Field evaluation of abundance estimates under binomial and multinomial N‐mixture models

Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories, (binomial N‐mixture models, or binmix) and on detection histories based on territory mapping data (multinomial N‐mixture models or multimix). However, calibration studies which evaluate these two N‐mixture model approaches are needed. We analysed conventional territory mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity‐nesting forest species (Great Tit Parus major and Eurasian Blue Tit Cyanistes caeruleus). In the same study area, we used two benchmarks: (i) occupancy data from a dense nest box scheme; (ii) total number of detected t...