Department of Statistics, Colorado State University, Fort Collins, 80523, USA.
Colorado Parks and Wildlife, Fort Collins, 80526, USA.
Sci Rep. 2022 Jul 18;12(1):12235. doi: 10.1038/s41598-022-15694-6.
Joint species distribution models have become ubiquitous for studying species-environment relationships and dependence among species. Accounting for community structure often improves predictive power, but can also affect inference on species-environment relationships. Specifically, some parameterizations of joint species distribution models allow interspecies dependence and environmental effects to explain the same sources of variability in species distributions, a phenomenon we call community confounding. We present a method for measuring community confounding and show how to orthogonalize the environmental and random species effects in suite of joint species distribution models. In a simulation study, we show that community confounding can lead to computational difficulties and that orthogonalizing the environmental and random species effects can alleviate these difficulties. We also discuss the inferential implications of community confounding and orthogonalizing the environmental and random species effects in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the outputs from occupancy models that treat species independently or account for interspecies dependence. We illustrate how joint species distribution models that restrict the random species effects to be orthogonal to the fixed effects can have computational benefits and still recover the inference provided by an unrestricted joint species distribution model.
联合物种分布模型已广泛用于研究物种-环境关系和物种间的相互依存关系。考虑群落结构通常可以提高预测能力，但也会影响对物种-环境关系的推断。具体来说，联合物种分布模型的一些参数化允许种间依存关系和环境效应解释物种分布中相同的变异性来源，我们称之为群落混淆。我们提出了一种测量群落混淆的方法，并展示了如何在联合物种分布模型中对环境和随机物种效应进行正交化。在一项模拟研究中，我们表明群落混淆可能导致计算困难，而对环境和随机物种效应进行正交化可以缓解这些困难。我们还讨论了在亚高山森林中哺乳动物对科罗拉多树皮甲虫流行的反应的案例研究中，群落混淆和对环境和随机物种效应进行正交化的推断意义，通过比较独立处理物种或考虑种间依存关系的占有模型的输出。我们说明了如何通过将随机物种效应限制为与固定效应正交，来使联合物种分布模型具有计算优势，同时仍能恢复不受限制的联合物种分布模型提供的推断。
