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bioRxiv2025

From microbiome to sperm motility traits: An inside out perspective

Growing interest in the relationship between microbiome composition and host biology has revealed the many ways host-associated microbes influence physiology, ecology, and evolution. However, microbial communities associated with reproductive organs - and their roles in reproduction - remain poorly understood. Here, we characterized the skin- and ejaculate-associated microbiomes in an internally fertilizing fish and tested whether microbial diversity and specific bacterial taxa correlate with sperm motility traits key for reproductive success. We used the guppy (Poecilia reticulata), a well-established model in ecology and evolutionary biology with well-characterised reproductive physiology. In guppies, sperm velocity is a validated predictor of male reproductive performance, making them a powerful system for exploring microbiome-fertility interactions. Our analyses reveal a correlation between skin microbiome diversity and sperm performance. Notably, increased skin microbiome total richness is associated with reduced sperm velocity, whereas no significant associations were detected for ejaculate-associated microbiomes. We also identified bacterial taxa across both tissues that were positively or negatively linked with sperm performance. These findings suggest that, while the ejaculate-associated microbiome may directly influence sperm traits, the skin microbiome could serve as a proxy for reproductive potential by reflecting systemic physiological and immunological states associated with fertility.

microbiology↗

bioRxiv2018

Multi-Hierarchical Dynamics of Antimicrobial Resistance Simulated in a Nested Membrane Computing Model

Membrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested \"membrane-surrounded entities\" able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

microbiology↗

bioRxiv2023

Regional specialization, polyploidy, and seminal fluid transcripts in the Drosophila female reproductive tract

Internal fertilization requires the choreographed interaction of female cells and molecules with seminal fluid and sperm. In many animals, including insects, the female reproductive tract is physically subdivided into sections that carry out specialized functions. For example, females of many species have specialized organs for sperm storage. Drosophila melanogaster is a premier model system for investigating many aspects of animal reproduction. Nevertheless, in contrast to males, much of the basic biology of the D. melanogaster female reproductive tract remains poorly understood or completely unknown. Here we use single-cell RNA-seq data and in situ hybridization to reveal a rich and previously unknown female reproductive tract cell diversity, including widespread variation in ploidy levels. We find that many so-called seminal fluid protein genes appear to be transcribed in specialized cells of the female reproductive tract, motivating a re-evaluation of the functional and evolutionary biology of this major class of proteins.

cell biology↗

bioRxiv2017

RecBlast: Cloud-Based Large Scale Orthology Detection

BackgroundThe effective detection and comparison of orthologues is crucial for answering many questions in comparative genomics, phylogenetics and evolutionary biology. One of the most common methods for discovering orthologues is widely known as Reciprocal Blast. While this method is simple when comparing only two genomes, performing a large-scale comparison of Multiple Genes across Multiple Taxa becomes a labor-intensive and inefficient task. The low efficiency of this complicated process limits the scope and breadth of questions that would otherwise benefit from this powerful method.\n\nFindingsHere we present RecBlast, an intuitive and easy-to-use pipeline that enables fast and easy discovery of orthologues along and across the evolutionary tree. RecBlast is capable of running heavy, large-scale and complex Reciprocal Blast comparisons across multiple genes and multiple taxa, in a completely automatic way. RecBlast is available as a cloud-based web server, which includes an easy-to-use user interface, implemented using cloud computing and an elastic and scalable server architecture. RecBlast is also available as a powerful standalone software supporting multi-processing for large datasets, and a cloud image which can be easily deployed on Amazon Web Services cloud. We also include sample results spanning 448 human genes, which illustrate the potential of RecBlast in detecting orthologues and in highlighting patterns and trends across multiple taxa.\n\nConclusionsRecBlast provides a fast, inexpensive and valuable insight into trends and phenomena across distance phyla, and provides data, visualizations and directions for downstream analysis. RecBlast's fully automatic pipeline provides a new and intuitive discovery platform for researchers from any domain in biology who are interested in evolution, comparative genomics and phylogenetics, regardless of their computational skills.

