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bioRxiv2022

Diversification of the Ruminant Skull Along an Evolutionary Line of Least Resistance

Morphological integration is relevant to evolutionary biology and paleontology because the structure of variation within populations determines the ways in which a population can respond to selective pressures. However, understanding the macroevolutionary consequences of morphological integration is elusive because the adaptive landscape is dynamic and population-level constraints themselves evolve. By analyzing a previously published dataset of 2859 ruminant crania with 3D geometric morphometrics and phylogenetic comparative methods, we find that variation within and between ruminant species is biased by a highly conserved mammalian-wide allometric pattern, CREA, where larger species have proportionally longer faces. More tightly integrated species and species more biased towards CREA have diverged farther from their ancestors, and Ruminantia as a clade diversified farther than expected in the direction anticipated by CREA. Our analyses indicate that CREA acts as an evolutionary line of least resistance and facilitates morphological diversification due to its alignment with the browser-grazer continuum. These results demonstrate that biological processes constraining variation at the microevolutionary level can produce highly directional phenotypic evolution over macroevolutionary timescales.

evolutionary biology↗

bioRxiv2019

Local adaptation fuels cryptic speciation in terrestrial annelids

Uncovering the genetic and evolutionary basis of cryptic speciation is a major focus of evolutionary biology. Next Generation Sequencing (NGS) allows the identification of genome-wide local adaptation signatures, but has rarely been applied to cryptic complexes - particularly in the soil milieu - as is the case with integrative taxonomy. The earthworm genus Carpetania, comprising six previously suggested putative cryptic lineages, is a promising model to study the evolutionary phenomena shaping cryptic speciation in soil-dwelling lineages. Genotyping-By-Sequencing (GBS) was used to provide genome-wide information about genetic variability between seventeen populations, and geometric morphometrics analyses of genital chaetae were performed to investigate unexplored cryptic morphological evolution. Genomic analyses revealed the existence of three cryptic species, with half of the previously-identified potential cryptic lineages clustering within them. Local adaptation was detected in more than 800 genes putatively involved in a plethora of biological functions (most notably reproduction, metabolism, immunological response and morphogenesis). Several genes with selection signatures showed shared mutations for each of the cryptic species, and genes under selection were enriched in functions related to regulation of transcription, including SNPs located in UTR regions. Finally, geometric morphometrics approaches partially confirmed the phylogenetic signal of relevant morphological characters such as genital chaetae. Our study therefore unveils that local adaptation and regulatory divergence are key evolutionary forces orchestrating genome evolution in soil fauna.

evolutionary biology↗

bioRxiv2018

Inferring the ancestry of everyone

A central problem in evolutionary biology is to infer the full genealogical history of a set of DNA sequences. This history contains rich information about the forces that have influenced a sexually reproducing species. However, existing methods are limited: the most accurate is unable to cope with more than a few dozen samples. With modern genetic data sets rapidly approaching millions of genomes, there is an urgent need for efficient inference methods to exploit such rich resources. We introduce an algorithm to infer whole-genome history which has comparable accuracy to the state-of-the-art but can process around four orders of magnitude more sequences. Additionally, our method results in an \"evolutionary encoding\" of the original sequence data, enabling efficient access to genealogies and calculation of genetic statistics over the data. We apply this technique to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the genealogies we estimate are both rich in biological signal and efficient to process.

evolutionary biology↗

bioRxiv2017

Can Ebola Virus evolve to be less virulent in humans?

