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


Carstens, Bryan Charles; Gruenstaeudl, Michael; Reid, Noah M. (2016), Data from: Community trees: identifying codiversification in the páramo dipteran community, Dryad, Dataset,


Groups of codistributed species that responded in a concerted manner to environmental events are expected to share patterns 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, given a group of species with a shared pattern of diversification, 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. In particular, both the PPS and K-F ANOVA perform acceptably when there are one or two outlier species, but less so as the number of outliers increase. This is likely a function of the corresponding degradation of the signal of community divergence; without a strong signal from a co-diversifying community, there is no dominant pattern from which to detect and outlier species. For this reason, both the magnitude of K-F distance distribution and outside knowledge about the phylogeographic history of each putative member of the community should be considered when interpreting results.

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Central America