Distance sampling analysis in unmarked

Distance sampling is a wildlife sampling technique used to estimate population size or density. Describing how density varies spatially is often of equal interest; however, conventional methods of analysis do not allow for explicit modeling of both density and detection probability. The function distsamp implements the multinomial-Poisson mixture model of Royle et al. (2004), which was developed to overcome this limitation. This model requires that lineor point-transects are spatially replicated and that distance data are recorded in discrete intervals. The function gdistsamp extends this basic model, by introducing the parameter φ, the probability of being available for detection (Chandler et al., 2011). Furthermore, this function allows abundance to be modeled using the negative binomial distribution, which may be useful for dealing with over-dispersion. This document describes how to format data, fit models, and manipulate results in package unmarked. It does not cover the statistical theory and assumptions underlying distance sampling (Buckland et al., 2001), which the user is expected to be familiar with.