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bioRxiv2025

Holotype genome of the lesula provides insights into demography and evolution of a threatened primate lineage

The rapid development of genome sequencing techniques in recent decades has revolutionized the field of evolutionary biology, facilitating the study of adaptation and speciation at the genome level. Genomic data has also become a cornerstone in conservation management, allowing inferences of population demography and genetic diversity. Here, we sequenced the genome of the holotype specimen of the elusive lesula (Cercopithecus lomamiensis), a recently described member of the guenons (tribe Cercopithecini) endemic to the Democratic Republic of the Congo. Using published and novel genomic data, we explore the evolutionary and demographic history of C. lomamiensis and its sister species C. hamlyni, the only two members of the hamlyni species group. We estimate that the two species split ca. 3-4 million years ago, and find that they both show high genetic diversity despite being listed as vulnerable on the IUCN Red List. We identify signatures of positive selection in the hamlyni group in genes involved in, e.g., pelage coloration and immune functions, as well as skeletal morphology and locomotor behavior, potentially related to their terrestrial lifestyle which stands out among the otherwise arboreal Cercopithecus genus. We specifically explored whether introgression from more distantly related terrestrial guenons was involved in the evolution of terrestriality in the hamlyni group. However, molecular convergence with other terrestrial species was low, suggesting that putative terrestrial adaptations occurred largely independently. By sequencing the C. lomamiensis genome, this study provides insights into the demography and evolutionary history in a threatened primate lineage, with relevance to conservation management.

evolutionary biology↗

bioRxiv2017

Sexual reproduction as bet-hedging

In evolutionary biology, bet-hedging refers to a strategy that reduces the variance of reproductive success at the cost of reduced mean reproductive success. In unpredictably fluctuating environments, bet-hedgers benefit from higher geometric mean fitness despite having lower arithmetic mean fitness than their specialist competitors. We examine the extent to which sexual reproduction can be considered a type of bet-hedging, by clarifying past arguments, examining parallels and differences to evolutionary games, and by presenting a simple model examining geometric and arithmetic mean payoffs of sexual and asexual reproduction. Sex typically has lower arithmetic mean fitness than asex, while the geometric mean fitness can be higher if sexually produced offspring are not identical. However, asexual individuals that are heterozygotes can gain conservative bet-hedging benefits of similar magnitude while avoiding the costs of sex. This highlights that bet-hedging always has to be specified relative to the payoff structure of relevant competitors. It also makes it unlikely that sex, at least when associated with significant male production, evolves solely based on bet-hedging in the context of frequently and repeatedly occupied environmental states. Future work could usefully consider bet-hedging in open-ended evolutionary scenarios with de novo mutations.

evolutionary biology↗

bioRxiv2025

DeePhy: A Deep Learning Model to Reconstruct Phylogenetic Tree from Unaligned Nucleotide Sequences

O_LIInferring phylogenetic trees for a set of taxa is one of the primary objective in the evolutionary biology. Numerous approaches exist to reconstruct the phylogenetic trees by considering different biological data, such as DNA sequence, protein sequence, protein-protein interaction graph, etc. However, each method has its own strengths and weaknesses. Till date, no existing method guarantees to determine true phylogenetic trees all the times. Various studies identified distinct branch length configurations where the existing methods are inefficient to infer the correct tree topologies. C_LIO_LIHere, we propose a novel deep convolutional neural network (CNN)-based model, DeePhy, to reconstruct the phylogenetic trees from the unaligned sequences. The sequences are repre- sented on a two-dimensional coordinate plane by utilizing a biological semantics-based map- ping. Additionally, to assess the robustness of a method, here we also propose a novel boot- strapping technique to generate replicas from the unaligned sequences. We train the model on the triplet sequences, where the output is a triplet tree topology. C_LIO_LIWe show that the well-trained DeePhy outperforms the state-of-the-art methods in inferring triplet tree topology. We experiment DeePhy on data simulated under numerous critical conditions and various branch length configurations. We conduct the McNemar test for comparing the performance of DeePhy and the state-of-the-art methods. The results exhibit that DeePhy is significantly more accurate and remarkably robust in determining the triplet tree topologies for most of the cases than that of the conventional methods. Again, various comparison metrics show that DeePhy also outperforms the conventional methods in inferring trees. Finally, to analyze the performance of DeePhy on real biological dataset, we apply it on Gadiformes dataset. Reassuringly, DeePhy reconstructs the phylogenetic tree from real biological data with known or widely accepted topologies. C_LIO_LIAlthough various practical challenges still need to be taken care of, the outcomes of our study suggest that the deep learning approaches be a successful endeavour in inferring the accurate phylogenetic trees. C_LI

evolutionary biology↗

bioRxiv2025

Eukaryotes evade information storage-replication rate trade-off with endosymbiont assistance leading to larger genomes