bioinformatics↗

bioRxiv2019

RAD-Behavior (Recombining Atomized, Discretized, Behavior): A new framework for the quantitative analysis of behavioral execution

The ability to precisely describe and numerically evaluate organismal phenotypes is a prerequisite for addressing most questions in evolutionary biology and ecology. The quantification and comparison of behavior, loosely defined as an external response to stimuli, is particularly challenging because the myriad axes of variation that exist make comparisons, both within and among species, difficult. Such evaluations often boil down to comparisons of time-budgets (e.g. relative investment in courtship displays) or probabilities (e.g. likelihood of engaging in a class of behaviors in a particular context) - which we refer to as behavioral strategies. A focus on variation in behavioral strategies underlies most research in evolutionary and ecological studies of behavior. Equally important, however, is perhaps the question of how animals are actually performing the complex motor sequences that comprise behaviors (i.e. behavioral execution). What are the patterns of movement, the relative transition rates, and kinematics underlying the behaviors exhibited in particular contexts? Understanding how behavioral execution differs among individuals, populations, and species has the potential to provide new insights into the factors shaping variation in behavior and the processes shaping behavioral evolution at different scales. Here, we propose a broad framework for comparing behavioral execution (RAD-behavior: recombining atomized, discretized behavior) that leverages string-matching/bioinformatic tools to understand phenotypic variation in behavioral execution and which holds the potential to yield novel insights about the evolutionary ecology of behavior at multiple scales.

animal behavior and cognition↗

bioRxiv2026

Convergent targeting of conserved regulatory networks during thermal evolution across Saccharomyces

Whether evolution follows predictable genetic paths across species remains a central question in evolutionary biology, particularly as rising temperatures reshape species distributions worldwide. Despite its importance, the genetic basis of thermal adaptation remains poorly understood across divergent species. Here, we use the yeast genus Saccharomyces as a comparative model to investigate how species with contrasting thermal niches adapt to rising temperatures. We combined experimental evolution under progressively increasing temperatures for up to [~]600 generations with whole-genome sequencing of 256 evolved genotypes, followed by transcriptomic, functional, and physiological analyses across eight species. Despite large differences in ancestral thermal tolerance and evolutionary outcomes, selection repeatedly targeted the same conserved regulatory pathways across species. Independent lineages accumulated de novo mutations in central growth and stress response networks, particularly in TORC1, PKA, and MAPK signaling pathways, revealing striking molecular convergence across species occupying distinct thermal environments. However, these shared genetic targets produced divergent transcriptional and physiological responses depending on species background, indicating that thermal adaptation primarily proceeds through rewiring of conserved regulatory hubs rather than changes in temperature-specific enzymes. Cold-tolerant species frequently lost mitochondrial DNA during thermal evolution, yet loss alone was insufficient to reproduce the adaptive thermal phenotypes of evolved populations. Together, our results show that adaptation to increasing temperature is driven by predictable changes in conserved regulatory networks, while species-specific constraints shape divergent phenotypic outcomes. These findings reveal both the predictability and contingency of evolutionary responses to rising temperature across species.

genomics↗

bioRxiv2026

Spatial expression evolution of duplicated genes post-whole genome duplication in cultivated tomato

The mechanisms by which whole genome duplication contributes to expression divergence of duplicate genes, which can impact the evolution of form and function, has been a long-standing question in evolutionary biology. We compared expression patterns of paralogs derived from the most recent Solanaceae-specific genome triplication event across 23 tissues and inferred ancestral expression states to investigate patterns of expression evolution. While the partitioning of ancestral expression states is traditionally considered the second most common (after the gene loss) trajectory of duplicate expression evolution, we found that the ancestral expression states tended to be retained by most (~66%) duplicates, with ~6% of gene lineages experiencing expression gain events and ~20% experiencing expression losses. The genes that gained expression in fruit tissues are significantly enriched with functions associated with fruit quality. Notably, asymmetrical expression divergence is prevalent among duplicates, especially when ancestral genes were either not expressed or were highly expressed. Our study demonstrates the utility of integrating paralog expression comparisons with phylogenetic analyses to document the potential evolutionary history of spatial expression patterns and gain insight into how expression divergence contributes to the evolution of function across tissues.

