Species distribution models (SDMs) are increasingly applied across macroscales. Such models typically assume that a single set of regression coefficients can adequately describe species-environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially-varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially-varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks. Here we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species-environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially-varying trends of 51 forest bird species in the eastern US over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on Grasshopper Sparrow occurrence across the continental US. We found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation.

翻译：物种分布模型（Species Distribution Models, SDMs）正日益广泛应用于宏观尺度。此类模型通常假设一组回归系数足以描述物种-环境关系和/或种群趋势。然而，这些关系往往表现出非线性及/或空间变化模式，这些模式源于不同尺度上非生物与生物过程的复杂相互作用。空间变化系数（Spatially-Varying Coefficient, SVC）模型能够便捷地解释环境协变量效应的变异性。然而，由于对SVC模型相较于更简单框架所能提供的推断优势理解不足，其在生态学中的应用仍相对稀少。本文展示了SVC SDMs的推断优势，特别关注该方法如何用于生成并检验关于种群趋势及物种-环境关系空间变异驱动因子的生态假设。我们通过模拟实验和两个案例研究阐述了SVC SDMs的推断优势：其一评估了美国东部51种森林鸟类在过去二十年间空间变化的种群趋势；其二评估了美国大陆范围内五十年来土地覆盖变化对草蝗鹀（Grasshopper Sparrow）出现率的空间效应变异性。在两个实证案例中，我们均发现SVC SDMs相比更简单的替代模型具有更强支持。这些应用展示了SVC SDMs在揭示驱动局部与广阔尺度上物种分布的环境因子方面的实用性。最后，我们讨论了SVC SDMs在生态学与保护生物学中的潜在应用。