Context Species are influenced by factors operating at multiple scales, but multi-scale species distribution and abundance models are rarely used. Though multi-scale species distribution models outperform single-scale models, when compared through model selection, multi- and single-scale models built with computer learning algorithms have not been compared. Objectives We compared the performance of models using a simple and accessible, multi-scale, machine learning, species distribution and abundance modeling framework to pseudo-optimized and unoptimized single-scale models. Methods We characterized environmental variables at four spatial scales and used boosted regression trees to build multi-scale and single-scale distribution and abundance models for 28 bird species. For each species and across species, we compared the performance of multi-scale models to pseudo-optimized and lowest-performing unoptimized single-scale models. Results Multi-scale distribution models consistently performed as well or better than pseudo-optimized single-scale models and significantly better than unoptimized single-scale models. Abundance model performance showed a similar, but less pronounced pattern. Mixed-effects models, that controlled for species, provided strong evidence that multi-scale models performed better than unoptimized single-scale models. Although mean improvement in model performance across species appeared minor, for individual species, arbitrary selection of scale could result in discrepancies of up to fourteen percent for area of suitable habitat and population estimates. Conclusions Scale selection should be explicitly addressed in distribution and abundance modeling. The multi-scale species distribution and abundance modeling framework presented here provides a concise and accessible alternative to standard pseudo-scale optimization while addressing the scale-dependent response of species to their environment.

中文翻译：

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比较使用增强回归树算法构建的多尺度和单尺度物种分布和丰度模型

上下文 物种受多尺度因素的影响，但很少使用多尺度物种分布和丰度模型。尽管多尺度物种分布模型优于单尺度模型，但通过模型选择进行比较时，使用计算机学习算法构建的多尺度和单尺度模型尚未进行比较。目标我们使用简单且可访问的多尺度机器学习、物种分布和丰度建模框架将模型的性能与伪优化和未优化的单尺度模型进行了比较。方法 我们在四个空间尺度上表征环境变量，并使用增强回归树为 28 种鸟类构建多尺度和单尺度分布和丰度模型。对于每个物种和跨物种，我们将多尺度模型的性能与伪优化和性能最低的未优化单尺度模型进行了比较。结果 多尺度分布模型的表现始终与伪优化的单尺度模型一样好或更好，并且明显优于未优化的单尺度模型。丰度模型性能表现出类似但不太明显的模式。控制物种的混合效应模型提供了强有力的证据，证明多尺度模型比未优化的单尺度模型表现更好。尽管跨物种模型性能的平均改进似乎很小，但对于单个物种，任意选择比例尺可能导致合适栖息地和种群估计面积的差异高达 14%。结论 应该在分布和丰度建模中明确解决尺度选择问题。此处介绍的多尺度物种分布和丰度建模框架为标准伪尺度优化提供了一种简洁易行的替代方案，同时解决了物种对其环境的尺度依赖性响应。