Suppr超能文献

检测选择的生态足迹。

Detecting the ecological footprint of selection.

机构信息

Département de Biologie, École Normale Supérieure-PSL, Paris, France.

INSA-Lyon, Inria, CNRS, Université Claude Bernard Lyon 1, ECL, Université Lumière Lyon 2, LIRIS UMR5205, Lyon, France.

出版信息

PLoS One. 2024 Jun 7;19(6):e0302794. doi: 10.1371/journal.pone.0302794. eCollection 2024.

Abstract

The structure of communities is influenced by many ecological and evolutionary processes, but the way these manifest in classic biodiversity patterns often remains unclear. Here we aim to distinguish the ecological footprint of selection-through competition or environmental filtering-from that of neutral processes that are invariant to species identity. We build on existing Massive Eco-evolutionary Synthesis Simulations (MESS), which uses information from three biodiversity axes-species abundances, genetic diversity, and trait variation-to distinguish between mechanistic processes. To correctly detect and characterise competition, we add a new and more realistic form of competition that explicitly compares the traits of each pair of individuals. Our results are qualitatively different to those of previous work in which competition is based on the distance of each individual's trait to the community mean. We find that our new form of competition is easier to identify in empirical data compared to the alternatives. This is especially true when trait data are available and used in the inference procedure. Our findings hint that signatures in empirical data previously attributed to neutrality may in fact be the result of pairwise-acting selective forces. We conclude that gathering more different types of data, together with more advanced mechanistic models and inference as done here, could be the key to unravelling the mechanisms of community assembly and question the relative roles of neutral and selective processes.

摘要

群落结构受许多生态和进化过程的影响,但这些过程在经典生物多样性模式中的表现方式往往不清楚。在这里,我们旨在区分选择的生态足迹——通过竞争或环境过滤——与对物种身份不变的中性过程的生态足迹。我们基于现有的大规模生态进化综合模拟(MESS),该模拟使用了三个生物多样性轴的信息——物种丰度、遗传多样性和性状变异——来区分机械过程。为了正确检测和描述竞争,我们添加了一种新的、更现实的竞争形式,该形式明确比较了每对个体的特征。我们的结果与之前基于每个个体的特征与群落平均值之间的距离来比较竞争的工作定性不同。我们发现,与替代方法相比,在实证数据中更容易识别我们新形式的竞争。当性状数据可用于推断过程时尤其如此。我们的研究结果表明,以前归因于中性的实证数据中的特征实际上可能是成对作用的选择力的结果。我们的结论是,收集更多不同类型的数据,结合更先进的机械模型和推理,如这里所做的那样,可能是揭示群落组装机制的关键,并质疑中性和选择过程的相对作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0375/11161045/903dee0d5901/pone.0302794.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验