plant biology↗

bioRxiv2026

Host adaptation drives genome evolution and virulence diversification in a bacterial zoonotic pathogen

Understanding how zoonotic pathogens diversify across reservoir hosts remains a central question in evolutionary biology and infectious disease research. Here, we address this challenge using Leptospira interrogans, a globally distributed bacterial pathogen with an extremely wide range of animal reservoirs, as a model. After defining 13 distinct genogroups that largely align with serogroups, we selected the strongly host-adapted rodent-associated lineage, which is more frequently associated with fatal outcomes in patients, and the cattle-associated lineage for further analysis. A comprehensive approach integrating multi-omics analyses and host-specific infection assays showed that these two genogroups have followed distinct evolutionary trajectories associated with host specialization. Genomics demonstrated that specialized genogroups are genetically less diverse, characterized by divergence in membrane and signaling genes, and by the acquisition of host-adaptive functions. Changes in gene expression and protein production revealed distinct regulatory programs, predominantly affecting virulence pathways in rodent-borne lineage and stress responses in cattle-borne lineage. Consistently, rodent-borne lineage causes greater disruption of human epithelial barrier integrity and elicits an attenuated host-dependent macrophage inflammatory response relative to cattle-borne lineage. Collectively, these findings reveal distinct host-adaptive strategies and remarkable evolutionary plasticity in a major zoonotic bacterium, highlighting the central role of intraspecies heterogeneity in shaping host specialization.

microbiology↗

bioRxiv2026

Comparative pangenomics of Trypanosoma brucei reveal host specialization is driven by targeted or segmental genome streamlining in human and equine-restricted lineages

How the eukaryotic parasite Trypanosoma brucei remodels its genome during host specialization remains a fundamental question in evolutionary biology. In contrast to the uniform, systemic gene loss seen in resident bacterial genomes, the structural variations driving niche restriction in extracellular pathogens are poorly defined. Here, we establish a high-resolution comparative structural pangenomic map of the T. brucei complex. By analyzing the human-restricted specialist DAL972 and the equine-restricted specialist Trypanosoma equiperdum IVM-t1 against the conserved TREU927 backbone, we determine how host-vector constraints dictate eukaryotic genome architecture. Our analyses reveal that host specialization is governed by a coordinated spatial blueprint that segregates structural variation into two distinct evolutionary pathways: targeted perimeter streamlining and systemic core erosion. The human specialist DAL972 preserves its core metabolic toolkit under intense purifying selection, restricting genomic variation to the compartmentalized trimming of its sub-telomeric variant surface glycoprotein (VSG) archives. Conversely, the equine specialist IVM-t1, which has completely abandoned cyclical tsetse fly transmission, displays multi-focal core erosion across internal domains. We demonstrate that vector abandonment forces a shift to strict asexual clonal population dynamics. This transition eases purifying selection, allowing obsolete, insect-stage flagellar and cytoskeletal structures to decay through neutral genetic drift. At the chromosomal perimeters, this structural allocation unmasks an unannotated sub-telomeric reservoir of 86 profile-validated retrotransposon hot spot (RHS) and catalytic DDE transposase domains organized into 23 discrete loci. Operating under adaptive positive selection in the human specialist, these silent, repetitive elements function as physical recombination anchors that stabilize strand exchange. This mechanism drives rapid antigenic diversification while insulating the core genome from dangerous macro-rearrangements. Importantly, we bridge this macro-scale chromosomal architecture to micro-scale proteomic execution through tertiary modeling of these unannotated specialized loci. This structural translation unmasks a biophysical mechanism of structural sequestration, where lineage-specific mutations and specificity-determining positions are deeply buried within hydrophobic protein cores to shield functional divergence from host immune surveillance. By pairing these rigid, hidden core packing networks with hyper-variable surface tiles and dynamic boundary hinges, the parasite deploys tiered molecular mimicry to selectively manipulate host cellular networks. Together, these findings redefine our understanding of structural and non-coding architecture as mechanical drivers of parasitic adaptability, uncovering a direct pipeline from pangenomic spatial variations to stable, structure-based allosteric drug targets.

