Abstract The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial‐statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest‐neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

中文翻译：

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大空间数据的计算高效联合物种分布建模

摘要 持续的全球变化和对宏观生态过程的兴趣日益增加，要求对物种群落的空间广泛数据进行分析，以了解和预测生物多样性的分布变化。最近开发的联合物种分布模型可以有效地处理众多物种，同时明确说明数据中的空间结构。然而，它们的适用性通常仅限于相对较小的空间数据集，因为随着空间位置数量的增加，它们的计算缩放比例很大。在这项工作中，我们通过利用两种空间统计技术来促进大型空间数据集的分析：高斯预测过程和最近邻高斯过程，提出了一种实际缓解联合物种建模的这种可扩展性约束的方法。我们为贝叶斯模型拟合设计了一种高效的 Gibbs 后验采样算法，使我们能够分析由从多达数十万个空间单位采样的数百种物种组成的群落数据集。使用包含 30,955 个空间单位的广泛植物数据集作为案例研究，证明了这些方法的性能。我们提供了所提出方法的实现，作为物种群落框架分层建模的扩展。