RPANDA: an R package for macroevolutionary analyses on phylogenetic trees

Abstract

Summary1. A number of approaches for studying macroevolution using phylogenetic trees have been developed in the last few years. Here, we present RPANDA, an R package that implements model-free and model-based phylogenetic comparative methods for macroevolutionary analyses. 2. The model-free approaches implemented in RPANDA are recently developed approaches stemming from graph theory that allow summarizing the information contained in phylogenetic trees, computing distances between trees, and clustering them accordingly. They also allow identifying distinct branching patterns within single trees. 3. RPANDA also implements likelihood-based models for fitting various diversification models to phylogenetic trees. It includes birth-death models with i) constant, ii) time-dependent and iii) environmental-dependent speciation and extinction rates. It also includes models with equilibrium diversity derived from the coalescent process, as well as a likelihood-based inference framework to fit the individual-based model of Speciation by Genetic Differentiation, which is an extension of Hubbell's neutral theory of biodiversity. 4. RPANDA can be used to (i) characterize trees by plotting their spectral density profiles (ii) compare trees and cluster them according to their similarities, (iii) identify and plot distinct branching patterns within trees, (iv) compare the fit of alternative diversification models to phylogenetic trees, (v) estimate rates of speciation and extinction, (vi) estimate and plot how these rates have varied with time and environmental variables and (vii) deduce and plot estimates of species richness through geological time. 5. RPANDA provides investigators with a set of tools for exploring patterns in phylogenetic trees and fitting various models to these trees, thereby contributing to the ongoing development of phylogenetics in the life sciences.

Transcriptomic and macroevolutionary evidence for phenotypic uncoupling between frog life history phases

“…To infer whether the mantellid radiation shows signatures of an adaptive diversification process, we tested for an ‘early burst' pattern in species diversification, consisting of high diversification rates at the beginning of the radiation followed by rate deceleration towards the present59. We compared the second order Akaike Information Criterion (AICc) for six alternative models of species diversification60 using the package RPANDA 61 in R62. We then used auteur 63 in R to model rate heterogeneity on a phylogeny (assuming a Brownian motion model) using a reversible-jump MCMC algorithm.…”

A process‐based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages

“…The association between species traits and clade diversity has mostly been documented using non-spatial phylogenetic approaches (Magallón and Sanderson 2001, FitzJohn et al 2009, Morlon et al 2016, Tedesco et al 2017 and generally restricted by the ability to integrate substantial historical information due to the lack of phylogenetic tree resolution linked to the paucity of the fossil record, especially in the marine realm. The association between species traits and clade diversity has mostly been documented using non-spatial phylogenetic approaches (Magallón and Sanderson 2001, FitzJohn et al 2009, Morlon et al 2016, Tedesco et al 2017 and generally restricted by the ability to integrate substantial historical information due to the lack of phylogenetic tree resolution linked to the paucity of the fossil record, especially in the marine realm.…”

Seed size and its rate of evolution correlate with species diversification across angiosperms

“…For each resulting clade in each time slice, we calculated diversification rates using a method-of-moments estimator, which assumes rates are constant over time [23]. We also fitted a series of time-dependent diversification models to each clade with R: Phylogenetic ANalyses of DiversificAtion (RPANDA), which uses a maximum likelihood approach to estimate speciation and extinction and allows for incomplete taxon sampling [24] (see S1 Table for a summary of the best fitting models for each time slice). Rates of seed size evolution were estimated within each clade that also had at least 4 species with seed size data by fitting both Brownian motion (BM) and early burst (EB) or accelerating decelerating [25] models of trait evolution.…”