Environmental fluctuations explain the universal decay of species-abundance correlations with phylogenetic distance

宏观生态学 丰度(生态学) 空模式 生态学 社区 相对丰度分布 生物 相对物种丰度 种间竞争 系统发育树 距离衰减 竞赛(生物学) 生物多样性 生态系统 基因 生物化学
作者
Matteo Sireci,Miguel A. Muñoz,Jacopo Grilli
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:120 (37) 被引量:4
标识
DOI:10.1073/pnas.2217144120
摘要

Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches-which combine phylogenetic relationships between species with community ecology-have the potential to disentangle such forces but are often hard to connect with quantitative predictions from theoretical models. On the other hand, macroecology, which focuses on statistical patterns of abundance and diversity, provides natural connections with theoretical models but often neglects interspecific correlations and interactions. Here, we propose a unified framework combining both such approaches to analyze microbial communities. In particular, by using both cross-sectional and longitudinal metagenomic data for species abundances, we reveal the existence of an empirical macroecological law establishing that correlations in species-abundance fluctuations across communities decay from positive to null values as a function of phylogenetic dissimilarity in a consistent manner across ecologically distinct microbiomes. We formulate three variants of a mechanistic model-each relying on alternative ecological forces-that lead to radically different predictions. From these analyses, we conclude that the empirically observed macroecological pattern can be quantitatively explained as a result of shared population-independent fluctuating resources, i.e., environmental filtering and not as a consequence of, e.g., species competition. Finally, we show that the macroecological law is also valid for temporal data of a single community and that the properties of delayed temporal correlations can be reproduced as well by the model with environmental filtering.
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