Biology subjects

Gompert, Z.

Publications and source records attributed to Gompert, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Inversion characterization in Timema stick insects: from local PCA to pangenome approaches

Structural variants (SVs) can strongly influence evolutionary processes by suppressing recombination to create jointly inherited regions and by altering gene expression and the 3D organization of DNA. Recent improvements in sequencing technologies have led to an influx of high-resolution genomic data, bolstering research on SVs. When detecting and characterizing SVs, researchers face trade-offs in data and methods concerning scale, resolution, and computational intensity. Here, we evaluate such trade-offs by quantifying how data type, analytical approach, and SV attributes affect the ability to detect and characterize SVs in Timema cristinae stick insects, with a focus on inversions. We specifically evaluate inversions characterized by (i) a population genomic approach based on patterns of local population structure inferred from genotyping-by-sequencing data, (ii) pairwise alignments of eight de novo genome assemblies and SV calling with SyRI, and (iii) a pangenome constructed from the alignment of the eight genome assemblies. Our results suggest that while broad genomic regions harboring SVs can be detected by any of the approaches, the approaches differ in how they characterize inversions, especially small or complex ones. The local population structure approach is exploratory and generally only detects the largest inversions. Pairwise comparative alignments identify the greatest number of inversions, but downstream analyses are influenced by identification of shared inversions across genome pairs. Pangenome approaches are scalable and produce network graphs that describe complex SVs but require a conceptual shift in visualization and interpretation. The collective results highlight limitations and benefits of different approaches in a burgeoning research field.

genomics↗

bioRxiv2026

Genomic offset is not predictive of recent demographic trends in Lycaeides butterflies

Genomic offset (GO) is increasingly used to predict population maladaptation risk under climate change, with larger offsets assumed to indicate greater vulnerability. Despite rapid adoption in conservation planning, it remains unclear how sensitive GO estimates are to key methodological choices, including SNP set composition, genotype-environment association (GEA) methods, and the specific GO metric used. Empirical validation against observed population dynamics also remains limited. Here, we evaluate the methodological robustness and predictive performance of GO using multidecadal demographic monitoring data from Lycaeides butterflies, a system with short generation times and high fecundity that may facilitate rapid adaptive responses. GO estimates were broadly consistent across SNP sets, regardless of composition or size, with climate-associated and randomly selected SNPs yielding largely concordant values. Consistency across GEA methods was moderate and depended on the SNP set used. In contrast, GO metrics differed substantially in the magnitude of maladaptation estimated, suggesting they capture distinct biological signals and should not be treated as interchangeable. Crucially, GO was a poor predictor of observed population trends, regardless of SNP set composition, GO metric, or GEA method, both at sites used to fit GEA models and when extrapolated to independent demographic sites. These findings suggest that, while GO provides a valuable conceptual framework for assessing potential maladaptation, its quantitative estimates and predictive power are sensitive to methodological choices and species-specific biological context. We therefore urge careful alignment of GO metric assumptions with conservation objectives, along with rigorous empirical validation, before GO estimates are used to inform management decisions.

evolutionary biology↗

bioRxiv2026

Genomic forecasts of maladptation in Lycaeides butterflies

Genomic forecasting approaches based on genotype-environment associations (GEAs) are increasingly used to estimate genomic offsets (GOs), which predict population maladaptation and extinction risk under current or future climatic conditions. Despite their widespread use, only a subset of studies have evaluated how accurately GOs predict (mal)adaptation, limiting their interpretation and application in policy and management. Here, we used GEA analyses to estimate GOs for past, present, and future climates in Lycaeides butterflies, focusing on the causes of variation in GOs among populations and their relationships with demographic parameters inferred from population genomic data. Using multivariate linear regression and genotyping-by-sequencing data from 42 Lycaeides populations (922 butterflies), we found that mean annual temperature, cumulative annual precipitation, and hybridization history together explained 47.6% of variation in genome-wide allele frequencies. Genomic offsets differed substantially among populations and across past, present, and future climates, with evidence for increasing maladaptation under more distant future climate scenarios. We found no relationship between GOs for present climates and contemporary effective population size. In contrast, genetic diversity, which reflects long-term effective population size, and local rates of gene flow together explained 27.3% of variation in contemporary GOs. Populations with higher genetic diversity and more gene flow exhibited lower GOs, consistent with the hypothesis that genetic diversity enhances adaptive capacity and that gene flow may introduce adaptive alleles. Overall, our results support the utility of GO predictions, particularly when validated with independent measures of adaptation, while cautioning against simplistic interpretations of GO as a direct measure of maladaptation in conservation and management contexts.

