Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.

中文翻译：

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各个苏格兰野猫混合栖息地模型的不同重要性和影响规模

了解物种-栖息地关系的尺度依赖性是物种分布模型研究的一个重要领域。很少有研究关注栖息地选择如何取决于生物体之间的个体差异、地理位置和该位置的生态环境。此外，对于居住在不同生态环境中的物种个体之间尺度依赖性异质性的程度和驱动因素知之甚少，也很少有研究在空间复制框架中比较尺度依赖性和变量重要性。解释栖息地关系模型的两个最重要的因素包括：（1）模型中变量的相对重要性；（2）每个变量具有最大影响力的空间尺度。根据现有证据，我们假设土地覆盖变量通常是最重要的预测因素，其次是地形，然后是土壤类型（影响植被和猎物），归一化植被指数（NDVI）作为总植被密度的指标，也许猎物密度、植被覆盖和兔子丰度的代表。我们还预计，与不同变量组相关的各个野猫混合模型之间将存在一致的规模依赖性模式。我们期望在大范围内选择地形特征，因为它们会影响大范围的气候和生态条件。鉴于过去的研究表明土地覆盖通常会在相对广泛的范围内影响栖息地选择，我们还预计将在相对广泛的范围内选择土地覆盖类别和植被覆盖密度。我们期望在更精细的尺度上选择 NDVI 和土壤类型，因为它们的变化会影响资源的分布和景观内的限制条件。最后，我们预计兔子的丰度和线性特征将在最精细的尺度上影响野猫杂交的发生，因为这些资源和条件在短距离内变化并强烈影响野猫和野猫杂交行为和栖息地利用。我们的结果与以下假设一致：关于哪些变量或变量组作为苏格兰野猫杂交发生的预测因素最重要，可能存在一致性。根据之前的研究，我们预计与不同变量组相关的各个野猫混合模型之间将存在一致的规模依赖性模式。最后，我们的结果确定了一个明确且一致的趋势，即在越来越广泛的范围内包含变量的频率不断增加。这是随着规模增加而保留的变量频率增加的线性趋势。这表明在越来越广泛的尺度上更频繁地保留变量的一致和单调的模式。