genomics↗

bioRxiv2017

Refactoring the Genetic Code for Increased Evolvability

The standard genetic code is robust to mutations and base-pairing errors during transcription and translation. Point mutations are most likely to be synonymous or preserve the chemical properties of the original amino acid. Saturation mutagenesis experiments suggest that in some cases the best performing mutant requires a replacement of more than a single nucleotide within a codon. These replacements are essentially inaccessible to common error-based laboratory engineering techniques that alter single nucleotide per mutation event, due to the extreme rarity of adjacent mutations. In this theoretical study, we suggest a radical reordering of the genetic code that maximizes the mutagenic potential of single nucleotide replacements. We explore several possible genetic codes that allow a greater degree of accessibility to the mutational landscape and may result in a hyper-evolvable organism serving as an ideal platform for directed evolution experiments. We then conclude by evaluating potential applications for recoded organisms within the synthetic biology field.\n\nSignificance StatementThe conservative nature of the genetic code prevents bioengineers from efficiently accessing the full mutational landscape of a gene using common error-prone methods. Here we present two computational approaches to generate alternative genetic codes with increased accessibility. These new codes allow mutational transition to a larger pool of amino acids and with a greater degree of chemical differences, using a single nucleotide replacement within the codon, thus increasing evolvability both at the single gene and at the genome levels. Given the widespread use of these techniques for strain and protein improvement along with more fundamental evolutionary biology questions, the use of recoded organisms that maximize evolvability should significantly improve the efficiency of directed evolution, library generation and fitness maximization.

bioengineering↗

bioRxiv2019

Single cell atlas of domestic pig brain illuminates the conservation and divergence of cell types at spatial and species levels

Domestic pig (Sus scrofa domesticus) has drawn much attention from researchers worldwide due to its implications in evolutionary biology, regenerative medicine and agriculture. The brain atlas of Homo sapiens (primate), Mus musculus (rodent), Danio rerio (fish) and Drosophila melanogaster (insect) have been constructed at single cell resolution, however, the cellular compositions of pig brain remain largely unexplored. In this study, we investigated the single-cell transcriptomic profiles of five distinct regions of domestic pig brain, from which we identified 21 clusters corresponding to six major cell types, characterized by unique spectrum of gene expression. By spatial comparison, we identified cell types enriched or depleted in certain brain regions. Inter-species comparison revealed cell-type similarities and divergences in hypothalamus between mouse and pig, providing invaluable resources for the evolutionary exploration of brain functions at single cell level. Besides, our study revealed cell types and molecular pathways closely associated with several diseases (obesity, anorexia, bulimia, epilepsy, intellectual disability, and autism spectrum disorder), bridging the gap between gene mutations and pathological phenotypes, which might be of great use to the development precise therapies against neural system disorders. Taken together, we reported, so far as we know, the first single cell brain atlas of Sus scrofa domesticus, followed by comprehensive comparisons across brain region and species, which could throw light upon future evo-devo, regenerative medicine, and agricultural studies.

bioinformatics↗

bioRxiv2024

First draft genome assembly and characterization of sponge Halisarca dujardinii reveals key components of basement membrane and broad repertoire of aggregation factors

How features characteristic of multicellular animals emerged in evolution and how the body plan of particular taxa was shaped are hotspots of modern evolutionary biology. We can get closer to answering them by studying animals that occupy a basal position on the phylogenetic tree, such as sponges (Porifera). We sequenced the genome of the sponge Halisarca dujardinii using Oxford Nanopore and Illumina technologies and made an assembly of long reads, followed by polishing with short reads. The resulting assembly had a size of 176 Mb, matching the prediction from the k-mer distribution, and an N50 of about 785 Kb. By analyzing transposable elements in the genomes of H. dujardinii and a number of other sponges, we found that a significant portion of the genome (more than half for Demospongiae) is occupied by repeats, most of which are evolutionary young. RNA-seq data were used to predict about 14000 genes in the genome, several times less than in other Demospongiae. By analyzing ortholog groups unique to H. dujardinii among sponges and higher invertebrates, we found overrepresented genes related to the extracellular matrix. The extracellular matrix of H. dujardinii contains, among others, key basement membrane components such as laminin, nidogen, fibronectin, and collagen IV, for which phylogenetic analysis has confirmed that it belongs to this type of nonfibrillar collagen. In addition, we showed in H. dujardinii 14 aggregation factor genes responsible for cell recognition and adhesion. They are organized in a genomic cluster and have at least two types of domains: Calx-beta, responsible for calcium ion binding, and Wreath domain, unique for this type of molecules. Our obtained assembly and annotation will further expand the understanding of genome evolution at the emergence of animal multicellularity, and will serve as a tool to study the regulation of gene expression by modern methods.