evolutionary biology↗

bioRxiv2025

Admixture in a butterfly species complex creates a genomic mosaic of ancestry with distinct histories for different chromosomes

Hybridization and admixture give rise to populations with mosaic genomes composed of ancestry segments with distinct origins and histories. Despite a growing number of documented cases of admixture in nature, relatively little is known about how mosaic patterns of ancestry vary simultaneously across the genome and among multiple populations or lineages of hybridizing species. Here, using pooled whole-genome sequence data from more than 20 populations representing three nominal species and multiple admixed or taxonomically ambiguous populations, we show that widespread admixture in Lycaeides butterflies has resulted in distinct evolutionary histories within and among chromosomes, with the most notable differences between the autosomes and the Z sex chromosome. Many populations exhibit substantial evidence of mixed ancestry on the autosomes, whereas the Z chromosome shows a more tree-like evolutionary history. In some cases, the predominant ancestry of the autosomes and the Z chromosome differ. We also find evidence of variation in ancestry within chromosomes, though this appears more idiosyncratic. We also show that differences in autosomal versus Z-chromosome ancestry have the potential to shape ecologically important trait variation. Specifically, using genome-wide association mapping, we demonstrate that wing-pattern elements--traits known to influence mate preference--are affected by genetic variants on both the autosomes and the Z chromosome. In sum, our results show that different chromosomes can exhibit distinct evolutionary histories, with the Z chromosome suggesting more discrete evolutionary lineages or species than the autosomes. These findings have implications both for our basic understanding of species and species boundaries and for how we view the sources of genetic and phenotypic variation that fuel ongoing evolutionary change.

evolutionary biology↗

bioRxiv2025

Dual roles of DNA methylation in gene expression and chromatin structure in a stick insect

Gene body methylation is one of the most taxonomically widespread epigenetic marks, yet its function and evolutionary history are poorly understood. While there is a positive association between gene body methylation and gene expression in many organisms, the mechanisms behind this relationship are unclear. Here, we investigate the function of gene body methylation in the stick insect Timema cristinae. We compare patterns of genome-wide gene body methylation to gene expression, open chromatin peaks, and chromatin compartments. We find that 95% of genes with gene body methylation occur in open chromatin compartments (euchromatin). Furthermore, highly expressed genes are impoverished in methylation at their transcription start site (TSS), which is associated with a peak of open chromatin. These findings suggest that the positive correlation between gene body methylation and gene expression is due to chromatin compartment structure. Yet, methylation around the TSS is associated with gene expression, possibly playing an inhibitory role as in vertebrates. Lastly, comparative analysis of Apis mellifera reveals a similar relationship between methylation and chromatin compartments, but not gene expression. These results suggest a possible explanation for the heterogeneous association between gene body methylation and gene expression and give insight into DNA methylations role in regulating gene expression.

evolutionary biology↗

bioRxiv2025

Genomic regions exhibiting divergent methylation patterns covary with loci associated with mate choice traits in a stick insect

Speciation involves the development of reproductive isolation between diverging populations. A potential key driver of reproductive isolation is mate choice, a behavioural mechanism that can limit gene flow based on divergence in signal traits. While the genetic basis of mating signal traits has been extensively studied, the contribution of epigenetic modifications to their variation remains underexplored, leaving the role of DNA methylation in mate choice unclear. Here, we focus on epigenetic variation and cuticular hydrocarbons (CHCs), the latter being chemical traits used for mate choice in insects. Specifically, we investigate the association between DNA methylation and regions associated with CHC variation in Timema cristinae stick insects. We integrate analyses of differentially methylated regions (DMRs) between individuals from different host-plant ecotypes with genomic sequencing and phenotypic data on CHCs. We find that DMRs are significantly enriched in genetic loci associated with CHCs, suggesting a non-random relationship between DNA methylation and loci associated with these signal traits. While further work is required to clarify causality, our results highlight the potential for epigenetic marks to be associated with traits involved in mate choice. Future studies should thus aim to establish causal links between DNA methylation and signal trait variation, which would clarify the contribution of methylation to mate choice, prezygotic isolation, and ultimately, speciation.

evolutionary biology↗

bioRxiv2025

Temporal occupancy distributions reveal multifaceted and heterogeneous effects of climatic variation on montane butterflies

