Data from: Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity

1.Natural communities commonly contain many different species and functional groups, and multiple types of species interactions act simultaneously, such as competition, predation, commensalism, or mutualism. However, experimental and theoretical investigations have generally been limited by focusing on one type of interaction at a time or by a lack of a common methodological and conceptual approach to measure species interactions.
2.We compared four methods to measure and express species interactions. These approaches are, with increasing degree of model complexity, an extinctionbased model, a relative-yield model, and two generalized Lotka-Volterra (LV) models. All four approaches have been individually applied in different fields of community ecology, but rarely integrated. We provide an overview of the definitions, assumptions and data needed for the specific methods, and apply them to empirical data by experimentally deriving the interaction matrices among 11 protist and rotifer species, belonging to three functional groups. Furthermore, we compare their advantages and limitations to predict multispeciescommunity dynamics and ecosystem functioning.
3.The relative-yield method is, in terms of final biomass production, the best method in predicting the 11-species community dynamics from the pairwise competition experiments. The LV model, which is considering equilibrium among the species, suffers from experimental constraints given the strict equilibrium assumption, and this may be rarely satisfied in ecological communities.
4.We show how simulations of a LV stochastic community model, derived from an empirical interaction matrix, can be used to predict multi-species community dynamics across multiple functional groups.
5.Our work unites available tools to measure species-interactions under one framework. This improves our ability to make management oriented predictions of species coexistence/extinction and to compare ecosystem processes across study systems.

1. 自然群落通常包含众多不同的物种与功能群（functional group），且多种类型的物种相互作用（species interaction）同时发生，例如竞争（competition）、捕食（predation）、偏利共生（commensalism）或互利共生（mutualism）。然而，现有实验与理论研究大多存在局限：要么仅聚焦于单一种类的物种相互作用，要么缺乏统一的方法论与概念框架来量化物种相互作用。
2. 本研究对比了四种用于量化与表征物种相互作用的方法。随着模型复杂程度递增，这四种方法分别为：基于灭绝的模型、相对产量模型（relative-yield model）以及两类广义洛特卡-沃尔泰拉模型（generalized Lotka-Volterra model，简称LV模型）。上述四种方法此前均已单独应用于群落生态学（community ecology）的不同分支领域，但极少被整合统一分析。我们系统梳理了各方法所需的定义、假设前提与数据类型，并将其应用于实证数据（empirical data）：通过实验推导了隶属于3个功能群的11种原生生物（protist）与轮虫（rotifer）之间的相互作用矩阵（interaction matrix）。此外，我们对比了各方法在预测多物种种群动态（multispecies community dynamics）与生态系统功能（ecosystem functioning）方面的优势与局限。
3. 就最终生物量产出（biomass production）而言，相对产量模型是从成对竞争实验（pairwise competition experiments）中预测11物种种群动态的最优方法。而考虑物种间平衡的LV模型，则因严格的平衡假设（equilibrium assumption）受到实验条件限制，而这类平衡在自然生态群落中极少得以满足。
4. 本研究展示了如何基于实证相互作用矩阵构建的LV随机群落模型（stochastic community model），来跨多个功能群预测多物种种群动态。
5. 本研究将现有量化物种相互作用的工具整合至统一框架中，这有助于提升我们针对物种共存/灭绝（species coexistence/extinction）开展管理导向型预测的能力，同时也便于在不同研究系统（study systems）间对比生态系统过程。