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.

研究目标：物种分布预测是生态学研究的核心议题之一，但野外调查中存在的空间限制采样、尺度依赖的生态关系，以及检测误差等问题，极大制约了该方向的发展。尽管如此，预测性物种分布模型（Species Distribution Models, SDMs）仍是众多物种保护工作的关键支撑工具。我们构建了一套基于多尺度数据的预测性物种分布模型构建框架，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的物种分布模型。研究区域：美国（USA）。

研究方法：本研究依托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，构建预测性物种分布模型。我们搭建了纳入采样阶段检测率异质性的分层占用模型（occupancy models），并采用贝叶斯模型选择方法，明确以空间预测能力为依据调节模型复杂度（协变量与尺度）。由此，本研究整合了多项核心技术：以优化样本外预测为目标的模型选择、覆盖宽泛环境条件的全分布范围采样、多尺度分析与尺度优化，以及针对多广布物种的物种特异性检测率校正。

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，同时还受到从局地尺度（100~500米）到流域尺度的农业、城市与水文干扰的作用。多数物种的分布预测模型因纳入人类干扰变量而提升了预测精度，且所有物种的分布均受到至少两种以上尺度的环境属性调控。本研究期间，有5个物种呈现出大陆尺度的分布范围收缩迹象。

主要结论：本研究证明了如何在大陆尺度的物种全分布范围内优化分层占用模型以实现精准预测，同时校正不完全检测（imperfect detection）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个研究物种中的绝大多数构建了首个基于全分布范围的、数据驱动的经验性物种分布模型，并证实：以往聚焦局部分布与精细尺度湿地植被效应的研究，忽略了湿地鸟类种群占据分布的重要宏观尺度驱动因子。