Empirical evaluation of the spatial scale and detection process of camera trap surveys

Kays, Roland; Hody, Allison; Jachowski, David S.; Parsons, Arielle W.

doi:10.1186/s40462-021-00277-3

Cited by 19 publications

(24 citation statements)

References 37 publications

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“…The human effort and cost associated with sampling more than 60 placements would not be feasible in some management programmes, which may limit the applicability of REM for wildlife monitoring (but CV of <0.2 may not always be necessary’). In this respect, some studies have shown seasonal variation in encounter rates (Kays et al., 2021; Kolowski et al., 2021), so a general recommendation when applying REM could be to survey populations when low aggregation is expected. This could help to optimise human effort.…”

Section: Discussionmentioning

confidence: 99%

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Palencia

Barroso

Vicente

et al. 2022

Remote Sens Ecol Conserv

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Population density estimates are important for wildlife conservation and management. Several camera trapping-based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for speciesspecific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REMreference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation-based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals.

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Section: Discussionmentioning

confidence: 99%

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Palencia

Barroso

Vicente

et al. 2022

Remote Sens Ecol Conserv

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“…To investigate how recreational activities impacted wildlife habitat use, we modeled the “probability of use” (Kays et al, 2021 ) for each of these focal species during a station‐week with a Bayesian generalized linear mixed‐effects model (GLMM), assuming a binomial response distribution and including station as a random intercept to account for non‐independence among weeks sampled at the same station. Using this GLMM specification, the probability of use of a station during each week for each species was modeled as a function of several hypothesized explanatory variables (Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…We therefore assumed that the potential influence of variation in detectability was minimized by our standardized protocols and inclusion of these sampling variables. We did not use an occupancy modeling approach to estimate detectability, as we were interested in variation in detections as a signal of local habitat use rather than as detection error, and we considered it unlikely that the assumptions of occupancy modeling would be met in our sampling context (e.g., site closure; Neilson et al, 2018 ; Kays et al, 2021 ). We assumed any temporal autocorrelation was accounted for through the inclusion of the random effect of camera station, and tested for spatial autocorrelation among stations using Mantel tests on model residuals.…”

Section: Methodsmentioning

confidence: 99%

Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns

Procko

Naidoo

LeMay

2022

Conservat Sci and Prac

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The dual mandate for many protected areas (PAs) to simultaneously promote recreation and conserve biodiversity may be hampered by negative effects of recreation on wildlife. However, reports of these effects are not consistent, presenting a knowledge gap that hinders evidence‐based decision‐making. We used camera traps to monitor human activity and terrestrial mammals in Golden Ears Provincial Park and the adjacent University of British Columbia Malcolm Knapp Research Forest near Vancouver, Canada, with the objective of discerning relative effects of various forms of recreation on cougars (Puma concolor), black bears (Ursus americanus), black‐tailed deer (Odocoileus hemionus), snowshoe hares (Lepus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Additionally, public closures of the study area associated with the COVD‐19 pandemic offered an unprecedented period of human‐exclusion through which to explore these effects. Using Bayesian generalized mixed‐effects models, we detected negative effects of hikers (mean posterior estimate = −0.58, 95% credible interval [CI] −1.09 to −0.12) on weekly bobcat habitat use and negative effects of motorized vehicles (estimate = −0.28, 95% CI −0.61 to −0.05) on weekly black bear habitat use. We also found increased cougar detection rates in the PA during the COVID‐19 closure (estimate = 0.007, 95% CI 0.005 to 0.009), but decreased cougar detection rates (estimate = −0.006, 95% CI −0.009 to −0.003) and increased black‐tailed deer detection rates (estimate = 0.014, 95% CI 0.002 to 0.026) upon reopening of the PA. Our results emphasize that effects of human activity on wildlife habitat use and movement may be species‐ and/or activity‐dependent, and that camera traps can be an invaluable tool for monitoring both wildlife and human activity, collecting data even when public access is barred. Further, we encourage PA managers seeking to promote both biodiversity conservation and recreation to explicitly assess trade‐offs between these two goals in their PAs.

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“…Missed detections could potentially generate misleading results in camera trapping studies, and therefore, create difficulties as regards direct comparison among studies (Jacobs & Ausband, 2018). In this respect, despite some statistical approaches accounting for the imperfect detection (Guillera-Arroita et al, 2010), recent studies have shown that biased and imprecise results can be obtained when detection probabilities are low (Hofmeester et al, 2021;Kays et al, 2021). Additionally, (1) there are a wide variety of studies that used the raw encounter rates without accounting for false-negatives (i.e.…”

Section: Introductionmentioning

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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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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Empirical evaluation of the spatial scale and detection process of camera trap surveys

Cited by 19 publications

References 37 publications

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns

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

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