Climate change has substantially altered the phenology of many organisms, with profound implications for biodiversity and species interactions. However, phenological studies of insects have often focused on shifts over time, without explicitly examining how climate drives these changes. In addition, the metrics used to measure changes in phenology often capture only limited information about the flight period. In this study, we analyzed multidecadal observational and climate data using a hierarchical Bayesian framework to model the annual probability of occurrence distributions for 135 butterfly species across five montane sites along an elevational gradient. Our analysis used polynomial models that account for shifts in abundance and the timing and length of flight periods to investigate the effect of climate on butterfly phenology. We found that spring maximum and minimum temperatures, as well as winter precipitation, are important predictors of butterfly phenology. High winter precipitation delayed phenology at high-elevation sites where substantial snowfall occurs, while increased spring maximum temperatures generally advanced phenology across all elevations. Even modest increases in spring minimum temperatures caused substantial shift in phenology. We documented variability in the effect of climate on phenology across sites, among species within a site and among populations of the same species across different sites with variability among species within a site being especially pronounced. We also found that climate influences different aspects of the flight period differently (e.g., timing versus duration), underscoring the need to move beyond single metrics such as day of first flight. These findings highlight the importance of examining the entire flight period and considering the interplay of species-specific traits to improve predictions of how climate change impacts phenology. Such approaches might be essential for designing more targeted and effective conservation strategies in response to ongoing climate change.

ecology↗

bioRxiv2024

Mosaic of Somatic Mutations in the Ancient and Still-Living Aspen Clone, Pando

While evolutionary biology traditionally focuses on the spread of mutations within populations, the dynamics of mutational spread within individuals, particularly in long-lived clonally-spreading organisms, remain poorly understood. Here we examine the genetic structure of Pando, Earths largest known quaking aspen (Populus tremuloides) clone. We sequenced over 500 samples across Pando and neighboring clones, including multiple tissue types. At fine spatial scales, we detected significant genetic structure, particularly in leaf tissue, but this signal weakened across larger distances, suggesting either rapid root growth homogenizes the system over time or mechanisms exist that prevent widespread mutation transmission. Phylogenetic analyses date Pando between [~]12,000 and 37,000 years old, supported by continuous aspen pollen presence in nearby lake sediments. Tissues accumulated mutations at different rates, with leaves showing significantly higher mutation loads than roots or branches. This work provides the first quantitative age estimate for this remarkable organism and offers initial insights into the spatial dynamics of somatic mutation in a massive clonal plant. While our reduced-representation sequencing approach limits detection of rare variants, these findings establish a foundation for understanding how long-lived modular organisms accumulate and distribute genetic variation, questions that will benefit from future high-coverage whole-genome sequencing across tissues.

evolutionary biology↗

bioRxiv2024

Extensive admixture among karst-obligate salamandersreveals evidence of recent divergence and gene exchangethrough aquifers

Karst ecosystems often contain extraordinary biodiversity, but the complex underground aquifers of karst regions present challenges for assessing and conserving stygobiont diversity and investigating their evolutionary history. We examined the karst-obligate salamanders of the Eurycea neotenes species complex in the Edwards Plateau region of central Texas using population genomics data to address questions about population connectivity and the potential for gene exchange within the underlying aquifer system. The Eurycea neotenes species complex has historically been divided into three nominal species, but their status, and spatial extent of species ranges, have remained uncertain. We discovered evidence of extensive admixture within the species complex and with adjacent lineages. We observed relatively low levels of differentiation among all sampling localities which supports the hypothesis of recent divergence. Nominal taxonomy, aquifer region and geography accounted for a modest amount of the overall population genomic variation, but these predictors were largely collinear and difficult to disentangle. Importantly, the taxonomy of the three nominal species does not reflect the admixture apparent in clustering analyses. Inference of migration events revealed a complex pattern of gene exchange, suggesting that Eurycea salamanders have a dynamic history of dispersal through the aquifer system. These results highlight the need for greater understanding of how stygobiont populations are connected via dispersal and gene exchange through karst aquifers.

evolutionary biology↗

bioRxiv2024

Ecology not genetics explains correlated trait divergence during speciation

The formation of new species often involves the correlated divergence of multiple traits and genetic regions. However, the mechanisms by which such trait covariation builds up remain poorly understood. In this context, we consider two non-exclusive hypotheses. First, genetic covariance between traits can cause divergent selection on one trait to promote population divergence in correlated traits (a genetic covariation hypothesis). Second, correlated environmental pressures can generate selection on multiple traits, facilitating the evolution of trait complexes (an environmental covariation hypothesis). Here, we test these hypotheses using cryptic coloration (controlled by an incipient supergene) and chemical traits (i.e., cuticular hydrocarbons, CHCs) involved in desiccation resistance and mate choice in Timema cristinae stick insects. We first demonstrate that population divergence in color-pattern is correlated with divergence in some (but not all) CHC traits. We show that when correlated population divergence does occur, it is unlikely to be explained by genetic covariation because within-population genetic covariance between color-pattern and CHCs traits is weak. In contrast, we find that correlated variation in climate and host plant likely generates selection jointly on color-pattern and some CHC traits. This supports the environmental covariation hypothesis, likely via the effects of two correlated environmental axes selecting on different traits. Finally, we provide evidence that misalignment between natural and sexual selection also contributes to patterns of correlated trait divergence. Our results shed light into transitions between phases of speciation by showing that environmental factors can promote population divergence in trait complexes, even without strong genetic covariance.

