Camera traps in animal ecology : methods and analyses

By A. F. O'connell, James D. Nichols, Ke. Ullāsa Kāranta. 2011

656 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c6dfab578246c051e9d03a70170eb4fee0cc552

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Citations in the corpus

From regional to global patterns in vertebrate scavenger communities subsidized by big game hunting (2015, methodology): “techniques used to record scavenging (Table S1) could underestimate it (O’Connell et al., 2010).”
Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna (2015, methodology): “Here we describe the datasets generated by Snapshot Serengeti, a large-scale survey that (1) deployed 225 camera traps across a 1,125 km(2) area in the Serengeti National Park, Tanzania from 2010–2013, (2) used a citizen science website (www.”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, background): “Biologist can monitor wildlife with camera traps for detecting rare species, delineating species distributions, monitoring animal behavior, and measuring other biological rates [1].”
Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps (2021, unknown): “Animal abundance can be estimated from camera trap footage using capture-recapture methods which require the individual identification of animals (O’Connell et al., 2011).”
Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: challenges and opportunities (2017, background): “Camera trapping is widely used in ecology and conservation for investigating species’ distributions, estimating population densities and inventorying biodiversity (O’Connell et al. 2011; Burton et al. 2015; Steenweg et al. 2017).”
Estimating mammalian species richness and occupancy in tropical forest canopies with arboreal camera traps (2017, methodology): “…method for assessing terrestrial mammal populations, measuring species richness, occupancy, absolute density and activity patterns (McCallum 2013; O’Connell et al. 2011; Ahumada et al. 2011; Tobler et al. 2015) and their potential for standardization has led to the prioritization of their use in…”
Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks (2016, background): “many are there?", among others. Also fundamental studies, like detecting rare species, delineating species distributions, documenting predation, monitoring animal behavior, and other vital rates [2] are carried out with this method. Hence, it allows biologist to protect animals and its environm”