Multiple observation processes in spatial capture-recapture models: How much do we gain?

Abstract

Population monitoring data may originate from multiple methods and are often sparse and fraught with incomplete information due to practical and economic constraints. Models that can integrate multiple survey methods and are able to cope with incomplete data may help investigators exploit available information more thoroughly. Here, we developed an integrated spatial capture-recapture (SCR) model to incorporate multiple data sources with imperfect individual identification. We contrast inferences drawn from this model with alternate models incorporating only subsets of the data available. Using extensive simulations and an empirical example of multi-method brown bear (Ursus arctos) monitoring data from northern Pakistan, we quantified the benefits of including multiple sources of information in SCR models in terms of parameter precision and bias. Our multiple observation processes SCR model (MOP) yielded a more complete picture of the underlying processes, reduced bias, and led to more precise parameter estimates. Our results suggest that the greatest gains from integrated SCR models can be expected in situations where detection probability is low, a large proportion of detections is not attributable to individuals, and the degree of overlap between individual home ranges is low.