genomics↗

bioRxiv2024

Selective expansion of motor cortical projections in the evolution of vocal novelty

Deciphering how cortical architecture evolves to drive behavioral innovations is a long-standing challenge in neuroscience and evolutionary biology. Here, we leverage a striking behavioral novelty in the Alstons singing mouse (Scotinomys teguina), compared to the laboratory mouse (Mus musculus), to quantitatively test models of motor cortical evolution. We used bulk tracing, serial two-photon tomography, and high-throughput DNA sequencing of over 76,000 barcoded neurons to discover a specific and substantial expansion ([~]200%) of orofacial motor cortical (OMC) projections to the auditory cortical region (AudR) and the midbrain periaqueductal gray (PAG), both implicated in vocal behaviors. Moreover, analysis of individual OMC neurons projection motifs revealed preferential expansion of exclusive projections to AudR. Our results imply that selective expansion of ancestral motor cortical projections can underlie behavioral divergence over short evolutionary timescales, suggesting potential mechanisms for the evolution of enhanced cortical control over vocalizations--a crucial preadaptation for human language.

neuroscience↗

bioRxiv2026

Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets

Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standard alphabet has been identified to be non-randomly distributed in physicochemical properties crucial to both structure-formation and function, often referred to as coverage theory. While machine learning models have drastically improved protein structure prediction, success of protein design models lags structure prediction, particularly for custom secondary structure motifs and amino acid alphabets. Here we therefore bridge contemporary biological theory with recent advancements in artificial intelligence (AI) to develop and evaluate a generative AI protein design model, trained on hundreds of thousands of proteins within the RSCB PDB, for custom secondary structure motifs using reduced amino acid alphabets (RAAs). Results indicate an overall success in designing novel proteins with desired secondary structure motifs for a broad range of amino acid alphabets and complexity of designs. Interestingly, this tool often captures the full three-dimensional tertiary structure of a target protein despite training only on physicochemical sequence space and secondary structure information. The development of this model advances research across multiple disciplines, from general scientific AI architecture development to protein design for biotechnology, astrobiology, and early-Earth evolutionary biology.

bioinformatics↗

bioRxiv2026

Subcellular spatial proteomics of the close animal relative, Capsaspora owczarzaki

Capsaspora owczarzaki is one of the closest relatives of animals and an increasingly tractable model for investigating the evolution of multicellularity. However, very little cell biological work has been done on the sister linages to animals. Subcellular spatial proteomics (SSP) is a technique that can localize thousands of proteins to subcellular compartments in a single experiment by combining cell fractionation with sensitive mass spectrometry. To provide broad insight into the localization of the Capsaspora proteome, we performed SSP based on a label-free version of LOPIT-DC (Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation). We detected 6,466 proteins, corresponding to 68.4% of the predicted Capsaspora proteome, and assigned high-confidence localizations to 3,106 of them across 13 subcellular compartments. This study serves as a proteomic dataset to resolve cellular compartmentalization in a close relative of animals, enabling downstream evolutionary, biological, and functional analyses. Investigators interested in using these data prior to publication are encouraged to contact us to avoid overlap and to facilitate coordinated and collaborative use of this resource.

