Biology subjects

Mendes, F. K.

Publications and source records attributed to Mendes, F. K..

10 recordsLinked to original sources

bioRxiv2026

Gene tree patterns help answer: vicariance or dispersal?

Historical biogeography seeks to understand the drivers of species distributions over space and time. One question of interest is how, out of many possible ways, does geography drive speciation. Vicariance, where geographic barriers arise splitting populations and limiting gene flow, can lead to allopatric speciation. Founder events, where a small number of individuals disperse over a barrier, can similarly lead to allopatric speciation if the individuals remain isolated. Both of these scenarios can lead to identical ranges of and relationships between modern species. Classic biogeographic approaches often focus on the history of populations of one or few species on shallow time scales or multiple species on deep time scales. We argue that focusing exclusively on either end of this time spectrum misses a venue for investigating the biogeographic drivers of speciation, at least for certain speciation events. With simple coalescent simulations with multiple species, we show as a proof of concept that gene tree distributions vary predictably between vicariance and founder event speciation. The existence of predictable patterns warrants the development of new approaches that capitalize the gene trees to distinguish vicariance and dispersal as drivers of speciation.

evolutionary biology↗

bioRxiv2025

The Goldilocks effect drives plant diversification on middle-aged Hawaiian islands

Islands are natural laboratories for studying dispersal, speciation, and extinction, yet the historical dynamics of many insular radiations remain poorly understood because of the difficulty aligning the tempo and mode of diversification with paleogeography during phylogenetic inference. We introduce a phylogenetic modeling framework, named TimeFIG, that combines data about species ranges and genetic variation with paleogeographic information to simultaneously infer divergence times, ancestral ranges, and historical diversification rates, without relying on fossils to time-calibrate the phylogeny. Applying TimeFIG to the spectacular radiation of Hawaiian Kadua (Rubiaceae) points to colonization of either modern Kaua'i or ancient now-eroded islands, with island isolation as the strongest correlate of dispersal, and with a tendency for net diversification to be highest on "middle-aged" islands. Modeling diversification within an explicit spatiotemporal context, while accounting for historical uncertainty regarding the timing and placement of phylogenetic and biogeographic events, enables the rigorous testing of foundational hypotheses in island biology and evolutionary theory for clades beyond Kadua.

evolutionary biology↗

bioRxiv2024

How to validate a Bayesian evolutionary model

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe and illustrate good practices for assessing the correctness of a model implementation, with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

evolutionary biology↗

bioRxiv2023

PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models

We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, testing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, through its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.

bioinformatics↗

bioRxiv2022

LinguaPhylo: a probabilistic model specification language for reproducible phylogenetic analyses

Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, LPhy, which is designed to be both human and machine-readable. A graphical user interface accompanies LPhy, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

evolutionary biology↗

bioRxiv2021

Scalable total-evidence inference from molecular and continuous characters in a Bayesian framework

Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remains challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counter-parts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our methods behavior, and suggest future research venues.

systems biology↗

bioRxiv2018

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.\n\nDeveloping such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.\n\nAuthor summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

evolutionary biology↗

bioRxiv2018

The perils of intralocus recombination for inferences of molecular convergence

Accurate inferences of convergence require that the appropriate tree topology be used. If there is a mismatch between the tree a trait has evolved along and the tree used for analysis, then false inferences of convergence (\"hemiplasy\") can occur. To avoid problems of hemiplasy when there are high levels of gene tree discordance with the species tree, researchers have begun to construct tree topologies from individual loci. However, due to intralocus recombination even locus-specific trees may contain multiple topologies within them. This implies that the use of individual tree topologies discordant with the species tree can still lead to incorrect inferences about molecular convergence. Here we examine the frequency with which single exons and single protein-coding genes contain multiple underlying tree topologies, in primates and Drosophila, and quantify the effects of hemiplasy when using trees inferred from individual loci. In both clades we find that there are most often multiple diagnosable topologies within single exons and whole genes, with 91% of Drosophila protein-coding genes containing multiple topologies. Because of this underlying topological heterogeneity, even using trees inferred from individual protein-coding genes results in 25% and 38% of substitutions falsely labeled as convergent in primates and Drosophila, respectively. While constructing local trees can reduce the problem of hemiplasy, our results suggest that it will be difficult to completely avoid false inferences of convergence. We conclude by suggesting several ways forward in the analysis of convergent evolution, for both molecular and morphological characters.

evolutionary biology↗

bioRxiv2018

Evolutionary inferences about quantitative traits are affected by underlying genealogical discordance

Modern phylogenetic methods used to study how traits evolve often require a single species tree as input, and do not take underlying gene tree discordance into account. Such approaches may lead to errors in phylogenetic inference because of hemiplasy -- the process by which single changes on discordant trees appear to be homoplastic when analyzed on a fixed species tree. Hemiplasy has been shown to affect inferences about discrete traits, but it is still unclear whether complications arise when quantitative traits are analyzed. In order to address this question and to characterize the effect of hemiplasy on traits controlled by a large number of loci, we present a multispecies coalescent model for quantitative traits evolving along a species tree. We demonstrate theoretically and through simulations that hemiplasy decreases the expected covariances in trait values between more closely related species relative to the covariances between more distantly related species. This effect leads to an overestimation of a traits evolutionary rate parameter, to a decrease of the traits phylogenetic signal, and to increased false positive rates in comparative methods such as the phylogenetic ANOVA. We also show that hemiplasy affects discrete, threshold traits that have an underlying continuous liability, leading to false inferences of convergent evolution. The number of loci controlling a quantitative trait appears to be irrelevant to the trends reported, for all analyses. Our results demonstrate that gene tree discordance and hemiplasy are a problem for all types of traits, across a wide range of methods. Our analyses also point to the conditions under which hemiplasy is most likely to be a factor, and suggest future approaches that may mitigate its effects.

evolutionary biology↗

bioRxiv2017

Why concatenation fails in the anomaly zone

AbstrctGenome-scale sequencing has been of great benefit in recovering species trees, but has not provided final answers. Despite the rapid accumulation of molecular sequences, resolving short and deep branches of the tree of life has remained a challenge, and has prompted the development of new strategies that can make the best use of available data. One such strategy - the concatenation of gene alignments - can be successful when coupled with many tree estimation methods, but has also been shown to fail when there are high levels of incomplete lineage sorting. Here, we focus on the failure of likelihood-based methods in retrieving a rooted, asymmetric four-taxon species tree from concatenated data when the species tree is in or near the anomaly zone - a region of parameter space where the most common gene tree does not match the species tree because of incomplete lineage sorting. First, we use coalescent theory to prove that most informative sites will support the species tree in the anomaly zone, and that as a consequence maximum-parsimony succeeds in recovering the species tree from concatenated data. We further show that maximum-likelihood tree estimation from concatenated data fails both inside and outside the anomaly zone, and that this failure is unconnected to the frequency of the most common gene tree. We provide support for a hypothesis that likelihood-based methods fail in and near the anomaly zone because discordant sites on the species tree have a lower likelihood than those that are discordant on alternative topologies. Our results confirm and extend previous reports of the failure and success of likelihood- and parsimony-based methods, and highlight avenues for future work improving the performance of methods aimed at recovering species tree.

evolutionary biology↗