Statistical Development of Animal Density Estimation Using Random Encounter Modelling

Jourdain, Natoya; Cole, Diana J.; Ridout, Martin S.; Rowcliffe, J. Marcus

doi:10.1007/s13253-020-00385-4

Cited by 10 publications

(10 citation statements)

References 43 publications

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“…All of the models mentioned above have had their accuracy tested using simulations, and in some cases with field data, but none has been demonstrated to consistently yield accurate estimates for a variety of species under field conditions. There has been ongoing development of the REM framework (Rowcliffe et al 2011, 2014, 2016, Lucas et al 2015, Gilbert et al 2020, Jourdain et al 2020), but practical applications have had mixed success due to inappropriate survey design or difficulties estimating the speed of animal movement (Rovero and Marshall 2009, Zero et al 2013, Anile et al 2014, Cusack et al 2015, Balestrieri 2016, Caravaggi et al 2016). Nakashima et al (2020) estimated densities and relationships between density and habitat covariates for sympatric duiker species and concluded that the method could be effective for estimating ungulate densities.…”

Section: Introductionmentioning

confidence: 99%

Estimating animal abundance and effort–precision relationship with camera trap distance sampling

et al. 2021

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Effective monitoring methods are needed for assessing the state of biodiversity and detecting population trends. The popularity of camera trapping in wildlife surveys continues to increase as they are able to detect species in remote and difficult-to-access areas. As a result, several statistical estimators of the abundance of unmarked animal populations have been developed, but none have been widely tested. Even where the potential for accurate estimation has been demonstrated, whether these methods estimators can yield estimates of sufficient precision to detect trends and inform conservation action remains questionable. Here, we assess the effort-precision relationship of camera trap distance sampling (CTDS) in order to help researchers design efficient surveys. A total of 200 cameras were deployed for 10 months across 200 km 2 in the Taï National Park, Côte d'Ivoire. We estimated abundance of Maxwell's duikers, western chimpanzees, leopards, and forest elephants that are challenging to enumerate due to rarity or semi-arboreality. To test the effects of spatial and temporal survey effort on the precision of CTDS estimates, we calculated coefficient of variation (CV) of the encounter rate from subsets of our complete data sets. Estimated abundance of leopard and Maxwell's duiker density (20% < CV < 30% and CV = 11%, respectively) were similar to prior estimates from the same area. Abundances of chimpanzees (20% < CV < 30%) were underestimated, but the quality of inference was similar to that reported after labor-intensive line transect surveys to nests. Estimates for the rare forest elephants were potentially unreliable since they were too imprecise (60% < CV < 200%). Generalized linear models coefficients indicated that for relatively common, ground-dwelling species, CVs between 10% and 20% are achievable from a variety of survey designs, including long-term (6+ months) surveys at few locations (50), or short term (2-week to 2-month) surveys at 100-150 locations. We conclude that CTDS can efficiently provide estimates of abundance of multiple species of sufficient quality and precision to inform conservation decisions. However, estimates for the rarest species will be imprecise even from ambitious surveys and may be biased for species that exhibit strong reactions to cameras.

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Section: Introductionmentioning

confidence: 99%

Estimating animal abundance and effort–precision relationship with camera trap distance sampling

et al. 2021

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“…Multiple detectors are also necessary to appropriately characterize and quantify the variance in the data, and thus estimate the error in the density estimates. Error estimation in REM models is a non-trivial issue, given that (1) the estimator is a ratio with variable numerator and denominator (Jourdain et al 2020), (2) process error and observation error are nearly indistinguishable (Rowcliffe et al 2008), and (3) the underlying data-generating process is not always the same (e.g. Poisson vs. negative binomial distribution of the number of detections).…”

Section: Discussionmentioning

confidence: 99%

“…Rowcliffe et al (2008) recognized that maximum likelihood methods would be ideal to characterize variance but, given these difficulties, they and other researchers have instead relied on nonparametric bootstrapping (Manzo et al 2012;Cusack et al 2015;Caravaggi et al 2016). More recently, Jourdain et al (2020) proposed suitable alternatives to estimate variance using the delta method, which compounds different sources of variability. Our model shares some of the same sources of error and underlying movement process as the 2D case, so these variance estimation methods would also be applicable in the 3D case.…”

Section: Discussionmentioning

confidence: 99%

“…Error estimation in REM models is a non‐trivial issue, given that (1) the estimator is a ratio with variable numerator and denominator (Jourdain et al. 2020), (2) process error and observation error are nearly indistinguishable (Rowcliffe et al. 2008), and (3) the underlying data‐generating process is not always the same (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Jourdain et al. (2020) proposed suitable alternatives to estimate variance using the delta method, which compounds different sources of variability. Our model shares some of the same sources of error and underlying movement process as the 2D case, so these variance estimation methods would also be applicable in the 3D case.…”

Section: Discussionmentioning

confidence: 99%

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Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Soto

Castañeda

Mandrak

et al. 2020

Remote Sens Ecol Conserv

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Remote detectors are being used increasingly often to study aquatic and aerial species, which move significantly differently from terrestrial species. Camera-trapping studies have used frameworks based on animal movement patterns to show that a species' detection frequency, along with movement speed and detector specifications, can be used to estimate absolute population density. This approach, however, has not yet been adapted to cases where movement is threedimensional. Here we adapt one such framework to three-dimensional movement, to characterize the relationship between population density, animal speed, characteristics of a remote sensor's detection zone and detection frequency. The derivation involves defining the detection zone mathematically and calculating the mean area of the profile it presents to approaching individuals. We developed two variants of the model-one assuming random movement of all individuals, and one allowing for different probabilities for each approach direction (e.g. that animals more often swim/fly horizontally than vertically). We used computer simulations to evaluate model performance for a wide range of animal and detector densities. Simulations show that in ideal conditions the method approximates true density well, and that estimates become increasingly accurate using more detectors, or sampling for longer. Moreover the method is robust to violations of assumptions, accuracy is decreased only in extreme cases where all detectors are facing the same way. We provide equations for estimating population density from detection frequency and outline how to estimate the necessary parameters. We discuss how environmental variables and species-specific characteristics affect parameter estimates and how to account for these differences in density estimations. Our method can be applied to common remote detection methods (cameras and acoustic detectors), which are currently being used to study a diversity of species and environments. Therefore, our work may significantly expand the number and diversity of species for which density can be estimated.

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Estimating effective survey duration in camera trap distance sampling surveys

Kühl,

Buckland,

Henrich

et al. 2023

Ecology and Evolution

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Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined ‘snapshot moments’. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. ‘camera recovery time’) or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. ‘retrigger delays’). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating ‘mean time intervals between triggers’ as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model‐based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS.

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Statistical Development of Animal Density Estimation Using Random Encounter Modelling

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 10 publications

References 43 publications

Estimating animal abundance and effort–precision relationship with camera trap distance sampling

Estimating animal abundance and effort–precision relationship with camera trap distance sampling

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Estimating effective survey duration in camera trap distance sampling surveys

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