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology ( ). Understanding Ebola Virus (EBOV) virulence evolution is not only timely but also raises specific questions because it causes one of the most virulent human infections and it is capable of transmission after the death of its host. Using a compartmental epidemiological model that captures three transmission routes (by regular contact, via dead bodies and by sexual contact), we infer the evolutionary dynamics of case fatality ratio (CFR) on the scale of an outbreak and on the long term. Our major finding is that the viruss specific life cycle imposes selection for high levels of virulence and that this pattern is robust to parameter variations in biological ranges. In addition to shedding a new light on the ultimate causes of EBOVs high virulence, these results generate testable predictions and contribute to informing public health policies. In particular, burial management stands out as the most appropriate intervention since it decreases the R0 of the epidemics, while imposing selection for less virulent strains.\n\nImpact SummaryThe severe haemorrhagic fever caused by Ebola Virus (EBOV) usually kills more than one infected individual out of two in the absence of treatment, which makes this pathogen one of the most virulent known to humans. The recent outbreak in West Africa (2013-2016) revealed that the virus is able to spread and persist for months across countries. It is often thought that virulence could be due to the fact that the virus is adapted to its reservoir host. Given that microbes evolve rapidly, it is important to determine whether EBOV virulence is likely to decrease as the virus adapts to its human host. To address this problem, we developed a novel mathematical model tailored to EBOVs life cycle, notably by capturing its three main transmission routes (by regular contact, sexual contact and via dead bodies). We investigated the evolutionary trends of EBOVs virulence on different time scales (outbreak initiation, short term and long term). Our results reveal that the virulence of EBOV might not be due to the maladaptation of the virus, but could rather originate from its unique life cycle. These results are robust to the parameter values chosen. From a public health perspective, burial management stands out as the main leverage to fight the virulence of EBOV, both on the short and long terms.

evolutionary biology↗

bioRxiv2026

Phylogenetic tree inference using generative models

Accurate inference of phylogenetic trees is fundamental to evolutionary biology, yet existing methods rely on complex pipelines involving multiple sequence alignment, explicit evolutionary models, and computationally intensive tree search procedures. Here, we present BetaInfer, a generative framework that reformulates phylogenetic tree inference as a sequence transduction problem. BetaInfer leverages hybrid transformer-based architectures to directly map sets of unaligned sequences to phylogenetic trees represented in Newick format. Trained on large-scale simulated evolutionary data with known ground truth, BetaInfer learns to capture complex evolutionary signals directly from sequence data. Ensemble-based generation of multiple candidate trees further improves robustness, reducing reconstruction error by over 30% relative to single predictions. Across extensive evaluations on both simulated and empirical datasets, BetaInfer achieves competitive performance relative to state-of-the-art phylogenetic pipelines, matching, and in some cases exceeding, the accuracy of established likelihood-based and distance-based methods under a wide range of conditions. Interpretability analyses reveal that BetaInfer leverages internal pairwise-distance computations to synthesize evolutionary relationships into an integrated, global representation that supports direct tree generation. Together, these results demonstrate that generative models can serve as a viable and scalable alternative to standard phylogenetic pipelines.

bioinformatics↗

bioRxiv2024

The comparative analysis of lineage-pair traits.

A powerful but poorly understood analysis in ecology and evolutionary biology is the comparative study of lineage-pair traits. "Lineage-pair traits" are characters like diet niche overlap and strength of reproductive isolation that are defined for pairs of lineages instead of individual taxa. Comparative tests for causal relationships among such variables have led to groundbreaking insights in several classic studies, but the statistical validity of these analyses has been unclear due to the complex dependency structure of the data. Specifically, lineage-pair datasets contain non-independent observations, but studies to-date have relied on untested workarounds for data dependency rather than direct models of linear-pair covariance, and the statistical consequences of non-independence have not been thoroughly explored. Here we consider how evolutionary relatedness among taxa translates into non-independence among taxonomic pairs. We develop models by which phylogenetic signal in an underlying character generates covariance among pairs in a lineage-pair trait. We incorporate the resulting lineage-pair covariance matrix into a modified version of phylogenetic generalized least squares and a new beta regression model suitable for bounded response variables. Both models outperform previous approaches in simulation tests. We re-analyze two empirical datasets and find dramatic improvements in model fit and, in the case of avian hybridization data, an even stronger relationship between pair age and RI than revealed by standard linear regression. We present a new tool, the R package phylopairs, to allow empiricists from a variety of biological fields to test relationships among pairwise-defined variables in a manner that is statistical robust and more straightforward to implement.

evolutionary biology↗

bioRxiv2024

Phenotypic characterization of cryptic species in the fungal pathogen Histoplasma

