Consequences of ignoring group association in spatial capture–recapture analysis

Abstract

Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.