Understanding how habitats shape species distributions and abundances across river networks remains a longstanding and fundamental challenge in ecology, with direct implications for effective biodiversity management and conservation. We introduce a scalable spatial stream network (S3N) model that enables estimation, inference, and prediction with greater computational efficiency than previously possible. S3Ns extend nearest-neighbor Gaussian processes (NNGPs) to include ecologically salient stream network dependence structure. Additionally, S3Ns implement more efficient preprocessing than SSNs; while the computational cost of estimation is a function of the number of observation points and not of the number of reaches, the preprocessing is a function of both. We demonstrate that S3Ns accurately recover spatial and covariance parameters 2-3 orders of magnitude faster than existing spatial stream network models. We then apply S3Ns to estimate the population sizes and geographic distributions of 285 fish species in the entire Ohio River Basin (>4,000 river km, approximately 170,000 reaches and 9,000 observation points) on a laptop. These results indicate the promise of S3Ns for mapping freshwater variables and quantifying the influence of environmental drivers across extensive, complex river networks with many observation points.

翻译：栖息地如何塑造河流网络中物种的分布与丰度，仍是生态学中一个长期且基础性的挑战，对生物多样性管理与保护具有直接意义。我们提出了一种可扩展的空间河流网络（S3N）模型，其计算效率较以往方法显著提升，可支持估计、推断与预测。S3N将最近邻高斯过程（NNGP）扩展至包含生态学上关键的河流网络依赖结构。此外，S3N实现了比SSN更高效的预处理：尽管估计的计算成本取决于观测点数量而非河段数量，但预处理则同时取决于两者。我们证明，S3N能以比现有空间河流网络模型快2-3个数量级的速度准确恢复空间与协方差参数。随后，我们应用S3N在笔记本电脑上估算了整个俄亥俄河流域（河流长度超过4000公里，约17万个河段及9000个观测点）中285种鱼类的种群规模与地理分布。这些结果表明，S3N在映射淡水变量及量化环境驱动因素对广泛复杂河流网络（含大量观测点）的影响方面具有广阔前景。