Genome length varies widely among organisms, from compact genomes of prokaryotes to vast and complex genomes of eukaryotes. In this study, we theoretically identify the evolutionary pressures that may have driven this divergence in genome length. We use a parameter-free model to study genome length evolution under selection pressure to minimize replication time and maximize information storage capacity. We show that prokaryotes tend to reduce genome length, constrained by a single replication origin, while eukaryotes expand their genomes by incorporating multiple replication origins. We propose a connection between genome length and cellular energetics, suggesting that endosymbiotic organelles, mitochondria and chloroplasts, evolutionarily regulate the number of replication origins, thereby influencing genome length in eukaryotes. We show that the above two selection pressures also lead to strict equalization of the number of purines and their corresponding base-pairing pyrimidines within a single DNA strand, known as Chagraffs second parity rule, a hitherto unexplained observation in genomes of nearly all known species. This arises from the symmetrization of replichore length, another observation that has been shown to hold across species, which our model reproduces. The model also reproduces other experimentally observed phenomena, such as a general preference for deletions over insertions, and elongation and high variance of genome lengths under reduced selection pressure for replication rate, termed the C-value paradox. We highlight the possibility of regulation of the firing of latent replication origins in response to cues from the extracellular environment leading to the regulation of cell cycle rates in multicellular eukaryotes. Significance StatementUnderstanding the forces shaping genome architecture is a long-standing challenge in evolutionary biology. Our study demonstrates that the balance between replication speed and information storage, constrained by cellular en-ergetics, drives the divergence in genome lengths between prokaryotes and eukaryotes. By quantifying selection pressure as the ratio of replication time to genomic information content, we show that this pressure enforces adaptive constraints, giving rise to features such as symmetric replichores and Chargaffs second parity rule. These insights not only help us resolve an enduring evolutionary puzzle, but also offer a unified framework linking genome organization, cellular specialization, and even potential mechanisms underlying carcinogenesis.

evolutionary biology↗

bioRxiv2026

The evolution of socially modulated dispersal

Dispersal plays a key role in ecology and evolutionary biology, including as a mechanism that reduces local competition. However, dispersers often suffer a fitness disadvantage relative to nondispersers, which may lead individuals to prefer that others disperse rather than dispersing themselves. Hence, if individuals can force their neighbours to disperse, they may be favoured to do so rather than voluntarily dispersing for the good of others. Besides forcing dispersal, individuals might also improve their neighbours ability to disperse. Here, we investigate the evolutionary drivers and consequences of socially modulated dispersal. We find that: (1) there are three possible stable evolutionary outcomes, involving only voluntary dispersal, only socially modulated dispersal, or coexistence of the two; (2) although social modulation that does not improve dispersal success can only be favoured if it is less costly than dispersal itself, social modulation that facilitates dispersal can be favoured even if it is highly expensive for the facilitator; (3) facilitated dispersal may, counterintuitively, lead to a reduced overall level of dispersal; and (4) social modulation can be either welcome or unwelcome from the perspective of the disperser. These predictions emphasise that the evolution of dispersal depends on individuals abilities to both disperse and modulate.

evolutionary biology↗

bioRxiv2026

Phenotypic complexity determines the predictability of molecular convergence

Whether the outcomes of evolution are predictable remains a central question in evolutionary biology. Convergent evolution, in which similar phenotypes arise independently in distinct lineages, is often interpreted as evidence of predictability under shared selective pressures. However, empirical studies have shown that convergent phenotypes can exhibit similar or disparate molecular bases, and no general frame-work predicts when convergence should occur. Here, we show that phenotypic complexity determines the extent of convergence under shared conditions. Using a deliberately minimal abstraction linking genotype and phenotype and evolutionary simulations, we investigated how regulatory gene expression programs evolve under selection for shared phenotypic optima. We find that phenotypes of low complexity can be produced by diverse regulator expression programs, permitting phenotypic convergence despite molecular divergence. However, as the complexity of the target phenotype increases, the variety of viable regulatory solutions decreases, funneling independently evolving lineages towards similar expression programs. In this regime, molecular convergence emerges as a predictable consequence of selection acting on finite regulatory repertoires, consistent with the phenomenon of "deep homology" observed in structures like eyes and limbs. Allowing evolutionary changes in regulator-target interactions relaxes these constraints and enhances divergence in expression between phenotypically convergent species. Together, our results provide general expectations for predicting when phenotypic convergence should be accompanied by convergent genetic mechanisms, and shed light on the causes of convergence in a broad range of phenotypic traits.

