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Under an adaptive hypothesis, the reciprocal influence between mutualistic plants and frugivores is expected to result in suites of matching frugivore and plant traits that structure fruit consumption. Recent work has suggested fruit traits can represent adaptations to broad groups of functionally similar frugivores, but the role of frugivore traits and within-species variation in structuring fruit consumption is less understood. To address these knowledge gaps, we assess the presence of reciprocal trait matching for the mutualistic ecological network comprising of Carollia bats that feed on and disperse Piper seeds. We used generalized joint attribute modeling (GJAM), a Bayesian modeling approach that simultaneously accounts for multiple sources of variance across trait types. In support of frugivore adaptation to their dietary composition and suggesting niche partitioning among Carollia bats, we find differential consumption of a suite of Piper species influenced by bat traits such as body size; however, the Piper morphological traits considered had no effect on bat consumption. Slow evolutionary rates, dispersal by other vertebrates, and unexamined fruit traits, such as Piper chemical bouquets, may explain the lack of association between bat Piper consumption and fruit morphological traits. We have identified a potential asymmetric influence of frugivore traits on plant–frugivore interactions, providing a template for future trait analyses of plant–animal networks. As intraspecific trait variation is rarely included in studies on trait matching, this paper contributes to closing that important knowledge gap. # Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling [https://doi.org/10.5061/dryad.2v6wwpzwg](https://doi.org/10.5061/dryad.2v6wwpzwg) Bayesian models relating: 1\. head.R: relates *Carollia* traits to bite force (performance) via hierarchical ML models 2\. carollia3_0.R: relates *Carollia* traits to bite force (performance) via hierarchical Bayesian models 3\. gjam generated model of consumption relationship to traits for *Carollia* bats 4\. gjam processed model outputs 5\. piper.R: relates *Piper* traits and GJAM coefficients obtained from 3 and 4 ## Description of the data and file structure All cells marked as NA lacked data and correspond to missing data. ## Code/Software 1\. head.R: relates *Carollia* traits to bite force (performance) via hierarchical ML models. Requires data file bat_l_biteBody.csv and R library lme4. Prints results to .txt file. 2\. carollia3_0.R: relates *Carollia* traits to bite force (performance) via hierarchical Bayesian models. Requires data file bat_l_biteBody.csv, R library R2Jags, and 3 Jags files. The three Jags files are: * carollia_bf_size.txt * carollia_bf_mass.txt * carollia_bf_head.txt carollia3_0.R prints out the results of hierarchical Bayesian regressions in txt and saves an Rdata file. 3\. niche_Carollia.R: generates gjam model of consumption relationship t traits for *Carollia* bats Requires data files: * carollia_niche_xdata.csv: x or explanatory variables, bat traits * carollia_niche_ydata_trim.csv: y or response variables, bat consumption of *Piper* fruit from different species * carollia_type.csv: individual bat assignment to one of 3 species * and R libraries gjam, reshape2 and plyr plus function * bayesReg.R (which codes a function to run a Tobit and Bayesian regression from the NEON example here: [https://rstudio-pubs-static.s3.amazonaws.com/710083_480b1b43b4f0470691e95302483fdc08.html](https://rstudio-pubs-static.s3.amazonaws.com/710083_480b1b43b4f0470691e95302483fdc08.html)). This script generates the bayesian gjam model and saves an Rdata file. 4\. plot_Carollia_v2.r processes gjam model outputs. Requires data files: * models_Carollia.Rdata * and R libraries gjam, reshape2, plyr, ggplot2, MCMCvis and wesanderson This script generates the standardized summary and prints out a file called piper_medians.csv 5\. piper.R relates *Piper* traits and gjam coefficients obtained from steps 3 and 4.. Requires data files: * piper.nex: phylogeny of *Piper* plants * piper_k_traits.csv: correspondence between *Piper* traits and *Piper* species * output.csv: this is processed from piper_medians.csv to separate relate Piper species to bat trait values resulting from gjam * Requires R libraries MCMCglmm and geiger This script prints out the results of phylogenetic Bayesian regressions of gjam outputs as a function of *Piper* traits in txt and saves a Rdata file. 

Gene duplication is an important process in the evolution of gene content in eukaryotic genomes. Understanding when gene duplicates contribute new molecular functions to genomes through molecular adaptation is one important goal in comparative genomics. In large gene families, however, characterizing adaptation and neofunctionalization across species is challenging, as models have traditionally quantified the timing of duplications without considering underlying gene trees. This protocol combines multiple approaches to detect adaptation in protein duplicates at a phylogenetic scale. We include a description of models for gene tree-species tree reconciliation that enable different types of inference, as well as a practical guide to their use. Although simulation-based approaches successfully detect shifts in the rate of duplica- tion/retention, the conflation between the duplication and retention processes, the distinct trajectories of duplicates under non-, sub-, and neofunctionalization, as well as dosage effects offer hitherto unexplored analytical avenues. We introduce mathematical descriptions of these probabilities and offer a road map to computational implementation whose starting point is parsimony reconciliation. Sequence evolution information based on the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) can be combined with duplicate survival probabilities to better predict the emergence of new molecular functions in retained duplicates. Together, these methods enable characterization of potentially adaptive candidate duplicates whose neofunctionalization may contribute to phenotypic divergence across species.