Abstract
ContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.
Highlights
Imperfect detection is one of the primary challenges to the estimation of the size of wild populations
We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of Spatial capture-recapture (SCR) methods that can take structured, unknown or partially unknown spatial variability in detection probability into account
Spatial variation in detection probability resulting from individual space use relative to detector locations, i.e. the declining probability of detection with increasing distance from an individual’s activity center (AC), is exploited by SCR models to estimate the distribution of individual ACs
Summary
Imperfect detection is one of the primary challenges to the estimation of the size of wild populations. Regardless of the data collection method employed, rarely, if ever, are all individuals in a population detected This challenge is amplified with the increasingly widespread application of non-invasive sampling methods for making landscape-level assessments across time and space, such as camera trapping and non-invasive DNA sampling (Burton et al 2015; Beng and Corlett 2020), which often trade off local sampling intensity for extent of spatial coverage (grain vs extent; Chandler and HepinstallCymerman 2016; Steenweg et al 2018). SCR models are well-suited to account for spatially variable detectability, as studies are usually configured into discrete detection locations referred to as detectors (or traps) that are distributed across the study area (Efford et al 2013; Royle et al 2014). Spatial variation in detection probability resulting from individual space use relative to detector locations, i.e. the declining probability of detection with increasing distance from an individual’s activity center (AC), is exploited by SCR models to estimate the distribution of individual ACs
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