Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

中文翻译：

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基于分层广义可加模型的资源选择函数为个体动物变异和物种分布提供了新的见解

栖息地选择研究旨在生成物种分布的预测或关于栖息地使用的一般栖息地关联和个体变化的推断。此类研究通常涉及在有限的空间范围内收集并使用资源选择函数 (RSF) 进行分析的单独索引位置，或者通常使用物种分布模型分析的没有单独分辨率的空间广泛的位置数据。两种分析方法都具有某些理想的特征，但将个体和群体水平推理与灵活的非线性函数相结合的分析可能会在考虑个体变异的同时提供改进的预测。在这里，我们描述了如何使用广泛可用的软件使用分层广义加性模型 (HGAM) 来拟合 RSF，提供一种方法来探索栖息地关联中的个体差异并生成物种分布图。我们使用了来自金鹰的 GPS 跟踪数据来自北美东部的Aquila chrysaetos有四个环境预测因子来生成月度分布模型。我们考虑了三种模型结构，它们假设预测因子和栖息地利用之间的函数关系存在不同数量的个体变异，并使用了k-fold 交叉验证以比较模型性能。考虑到功能反应的形状和平滑度的个体差异的模型表现最好。鹰在冬季对土地覆盖变量的反应中表现出最少的个体变异，大多数个体更接近于种群水平的趋势。在夏季，鹰在形状和功能关系的平滑度方面表现出更显着的个体差异，这表明在一年中的这个时候，出于推理和预测目的，需要考虑鹰栖息地使用的个体差异。因为它们允许用户将灵活的功能与随机效果结构相结合，并得到各种软件平台的良好支持，