Species distribution models (SDMs) are increasingly applied across macroscales using detection-nondetection data. These 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.

中文翻译：

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在物种分布模型中使用空间变化系数的指南

物种分布模型 (SDM) 越来越多地使用检测-非检测数据在宏观尺度上应用。这些模型通常假设一组回归系数可以充分描述物种-环境关系和/或种群趋势。然而，这种关系通常表现出非线性和/或空间变化的模式，这些模式是由不同尺度下运行的非生物和生物过程的复杂相互作用引起的。空间变化系数 (SVC) 模型可以轻松解释环境协变量影响的变异性。然而，由于对 SVC 模型与更简单的框架相比所能提供的推理优势的理解存在差距，它们在生态学中的应用相对较少。