Data from: Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence

A key focus in ecology is to search for community assembly rules. Here we compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence-absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM). The frameworks use a dichotomous logic tree that distinguishes dispersal limitation, environmental requirements, and interspecific interactions as causes of segregated or aggregated species pairs. The first framework is based on a classical null model analysis complemented by tests of spatial arrangement and environmental characteristics of the sites occupied by the members of each species pair (Classic framework). The second framework, (SDM framework) implemented here for the first time, builds on the application of environmentally-constrained null models (or JSDMs) to partial out the influence of the environment, and includes an analysis of the geographical configuration of species ranges to account for dispersal effects. We applied these approaches to examine plot-level species co-occurrence in plant communities sampled along a wide elevation gradient in the Swiss Alps. According to the frameworks, the majority of species pairs were randomly associated, and most of the non-random positive and negative species associations could be attributed to environmental filtering and/or dispersal limitation. These patterns were partly detected also with JSDM. Biotic interactions were detected more frequently in the SDM framework, and by JSDM, than in the Classic framework. All approaches detected species aggregation more often than segregation, perhaps reflecting the important role of facilitation in stressful high-elevation environments. Differences between the frameworks may reflect the explicit incorporation of elevational segregation in the SDM framework and the sensitivity of JSDM to the environmental data. Nevertheless, all methods have the potential to reveal general patterns of species co-occurrence for different taxa, spatial scales, and environmental conditions.生态学研究的核心议题之一是探索群落构建规则（community assembly rules）。本研究对比了两种整合环境与空间数据的群落建模框架，旨在从存在-缺失矩阵（presence-absence matrices）中识别物种间的正负关联，并额外采用联合物种分布模型（joint species distribution models, JSDM）开展对比分析。两类框架均采用二分逻辑树（dichotomous logic tree），将扩散限制（dispersal limitation）、环境需求（environmental requirements）与种间相互作用（interspecific interactions）作为导致物种对呈现分离或聚集分布的成因加以区分。第一种框架基于经典零模型分析（classical null model analysis），并辅以针对每个物种对所占据样地的空间格局与环境特征的检验（以下简称经典框架）。第二种框架——本研究首次实现的SDM框架——则以环境约束零模型（或联合物种分布模型）为基础，剥离环境因素的影响，同时通过分析物种分布区的地理格局来考量扩散效应的作用。我们将上述方法应用于瑞士阿尔卑斯山沿宽海拔梯度采样的植物群落，以探究样方水平的物种共现模式。基于两类框架的分析结果显示，绝大多数物种对呈随机关联，多数非随机的正负物种关联可归因于环境过滤（environmental filtering）和/或扩散限制。联合物种分布模型也部分检测到了上述模式。相较于经典框架，SDM框架与联合物种分布模型更频繁地检测到生物相互作用（biotic interactions）。所有方法均更易检测到物种聚集而非分离，这或许反映了促进作用（facilitation）在高海拔胁迫环境中的重要作用。不同框架间的差异可能源于SDM框架明确纳入了海拔分离效应，以及联合物种分布模型对环境数据的敏感性差异。尽管如此，所有方法均有望揭示不同类群、空间尺度与环境条件下的物种共现通用模式。