evolutionary biology↗

bioRxiv2021

Correlated Evolution in the Small Parsimony Framework

When studying the evolutionary relationships among a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, e.g., Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available. In this work, we devise a method for determining if pairs of discrete characters are significantly correlated across all most parsimonious reconstructions, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoffs algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by the (co-) occurrence patterns between the sets of their respective ancestral assignments. The probability of obtaining a correlation this extreme (or more) under a null hypothesis where the events happen randomly on the evolutionary tree is then used to assess the significance of this correlation. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to identify significantly correlated evolution among vocalizations and morphological characters in the Felidae family. The parcours tool is freely available at https://github.com/murraypatterson/parcours

evolutionary biology↗

bioRxiv2025

Microbiome evolution plays a secondary role in host rapid adaptation

Understanding how populations adapt to environmental change is a central goal in evolutionary biology. Microbiomes have been proposed as a source of heritable variation that is central to rapid adaptation in hosts, yet empirical evidence supporting this remains limited, particularly in naturalistic settings. We combined a field evolution experiment in Drosophila melanogaster exposed to an insecticide with microbiome manipulations to disentangle the contributions of host standing genetic variation and microbiome evolution to adaptation. Within three generations, independent populations rapidly and repeatedly evolved increased survivorship, a defining feature of resistance evolution. Adaptive changes in sub-lethal traits such as reproductive output, stress tolerance, and body size occurred with a delayed response following the evolution of resistance. Core microbiome taxa declined following insecticide exposure, and resistant populations evolved to house lower microbial abundances. Axenic rearing and microbiome transplant experiments demonstrated that adaptation via host standing genetic variation was the mechanism for resistance evolution. Microbiome evolution played a secondary and cryptic role in host adaptation by masking slowed development rates that evolved in resistant populations. Together, these results reinforce the primacy of adaptation occurring through selection on host standing genetic variation while also demonstrating the contributions of microbiome evolution in host adaptation. SignificanceIdentifying the mechanisms that allow organisms to adapt to environmental stress is a foundational goal in biology. Using field experimental evolution and microbiome manipulations in Drosophila melanogaster, we directly tested the relative contributions of host genomic evolution and microbiome evolution to adaptation. We found that adaptation to environmental stress occurred rapidly and repeatedly, driven primarily by selection on host standing genetic variation, with microbiome evolution acting as a secondary contributor. These findings reinforce the importance of host genetic variation in rapid adaptation and demonstrate that microbiome evolution can contribute to host evolutionary trajectories in a cryptic manner.

evolutionary biology↗

bioRxiv2019

A large-scale species level dated angiosperm phylogeny for evolutionary and ecological analyses

Phylogenies are a central and indispensable tool for evolutionary and ecological research. Even though most angiosperm families are well investigated from a phylogenetic point of view, there are far less possibilities to carry out large-scale meta-analyses at order level or higher. Here, we reconstructed a large-scale dated phylogeny including nearly 1/8th of all angiosperm species based on two plastid barcoding genes, matK and rbcL. Novel sequences were generated for several species, while the rest of the data were mined from GenBank. The resulting tree was dated using 56 angiosperm fossils as calibration points. The resulting megaphylogeny is one of the largest dated phylogenetic tree of angiosperms yet, consisting of 36,101 sampled species, representing 8,399 genera, 426 families and all orders. This novel framework will be useful to investigate different broad scale research questions in ecological and evolutionary biology.

evolutionary biology↗

bioRxiv2019

Integrated evolutionary and structural analysis reveals xenobiotics and pathogens as the major drivers of mammalian adaptation

