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150 summaries in Camera-Trap Methods
A critique of density estimation from camera-trap data
ReviewFoster and Harmsen critically reviewed the assumptions and pitfalls of estimating animal density from camera traps, focusing on how researchers define the effective sampling area and handle heterogene…
Rebecca J. Foster, Bart J. Harmsen · Journal of Wildlife Management · 2012
A deep active learning system for species identification and counting in camera trap images
PaperNorouzzadeh and colleagues built an end-to-end active-learning system that combines automated detection, classification, and counting with strategic human input, asking experts to label only the most…
Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune · Methods in Ecology and Evolution · 2021
A gentle introduction to camera-trap data analysis
ReviewSollmann wrote an accessible tutorial-style review aimed at newcomers, walking through the main questions camera-trap data can answer and the analytical tools suited to each. She covered relative-abun…
Rahel Sollmann · African Journal of Ecology · 2018
A hierarchical model for spatial capture-recapture data
PaperRoyle and Young formulated a hierarchical Bayesian model for spatial capture-recapture in which individual animal locations are treated as latent variables drawn from a spatial point process, and dete…
J. Andrew Royle, Kevin V. Young · Ecology · 2008
A review of camera trapping for conservation behaviour research
ReviewCaravaggi and colleagues reviewed how camera traps can be used to study animal behavior, not just presence and abundance, for conservation purposes. They catalogued behavioral metrics extractable from…
Anthony Caravaggi, Peter B. Banks, A. Cole Burton, Caroline M. V. Finlay, Peter M. Haswell, et al. · Remote Sensing in Ecology and Conservation · 2017
A simple method for estimating the effective detection distance of camera traps
PaperHofmeester and colleagues addressed a key parameter for density methods like the Random Encounter Model and distance sampling: how far in front of a camera animals are actually detected. Because the p…
Tim R. Hofmeester, J. Marcus Rowcliffe, Patrick A. Jansen · Remote Sensing in Ecology and Conservation · 2019
Abundance estimation of unmarked animals based on camera-trap data
ReviewGilbert and colleagues reviewed the growing array of methods for estimating abundance and density of animals that cannot be individually identified from camera-trap images, a category that includes ma…
Neil A. Gilbert, John D. J. Clare, Jennifer L. Stenglein, Benjamin Zuckerberg · Conservation Biology · 2021
Advances and applications of occupancy models
ReviewBailey, MacKenzie, and Nichols reviewed the rapid development of occupancy modeling in the decade after its introduction, cataloguing extensions such as multi-season dynamics, multi-species and commun…
Larissa L. Bailey, Darryl I. MacKenzie, James D. Nichols · Methods in Ecology and Evolution · 2014
An empirical evaluation of camera trap study design: how many, how long and when?
PaperKays and colleagues used large multi-site camera-trap datasets to empirically assess how the number of cameras, deployment duration, and timing affect the precision and reliability of estimates for sp…
Roland Kays, Brian S. Arbogast, Megan Baker-Whatton, Chris Beirne, Hailey M. Boone, et al. · Methods in Ecology and Evolution · 2020
An evaluation of camera traps for inventorying large- and medium-sized terrestrial rainforest mammals
PaperTobler and colleagues assessed how effectively camera traps inventory the terrestrial mammal community of a Peruvian rainforest, quantifying sampling effort needed to detect species and how detection…
Mathias W. Tobler, Samia E. Carrillo-Percastegui, Renata Leite Pitman, Rosa Mares, George Powell · Animal Conservation · 2008
An evaluation of platforms for processing camera-trap data using artificial intelligence
PaperVelez and colleagues compared the major software platforms and tools that apply artificial intelligence to camera-trap images, including options such as Wildlife Insights, MegaDetector-based pipelines…
Juliana Velez, William McShea, Hila Shamon, Paula J. Castiblanco-Camacho, Michael A. Tabak, et al. · Methods in Ecology and Evolution · 2023
Applications for deep learning in ecology
ReviewChristin, Hervet, and Lecomte reviewed how deep learning is transforming ecology, with camera-trap image classification as a prominent application alongside acoustic analysis and remote sensing. Writt…
Sylvain Christin, Eric Hervet, Nicolas Lecomte · Methods in Ecology and Evolution · 2019
Assessing tiger population dynamics using photographic capture-recapture sampling
PaperThis study moved camera-trap monitoring beyond single snapshots to open-population modeling, using repeated annual photographic surveys at Nagarahole to estimate survival, recruitment, and population…
K. Ullas Karanth, James D. Nichols, N. Samba Kumar, James E. Hines · Ecology · 2006
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
PaperNorouzzadeh and colleagues demonstrated that deep convolutional neural networks could automate the labor-intensive task of processing millions of camera-trap images. Training on the labeled Snapshot S…
Mohammad Sadegh Norouzzadeh, Anh Nguyen, Margaret Kosmala, Alexandra Swanson, Meredith S. Palmer, Craig Packer, Jeff Clune · Proceedings of the National Academy of Sciences · 2018
Bayesian inference in camera trapping studies for a class of spatial capture-recapture models
PaperRoyle, Karanth, Gopalaswamy, and Kumar developed a Bayesian implementation of spatially explicit capture-recapture tailored to camera-trap data, using data augmentation and Markov chain Monte Carlo to…
