Quantifying the sensitivity of camera traps: an adapted distance sampling approach

By J. Marcus Rowcliffe, Chris Carbone, Patrick A. Jansen, R. Kays, Bart Kranstauber. 2011

281 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/163dc3837443bc3e63534bbf102d03b12e1ce454

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In Ecological Modeling

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Citations in the corpus

Distance sampling with camera traps (2017, methodology): “Below, we describe an approach for accurately assigning animals to distance intervals; Rowcliffe et al. (2011) and Caravaggi et al. (2016) describe methods for measuring continuous distances betweenCTs and detected animals.”
An invasive‐native mammalian species replacement process captured by camera trap survey random encounter models (2016, methodology): “The angle (ai) to the backpack from the camera was measured using a handheld compass, following Rowcliffe et al. (2008, 2011).”
Automated distance estimation for wildlife camera trapping (2022, methodology): “, 2017), even more time-consuming on-site distance measurements (Rowcliffe et al., 2011) or semi-automatic calibration of relative depth images for the specific sequence (Haucke et al.”
Domestic dogs shape the landscape-scale distribution of a threatened forest ungulate (2012, methodology): “For each camera we also recorded the maximum distance at which the camera detected movement which is one of the main factors that determine whether a camera is triggered and consequently the probability of detection in camera traps (Rowcliffe et al., 2011).”
A simple method for estimating the effective detection distance of camera traps (2017, methodology): “For each sample, we fitted a point detection function model to the direct trigger distance data to provide baseline expected EDD, using previously published methods (Rowcliffe et al. 2011).”
Towards good practice guidance in using camera‐traps in ecology: influence of sampling design on validity of ecological inferences (2013, background): “Several other studies also emphasized on the factors affecting trapping efficiency and sensitivity such as sex, species, season and camera position (Rowcliffe et al. 2008, 2011; Guil et al. 2010; Harmsen, Foster & Doncaster 2011; Foster & Harmsen 2012).”
Quantifying levels of animal activity using camera trap data (2014, methodology): “However, using the covariate model for camera sensitivity described in Rowcliffe et al. (2011), we find that camera detection radius is 21% higher during the day than during the night (Appendix S2).”
Building capacity in biodiversity monitoring at the global scale (2017, methodology): “Important advances have been made in estimating densities and population sizes from camera trap data using Random Encounter Models and Non-Instantaneous Plot Sampling (Rowcliffe et al. 2008, 2011, 2012), and in integrating data derived from different monitoring techniques (Popescu et al. 2014).”
Estimating animal density without individual recognition using information derivable exclusively from camera traps (2018, methodology): “The detection zone can be defined as the area in which the number of detections is maximized using the approach of Rowcliffe et al. (2011).”
"Which camera trap type and how many do I need?" A review of camera features and study designs for a range of wildlife research applications (2013, methodology): “…frameworks (review in Nichols et al. 2011); see also O’Brien et al. (2010); Kinnaird and O’Brien (2012) for species richness and occupancy analysis; Rowcliffe et al. (2011) for REM; Bengsen et al. (2011) for a Generalised Index Approach; Schaub et al. (2004) for multistate CMRmodels; Gardner…”