Understanding the molecular basis of adaptation to the environment is a central question in evolutionary biology, yet linking detected signatures of positive selection to molecular mechanisms remains challenging. Here we demonstrate that combining sequence-based phylogenetic methods with structural information assists in making such mechanistic interpretations on a genomic scale. Our integrative analysis shows that positively selected sites tend to co-localise on protein structures and that positively selected clusters are found in functionally important regions of proteins, indicating that positive selection can contravene the well-known principle of evolutionary conservation of functionally important regions. This unexpected finding, along with our discovery that positive selection acts on structural clusters, opens new strategies for the development of better models of protein evolution. Remarkably, proteins where we detect the strongest evidence of clustering belong to just two functional groups: components of immune response and metabolic enzymes. This gives a coherent picture of immune response and xenobiotic metabolism as the drivers of adaptive evolution of mammals.

evolutionary biology↗

bioRxiv2021

Unearthing modes of climatic adaptation in underground storage organs across Liliales

Testing adaptive hypotheses about how continuous traits evolve in association with developmentally-structured discrete traits, while accounting for the confounding influence of other, hidden, evolutionary forces, remains a challenge in evolutionary biology. For example, geophytes are herbaceous plants--with underground buds--that use underground storage organs (USOs) to survive extended periods of unfavorable conditions. Such plants have evolved multiple times independently across all major vascular plant lineages. Even within closely related lineages, however, geophytes show impressive variation in the morphological modifications and structures (i.e., "types" of USOs) that allow them to survive underground. Despite the developmental and structural complexity of USOs, the prevailing hypothesis is that they represent convergent evolutionary "solutions" to a common ecological problem, though some recent research has drawn this conclusion into question. We extend existing phylogenetic comparative methods to test for links between the hierarchical discrete morphological traits associated with USOs and adaptation to environmental variables, using a phylogeny of 621 species in Liliales. We found that plants with different USO type do not differ in climatic niche more than expected by chance, with the exception of root morphology, where modified roots are associated with lower temperature seasonality. These findings suggest that root tubers may reflect adaptations to different climatic conditions than those represented by other types of USOs. Thus, the tissue type and developmental origin of the USO structure may influence the way it mediates ecological relationships, which draws into question the appropriateness of ascribing broad ecological patterns uniformly across geophytic taxa. This work provides a new framework for testing adaptive hypotheses and for linking ecological patterns across morphologically varying taxa while accounting for developmental (non-independent) relationships in morphological data. [Macroevolution, geophytes, climatic niche evolution, imperfect correspondence]

evolutionary biology↗

bioRxiv2023

Why Do Hybrids Turn Down Sex?

Asexual reproduction is ancestral in prokaryotes; the switch to sexuality in eukaryotes is one of the major transitions in the history of life. The study of the maintenance of sex in eukaryotes has raised considerable interest for decades and is still one of evolutionary biologys most prominent question. The observation that many asexual species are of hybrid origin have led some to propose that asexuality in hybrids results from sexual processes being disturbed because of incompatibilities between the two parental species genomes. This proximate theory appears difficult in real life, as it requires fundamental reproductive traits to be profoundly altered without collapsing individuals fertility. Repeated failures to produce asexual Amazon Molly in the lab through crossing experiments show that we are still in need of an evolutionary explanation. Here, we present a mathematical model and propose an adaptive route for the evolution of asexuality from previously sexual hybrids. Under smaller reproductive alterations, we show that asexuality can evolve to rescue hybrids reproduction. Importantly, we highlight that when incompatibilities only affect the fusion of sperm and eggs genomes, unreduced meiosis and paternal genome elimination can evolve separately, greatly facilitating the overall evolutionary route.

evolutionary biology↗

bioRxiv2018

Ancestral Reconstruction of Protein Interaction Networks

The molecular and cellular basis of novelty is a major open question in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term \"phylogenetic latent variable models\" (PLVMs, pronounced \"plums\") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

evolutionary biology↗

bioRxiv2021

Fine scale population structure and extensive gene flow within an Eastern Nearctic snake complex (Pituophis melanoleucus)

