Spatially explicit capture–recapture models for relative density

Spatially explicit capture–recapture (SECR) methods are used widely to estimate animal population density and related parameters. Maximum likelihood has been applied to two flavors of SECR model - a full model that includes absolute density as a spatially varying parameter, and a model conditional on the number of detected individuals that assumes uniform density. We show how the conditional model may be extended to estimate relative density as a function of habitat and other spatial variables. The conditional model has the major advantage of seamlessly including individual covariates of detection. It is also faster to fit than the full model and more robust with an ‘identity’ link. The spatial absolute density surface may be derived from the conditional model fit. We demonstrate this with a spline surface fitted to data on American black bears ( Ursus americanus ). The relative density formulation also allows an adjustment for spatially selective prior marking. The problem arises when fish are caught and implanted with transponders, such as acoustic tags, and later monitored intensively with passive recorders. The confounding spatial effect of undocumented initial sampling may be removed from spatial analyses of the monitoring data by including an offset proportional to the smoothed sampling effort.