Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These joint models capture species dependence through an associated correlation matrix arising from a set of latent multivariate normal variables. However, these associations offer limited insight into realized dependence behavior between species at sites. We focus on presence/absence data using joint species modeling, which, in addition, incorporates spatial dependence between sites. For pairs of species selected from a collection, we emphasize the induced odds ratios (along with the joint occurrence probabilities); they provide a better appreciation of the practical dependence between species that is implicit in these joint species distribution modeling specifications. For any pair of species, the spatial structure enables a spatial odds ratio surface to illuminate how dependence varies over the region of interest. We illustrate with a dataset from the Cape Floristic Region of South Africa consisting of more than 600 species at more than 600 sites. We present the spatial distribution of odds ratios for pairs of species that are positively correlated and pairs that are negatively correlated under the joint species distribution model.

如今，联合物种分布建模越来越引起人们的关注，承认个体级建模未能考虑到物种之间的预期依赖性/相互作用这一事实。这些联合模型通过一个潜在的多元正态变量集所产生的关联相关性矩阵来捕获物种依赖性。但是，这些关联对站点之间物种之间已实现的依赖行为的了解有限。我们关注使用联合物种建模的存在/不存在数据，此外，它还结合了站点之间的空间依赖性。对于从一个集合中选择的成对物种，我们强调诱导的优势比（以及联合发生概率）；他们提供了对物种之间实际依赖性的更好理解，这些依赖性在这些联合物种分布建模规范中是隐含的。对于任何一对物种，空间结构都能使空间比值比表面显示出相关区域内相关性的变化方式。我们以南非开普植物区的数据集为例，该数据集由600多个地点的600多个物种组成。在联合物种分布模型下，我们给出了正相关的物种对和负相关的对的优势比的空间分布。我们用来自南非开普植物区的数据集进行说明，该数据集由600多个地点的600多个物种组成。在联合物种分布模型下，我们给出了正相关的物种对和负相关的对的优势比的空间分布。我们以南非开普植物区的数据集为例，该数据集由600多个地点的600多个物种组成。在联合物种分布模型下，我们给出了正相关的物种对和负相关的对的优势比的空间分布。