Community occupancy models estimate species‐specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates toward the community mean can mask differences among groups. Infinite‐mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species‐level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP‐COM with different dimensions (i.e., different numbers of model parameters with species‐level DP random effects) and under varying cluster differences. Because the DP‐COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP‐COM model to a bird data set from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community‐level covariate coefficients on occupancy probability were generally unbiased with (near‐) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP‐COM was similar. The DP‐COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP‐COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP‐COM, and considering the computational burden for the DP models, we recommend using the DP‐COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP‐COM.

中文翻译：

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贝叶斯Dirichlet过程群落占用模型，用于估计群落结构和物种相似性

群落占用模型估计特定物种的参数，同时通过处理从共同分布中采样的参数，在物种间共享信息。当社区由离散的群体组成时，估计值向社区均值的缩小会掩盖群体之间的差异。已经提出了使用狄利克雷过程（DP）分布的无限混合模型的解决方案，在该模型中，根据数据估算了潜在基团的数量。除了群落结构以外，这些模型还可以估计物种的相似性，从而可以检验有关性状是否驱动物种对环境条件做出响应的假设。我们使用DP分布开发了种群占用模型（COM），以对物种级别的参数进行建模。由于聚类算法对聚类的维数和独特性敏感，我们进行了仿真研究，以探索不同尺寸（即具有物种级DP随机效应的不同数量的模型参数）和集群差异变化下的DP-COM的性能。由于DP-COM的计算量很大，因此我们将其估计值与具有正常随机物种效应的COM进行了比较。我们进一步将DP-COM模型应用于乌干达的鸟类数据集。随着簇间差异的增加和DP尺度的增加，对簇数和物种簇同一性的估计也有所提高。但是簇的数量总是被高估了。通常，在（接近）名义上的95％贝叶斯可信区间覆盖范围内，对所占站点的数量以及物种和社区级别的占用概率协变量系数的估计是无偏的。正常值和DP-COM估计值的准确性相似。DP-COM将它们与开放或林地栖息地以及与油井的距离联系起来，将166种鸟类分为27种。正常变量和DP-COM之间的协变量系数估计值相似。除太阳鸟外，一个家庭中的物种对这些协变量的反应与整个社区的相似度更高。鉴于正常值和DP-COM之间的估计值是一致的，并且考虑到DP模型的计算负担，我们建议仅在分析着重于群落结构和物种相似性时才使用DP-COM，因为只能获得这些数量在DP-COM下。DP-COM将它们与开放或林地栖息地以及与油井的距离联系起来，将166种鸟类分为27种。正常变量和DP-COM之间的协变量系数估计值相似。除太阳鸟外，一个家庭中的物种对这些协变量的反应与整个社区的相似度更高。鉴于正常值和DP-COM之间的估计值是一致的，并且考虑到DP模型的计算负担，我们建议仅在分析着重于群落结构和物种相似性时才使用DP-COM，因为只能获得这些数量在DP-COM下。DP-COM将它们与开放或林地栖息地以及与油井的距离联系起来，将166种鸟类分为27种。正常变量和DP-COM之间的协变量系数估计值相似。除太阳鸟外，一个家庭中的物种对这些协变量的反应与整个社区的相似度更高。鉴于正常值和DP-COM之间的估计值是一致的，并且考虑到DP模型的计算负担，我们建议仅在分析着重于群落结构和物种相似性时才使用DP-COM，因为只能获得这些数量在DP-COM下。一个家庭中的物种对这些协变量的反应与整个社区的相似度更高。鉴于正常值和DP-COM之间的估计值是一致的，并且考虑到DP模型的计算负担，我们建议仅在分析着重于群落结构和物种相似性时才使用DP-COM，因为只能获得这些数量在DP-COM下。一个家庭中的物种对这些协变量的反应并不比整个社区更相似。鉴于正常值和DP-COM之间的估计值是一致的，并且考虑到DP模型的计算负担，我们建议仅在分析着重于群落结构和物种相似性时才使用DP-COM，因为只能获得这些数量在DP-COM下。