复制的多尺度物种分布模型(SDM)对于确定确定物种分布及其对生态响应的影响的正确变量变得越来越重要。本研究探讨了中国西部青藏高原两个研究区雪豹(Pantherauncia)的多尺度栖息地关系。我们的主要目标是评估雪豹栖息地关系的程度,用预测因子表示,反应的尺度,以及影响的大小,在不同研究区域或当地景观特定的情况下是一致的。我们耦合单变量尺度优化和最大熵算法来产生多变量SDM,通过集成性能最高的模型来推断该物种的相对适用性。我们基于顶级模型的平均遗漏率以及与模拟参考模型的集合重叠来优化SDM。两个研究区域中SDM的比较突出了景观对限制因素的特定反应。这些取决于水文网络的影响,人为特征,地形复杂性,以及土地覆盖斑块马赛克的异质性。总的来说,甚至考虑到特定的局部差异,我们发现了与雪豹生态要求相关的一般景观属性,由与高地和山脊的正相关组成,聚集的低对比度景观,以及大量的草地和草本植物。作为评估两种偏差校正方法性能的一种手段,我们探索了它们对三个数据集的影响,这些数据集显示了一系列的偏倚强度。校正的性能取决于偏置强度;但是,密度内核在所有情况下都提供了可靠的校正策略。这项研究揭示了雪豹对环境属性的多尺度响应,并证实了元复制研究设计在识别空间变化的限制因素中的作用。此外,这项研究为正在进行的关于抽样偏差校正的最佳方法的讨论做出了重要贡献。
Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi-scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai-Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape-specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles\' overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape-specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low-contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi-scale response of snow leopards to environmental attributes and confirms the role of meta-replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.