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ABSTRACT A core hypothesis in invasion and community ecology is that species interaction patterns should differ between native and non‐native species due to non‐native species lacking a long evolutionary history in their resident communities. Numerous studies testing this hypothesis yield conflicting results, often focusing on mean interaction rates and overlooking the substantial within‐population variability in species interactions. We explored plant‐herbivore interactions in populations of native and established non‐native plant species by quantifying differences in mean herbivory and added a novel approach by comparing within‐population variability in herbivory. We include as covariates latitude, plant richness, plant growth form and cover. Using leaf herbivory data from the Herbivory Variability Network for 788 plant populations spanning 504 species globally distributed, we found no overall differences in mean herbivory or variability between native and non‐native plants. These results suggest native and established non‐native plants interact similarly with herbivores, indicating non‐native status is not a strong predictor of ecological roles. 

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. 

Data from: Interactions between nutrients and fruit secondary metabolites shape bat foraging behavior and nutrient absorption; by Gelambi, M., Morales-M. E., & Whitehead, S. R. Published in Ecosphere, 2024. The study was conducted at La Selva Biological Station, Costa Rica during June-July 2021. We employed neotropical fruit bats (Carollia perspicilla) as a model to investigate how nutrients and a broadly bioactive fruit secondary metabolite, piperine (Sigma-Aldrich), interact and influence two critical aspects of nutrient acquisition: foraging behavior and nutrient absorption. By manipulating nutrient and piperine concentrations in artificial diets, we reveal that captive fruit bats prioritize nutrient concentrations, even in the presence of piperine's potent deterrent effects. Additionally, our findings indicate that while piperine exerts no detectable influence on total sugar absorption, it significantly reduces protein absorption. 

The Enemy Release Hypothesis (ERH) proposes that non-native plants escape their co-evolved herbivores and benefit from reduced herbivory in their introduced ranges. Numerous studies have tested this hypothesis, with conflicting results, but previous studies focus on average levels of herbivory and overlook the substantial within-population variability in herbivory, which may provide unique insights into the ERH. We tested differences in mean herbivory and added a novel approach to the ERH by comparing within-population variability in herbivory between native and non-native plant populations. We include several covariates that might mask an effect of enemy release, including latitude, regional plant richness, plant growth form and plant cover. We use leaf herbivory data collected by the Herbivory Variability Network for 788 plant populations (616 native range populations and 172 introduced range populations) of 503 different native and non-native species distributed worldwide. We found no overall differences in mean herbivory or herbivory variability between native and non-native plant populations. Taken together, our results indicate no evidence of enemy release for non-native plants, suggesting that enemy release is not a generalized mechanism favoring the success of non-native species. 

Abstract Frugivore foraging behavior is largely influenced by two key groups of chemical traits: nutrients and secondary metabolites. Many secondary metabolites function in plant defense, but their consumption can negatively impact both mutualistic and antagonistic frugivores, often due to toxic properties of the metabolites or through nutrient absorption interference. Frugivores are assumed to maximize nutrient acquisition while avoiding or minimizing toxic metabolite intake, but the relative roles of co‐occurring nutrients and secondary metabolites in foraging behavior are not well understood. Here, we used a neotropical fruit bat to investigate the interactive effects of nutrients and a broadly bioactive fruit secondary metabolite, piperine, on two essential processes in nutrient acquisition, namely foraging behavior and nutrient absorption. Through the manipulation of nutrient and piperine concentrations in artificial diets, we showed that captive fruit bats prioritize nutrient concentrations regardless of the levels of piperine, even though piperine is a strong deterrent on its own. Furthermore, our findings reveal that while piperine has no detectable influence on total sugar absorption, it reduces protein absorption, which is a crucial and limited nutrient in the frugivore diet. Overall, our results demonstrate the importance of considering the interaction between co‐occurring chemical traits in fruit pulp to better understand frugivore foraging and physiology. 

