Despite their interdependence, community ecology and evolutionary biology have proven difficult to synthesize empirically in studies of community assembly. This is primarily due to differing temporal and spatial scales of ecological and evolutionary dynamics, ranging from broad‐scale processes like speciation and environmental filtering to local‐scale past and present‐day niche dynamics. Phylogenetic generalized linear mixed modeling (PGLMM) offers a solution to this problem, it can be used to integrate through time by modeling the evolution of trait‐based community assembly, and across space ranging from broad‐scale environmental sensitivities to local‐scale co‐occurrences. As such, PGLMM provides the ability to compare the relative strength of deep versus shallow‐time drivers of biodiversity by including them in a single model. Despite its unique value, the application of PGLMM has been limited because statistical advances have not been adequately matched by conceptual progress. Recent expansion in the availability of cross‐clade assemblage data and phylogenetic tools have increased the urgency of conceptual unification. Here we describe the potential of PGLMM for parsing the evolutionary and ecological drivers of community assembly, focusing on how three major drivers – environmental sensitivities, within‐clade interactions (e.g. competition), and cross‐clade associations (e.g. herbivory) – shape historical and present‐day assemblages. We outline three fundamental questions that PGLMM can address, linked to each of the aforementioned drivers: 1) are species' regional‐scale environmental responses evolutionarily constrained? 2) Do evolved responses to past competition minimize or enhance present‐day competition? 3) Are cross‐clade associations evolutionarily constrained? For each question, we review conceptual advances and opportunities, and demonstrate the application of PGLMM in a supplementary tutorial. We focus on the ecological and evolutionary outcomes of PGLMM and describe the value of these outcomes for conservation and natural resource management, in order to move PGLMM beyond statistical complexities and toward a future with clear conceptual and practical goals.

中文翻译：

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系统发育的广义线性混合建模为生态进化综合提供了新的机会

尽管它们相互依存，但在社区组装研究中，已证明难以凭经验综合合成社区生态学和进化生物学。这主要是由于生态和进化动力学的时空尺度不同，范围从物种形成和环境过滤等大规模过程到过去和现在的局部生态位动力学。系统发育广义线性混合建模（PGLMM）提供了解决此问题的方法，它可以通过对基于特征的社区装配的演变进行建模，并在整个时间范围内进行整合，范围涵盖从广泛的环境敏感性到局部规模的合作环境。发生。因此，PGLMM通过将生物多样性的深层驱动力与浅层驱动力的相对强度纳入一个模型，从而能够比较它们的相对强度。尽管PGLMM具有独特的价值，但它的应用受到了限制，因为统计上的进展还没有与概念上的进展充分匹配。跨组合数据和系统发育工具的可用性的最新扩展增加了概念统一的紧迫性。在这里，我们将介绍PGLMM在解析社区集会的进化和生态驱动因素方面的潜力，重点关注三个主要驱动因素-环境敏感性，进化内相互作用（例如竞争）和跨进化联系（例如食草动物）如何塑造历史和当前的集合。我们概述了PGLMM可以解决的三个基本问题，这些问题与上述每个驱动因素相关：1）是物种的 区域规模的环境反应在进化上受到约束吗？2）对过去竞争的逐渐反应会最小化或增强当前的竞争吗？3）跨类别关联在进化上受到约束吗？对于每个问题，我们都会回顾概念上的进步和机遇，并在补充教程中演示PGLMM的应用。我们将重点放在PGLMM的生态和进化成果上，并描述这些成果在保护和自然资源管理中的价值，以使PGLMM摆脱统计上的复杂性，走向具有明确概念和实际目标的未来。并在补充教程中演示PGLMM的应用。我们将重点放在PGLMM的生态和进化成果上，并描述这些成果在保护和自然资源管理中的价值，以使PGLMM摆脱统计上的复杂性，走向具有明确概念和实际目标的未来。并在补充教程中演示PGLMM的应用。我们将重点放在PGLMM的生态和进化成果上，并描述这些成果在保护和自然资源管理中的价值，以使PGLMM摆脱统计上的复杂性，走向具有明确概念和实际目标的未来。