Consequences of ignoring variable and spatially-autocorrelated detection probability in spatial capture-recapture

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, intrinsic and extrinsic to the study system. ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates. MethodsWe simulated realistic SCR data with spatially variable and autocorrelated detection probability. We then fitted a single-session 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 (Morans I = 0.85 - 0.96), modulated by the magnitude of that variation, can lead to pronounced negative bias (up to 75%), reduction in precision (249%), and decreasing coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Morans 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.