Data from: Untargeted Metabolomics Reveals Fruit Secondary Metabolites Alter Bat Nutrient Absorption; by Gelambi, M. & Whitehead, S. R. Published in the Journal of Chemical Ecology, 2024. Using a mutualistic fruit bat (Carollia perspicillata), our research explores how four secondary metabolites (piperine, tannin acid, eugenol, and phytol) commonly found in plant tissues affect the foraging behavior and induce changes in the fecal metabolome. In this study, bats were captured and housed in flight cages. Nightly trials exposed them to varying concentrations of secondary metabolites. Objective 1 involved non-choice trials to measure food consumption, while Objective 2 evaluated the impact of metabolite consumption on the bat fecal metabolome. Fecal samples were collected, stored, and later analyzed to understand how secondary metabolites influence bat behavior and metabolism. All the analyses were performed in R v. 4.2.1. 

This repository contains scripts, information, and figures related to the statistical analyses conducted for the research paper “Testing the effectiveness of synthetic chemical lures to increase fruit bat-mediated seed dispersal in a tropical forest.” Our research explores the effectiveness of synthetic chemical lures as a novel strategy to attract fruit bats and enhance seed rain in a lowland rainforest in northeastern Costa Rica (La Selva Biological Station). We investigated the impact of chemical lures on increasing bat activity and seed rain. All the analyses were performed in R v. 4.2.1. Scripts 1. Objective 1: Assess the effectiveness of the chemical lure to increase bat activity in open and semi-open areas (script1.R and script2.R) These scripts details the process of analyzing the impact of chemical lures on bat activity. script1.R compares bat communities across different sites and treatments. Non-metric multidimensional scaling (NMDS) was employed for visualization. Homogeneity of variances was checked using the ‘betadisper()’ function, followed by permutational multivariate analysis of variance (PERMANOVA) using the ‘adonis2()’ function with 999 permutations. In script2.R GLMMs were employed using the glmmTMB package. The models included the bat abundance as fixed effect, and site and capture date as random effects. The analysis was performed using various count data distributions from the glmmTMB package Overdispersion and zero inflation were assessed using the ‘check_overdispersion()’ and ‘check_zeroinflation()’ functions from the performance package. Effect sizes were computed based on estimated marginal means using the ‘emmeans()’ function from the emmeans package. We performed an autocorrelation analysis on the residuals of each model fitted. First, we performed a Durbin-Watson (DW) using the function ‘dwtest()’ from the lmtest package to test to assess temporal autocorrelation in these residuals. Then, we generated a visual representation of the autocorrelation function (ACF). Our results indicate that there is no temporal autocorrelation present in our bat data. 1. Objective 2: Assess the effectiveness of the chemical lure to increase seed rain of open and semi-open areas (script3.R) This script outlines the analysis of the impact of chemical lures on seed rain NMDS was used for visualization, and homogeneity of variances was checked with ‘betadisper()’. PERMANOVA was conducted using the ‘adonis2()’ function with 999 permutations to test for statistical significance.   Data Files Folder Objective 1: data.csv and data_nodates.csv Contains the data used for analyzing bat activity. The columns in the dataset are as follows: date: The date of bat capture. site: The site where the capture took place. bats: Total number of bats captured. fruit_bats: Total number of captured fruit bats. cperspicillata: Number of captured Carollia perspicillata bats. csowelli: Number of captured Carollia sowelli bats. ccastanea: Number of captured Carollia castanea bats. carollia_spp: Total number of captured bats from the Carollia genus. uroderma_spp: Number of captured bats from the Uroderma genus. sturnira_spp: Number of captured bats from the Sturnira genus. ectophylla_alba: Number of captured Ectophylla alba bats. artibeus_spp: Number of captured bats from the Artibeus genus. desmodus_rotundus: Number of captured Desmodus rotundus bats. nectarivorous_bats: Number of captured nectarivorous bats. insectivorous_bats: Number of captured insectivorous bats. treatment: The treatment applied (“control” or “lures”). hours: Total hours of mist nesting. nets: Total number of nets used for bat capture. Folder Objective 2: seed_data.csv Contains the data used for analyzing seed rain. The columns in the dataset are as follows: week: The week of the seed collection. Seeds were collected every 15 days. site_name: The name of the site where the observation took place at La Selva (“Zompopa”, “Lab”, “STR - Sendero Tres Rios”, “PS - Parcelas de Sucesion”). site_letter: The site’s letter designation (“A”, “B”, “C”, “D”) collection_date: The date of seed collection. treatment: The treatment applied (“baseline”, “control”, “treatment”). Columns for various plant species/plant families, indicating the count of seeds for each species. total: The total count of seeds for all plant species per collection week. comments: Additional comments or notes about the observation. Figures folder The ‘Figures’ folder contains various output files generated from the analyses conducted in the main scripts. These figures represent the results and insights obtained from the data exploration and statistical modeling. 

