Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space‐use intensity beyond the place and time of data collection, presumably because space‐use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance‐decomposition of habitat‐selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana, across three years in Utah, USA, we combine individual‐year‐season‐specific exponential habitat‐selection models with weighted mixed‐effects regressions to both draw inference about the drivers of habitat selection and predict space‐use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter‐ and intra‐individual components. We then used the results to predict population‐level, spatially and temporally dynamic, habitat‐selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species‐habitat associations across space and time.

中文翻译：

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通过个体栖息地选择预测动物分布：种群推断和可转移预测的见解


栖息地选择模型经常使用在短时间内从较小地理区域收集的数据，将相对丰度模式外推到未观察到的区域或时间段。然而，此类模型通常很难预测数据收集地点和时间之外的动物空间使用强度的分布，大概是因为空间使用行为因个体和环境背景而异。同样，基于栖息地选择模型的生态推断可能会由于未考虑的个体和环境依赖性而变得混乱或有偏差。在这里，我们提出了一个建模工作流程，旨在允许对栖息地选择模式进行透明的方差分解，从而提高推理和预测能力。利用三年来在美国犹他州收集的 238 只叉角羚个体（Antilocapra americana）的全球定位系统 (GPS) 数据，我们将个体年份特定指数栖息地选择模型与加权混合效应回归相结合，得出关于栖息地选择的驱动因素并预测叉角羚未受到监测的地区/时间的空间利用。我们发现，不同季节、不同个体、不同地理区域和不同年份的栖息地选择行为的幅度和方向都存在巨大差异。我们能够将这种变化的部分原因归因于季节、运动策略、性别以及资源、条件和风险的区域变化。我们还能够将残余变异分为个体间和个体内部分。 然后，我们利用这些结果来预测犹他州的种群水平、空间和时间动态以及栖息地选择系数，从而生成了叉角羚分布的时间动态图，分辨率为 30 × 30 m，范围为 220 000 km2。我们相信，我们的可转移工作流程可以为管理者和研究人员提供一种方法，将传统栖息地选择模型的局限性（栖息地选择的可变性）转变为理解和预测跨空间和时间的物种栖息地关联的工具。