Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland.
Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA.
Nat Ecol Evol. 2020 Jan;4(1):91-100. doi: 10.1038/s41559-019-1059-z. Epub 2019 Dec 16.
The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model or analyse the community vastly exceeds what is feasible using current methods. Here, we address this challenge by presenting a statistical approach that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Using three well-studied experimental systems-encompassing plants, protists, and algae with grazers-we show that this method predicts the results of unobserved experiments with high accuracy, while making no assumptions about the dynamics of the systems. These results demonstrate a fundamentally different way of building and quantifying experimental systems, requiring far fewer experiments than traditional study designs. By developing a scalable method for navigating large systems, this work provides an efficient approach to studying highly diverse experimental communities.
实验群落的研究是生态学发展的基础。然而，对于大多数生态系统来说，构建、建模或分析群落所需的实验数量远远超过了当前方法所能实现的数量。在这里，我们通过提出一种统计方法来解决这一挑战，该方法利用有限数量的实验结果来预测从给定物种库中形成的所有可能组合的结果（共存和物种丰度）。我们使用三个经过充分研究的实验系统——包括植物、原生动物和有食草动物的藻类——表明该方法可以高精度地预测未观察到的实验结果，而无需对系统的动态做出任何假设。这些结果展示了一种构建和量化实验系统的根本不同的方法，与传统的研究设计相比，所需的实验数量要少得多。通过开发一种可扩展的方法来探索大型系统，这项工作为研究高度多样化的实验群落提供了一种高效的方法。
