Modeling variation in abundance using capture-recapture data

The “un” in unmarked is somewhat misleading because the package can be used to analyze data from marked animals. The three most common sampling methods that produce suitable data are removal sampling, double observer sampling, and capture-recapture methods. This document focuses on the analysis of capture-recapture data using a class of models known as multinomial N -mixture models (Royle, 2004; Dorazio et al., 2005), which assume that capture-recapture data have been collected at a collection of sample locations (“sites”). Capture-recapture models can be fitted with constant parameters (M0), time-specific parameters (Mt), and behavioral responses (Mb). In addition, spatial variation in abundance and capture probability can also be modeled using covariates. unmarked has two functions for fitting capture-recapture models: multinomPois and gmultmix. Both allow for user-defined functions to describe the capture process, and the latter allows for modeling of temporary emigration when data have been collected using the so-called robust design (Kendall et al., 1997; Chandler et al., 2011).