Histoplasmosis is an endemic mycosis that often presents as a respiratory infection in immunocompromised patients. Hundreds of thousands of new infections are reported annually around the world. The etiological agent of the disease, Histoplasma, is a dimorphic fungus commonly found in the soil where it grows as mycelia. Humans can become infected by Histoplasma through inhalation of its spores (conidia) or mycelial particles. The fungi transitions into the yeast phase in the lungs at 37{degrees}C. Once in the lungs, yeast cells reside and proliferate inside alveolar macrophages. We have previously described that Histoplasma is composed of at least five cryptic species that differ genetically, and assigned new names to the lineages. Here we evaluated multiple phenotypic characteristics of 12 strains from five phylogenetic species of Histoplasma to identify phenotypic traits that differentiate between these species: H. capsulatum sensu stricto, H. ohiense, H. mississippiense, H. suramericanum, and an African lineage. We report diagnostic traits for two species. The other three species can be identified by a combination of traits. Our results suggest that 1) there are significant phenotypic differences among the cryptic species of Histoplasma, and 2) that those differences can be used to positively distinguish those species in a clinical setting and for further study of the evolution of this fungal pathogen. IMPORTANCEIdentifying species boundaries is a critical component of evolutionary biology. Genome sequencing and the use of molecular markers have advanced our understanding of the evolutionary history of fungal pathogens, including Histoplasma, and have allowed for the identification of new species. This is especially important in organisms where morphological characteristics cannot be used for this purpose. In this study, we revise the taxonomic status of the four named species of the genus Histoplasma: H. capsulatum sensu stricto, H. ohiense, H. mississippiense, and H. suramericanum and propose the use of species-specific phenotypic traits to aid their identification when genome sequencing is not available. These results have implications not only for evolutionary study of Histoplasma, but also for clinicians, as the Histoplasma species could determine the outcome of disease and treatment needed.

microbiology↗

bioRxiv2026

A subcellular spatial proteome of the non-photosynthetic euglenid Euglena longa

Euglena longa is a heterotrophic model euglenid, of significant research interest as a unicellular eukaryote. To provide broad insight into the localisation of its proteome we used subcellular spatial proteomics (SSP), based on a label-free version of LOPIT-DC (Localisation of Organelle Proteins by Isotope Tagging after Differential Centrifugation), in which organelles are separated by differential centrifugation and each protein is assigned to a compartment from its abundance profile across the fractions. We detected 5,909 proteins, using protein abundance profiles across fractions to resolve 16 different subcellular compartments, such as the plastid, pellicle, and peroxisomes, and assigned high-confidence localisations to 2,955 of them. This study serves as a proteomic dataset to resolve protein compartmentalisation for this euglenid, enabling evolutionary, biological and functional insight and analysis. We are drafting manuscripts concerning the metabolic nature of the plastid, the evolutionary origins of the pellicle, and the functions of the peroxisome. Investigators interested in using this data prior to publication are encouraged to contact us to avoid overlap and to facilitate coordinated and collaborative use of this resource.

cell biology↗

bioRxiv2016

Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

Bioinformatics↗

bioRxiv2025

MaizeGDB Phylostrata Tool: Exploring evolutionary origins of maize proteins

MotivationPhylostratigraphic analysis identifies the evolutionary origins and level of conservation of proteins, facilitating research in evolutionary biology and comparative genomics. ResultsWe developed the MaizeGDB Phylostrata Tool, a custom web application that enables users to explore the evolutionary origins of maize proteins. This tool features interactive visualizations and detailed gene pages incorporating subcellular localization, Gene Ontology (GO) terms, and other resources for homologs. Full-proteome downloads are available for 26 maize inbreds (B73 and the NAM founders). We also provide an updated version of the phylostratr R package that makes it more robust to taxonomic updates, as well as example scripts for phylostratigraphic analysis and webtool development for the use of researchers and curators of other species. Availability and ImplementationThe MaizeGDB Phylostrata Tool is freely available at phylostrata.maizegdb.org. Scripts used for the analysis and web tool are available at https://github.com/LTibbs/PhylostrataWebtool. Contactcarson.andorf@usda.gov

bioinformatics↗

bioRxiv2023

Arctos: Community-driven innovations for managing biodiversity and cultural collections