Plants and herbivores are remarkably variable in space and time, and variability has been considered a defining feature of their interactions. Empirical research, however, has traditionally focused on understanding differences in means and overlooked the theoretically significant ecological and evolutionary roles of variability itself. We review the literature with the goal of showing how variability-explicit research expands our perspective on plant–herbivore ecology and evolution. We first clarify terminology for describing variation and then review patterns, causes, and consequences of variation in herbivory across scales of space, time, and biological organization. We consider how incorporating variability improves existing hypotheses and leads to new ones. We conclude by suggesting future work that reports full distributions, integrates effects of variation across scales, describes nonlinearities, and considers how stochastic and deterministic variation combine to determine herbivory distributions. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Original data and R code accompanying our paper in Ecology & Evolution by Gelambi M. & Whitehead, S. R. (2023).  Ripe fleshy fruits contain not only nutrients but also a diverse array of many secondary metabolites. Nutrients serve as a reward for mutualists, whereas defensive metabolites protect the fruit against pests and predators. The composition of these chemical traits is highly variable, both across different plants and even within repeating structures on the same individual plant. This intraspecific and intraindividual variation has important fitness consequences for both plants and animals, yet patterns of variation and covariation in nutrients and secondary metabolites are not well understood, especially at smaller scales. Here, we investigate the multiscale variation and covariation between nutrients and defensive metabolites in Piper sancti-felicis ripe fruits. Means and variances of sugars, proteins, phenolics, and alkenylphenols vary greatly among plants, and at least 50% of the trait variation occurs at the intraindividual level. Also, we found that proteins, but not sugars, were correlated with phenolics and alkenylphenols at multiple scales, suggesting trait variation in protein content may be more constrained than sugars. Our findings emphasize the importance of examining patterns across scales and provide the groundwork to better understand how complex patterns of variation and covariation in nutrients and defensive metabolites shape ecological interactions surrounding fruits. 

Plant secondary metabolites are key mechanistic drivers of species interactions. These metabolites have primarily been studied for their role in defense, but they can also have complex consequences for mutualisms, including seed dispersal. Although the primary function of fleshy fruits is to attract seed-dispersing animals, fruits often contain complex mixtures of toxic or deterrent secondary metabolites that can reduce the quantity or quality of seed dispersal mutualisms. Furthermore, because seeds are often dispersed across multiple stages by several dispersers, the net consequences of fruit secondary metabolites for the effectiveness of seed dispersal and ultimately plant fitness are poorly understood. Here, we tested the effects of amides, nitrogen-based defensive compounds common in fruits of the neotropical plant genus Piper (Piperaceae), on seed dispersal effectiveness (SDE) by ants, which are common secondary seed dispersers. We experimentally added amide extracts to Piper fruits both in the field and lab, finding that amides reduced the quantity of secondary seed dispersal by reducing ant recruitment (87%) and fruit removal rates (58% and 66% in the field and lab, respectively). Moreover, amides not only reduced dispersal quantity but also altered seed dispersal quality by shifting the community composition of recruiting ants (notably by reducing the recruitment of the most effective disperser by 90% but having no detectable effect on the recruitment of a cheater species that removes fruit pulp without dispersing seeds). Although amides did not affect the distance ants initially carried seeds, they altered the quality of seed dispersal by reducing the likelihood of ants cleaning seeds (67%) and increasing their likelihood of redispersing seeds outside of the nest (200%). Overall, these results demonstrate that secondary metabolites can alter the effectiveness of plant mutualisms, by both reducing mutualism quantity and altering mutualism quality through multiple mechanisms. These findings present a critical step in understanding the factors mediating the outcomes of seed dispersal and, more broadly, demonstrate the importance of considering how defensive secondary metabolites influence the outcomes of mutualisms surrounding plants.