Assessing the camera trap methodologies used to estimate density of unmarked populations

Palencia, Pablo; Rowcliffe, J. Marcus; Vicente, Joaquín; Acevedo, Pelayo

doi:10.1111/1365-2664.13913

Cited by 86 publications

(112 citation statements)

References 48 publications

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“…It should be noted that despite the significant effect of the camera trap model, we did not find any association between theoretical trigger speed (ranging from 0.25 to 1.2, Table 1) and the probability of rapid activation. This can be explained as a consequence of the variability in intra-camera trigger times observed in other studies (https:// www.trailcampro.com/collections/trail-camera-reviews; Palencia, Rowcliffe et al, 2021), the type of detection zone (zonal or conical, Driessen et al, 2017), and the relatively wide intervals (width = 2.5 m) that have been considered here to evaluate the trigger speed. Future studies therefore should be designed to explore the relevance of the trigger speed by considering shorter intervals, and potentially, not only radials but also angled intervals (Howe et al, 2017;Rowcliffe et al, 2011).…”

Section: Discussionmentioning

confidence: 92%

“…a common detection zone for the 15 camera traps at each sampling point) and were later used to locate the position of the individuals registered. We considered 10 m as maximum distance based on previous studies that reported detection distances for the species sampled in this study (Hofmeester et al, 2017;Palencia, Rowcliffe et al, 2021). The cameras were angled to be parallel to the slope of the ground.…”

Section: Experimental Designmentioning

confidence: 99%

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Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

et al. 2021

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Camera trapping is a widely used tool in wildlife research and conservation, and a plethora of makes and models of camera traps have emerged. However, insufficient attention has been paid to testing their performance, particularly under field conditions. In this study, we have comparatively tested five of the most frequently used makes of camera trap (Bushnell, KeepGuard, Ltl Acorn, Reconyx and Scoutguard) to identify the key factors behind their probability of detection (i.e. the probability that the camera successfully capturing a usable photograph of an animal passing through the field of view) and trigger speed (i.e. the time delay between the instant at which a motion is detected, and the time at which the picture is taken). We used 45 cameras (nine devices of each make) with infrared flash in a field experiment in which a continuous remote video was used in parallel (as a gold-standard) to discover the animals that entered the camera trap detection zone. The period (day/ night), distance between animals and cameras, model, species, deployment height and activation sensitivity were significantly related to the probability of detection. This probability was lower during the night than during the day. There was a greater probability of detecting a given species when the cameras were set at its shoulder height. The interaction between species and the distance between the animals and the cameras significantly affected the trigger speed, meaning that the closer the animals that entered the detection zone, the higher the trigger speed, with substantial differences among species. This was probably related by movement speed. In conclusion, this study shows differences in the performance of camera trap models and settings, signifying that caution is required when making direct comparisons among results obtained in different experiments, or when designing new ones. These results provide empirical guidelines for best practices in camera trapping and highlight the relevance of field experiments for testing the performance of the camera traps.

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Section: Discussionmentioning

confidence: 92%

Section: Experimental Designmentioning

confidence: 99%

Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

et al. 2021

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“…The REST model requires some assumptions, including the certain detection of animals entering the focal area (Nakashima et al 2018); however, it is an efficient and realistic method for estimating the density of ground‐dwelling mammals lacking individually recognizable markings (Palencia et al 2021). In the model, the relationship between animal density D and camera trap data is described by the following equation:

D = E (Y) \times E (T) / (s H a)

…”

Section: Methodsmentioning

confidence: 99%

“…For example, nocturnal distance sampling with thermal imagers (Franzetti et al 2012, Focardi et al 2020) and capture-mark-recapture with ear tags (Hebeisen et al 2008) or DNA (Ebert et al 2012) are used to estimate the absolute density of wild boar. Recently, methods for the estimation of the absolute density of mammals from camera trap data without individual identification have been devised and used, such as the random encounter model (REM; Rowcliffe et al 2008), spatially explicit N-mixture model (Chandler and Royle 2013), distance sampling (CTDS; Howe et al 2017), and random encounter and staying time model (REST; Nakashima et al 2018), and some of these have been applied to wild boar (Palencia et al 2021). However, methods for estimating absolute density are often not suitable for monitoring over large spatial areas (e.g.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of signs of activity as relative abundance indices for wild boar

et al. 2021

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“…In addition, time series of WVC data could be used for monitoring population trends in a multi season framework. In this context, abundance predictions from the Royle–Nichols models should be evaluated to study their ability to be extrapolated, down or upscaled, etc., using other independent sources such as telemetry or camera trapping studies (Palencia et al 2021).…”

Section: Concluding Remarks and Recommendationsmentioning

confidence: 99%

Can we model distribution of population abundance from wildlife–vehicles collision data?

et al. 2022

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Reliable estimates of the distribution of species abundance are a key element in wildlife studies, but such information is usually difficult to obtain for large spatial or long temporal scales. Wildlife–vehicle collision (WVC) data is systematically registered in many countries and could be used as a proxy of population abundance if the number of WVC in each territory increase with the population abundance. However, factors such as road density or human population should be controlled to obtain accurate abundance estimations from WVC data. Here, we propose a hierarchical modeling approach using the Royle–Nichols model for detection–non‐detection data to obtain population abundance indices from WVC. Relative abundance and individual detectability were modeled for two species, wild boar Sus scrofa and roe deer Capreolus capreolus at 10 × 10 km cells in mainland Spain from WVC data using environmental, anthropological and temporal covariates. For each cell, a detection was annotated if at least one WVC was recorded at each month (used as survey occasion). The predicted abundance indices were compared with raw hunting statistics at region level to assess the performance of the modeling approach. Site specific covariates such as road density or administrative region and the month of the year, affected individual detectability, with higher WVC probability between October and December for wild boar and between April and July for roe deer. Wild boar and roe deer abundance can be explained by both, bioclimatic and land cover covariates. Abundance indices obtained from WVC data were significantly positively correlated with regional raw hunting yields for both species. We presented empirical evidence supporting that accurate wildlife abundance indices at fine spatial resolution can be generated from WVC data when individual detectability is considered in the modeling process.

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Assessing the camera trap methodologies used to estimate density of unmarked populations

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 86 publications

References 48 publications

Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions

Effectiveness of signs of activity as relative abundance indices for wild boar

Can we model distribution of population abundance from wildlife–vehicles collision data?

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