We present a multivariate occupancy model to simultaneously model the presence/absence of multiple species, and demonstrate its use with a goal of estimating parameters related to occupancy. The proposed model accounts for both spatial and temporal dependence within each species, as well as dependence across all species. These dependencies are addressed through random effects, defined so there is no confounding with estimating occupancy covariate effects. Data augmentation and specific choices for the random effects permit all Gibbs updates in the Markov chain Monte Carlo algorithm, making the model computationally efficient and scalable with the number of species and size of spatial domain. A simulation study shows that the model outperforms single‐species spatiotemporal occupancy models with regard to estimating occupancy parameters. We demonstrate the model with a three species camera trap study on Thomson's gazelle, wildebeest, and zebra in the Serengeti National Park of Tanzania, Africa.

中文翻译：

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多元占用数据的时空模型

我们提出了一个多元占用模型，以同时对多个物种的存在/不存在进行建模，并证明了其使用目的是估计与占用相关的参数。提出的模型考虑了每个物种内的空间和时间依赖性，以及所有物种之间的依赖性。这些依赖性通过定义的随机效应来解决，因此不会与估计占用协变量效应混淆。数据增强和随机效应的特定选择允许Markov链蒙特卡洛算法中的所有Gibbs更新，从而使模型的计算效率高且可扩展，具有物种数和空间域大小。仿真研究表明，在估计占用参数方面，该模型优于单物种时空占用模型。