric Fegraus, Tanya Birch, Nicole Flores, Roland Kays, et al. · Environmental Conservation · 2020
Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes
ReviewBurton and colleagues synthesized the rapidly expanding camera-trap literature, evaluating how studies define their sampling, handle detection, and connect raw detections to the ecological quantities…
A. Cole Burton, Eric Neilson, Dario Moreira, Andrew Ladle, Robin Steenweg, et al. · Journal of Applied Ecology · 2015