PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences

Aim: Understanding how ecosystem functioning and evolution shape taxonomic as- semblages is a lively debate basically involving two major opposite views: the niche- and dispersal-assembly hypotheses. Here, we introduce a new method allowing for the identification of the first-order process of assembly underlying a set of taxonomic assemblages.

Methods: Building on Clarke’s SIMPER (for “similarity percentage”) analysis of a taxon/ locality occurrence data set, we develop a permutation-based algorithm named PER- SIMPER, allowing for the identification of the first-order process—either niche- or dispersal-assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton-like simulations generating niche-assembled and/or dispersal-as- sembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, includin..., R PerSIMPER functionBased on a presence/absence matrix, this function allows the identification of the first-order process of assembly underlying a set of taxonomic assemblages. Its use should therefore be limited to the comparison of significantly distinct taxonomic sets. But at the same time connected enough to allow the potential dispersal of species between these different sets. The PER-SIMPER method distinguishes the main ecological assembly process (between species dispersal capacity and niche richness) at the origin of the observed taxonomic differences between two (or more) compared sets of assemblages. The PER-SIMPER method is associated with the calculation of the E index (the logarithm of the sum of squared deviations between empirical and simulated SIMPER profiles) to assist in distinguishing the result of PER-SIMPER analyses.PERSIMPER.RHelp notes PER-SIMPERHelp notes of the PER-SIMPER R function. R function included too.PerSIMPERgroupsPerSIMPERmatrix,

研究目标：生态系统功能与演化如何塑造分类组合是当前活跃的学术争论，核心围绕两种对立假说展开——生态位组装假说（niche-assembly hypothesis）与扩散组装假说（dispersal-assembly hypothesis）。在此，我们提出一种新方法，可识别一组分类组合背后的一阶组装过程。

研究方法：基于Clarke的SIMPER（相似性百分比）分析（针对分类群/地点出现数据集），我们开发了一种基于置换的算法PER-SIMPER，能够识别驱动两个或多个组合群内物种分布的一阶过程（生态位组装或扩散组装）。我们通过类元胞自动机模拟（cellular automaton-like simulations）生成生态位组装和/或扩散组装的物种出现数据集，验证了该方法的可靠性与稳健性。敏感性分析进一步评估了其在不同采样强度下的准确性与稳健性（包括...）。R语言PER-SIMPER函数：基于存在/缺失矩阵（presence/absence matrix），该函数可识别一组分类组合背后的一阶组装过程。因此，其应用范围应限于比较显著差异的分类集合，但这些集合需具备足够连通性，以允许物种在集合间潜在扩散。PER-SIMPER方法可区分主导的生态组装过程（物种扩散能力与生态位丰富度之间的关系），该过程是导致两组（或多组）对比组合集合间分类差异的根本原因。PER-SIMPER方法与E指数（E index）计算相关联，E指数为经验SIMPER谱与模拟SIMPER谱间平方偏差之和的对数，用于辅助解读PER-SIMPER分析结果。此外，数据集包含PER-SIMPER的R语言函数及其帮助文档、PER-SIMPER分组信息与PER-SIMPER矩阵。