Museum collections house millions of objects and associated data records that document biological and cultural diversity. In recent decades, digitization efforts have greatly increased accessibility to these data, thereby revolutionizing interdisciplinary studies in evolutionary biology, biogeography, epidemiology, cultural change, and human-mediated environmental impacts. Curators and collection managers can make museum data as accessible as possible to scientists and learners by using a collection management system. However, selecting a system can be a challenging task. Here, we describe Arctos, a community solution for managing and accessing collections data for research and education. Specific goals are to: (1) Describe the core elements of Arctos for a broad audience with respect to the biodiversity informatics principles that enable high quality research; (2) Highlight the unique aspects of Arctos; (3) Illustrate Arctos as a model for supporting and enhancing the Digital Extended Specimen; and (4) Emphasize the role of the Arctos community for improving data discovery and enabling cross-disciplinary, integrative studies within a sustainable governance model. In addition to detailing Arctos as both a community of museum professionals and a collection database platform, we discuss how Arctos achieves its richly annotated data by creating a web of knowledge with deep connections between catalog records and derived or associated data. We also highlight the value of Arctos as an educational resource. Finally, we present a financial model of fiscal sponsorship by a non-profit organization, implemented in 2022, to ensure the long-term success and sustainability of Arctos. We attribute Arctos longevity of nearly three decades to its core development principles of standardization, flexibility, interdisciplinarity, and connectivity within a nimble development model for addressing novel needs and information types in response to changing technology, workflows, ethical considerations, and regulations.

bioinformatics↗

bioRxiv2026

Spatial heterogeneity shapes microbial eco-evolutionary dynamics of soil carbon

The study of reciprocal influences between ecological and evolutionary processes has advanced considerably, yet integration between evolutionary biology and ecosystem-level ecology remains limited. Here we contribute to this integration by advancing the theory of eco-evolutionary feedbacks between soil microbial adaptation and soil-atmosphere carbon fluxes in a warming climate. We develop a spatially structured model of soil organic matter decomposition that represents microbial populations in microsites embedded in a bulk-soil matrix and focuses on exoenzyme production as a key resource-acquisition trait. The evolutionarily adapted investment in exoenzyme production is shaped by opposing selective forces: negative selection within microsites, where lower-investing mutants exploit exoenzymes as public goods, and positive selection in the soil matrix, where exoenzyme production directly benefits individual cells. Microsite density emerges as a critical determinant of microbial adaptation to warming and its consequences for soil carbon loss. Even small changes in microsite density across a threshold can reverse the ecosystem-level effect of adaptation, from buffering to amplifying carbon loss. High microsite density generally promotes buffering, whereas low microsite density has little effect in cool ecosystems but can strongly amplify carbon loss in warm ecosystems, especially when microbial mobility is low. These results identify soil spatial structure at microsite scale as a key mediator of microbial evolutionary adaptation and soil carbon-climate feedback under global environmental change, with implications for quantitatively improving Earth system models.

ecology↗

bioRxiv2025

Linking genomic evolutionary transitions to ecological phenotypic adaptations in Spirochaetes

Understanding the genetic basis of ecological adaptation is a fundamental challenge of evolutionary biology. Spirochaetes are an ancient bacterial phylum of exceptional ecological breadth, encompassing major human pathogens such as the causative agents of syphilis, Lyme disease, and leptospirosis, alongside free-living species from contrasting environments and recently described species that lack the canonical spiral morphology of the phylum. Here, using a curated dataset of genomes representing all cultivable spirochete species, we show that functional genome architecture (defined as the proportional allocation of coding capacity across biological processes) correlates with ecological lifestyle and phenotypic traits in Spirochaetes independently of phylogenetic relationships, with host-dependent lineages from distinct evolutionary origins converging on shared functional profiles. Phylum-wide phylogenomic analyses identified Brachyspira as the earliest-diverging lineage, revisiting the evolutionary rooting of the phylum, and establishing that the spiral morphology is ancestral and was lost in a single evolutionary transition also associated with coordinated functional changes. Lastly, ancestral genome reconstruction uncovers the common ancestor as a motile, heterotrophic, and spiral-shaped bacterium, with metabolic and structural features not previously described. Together, these results provide an integrated and functional framework for Spirochaetes, illustrating how genome architecture can be used to track the ecological diversification of a widespread bacterial phylum.

