Plant-associate interactions and diversification across trophic levels
Yoder, Jeremy; Dang, Albert; MacGregor, Caitlin; Plaza, Mikhail (2022), Plant-associate interactions and diversification across trophic levels, Dryad, Dataset, https://doi.org/10.5061/dryad.fxpnvx0w3
Interactions between species are widely understood to have promoted the diversification of life on Earth, but how interactions spur the formation of new species remains unclear. Interacting species often become locally adapted to each other, but they may also be subject to shared dispersal limitations and environmental conditions. Moreover, theory predicts that different kinds of interactions have different effects on diversification. To better understand how species interactions promote diversification, we compiled population genetic studies of host plants and intimately associated herbivores, parasites, and mutualists. We used Bayesian multiple regressions and the BEDASSLE modeling framework to test whether host and associate population structures were correlated over and above the potentially confounding effects of geography and shared environmental variation. We found that associates' population structure often paralleled their hosts' population structure, and that this effect is robust to accounting for geographic distance and climate. Associate genetic structure was significantly explained by plant genetic structure somewhat more often in antagonistic interactions than in mutualistic ones. This aligns with a key prediction of coevolutionary theory, that antagonistic interactions promote diversity through local adaptation of antagonists to hosts, while mutualistic interactions more often promote diversity via the effect of hosts' geographic distribution on mutualists' dispersal.
Data were compiled from published papers describing population genetic structure in host plants and at least one herbivore, pathogen, parasite, or mutualist, sampled across the same geographic sites. Literature search parameters for included studies, data extraction and processing, and data analysis are described in the paper's Methods.
Data are provided as delimited text files, with R scripts for all analysis and visualization in the paper.
National Science Foundation, Award: DEB-2001180
California State University, Northridge