Understanding the processes and mechanisms that promote lineage divergence is a central goal in evolutionary biology. For instance, studies investigating the spatial distribution of genomic variation often highlight biogeographic barriers underpinning geographic isolation, as well as patterns of isolation by environment and isolation by distance that can also lead to lineage divergence. However, the patterns and processes that shape genomic variation and drive lineage divergence may be taxa-specific, even across closely related taxa co-occurring within the same biogeographic region. Here, we use molecular data in the form of ultra-conserved elements (UCEs) to infer the evolutionary relationships and population genomic structure of the Eastern Pinesnake complex (Pituophis melanoleucus) - a polytypic wide-ranging species that occupies much of the Eastern Nearctic. In addition to inferring evolutionary relationships, population genomic structure, and gene flow, we also test relationships between genomic diversity and putative barriers to dispersal, environmental variation, and geographic distance. We present results that reveal shallow population genomic structure and ongoing gene flow, despite an extensive geographic range that transcends geographic features found to reduce gene flow among many taxa, including other squamate reptiles within the Eastern Nearctic. Further, our results indicate that the observed genomic diversity is spatially distributed as a pattern of isolation by distance and suggest that the current subspecific taxonomy do not adhere to independent lineages, but rather, show a significant amount of admixture across the entire P. melanoleucus range.

evolutionary biology↗

bioRxiv2023

The Evolution of Democratic Peace

A major goal in evolutionary biology is to elucidate common principles that drive human and other animal societies to adopt either a warlike or peaceful nature. One proposed explanation for the variation in aggression between human societies is the democratic peace hypothesis. According to this theory, autocracies are more warlike than democracies because autocratic leaders can pursue fights for private gain. However, autocratic and democratic decision-making processes are not unique to humans and are widely observed across a diverse range of non-human animal societies. We use evolutionary game theory to evaluate whether the logic of democratic peace may apply across taxa; specifically adapting the classic Hawk-Dove model to consider conflict decisions made by groups rather than individuals. We find support for the democratic peace hypothesis without mechanisms involving complex human institutions and discuss how these findings might be relevant to non-human animal societies. We suggest that the degree to which collective decisions are shared may explain variation in the intensity of intergroup conflict in nature.

evolutionary biology↗

bioRxiv2023

The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change

Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 years, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets. Significance statementThe relative contribution of random genetic drift and natural selection to the change in allele frequencies through time is a long standing question in Evolutionary Biology. We show through theory and simulation how genomic time series - such as ancient DNA datasets - can be used to decompose the genome-wide contributions of selection, gene flow, and genetic drift to allele frequency change. We apply these methods to two time time series from ancient Europeans and show that gene flow accounts for most allele frequency change over the last few thousand years, with genetic drift and not selection making up much of the rest of the contribution to genome-wide evolutionary change.

evolutionary biology↗

bioRxiv2016

Going down the rabbit hole: a review on how to link genome-wide data with ecology and evolution in natural populations

O_LICharacterizing species history and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The ongoing and constant improvement of next-generation sequencing (NGS) techniques has facilitated the production of an ever-increasing number of genetic markers across genomes of non-model species.\nC_LIO_LIThe study of variation in these markers across natural populations has deepened the understanding of how population history and selection act on genomes. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a burst of analytical tools that can confuse users.\nC_LIO_LISuch confusion can limit the amount of information effectively retrieved from complex genomic datasets. In addition, the lack of a unified analytical pipeline impairs the diffusion of the most recent analytical tools into fields like conservation biology.\nC_LIO_LITo address this need, we describe possible analytical protocols and link these with more than 70 methods dealing with genome-scale datasets. We summarise the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.\nC_LI

Evolutionary Biology↗

bioRxiv2023

rphylopic: An R package for fetching, transforming, and visualising PhyloPic silhouettes

O_LIData visualisation is vital for data exploration, analysis, and communication in research. Moreover, it can bridge gaps between researchers and the general public by making research findings more accessible and engaging. Today, researchers increasingly conduct their data analyses in programming languages such as R and Python. The availability of data visualisation tools within these environments supports the generation of reproducible data analyses and visualisation workflows. However, resources from external databases are frequently required for data visualisation necessitating integration between these databases and the platforms in which researchers conduct their analyses. C_LIO_LIHere, we introduce rphylopic, an R package for fetching, transforming, and visualising silhouettes of organisms from the PhyloPic database. In addition to making over 7,000 organism silhouettes available within the R programming language, rphylopic empowers users to modify the appearance of these silhouettes for ultimate customisability when coding production-quality visualisations in both base R and ggplot2 workflows. C_LIO_LIIn this work, we provide details about how the package can be installed, its implementation, and potential use cases. For the latter, we showcase three examples across the ecology and evolutionary biology spectrum. C_LIO_LIOur hope is that rphylopic will make it easier for biologists to develop more accessible and engaging data visualisations by making external resources readily accessible, customisable, and usable within R. In turn, by integrating into existing workflows, rphylopic helps to ensure that science is reproducible and accessible. C_LI

evolutionary biology↗