Data from: Deconstruction of a plant-arthropod community reveals influential plant traits with nonlinear effects on arthropod assemblages
Harrison, Joshua G. et al. (2019), Data from: Deconstruction of a plant-arthropod community reveals influential plant traits with nonlinear effects on arthropod assemblages, Dryad, Dataset, https://doi.org/10.5061/dryad.vg089
1. Studies of herbivores and secondary consumer communities rarely incorporate a comprehensive characterization of primary producer trait variation, thus limiting our understanding of how plants mediate community assembly of consumers. 2. We took advantage of recent technological developments for efficient generation of phytochemical, microbial, and genomic data to characterize individual alfalfa plants (Medicago sativa; Fabaceae) growing in an old-field, semi-naturalized state for 770 traits (including 753 chemical features). Using random forest modeling, we investigated the effect of variation in these traits on arthropod and fungal assemblages while accounting for plant genetic structure. 3. We found that traits indicative of plant vigor, including size, percentage of flowering stems, and leaf area, were positively associated with arthropod richness and abundance. Most phytochemicals were, by comparison, poor predictors, although phytochemical diversity and several individual phenolic compounds were important. Plants with a higher proportion of flowering stems were hotspots of inter-trophic interactions with higher species richness of secondary consumers. The effects of many traits on plant-associated assemblages were best modeled as nonlinear functions, often incorporating threshold effects. Foliar fungal richness was not well predicted by our models, suggesting we have much to learn regarding the role of plant traits on phyllosphere fungi at small spatial scales. 4. Our results support the need for characterization of multiple axes of plant phenotypes in studies of plant-arthropod-microbe communities, and demonstrate the value of modern analytical techniques for understanding the nonlinear ways in which plant traits mediate the structure of associated biotic communities.
National Science Foundation, Award: DEB 1638768 & 1638793