Guidelines for the use of spatially varying coefficients in species distribution models
Guidelines for the use of spatially varying coefficients in species distribution models
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在物种分布模型中使用空间变化系数的指南
DOI：
10.1111/geb.13814
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发表时间：
2024
影响因子：
6.4
通讯作者：
Zipkin, Elise F.
中科院分区：
文献类型：
--
作者：
Doser, Jeffrey W.;Kéry, Marc;Saunders, Sarah P.;Finley, Andrew O.;Bateman, Brooke L.;Grand, Joanna;Reault, Shannon;Weed, Aaron S.;Zipkin, Elise F.
目标物种分布模型（SDM）越来越多地应用于使用检测-非检测数据的宏观尺度。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，这种关系往往表现出非线性和/或空间变化的模式，从复杂的相互作用与非生物和生物过程，在不同的尺度上运行。空间变化系数（SVC）模型可以很容易地解释环境协变量的影响的变异性。然而，它们在生态学中的使用是相对稀缺的，由于理解的差距SVC模型可以提供的推理优势相比，简单的framework.InnovationHere，我们展示了SVC SDMs的推理优势，特别关注这种方法可以用来生成和测试生态假说的驱动程序的空间变异的人口趋势和物种与环境的关系。我们通过模拟和两个案例研究来说明SVC SDM的推论优势：第一个评估了美国东部51种森林鸟类在20年内的空间变化趋势，第二个评估了50年土地覆盖变化对蚱蜢麻雀影响的空间变化（Ammodramus savannarum）发生在整个大陆的美国。主要conclusionsWe发现强有力的支持SVC SDM相比，简单的替代品在两个实证案例研究。在精细的空间尺度上操作的因素，占的SVC，森林鸟类发生趋势的空间变异的主要潜水员。此外，SVCs揭示了蝗虫麻雀与草地和农田面积的复杂物种-栖息地关系，为未来土地利用变化如何塑造其分布提供了细致入微的见解。这些应用程序显示SVC SDM的效用，以帮助揭示环境因素，推动物种分布在本地和广泛的规模。最后，我们讨论了潜在的应用SVC SDM在生态和保护。
AimSpecies 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.InnovationHere 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 United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States.Main conclusionsWe found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. 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.
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