J. Andrew Royle, K. Ullas Karanth, Arjun M. Gopalaswamy, N. Samba Kumar · Ecology · 2009
Camera Traps in Animal Ecology: Methods and Analyses
ReviewEdited by O'Connell, Nichols, and Karanth, this book was the first comprehensive volume devoted to camera-trap methodology, bringing together leading practitioners to cover study design, individual id…
Allan F. O'Connell, James D. Nichols, K. Ullas Karanth (editors) · Springer · 2011
Camera trapping photographic rate as an index of density in forest ungulates
PaperRovero and Marshall tested whether the rate at which animals are photographed can serve as a reliable index of their density, a crucial question because many studies use detection rates as abundance p…
Francesco Rovero, Andrew R. Marshall · Journal of Applied Ecology · 2009
Camera traps for estimating tiger and leopard populations in the high mountains of Bhutan
PaperWang and Macdonald applied camera-trap capture-recapture to tigers and leopards in the high-altitude mountains of Bhutan, documenting the surprising presence of tigers at unusually high elevations and…
Sherub Wang, David W. Macdonald · Biological Conservation · 2009
Camera-trapping version 3.0: current constraints and future priorities for development
ReviewGlover-Kapfer, Soto-Navarro, and Wearn surveyed the state of camera-trap technology and practice, identifying the constraints that limit the method and the innovations needed to overcome them. They re…
Paul Glover-Kapfer, Carolina A. Soto-Navarro, Oliver R. Wearn · Remote Sensing in Ecology and Conservation · 2019
Density estimation by spatially explicit capture-recapture: likelihood-based methods
PaperIn this book chapter, Efford, Borchers, and Byrom consolidated the practical likelihood machinery for spatially explicit capture-recapture, explaining how to fit detection functions, choose detector t…
Murray G. Efford, David L. Borchers, Andrea E. Byrom · Modeling Demographic Processes in Marked Populations (Springer) · 2009
Density estimation in live-trapping studies
PaperMurray Efford introduced spatially explicit density estimation, addressing the long-standing problem that traditional capture-recapture estimates abundance without a well-defined area, forcing ad hoc…
Murray G. Efford · Oikos · 2004
Density, habitat use and activity patterns of ocelots in the Atlantic Forest of Misiones, Argentina
PaperDi Bitetti, Paviolo, and De Angelo combined camera-trap capture-recapture with activity-pattern analysis to characterize ocelot ecology in the threatened Atlantic Forest. They estimated density from i…
Mario S. Di Bitetti, Agustin Paviolo, Carlos De Angelo · Journal of Zoology · 2006
Designing occupancy studies: general advice and allocating survey effort
PaperMacKenzie and Royle tackled the design side of occupancy studies, asking how to allocate limited survey effort between visiting more sites and surveying each site more intensively to best estimate occ…
Darryl I. MacKenzie, J. Andrew Royle · Journal of Applied Ecology · 2005
Differential use of trails by forest mammals and implications for camera-trap studies in Belize
PaperHarmsen and colleagues documented how different mammal species use trails versus off-trail forest to varying degrees, and showed that this differential trail use biases camera-trap data when stations…
Bart J. Harmsen, Rebecca J. Foster, Scott C. Silver, Linde E. T. Ostro, C. Patrick Doncaster · Biotropica · 2010
Distance sampling with camera traps
PaperHowe and colleagues adapted distance-sampling theory to camera traps, providing a way to estimate density of unmarked animals from the distances at which they are detected in front of the camera. By m…
Eric J. Howe, Stephen T. Buckland, Marie-Lyne Despres-Einspenner, Hjalmar S. Kuhl · Methods in Ecology and Evolution · 2017
Efficient pipeline for camera trap image review
PaperBeery, Morris, and Yang described the approach behind MegaDetector, a general-purpose object-detection model trained to locate animals, people, and vehicles in camera-trap images regardless of species…
Sara Beery, Dan Morris, Siyu Yang · arXiv preprint · 2019
Estimating abundance from repeated presence-absence data or point counts
PaperRoyle and Nichols showed that variation in detection probability across sites carries information about local abundance, because sites with more individuals are more likely to yield a detection. Their…
J. Andrew Royle, James D. Nichols · Ecology · 2003
Estimating animal density using camera traps without the need for individual recognition
PaperRowcliffe and colleagues introduced the Random Encounter Model, a method to estimate density for species that cannot be individually identified, such as unmarked cats and many prey species. Borrowing…
J. Marcus Rowcliffe, Juliet Field, Samuel T. Turvey, Chris Carbone · Journal of Applied Ecology · 2008
Estimating animal density without individual recognition using camera-trap-derived information
PaperNakashima, Fukasawa, and Samejima proposed the Random Encounter and Staying Time (REST) model, which estimates density from the number of detections and the time animals remain within a defined detect…
Yoshihiro Nakashima, Keita Fukasawa, Hiroshi Samejima · Journal of Applied Ecology · 2018
Estimating detection and density of the Andean cat in the high Andes
PaperReppucci, Gardner, and Lucherini tackled one of the hardest felid monitoring problems: estimating density of the rare, cryptic Andean cat in the high Andes, where it coexists with the more common pamp…
Juan I. Reppucci, Beth Gardner, Mauro Lucherini · Journal of Mammalogy · 2011