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: occupancy data from a dense nestbox scheme and total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nestbox occupancy yields a minimum number of territories, as some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates using the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the ‘truth’. We conclude that N‐mixture models estimated abundance reliably, even for our very small sample sizes. Territory‐mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a ‘territory editor’ factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.

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二项式和多项式N混合模型下的丰度估计值的现场评估

从动物计数数据评估和建模丰度是生态和管理中非常普遍的任务。可以说检测永远不可能是完美的，但是现代的分层模型可以合并检测概率并产生为不完善的检测而校正的丰度估计。这些模型的两个变体依赖于未标记的个体或区域的数量（二项式N混合模型或binmix），以及基于区域映射数据的检测历史（多项式N混合模型或multimix）。但是，评估这两个N的校准研究需要混合模型方法。我们分析了传统的领土映射数据（2014年三次调查，四个在2015年），同时使用binmix和MULTIMIX模型来估计丰两种常见鸟类洞巢森林物种（大山雀大山雀和蓝山雀Cyanistes青色）。在同一研究区域中，我们使用了两个基准：密集巢箱方案的占用数据和检测到的领土总数。为了调查由于地域分配而引起的估计差异，三位独立的鸟类学家进行了地域分配。由于可能已使用某些自然型腔，因此Nestbox占用的区域数量最少，并且binmix模型的估算值通常高于此基准。使用multimix模型的估计比binmix模型的估计更精确。取决于分配地区的人，多重混合模型的估计变得非常不同，要么高估要么低估了“真相”。我们得出结论，N混合模型可以可靠地估计丰度，即使对于很小的样本量也是如此。地域映射计数取决于地域分配，并在多重混合模型下结转到估计中。当比较站点或年份之间的丰度估计时，必须考虑此限制。建议在可能的情况下，通过包括“领土编辑器”因素在内的鸟类调查原始数据中解决这种隐藏的异质性。随机（按时间和空间）将调查问卷分发给编辑人员也可以缓解此问题。