Schmidt Thomas Sebastian Benedikt, Matias Rodrigues João Frederico, von Mering Christian
Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zürich, Switzerland.
ISME J. 2017 Mar;11(3):791-807. doi: 10.1038/ismej.2016.139. Epub 2016 Dec 9.
Interactions between taxa are essential drivers of ecological community structure and dynamics, but they are not taken into account by traditional indices of β diversity. In this study, we propose a novel family of indices that quantify community similarity in the context of taxa interaction networks. Using publicly available datasets, we assessed the performance of two specific indices that are Taxa INteraction-Adjusted (TINA, based on taxa co-occurrence networks), and Phylogenetic INteraction-Adjusted (PINA, based on phylogenetic similarities). TINA and PINA outperformed traditional indices when partitioning human-associated microbial communities according to habitat, even for extremely downsampled datasets, and when organising ocean micro-eukaryotic plankton diversity according to geographical and physicochemical gradients. We argue that interaction-adjusted indices capture novel aspects of diversity outside the scope of traditional approaches, highlighting the biological significance of ecological association networks in the interpretation of community similarity.
分类群之间的相互作用是生态群落结构和动态的重要驱动因素，但传统的β多样性指数并未将其考虑在内。在本研究中，我们提出了一类新的指数，用于在分类群相互作用网络的背景下量化群落相似性。利用公开可用的数据集，我们评估了两个特定指数的性能，即分类群相互作用调整指数（TINA，基于分类群共现网络）和系统发育相互作用调整指数（PINA，基于系统发育相似性）。当根据栖息地对人类相关微生物群落进行划分时，即使是对于极度下采样的数据集，以及根据地理和理化梯度组织海洋微型真核浮游生物多样性时，TINA和PINA的表现均优于传统指数。我们认为，相互作用调整指数捕捉到了传统方法范围之外的多样性新方面，突出了生态关联网络在解释群落相似性方面的生物学意义。