cell biology↗

bioRxiv2024

This is SPRTA: assessing phylogenetic confidence at pandemic scales

Phylogenetics plays a central role in evolutionary biology and genomic epidemiology. Assessing phylogenetic confidence and reliability is therefore crucial and methods to do this, such as Felsensteins bootstrap, are among the most used in modern science. However, methods based on Felsensteins bootstrap suffer from excessive computational demand, and are unsuitable for large datasets. Furthermore, most of these methods emerge from a cladistic framework which makes their results hard to interpret in the context of genomic epidemiology. We propose SPRTA (" SPR-based Tree Assessment"), an efficient and interpretable approach to assess confidence in phylogenetic trees. SPRTA shifts the paradigm of phylogenetic support measurement from evaluating the confidence in clades (groupings of taxa) to genome evolution histories, for example assessing if a lineage evolved from another considered lineage or not. This focus on evolutionary histories is particularly valuable in genomic epidemiology, where typically the evolutionary and transmission history of a pathogen are of interest, not clade content. We illustrate the use of SPRTA by investigating a global SARS-CoV-2 phylogenetic tree relating > 2M genomes, highlighting plausible alternative evolutionary origins of many SARS-CoV-2 variants. We have implemented SPRTA within the free and open source maximum likelihood phylogenetic software MAPLE, available from https://github.com/NicolaDM/MAPLE.

bioinformatics↗

bioRxiv2018

Inferring biochemical reactions and metabolite structures to cope with metabolic pathway drift

Inferring genome-scale metabolic networks in emerging model organisms is challenging because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution. This limits the possibility to automatically transfer knowledge from well-established model organisms. Therefore, specific bioinformatic tools are necessary to infer new biochemical reactions and new metabolic structures that can be checked experimentally. Using an integrative approach combining both genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterol or mycosporine-like amino acids (MAA) synthesis pathways, undergo substantial turnover. This phenomenon, which we formally define as \"metabolic pathway drift\", is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a new methodological approach to formalize the logical reasoning necessary to infer new reactions and new molecular structures, based on previous biochemical knowledge. We use this approach to infer previously unknown reactions in the sterol and MAA pathways.\n\nAuthor summaryGenome-scale metabolic models describe our current understanding of all metabolic pathways occuring in a given organism. For emerging model species, where few biochemical data are available about really occurring enzymatic activities, such metabolic models are mainly based on transferring knowledge from other more studied species, based on the assumption that the same genes have the same function in the compared species. However, integration of metabolomic data into genome-scale metabolic models leads to situations where gaps in pathways cannot be filled by known enzymatic reactions from existing databases. This is due to structural variation in metabolic pathways accross evolutionary time. In such cases, it is necessary to use complementary approaches to infer new reactions and new metabolic intermediates using logical reasoning, based on available partial biochemical knowledge. Here we present a proof of concept that this is feasible and leads to hypotheses that are precise enough to be a starting point for new experimental work.

systems biology↗

bioRxiv2024

Museomics of Carabus giant ground beetles evidences an Oligocene origin and in situ Alpine diversification

The development of museomics represents a major paradigm shift in the use of natural history collection specimens for systematics and evolutionary biology. New approaches in this field allow the sequencing of hundreds to thousands of loci from across the genome using historical DNA. HyRAD-X, a recently introduced capture method using bench-top designed probes, has proved very efficient for recovering genomic-scale datasets using natural history collection specimens. Using this technique at both the intra- and interspecific levels, we infer the most robust phylogeny to date for Arcifera, an ecologically and morphologically diverse clade of Carabus giant ground beetles. We successfully generated a genomic dataset of up to 1965 HyRAD-X loci for all described species, permitting inference of a robust dated phylogenomic tree for this clade. Our species delimitation and population genomic analyses suggest that the current classification for Arcifera is in line with its evolutionary history. Our results suggest an origin of Arcifera in the late Oligocene followed by speciation events during the warm mid-Miocene unlinked to Pleistocene glaciations. The dynamic paleogeographic history of the Palearctic region likely contributed to the diversification of this lineage with a relatively ancient colonization of the proto-Alps followed by in situ speciation where most species of Arcifera are currently found sometimes syntopically likely as a result of post-glaciations secondary contacts.

zoology↗