Data from: An a posteriori species clustering for quantifying the effects of species interactions on ecosystem functioning

1. Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group-based approaches. Here we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning. 2. We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co-occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long-term Cedar Creek Biodiversity II experiment. 3. We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species’ functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning. 4. Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.1. 量化物种相互作用的效应，是理解生物多样性与生态系统功能之间关联的核心议题，但受限于组合学（combinatorics）难题，该问题至今仍悬而未决。功能类群（functional groups）常被用于表征生物形态与功能的多样性，以此简化现实场景。然而，基于功能类群的研究框架仍未显性纳入物种相互作用。本文提出一种基于物种后验聚类（a posteriori clustering）的新方法，以量化物种相互作用对生态系统功能的影响。 2. 我们首先利用零模型（null models，该模型设定物种多样性不影响生态系统功能）对观测得到的生态系统功能进行分解，以分离物种相互作用与物种组成的效应。借此可识别出对生态系统功能具有差异化多样性效应的后验功能类群。随后，我们构建了形式化的物种相互作用组合模型：将生态系统描述为共现功能类群的组合，我们将其命名为装配基序（assembly motif），每个装配基序对应一类特定的生物环境。我们通过微生物实验数据集与长期运行的Cedar Creek生物多样性II实验（Cedar Creek Biodiversity II experiment）验证了本方法的适用性。 3. 研究表明，相较于先验（a priori）方法，本后验方法具备更高的准确性、效率与简约性。先验与后验方法的差异源于二者聚类逻辑的不同：先验方法基于生态系统或物种属性，例如生态系统规模（物种或功能类群的数量）或物种功能性状（functional traits）；而本后验方法仅基于观测到的相互作用与组成效应对生态系统功能的影响。 4. 我们的研究结果证实，后验方法具备极强的解释力：它可识别物种间的相互作用关系，并量化物种相互作用对生态系统功能的效应。同时，研究还表明，生态系统功能的组合建模可无需任何关于生物或环境决定因子的假设，也无需获取物种功能性状的相关信息，即可预测生态系统功能。该方法仅需知晓生态系统的物种组成，以及共享同一装配基序的其他生态系统的观测功能即可。