When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric–the mean pairwise distance (MPD) between taxa–and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model, suggesting freeze tolerance evolved recently. We suggest that multiple data types that are ordered on phylogenies, such as trait values, species interactions, or community presence/absence, are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package kitchen to perform CKS for generic application of the technique.

中文翻译：

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用卷积厨房水槽推断社区结构特征的进化模型


当通过对系统发育保守性状进行过滤或限制相似性等过程来组装群落时，这些性状的进化特征可能会反映在群落成员模式中。我们展示了如何使用传统生态系统发育指标的变体（类群之间的平均成对距离（MPD））和最新的机器学习工具卷积从群落成员数据中推断出群落结构性状背后的性状进化模型。厨房水槽（CKS）。这两种方法在一系列系统发育信息丰富的进化模型中都表现良好，但随着树大小的增加，CKS 的表现优于 MPD。我们通过推断被子植物耐冻性的进化历史来证明 CKS。我们的分析与晚期爆发模型一致，表明耐冻性最近发生了变化。我们建议按系统发育排序的多种数据类型（例如性状值、物种相互作用或群落存在/不存在）是 CKS 建模的良好候选者，因为生成模型会在 CKS 非常适合的相邻点之间产生结构化差异。我们引入 R 包 kitchen 来执行 CKS，以实现该技术的通用应用。