Data from: Community trees: identifying codiversification in the Páramo dipteran community

Groups of codistributed species that responded in a concerted manner to environmental events are expected to share a pattern of evolutionary diversification. However, the identification of such groups has largely been based on qualitative, post hoc analyses. We develop here two methods (PPS, K-F ANOVA) for the analysis of codistributed species that, allow empiricists to identify those taxa that do not codiversify (i.e., "outlier" species). The identification of outlier species makes it possible to jointly estimate the evolutionary history of co-diversifying taxa. To evaluate the approaches presented here, we collected data from Páramo dipterans, identified outlier species, and estimated a "community tree" from species that are identified as having co-diversified. Our results demonstrate that dipteran communities from different Páramo habitats in the same mountain range are more closely related than communities in other ranges. We also conduct simulation testing to evaluate this approach. Results suggest that our approach provides a useful addition to comparative phylogeographic methods, while identifying aspects of the analysis that require careful interpretation. By modeling a common history and explicitly seeking to identify outlier species, the community tree framework offers an approach for the identification of evolutionary communities – species that share both ecological interactions and evolutionary history.对环境事件做出协同响应的共分布物种类群，预期会共享一致的进化分化模式。然而，此类物种类群的识别迄今大多基于定性的事后分析。本文开发了两种用于共分布物种类群分析的方法（PPS、K-F ANOVA），可帮助实验研究者甄别未发生共分化的类群（即"异常物种"）。甄别异常物种后，即可联合估算发生共分化的类群的进化历史。为评估本文提出的分析方法，研究团队采集了帕拉莫（Páramo）高海拔生态系统中的双翅目昆虫数据，甄别出其中的异常物种，并基于被判定为发生共分化的物种构建了"群落系统发育树"。研究结果显示，同一山脉范围内不同帕拉莫生境中的双翅目昆虫群落，与其他山脉的同类群落相比，亲缘关系更为紧密。本研究还通过模拟测试对该方法进行了评估，结果表明，本方法可为比较系统地理学研究方法提供有价值的补充，同时也明确了分析过程中需要谨慎解读的环节。通过构建共同进化历史模型并明确甄别异常物种，群落系统发育树框架为进化群落的识别提供了可行路径——进化群落指同时共享生态相互作用与进化历史的物种类群。