Understanding how habitats shape species distributions and abundances across spatially complex, dendritic freshwater networks remains a longstanding and fundamental challenge in ecology, with direct implications for effective biodiversity management and conservation. Existing spatial stream network (SSN) models adapt spatial process models to river networks by creating covariance functions that account for stream distance, but preprocessing and estimation with these models is both computationally and time intensive, thus precluding the application of these models to regional or continental scales. This paper introduces a new class of Scalable Spatial Stream Network (S3N) models, which extend nearest-neighbor Gaussian processes to incorporate ecologically relevant spatial dependence while greatly improving computational efficiency. The S3N framework enables scalable modeling of spatial stream networks, demonstrated here for 285 fish species in the Ohio River Basin (>4,000 river km). Validation analyses show that S3N accurately recovers spatial and covariance parameters, even with reduced bias and variance compared to standard SSN implementations. These results represent a key advancement toward large-scale mapping of freshwater fish distributions and quantifying the influence of environmental drivers across extensive river networks.

翻译：理解栖息地如何在空间复杂、呈树状结构的淡水网络中塑造物种分布与丰度，一直是生态学中长期存在的基础性挑战，对有效的生物多样性管理与保护具有直接意义。现有的空间河流网络（SSN）模型通过构建考虑河流距离的协方差函数，将空间过程模型适配至河流网络，但这些模型的预处理与估计过程在计算和时间上均极为耗时，从而阻碍了这些模型在区域或大陆尺度上的应用。本文提出了一类新的可扩展空间河流网络（S3N）模型，该模型将最近邻高斯过程扩展至包含生态学相关的空间依赖性，同时显著提升了计算效率。S3N框架实现了对空间河流网络的可扩展建模，本文以俄亥俄河流域（>4,000 河流公里）的285种鱼类为例进行了演示。验证分析表明，S3N能够准确恢复空间及协方差参数，与标准SSN实现相比，其偏差和方差均有所降低。这些成果标志着在实现大规模淡水鱼类分布制图及量化广泛河流网络中环境驱动因素的影响方面取得了关键进展。