evolutionary biology↗

bioRxiv2024

A next generation of hierarchical Bayesian analyses of hybrid zones enables direct quantification of variation in introgression in R

Hybrid zones, where genetically distinct groups of organisms meet and interbreed, offer valuable insights into the nature of species and speciation. Here, we present a new R package bgchm, for population genomic analyses of hybrid zones. This R package extends and updates the existing bgc software and combines Bayesian analyses of hierarchical genomic clines with Bayesian methods for estimating hybrid indexes, interpopulation ancestry proportions, and geographic clines. Compared to existing software, bgchm offers enhanced efficiency through Hamiltonian Monte Carlo sampling and the ability to work with genotype likelihoods combined with a hierarchical Bayesian approach, enabling accurate inference for diverse types of genetic datasets. The package also facilitates the quantification of introgression patterns across genomes, which is crucial for understanding reproductive isolation and speciation genetics. We first describe the models underlying bgchm and then provide an overview of the R package and illustrate its use through the analysis of simulated and empirical data sets. We show that bgchm generates accurate estimates of model parameters under a variety of conditions, especially when the genetic loci analyzed are highly ancestry informative. This includes relatively robust estimates of genome-wide variability in clines, which has not been the focus of previous models and methods. We also illustrate how both selection and genetic drift contribute to variability in introgression among loci and how additional information can be used to help distinguish these contributions. We conclude by describing the promises and limitations of bgchm, comparing bgchm to other software for genomic cline analyses, and identifying areas for fruitful future development.

evolutionary biology↗

bioRxiv2022

Spatiotemporal and ontogenetic variation, microbial selection, and predicted antifungal function in the skin-associated microbiome of a Rocky Mountain amphibian

Host-associated microbiomes play important roles in host health and pathogen defense. In amphibians, the skin-associated microbiome serves as an innate immune defense with potential implications for disease management. Few studies have examined season-long temporal variation in the amphibian skin-associated microbiome, and the interactions between bacteria and fungi on amphibian skin remain poorly understood. We characterize season-long temporal variation in the skin-associated microbiome of the western tiger salamander (Ambystoma mavortium) for both bacteria and fungi between sites and across salamander life stages. 207 skin-associated microbiome samples were collected from salamanders at two Rocky Mountain lakes throughout the summer and fall of 2018, and 127 additional microbiome samples were collected from lake water and lake substrate. We used 16S and ITS next-generation sequencing data with Bayesian Dirichlet-multinomial regression to estimate the relative abundances of bacterial and fungal taxa, test for differential abundance, examine microbial selection, and derive alpha and beta diversity. The antifungal function of bacterial communities was predicted using stochastic character mapping and a database of antifungal bacterial isolates. We examined microbial absolute abundances using Bayesian negative binomial LASSO coupled with synthetic gene spike-ins. For both bacteria and fungi, we observed variation in community composition through time, between sites, and with salamander age and life stage. We found salamander skin to be selective for microbes, with many taxa disproportionately represented relative to the environment, and we observed selection for predicted antifungal bacteria. Ultimately, this ecological knowledge may assist in the conservation of amphibian species threatened by chytridiomycosis and other emerging diseases.

microbiology↗

bioRxiv2022

Additive genetic effects in interacting species jointly determine the outcome of caterpillar herbivory

Plant-insect interactions are common and important in basic and applied biology. Trait and genetic variation can affect the outcome and evolution of these interactions, but the relative contributions of plant and insect genetic variation and how these interact remain unclear and are rarely subject to assessment in the same experimental context. Here we address this knowledge gap using a recent host range expansion onto alfalfa by the Melissa blue butterfly. Common garden rearing experiments and genomic data show that caterpillar performance depends on plant and insect genetic variation, with insect genetics contributing to performance earlier in development and plant genetics later. Our models of performance based on caterpillar genetics retained predictive power when applied to a second common garden. Much of the plant genetic effect could be explained by heritable variation in plant phytochemicals, especially saponins, peptides, and phosphatidyl cholines, providing a mechanistic understanding of variation in the species interaction. We find evidence of polygenic, mostly additive effects within and between species, with consistent effects of plant genotype on growth and development across multiple butterfly species. Our results inform theories of plant-insect coevolution and the evolution of diet breadth in herbivorous insects and other host-specific parasites. Teaser summaryThe combined, additive effects of plant and insect genetic variation explain Melissa blue caterpillar growth and development on alfalfa plants.

