Data from: Varyingly hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforest

A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of plant host use in speciose groups like tropical Lepidoptera. Here we used standardised food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of exhaustively sampled forest. For pyraloids we relied on phylogeny alone and predicted 54% of species level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food-web only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity), was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid-plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.Data and Code for Hungry CaterpillarsAn archive folder containing the 54 files needed to reproduce the results and figures reported in 'Varyingly Hungry Caterpillars: Predictive Models and Foliar Chemistry Suggest How to Eat a Rainforest'. The folder includes both raw data and files for analysis and one README file. Each file is described in the README file and is presented in the same order as in the R file containing the script necessary for the analyses.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 9707928, 0211591 and 0515678, 0816749, and 084188