DOI: 10.5061/dryad.83bk3j9vk
title: What is a mammalian omnivore? Insights into terrestrial mammalian diet diversity, body mass, and evolution

description: Mammalian omnivores are a broad and diverse group of mammals that are
 often lumped together in ecological studies. As a result, there are open
 questions about what macroevolutionary and macroecological information can
 be gained when omnivorous dietary differences are investigated in
 ecological and phylogenetic comparative studies. In this study, we
 investigate the frequency at which vertebrate prey, invertebrate prey,
 fibrous plant material, and non-fibrous plant material co-occur in the
 diets of omnivorous species. We quantify the body size distributions and
 phylogenetic signal of terrestrial mammals that consume different
 omnivorous diets and using multistate reversible jump MCMC, we assess the
 transition rates between mammalian diet strategies on the mammalian
 phylogenetic tree. We find that omnivores that consume all four food types
 are rare and most omnivorous mammals consume only invertebrate prey and
 non-fibrous plants. We also find that omnivores that only consume
 invertebrate prey are on average smaller than omnivores that incorporate
 vertebrate prey as many are from within Rodentia. Our transition rate
 models show that there are high transition rates from invertivorous
 omnivory to herbivory, and from vertebrate predation to prey mixing and
 ultimately invertivory. Our results suggest that prey type is an important
 aspect of omnivore macroevolution and macroecology, as it is correlated
 with body mass, evolutionary history, and diet-related evolutionary
 transition rates.
description: Dataset and phylogenetic tree Using previously
 published datasets, we compiled diet data and body masses for 1437 extant
 terrestrial mammals (about a quarter of all mammal species) (Supplemental
 data 1). Aquatic mammals, dependent on food webs with a dramatically
 different structure than their terrestrial counterparts, are expected to
 experience different ecological and evolutionary pressures than
 terrestrial mammals and have been excluded from this analysis. We chose to
 use a dataset compiled from primary literature sources generated
 previously by some of the authors (Price et al. 2012). This dataset did
 not use dietary inferences based on phylogeny or morphology, only using
 studies that reported stomach contents or cheek-pouch contents, the
 contents of food stores, direct behavioral observations, or fecal
 analysis. While this dataset contains fewer species, and uses a
 categorical schema, we use it because of the high diet resolution and
 because it allows us to directly compare our results to those previously
 published by Price et al. 2012. The dataset has broad taxonomic and
 phylogenetic representation and good coverage of all the mammalian orders
 (Supplemental data 1). As it was compiled, they prioritized species from
 underrepresented families in the diet literature, as opposed to obtaining
 exhaustive samples of well-represented genera. The data in Price et al.
 2012 were analyzed using the three basic trophic categories, however, it
 was originally collected using more detailed categories and dietary
 descriptions. Food materials were split into four food categories:
 invertebrate prey, vertebrate prey, fibrous plant parts (mature leaves,
 stems, wood, and bark), and non-fibrous plant parts (any other parts of
 plants). A category was scored as present in the diet of the organism if,
 in the primary literature, it constituted at least 5% of the food consumed
 by volume, weight, or feeding time. Body masses for omnivorous species
 were gathered from the PanTHERIA database. For all
 phylogenetically-informed analyses, we used a fully resolved set of
 phylogenetic trees from Faurby and Svenning 2015.
 Phylogenetic signal We calculated the
 phylogenetic signal of each diet category, treating each diet category as
 a binary trait over ten randomly selected trees with the phylo.d function
 in the caper package in R. This method calculates a D statistic which is
 close to 1 if the observed trait has a phylogenetically random
 distribution or 0 if the observed trait is dispersed on the tree in a way
 that is consistent with a threshold model of Brownian motion evolution.
 The trait distribution for the Brownian motion model is calculated by
 simulating a continuous trait along the phylogeny, defining a threshold
 value that ensures that the number of tips with each character state
 remains the same as in the original dataset, then defining the character
 state at each tip using the threshold value of the continuous trait.
 Values lower than 0 indicate phylogenetic clustering beyond what is
 expected by the Brownian motion threshold model. The phylo.d function also
 tests for significant departure from both a phylogenetically random
 distribution and the phylogenetic distribution generated under the
 threshold model. Transition rates We
 calculated transition rates between the seven dietary guilds using
 Bayesian Markov Chain Monte Carlo (MCMC) methods in the program
 BayesTraits. Specifically, a multistate reversible jump MCMC was used to
 estimate transition rates without assuming a single model of trait
 evolution. Reversible jump MCMC explores all possible models and generates
 a posterior distribution of models and parameter estimates by setting each
 transition rate parameter to either a unique value, equal to one or more
 of the other transition rates, or zero. This process allows for the
 exploration of loglikelihood especially when there is a large number of
 possible models. Because this is computationally intensive, all
 BayesTraits analyses were run on the University of Oregon Talapas High
 Performance Computing cluster. To take into account
 variability in tree topology we ran independent chains on 100 randomly
 selected fully resolved trees. We used uniform hyperpriors to seed the
 exponential prior on the parameters, seeding the mean of the exponential
 prior from a uniform distribution on the interval 0 to 10 or 0 to 2. To
 ensure stationarity was reached each chain was run for 1 billion
 iterations with a sampling interval of 300,000 and a burn-in of 100,000
 iterations. We examined the effective sample sizes, autocorrelation, and
 convergence using packages coda and btw in R (Supplemental data 23). We
 also checked the autotuning mechanism by examining schedule files to make
 sure the chains were mixing appropriately. The medians and interquartile
 ranges (IQR) were then calculated for each transition rate along with the
 frequency with which a transition rate was reconstructed as zero (% Z). To
 investigate whether the differences in transition rates were meaningful,
 we ran the same analyses on a tree with randomly reassigned dietary
 categories. We produced a random dataset in R using the sample function on
 our existing data to guarantee the same number of individuals in each
 dietary guild. We then used the same reversible jump MCMC procedure in
 BayesTraits to calculate median transition rates, % Z, and model posterior
 distribution. This allowed us to determine whether our observed results
 differed from those expected when there is no phylogenetic signal in
 dietary guilds. Additionally, to accurately compare our
 transition rates to those estimated by Price et al. 2012, which used a
 different phylogeny and methods, we used the same reversible jump MCMC
 procedure in BayesTraits to estimate transition rates between the three
 main trophic categories (herbivory, carnivory, omnivory). We ran ten
 independent chains using 10 randomly selected trees. Furthermore,
 following the procedures of Price et al. 2012, we conducted analyses using
 the “Multiple State Speciation Extinction” (MuSSE) model in the
 diversitree package on our seven dietary guilds. We estimated model fit
 using maximum likelihood instead of Bayesian MCMC methods because of
 computational constraints. We ran two models one with the speciation and
 extinction rates constrained to those reported in Price et al. 2012 and
 one with no constraints. We incorporated the sampling frequency to be
 0.224 of all Mammalia as the current reported number of mammalian species
 is 6,495. We ran each model on 10 randomly selected trees.