Predictive multi-scale occupancy models at range-wide extents: effects of habitat and human disturbance on distributions of wetland birds

Aim: Predicting distributions is fundamental to ecology, yet hindered by spatially-restricted sampling, scale-dependent relationships, and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data, and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.

Location: USA.

Methods: We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale-optimization, and species-specific detectability for a suite of wide-ranging species.

Results: Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban, and hydrologic disturbances operating from local scales (100 – 500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at > 1 scale. Five species showed evidence for continental-scale range contraction during the study.

Main conclusions: We demonstrate how hierarchical occupancy models can be optimized for prediction across a species’ range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broad-scale drivers of occupancy for marsh birds.

Methods
Data used to fit hierarchical multi-scale occupancy models for wetland birds.

研究目标：物种分布预测是生态学研究的核心内容之一，但该工作常受限于空间受限的采样方案、尺度依赖的生态关系，以及野外调查带来的检测误差。尽管如此，预测性物种分布模型（SDMs）仍是多数物种保护工作的关键工具。本研究构建了一套基于多尺度数据的预测性SDMs构建框架，并利用该框架为14种受关注的湿地鸟类构建了全分布范围繁殖季SDMs。
研究区域：美国。
研究方法：我们基于14年间开展的全分布范围调查数据，以及多空间尺度下获取的生境与干扰协变量，构建了SDMs。本研究构建了包含采样过程中检测异质性的层级占用模型（hierarchical occupancy models），并采用贝叶斯模型选择方法，明确基于空间预测能力来调控模型复杂度（协变量与尺度）。由此，我们整合了用于优化样本外预测的模型选择、基于广泛条件下的全分布范围采样、多尺度分析与尺度优化，以及针对一系列广泛分布物种的物种特异性检测能力。
研究结果：湿地鸟类的分布不仅受局地湿地环境条件的影响，同时也受到从局地尺度（100~500米）到流域尺度的农业、城市与水文干扰的作用。表征人类干扰的变量对多数物种的预测精度均有提升，且每个物种的分布均受到多于1个尺度的属性因子影响。在本研究周期内，有5个物种表现出大陆尺度分布范围收缩的迹象。
主要结论：本研究阐明了如何在大陆尺度下针对物种全分布范围优化层级占用模型的预测性能，同时兼顾不完全检测问题，由此提出了一套可推广至任意物种的通用研究方法。本研究为14种研究物种中的绝大多数构建了首个基于全分布范围的、数据驱动的经验性SDMs，并证实此前聚焦于局部分布与精细尺度湿地植被效应的研究，遗漏了湿地鸟类占用行为的重要大尺度驱动因子。
方法
用于拟合湿地鸟类层级多尺度占用模型的数据。