Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (so-called abiotic features) present at the site. Historically, such models have considered species individually. However, it is well-established that species interact to influence presence-absence and abundance (envisioned as biotic factors). As a result, there has been substantial recent interest in joint species distribution models with various types of response, e.g., presence-absence, continuous and ordinal data. Such models incorporate dependence between species response as a surrogate for interaction. The challenge we address here is how to accommodate such modeling in the context of a large number of species (e.g., order 102) across sites numbering on the order of 102 or 103 when, in practice, only a few species are found at any observed site. Again, there is some recent literature to address this; we adopt a dimension reduction approach. The novel wrinkle we add here is spatial dependence. That is, we have a collection of sites over a relatively small spatial region so it is anticipated that species distribution at a given site would be similar to that at a nearby site. Specifically, we handle dimension reduction through Dirichlet processes, enabling clustering of species, joined with spatial dependence across sites through Gaussian processes. We use both simulated data and a plant communities dataset for the Cape Floristic Region (CFR) of South Africa to demonstrate our approach. The latter consists of presence-absence measurements for 639 tree species at 662 locations. Through both data examples we are able to demonstrate improved predictive performance using the foregoing specification.

中文翻译：

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空间联合物种分布建模

物种分布模型通常试图根据该地点存在的环境特征（所谓的非生物特征）来解释该地点物种的存在-不存在或丰富度。从历史上看，此类模型单独考虑了物种。然而，众所周知，物种相互作用会影响存在与不存在和丰度（设想为生物因素）。因此，最近人们对具有各种类型响应（例如存在-不存在、连续和有序数据）的联合物种分布模型产生了极大的兴趣。这些模型将物种反应之间的依赖性作为相互作用的替代。我们在这里面临的挑战是如何在编号为 102 或 103 的地点的大量物种（例如，102 目）的背景下适应这种建模，而实际上，在任何观察到的地点都只发现了少数物种。地点。同样，最近有一些文献解决了这个问题；我们采用降维方法。我们在这里添加的新问题是空间依赖性。也就是说，我们在相对较小的空间区域内收集了多个地点，因此预计给定地点的物种分布将与附近地点的物种分布相似。具体来说，我们通过狄利克雷过程处理降维，从而实现物种聚类，并通过高斯过程与站点之间的空间依赖性相结合。我们使用南非开普弗洛里地区 (CFR) 的模拟数据和植物群落数据集来演示我们的方法。后者包括 662 个地点的 639 种树种的存在与不存在测量。通过这两个数据示例，我们能够证明使用上述规范改进的预测性能。