Abundance estimation from point counts when replication is spatially intensive but temporally limited: comparing binomial N-mixture and hierarchical distance sampling models

Abstract

We investigated the performance of hierarchical distance sampling (HDS) versus binomial N-mixture (binmix) models, both aiming at abundance (or, equivalently, density) estimation. We tested the accuracy of density estimates using simulated data and compared them to the estimates coming from a Red-breasted Flycatcher (Ficedula parva) point count survey in the Dar¿lubie Forest (N Poland). In both the simulations and the actual data, we mimicked varying plot size (i.e., radius length, and site area) and song loudness from quiet to loud by modifying detection functions. We found that the resolution at which distance detection data are collected (i.e., the number and width of distance classes) had essentially no effect on estimates and their precision in HDS, even when birds were only assigned into two, wide distance classes, such as “close” and “far”. Both site size (radius length) and song loudness affected density estimates in HDS only slightly: a positive bias (by 5%–17%) occurred when sites were small and a lower precision occurred for quiet singers.