genomics↗

bioRxiv2022

Population genomic evidence of selection on structural variants in a natural hybrid zone

Structural variants (SVs) can promote speciation by directly causing reproductive isolation or by suppressing recombination across large genomic regions. Whereas examples of each mechanism have been documented, systematic tests of the role of SVs in speciation are lacking. Here, we take advantage of long-read (Oxford nanopore) whole-genome sequencing and a hybrid zone between two Lycaeides butterfly taxa (L. melissa and Jackson Hole Lycaeides) to comprehensively evaluate genome-wide patterns of introgression for SVs and relate these patterns to hypotheses about speciation. We found >100,000 SVs segregating within or between the two hybridizing species. SVs and SNPs exhibited similar levels of genetic differentiation between species, with the exception of inversions, which were more differentiated. We detected credible variation in patterns of introgression among SV loci in the hybrid zone, with 562 of 1419 ancestry-informative SVs exhibiting genomic clines that deviating from null expectations based on genome-average ancestry. Overall, hybrids exhibited a directional shift towards Jackson Hole Lycaeides ancestry at SV loci, consistent with the hypothesis that these loci experienced more selection on average then SNP loci. Surprisingly, we found that deletions, rather than inversions, showed the highest skew towards excess introgression from Jackson Hole Lycaeides. Excess Jackson Hole Lycaeides ancestry in hybrids was also especially pronounced for Z-linked SVs and inversions containing many genes. In conclusion, our results show that SVs are ubiquitous and suggest that SVs in general, but especially deletions, might contribute disproportionately to hybrid fitness and thus (partial) reproductive isolation.

genomics↗

bioRxiv2021

Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species

Effective population size affects the efficacy of selection, rate of evolution by drift, and neutral diversity levels. When species are subdivided into multiple populations connected by gene flow, evolutionary processes can depend on global or local effective population sizes. Theory predicts that high levels of diversity might be maintained by gene flow, even very low levels of gene flow, consistent with species long-term effective population size, but tests of this idea are mostly lacking. Here, we show that Lycaeides butterfly populations maintain low contemporary (variance) effective population sizes (e.g., [~]200 individuals) and thus evolve rapidly by genetic drift. Contemporary effective sizes were consistent with local census populations sizes. In contrast, populations harbored high levels of genetic diversity consistent with an effective population size several orders of magnitude larger. We hypothesized that the differences in the magnitude and variability of contemporary versus long-term effective population sizes were caused by gene flow of sufficient magnitude to maintain diversity but only subtly affect evolution on generational time scales. Consistent with this hypothesis, we detected low but non-trivial gene flow among populations. Furthermore, using population-genomic time-series data, we documented patterns consistent with predictions from this hypothesis, including a weak but detectable excess of evolutionary change in the direction of the mean (migrant gene pool) allele frequencies across populations, and consistency in the direction of allele frequency change over time. The documented decoupling of diversity levels and short-term change by drift in Lycaeides has implications for our understanding of contemporary evolution and the maintenance of genetic variation in the wild.

evolutionary biology↗

bioRxiv2020

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

genetics↗

bioRxiv2020

A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on poly-genic traits from population-genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modeled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. Performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Cal-losobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open source computer software (fsabc) implementing this method are available from GitHub (https://github.com/zgompert/fsabc.git).

evolutionary biology↗

bioRxiv2020

Leveraging biological complexity to predict patch occupancy in a recent host range expansion

Specialized plant-insect interactions are a defining feature of life on earth, yet we are only beginning to understand the factors that set limits on host ranges in herbivorous insects. To understand the colonization of alfalfa by the Melissa blue butterfly, we quantified arthropod assemblages and plant metabolites across a wide geographic region, while controlling for climate and dispersal inferred from population genomic variation. The presence of the butterfly is successfully predicted by direct and indirect effects of plant traits and interactions with other species. Results are consistent with the predictions of a theoretical model of parasite host range in which specialization is an epiphenomenon of the many barriers to be overcome rather than a consequence of trade-offs in developmental physiology. One sentence summaryThe formation of a novel plant-insect interaction can be predicted with a combination of biotic and abiotic factors, with comparable importance revealed for metabolomic variation in plants and interactions with mutualists, competitors and enemies.

ecology↗