microbiology↗

bioRxiv2024

An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing

We present the Evolutionary Statistics Toolkit, a user-friendly web-based platform designed for specialized analysis of genetic sequences, which integrates multiple evolutionary statistics. The toolkit focuses on a selection of specialized tools, including Tajimas D calculator with Site Frequency Spectrum (SFS), Shannons Entropy (H), alignment re-formatting, HGSV to FASTA conversion, pair-wise frequency analysis, FASTA to SEQRES, RNA 2D structure alignment, Kyte-Doolittle hydrophilicity plot tool, Chou-Fasman tool, and kurtosis coefficient calculator. Tajimas D is calculated using the reference formula: D = ({pi} - {theta}W) / sqrt(VD), where {pi} corresponds to the average number of differences, {theta}W is Wattersons estimator of {theta}, and VD is the variance of {pi} - {theta}W. Shannons Entropy is defined as H = -{sum} pi * log2(pi), where pi is the probability of occurrence of each unique character (nucleotide or amino acid) in the sequence. The toolkit facilitates streamlined workflows for early researchers in evolutionary biology, genomics, and related fields. With comparing with existing codes, we propose it also emerges as an educational interactive website for beginners in evolutionary statistics. The source code for each tool in the toolkit is available through GitHub links provided on the website. This open-source approach allows users to inspect the code, suggest improvements, or further adapt the tools for their specific usage and research needs. This article describes the functionalities, and validation of each tool within the platform, along with comparison with accessible existing statistical utilities. The toolkit is freely accessible on: https://www.alperkaragol.com/toolkit

bioinformatics↗

bioRxiv2025

Advancing FAIR Data Management through AI-Assisted Curation of Morphological Data Matrices

Curation of biological and paleontological datasets is a labor-intensive process that requires standardization and validation to ensure data integrity. In particular, manual curation of datasets is prone to human errors such as typographical errors, inconsistent formatting, and incomplete metadata, which hinder reproducibility and compliance with Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Artificial Intelligence (AI) offers a transformative solution for enhancing research efficiency by automating data validation, improving accuracy, and streamlining curation workflows. This study presents a proof-of-concept implementation of an AI-assisted curation tool developed for MorphoBank, an open access repository established to enhance standardization and usability of morphological character datasets. Specifically, this work presents an AI tool designed to extract, structure, and standardize morphological character data from published literature into the NEXUS file format, a widely used format for phylogenetic analyses. This tool leverages machine learning techniques, including Large Language Models (LLMs), to automate the extraction of character names and states from text in various formats, reducing manual data entry errors and improving data completeness. The system enables efficient conversion of matrix-only files into complete, machine- and human-readable datasets that include key character metadata. By assisting with these tasks, the tool reduces the manual effort required for curation while improving consistency and standardization. This approach increases the FAIRness of morphological character data and provides a framework for extending AI-assisted curation to other types of biological data. These results illustrate the potential of AI-assisted workflows to support scalable data curation and reuse in paleontology, systematics, and evolutionary biology.

bioinformatics↗

bioRxiv2024

Scaling ecological niches from individuals to populations and beyond

The niche is a key concept that unifies ecology and evolutionary biology. However, empirical and theoretical treatments of the niche are mostly performed at the species level, neglecting individuals as important units of ecological and evolutionary processes. So far, a formal mathematical link between individual-level niches and higher organismal-level niches has been lacking, hampering the unification of ecological theories and more accurate forecasts of biodiversity change. To fill in this gap, we propose a bottom-up approach to derive population and higher organismal-level niches from individual niches. We demonstrate the power of our framework by showing that 1) the statistical properties of higher organismal-level niches (e.g. niche breadth, skewness etc.) can be partitioned into individual contributions; 2) the species-level niche shifts can be estimated by tracing the responses of individuals. Our method paves the way for a unifying niche theory and enables mechanistic assessments of organism-environment relationships across organismal scales.

ecology↗

bioRxiv2019

Searching for Simple Rules in Pseudomonas aeruginosa Biofilm Formation

Living cells display complex and non-linear behaviors, especially when posed to environmental threats. Here, to understand the self-organizing cooperative behavior of a microorganism Pseudomonas aeruginosa, we developed a discrete spatiotemporal cellular automata model based on simple physical rules, similar to Conways game of life. The time evolution model simulations were experimentally verified for P. aeroginosa biofilm for both control and antibiotic azithromycin (AZM) treated condition. Our model suggests that AZM regulates the single cell motility, thereby resulting in delayed, but not abolished, biofilm formation. In addition, the model highlights the importance of reproduction by cell to cell interaction is key for biofilm formation. Overall, this work highlights another example where biological evolutionary complexity may be interpreted using rules taken from theoretical disciplines.

systems 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↗