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Buerkle, C. A.

Publications and source records attributed to Buerkle, C. A..

15 recordsLinked to original sources
bioRxiv2025

Temporal dynamics of color polymorphism and hybridization in Colias butterflies

Investigations into the genetic basis of color polymorphism have played a key role in our understanding of genetic architecture and the evolution of mating systems. Sulphur butterflies (Colias) have been models in this field, but also contain unsolved puzzles with respect to species boundaries and hybridization. We surveyed genomic variation across five years in a location where phenotypic intermediates between Colias eurytheme and C. eriphyle are observed, but ancestry variation of potential hybrids has not been quantified. Our results reveal individuals with hybrid ancestry roughly in proportion to the frequency of individuals of intermediate phenotype recorded in the wild. Individuals with hybrid ancestry were predominantly those with intermediate morphologies, but morphologically intermediate individuals were not always of hybrid origin, which raises alternative possibilities for the origin and maintenance of color variation in the system. Genomic regions differentiated between species are largely located on the Z chromosome, and we find more candidates on autosomes than in another Colias contact zone. The dynamics of hybridization in this system are highly variable through time, suggesting fertile avenues for future study into the maintenance of species boundaries in the face of temporally-variable, climatically influenced, and pervasive hybridization.

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↗
bioRxiv2024

Interpretable and predictive models to harness the life science data revolution

The proliferation of high-dimensional data in ecology and evolutionary biology raises the promise of statistical and machine learning models that are highly predictive and interpretable. However, high-dimensional data are commonly burdened with an inherent trade-off: in-sample prediction of outcomes will improve as additional variables are included in the model, but this may come at the cost of poor predictive accuracy and limited generalizability for future or unsampled observations (out-of-sample prediction). To confront this problem of overfitting, sparse models can focus on key variables by correctly placing low weight on unimportant variables. We competed nine methods to quantify their performance in variable selection and prediction using simulated data with different sample sizes, numbers of variables, and strengths of effects. Overfitting was typical for many methods and simulation scenarios. Despite this, in-sample and out-of-sample prediction converged on the true predictive target for simulations with more observations, larger causal effects, and fewer variables. Accurate variable selection to support process-based understanding will be unattainable for many realistic sampling schemes in ecology and evolution. We use our analyses to characterize data attributes for which statistical learning is possible, and illustrate how some sparse methods can achieve predictive accuracy while mitigating and learning the extent of overfitting.

genomics↗
bioRxiv2023

The effects of reference panel perturbations on the accuracy of genotype imputation

Reference-based genotype imputation is a standard technique that has become increasingly popular in large-scale studies involving genomic data. The two key elements involved in the process of genotype imputation are (1) the haplotype reference panel to which a target individual is being imputed, and (2) the imputation algorithm used to infer missing genotypes in the target individual. The imputation literature has historically focused mainly on (2), with a typical comparative study investigating the relative performance of various imputation algorithms while holding the reference panel constant. However, the role of the reference panel itself (1) on overall imputation performance is equally, if not more, important than the choice among many high-performing algorithms. Even though it is intuitive that the quality of a reference panel should play a role in the accuracy of imputation, it is nonetheless unclear to what extent common errors during panel creation (e.g., genotyping and phase error) lead to suboptimal imputation performance. In this study, we investigate the effects of applying three distinct modes of perturbations to a widely used haplotype reference panel in human genetics on the resulting imputation accuracy. Specifically, we perturb the reference panel by (1) randomly introducing phase errors, (2) randomly introducing genotype errors, and (3) randomly pruning variants from the panel (all at varying magnitudes). We then impute a set of diverse individuals at various sequencing coverages (0.5x, 1.0x, and 2.0x) to these various perturbed panels and evaluate imputation accuracy using the r2 metric for the entire cohort as well as ancestry-stratified subsets. We observe that both phase- and genotype-perturbations can dramatically affect imputation accuracy, particularly at very low allele frequencies, while pruning variants has a far smaller effect. We then empirically verified that our simulations reliably predict the impact of potential filtering techniques in a real-world dataset. In the context of haplotype reference panels, these results suggest that phasing and genotyping accuracy are far more important than the density of a reference panel used for imputation.

genomics↗
bioRxiv2022

Selection leads to remarkable variability in the outcomes of hybridization across replicate hybrid zones

Hybrid zones have been viewed as an opportunity to see speciation in action. When hybrid zones are replicated, it is assumed that if the same genetic incompatibilities are maintaining reproductive isolation across all instances of secondary contact, those incompatibilities should be identifiable by consistent patterns in the genome. In contrast, changes in allele frequencies due to genetic drift should be idiosyncratic for each hybrid zone. To test this assumption, we simulated 20 replicates of each of 12 hybrid zone scenarios with varied genetic incompatibilities, rates of migration, selection and different starting population size ratios of parental species. We found remarkable variability in the outcomes of hybridization in replicate hybrid zones, particularly with Bateson-Dobzhansky-Muller incompatibilities and strong selection. We found substantial differences among replicates in the overall genomic composition of individuals, including admixture proportions, inter-specific ancestry complement, and number of ancestry junctions. Additionally, we found substantial variation in genomic clines among replicates at focal loci, regardless of locus-specific selection. We conclude that processes other than selection are responsible for some consistent outcomes of hybridization, whereas selection on incompatibilities can lead to genomically widespread and highly variable outcomes. We highlight the challenge of mapping between pattern and process in hybrid zones and call attention to how selection against incompatibilities will commonly lead to variable outcomes. We hope that this study informs future research on replicate hybrid zones and encourages further development of statistical techniques, theoretical models, and exploration of additional axes of variation to understand reproductive isolation.

evolutionary biology↗
bioRxiv2022

The abundances and occurrences of foliar microbes are poorly predicted by variation in plant traits and abiotic conditions

Much effort has been made to understand why foliar microbes live where they do. However, whether foliar microbiome composition can be predicted is unknown. Here, we determine the limits of prediction using metabarcoding data of both fungal and bacterial assemblages that occur within (endophytes) and without (epiphytes) leaves from 59 plant taxa. We built random forest models for prevalent taxa and quantified the combined predictive power of 24 plant traits, 12 abiotic conditions and 7 additional features. As response variables, we considered microbial relative and absolute abundances, and occurrences. Most microbial taxa were too rare to effectively model, but model performance was generally poor even for the most prevalent and abundant taxa (model R2 was typically <0.1). Fungi were more tractable for modeling than bacteria. Models of Shannons diversity were moderately successful but those for richness were not. Taxa responded idiosyncratically and non-linearly to variation in the foliar habitat. When prevalent microbes were included as features in models, performance improved. Our results suggest that easily measurable aspects of the phyllosphere habitat are poor predictors of microbiome composition. These results pose a challenge for the study of microbial biogeography and we discuss possible ways forward.

ecology↗
bioRxiv2021

A polygenic architecture with conditionally neutral effects underlies ecological differentiation in Silene

O_LIEcological differentiation can drive speciation but it is unclear how the genetic architecture of habitat-dependent fitness contributes to lineage divergence. We investigated the genetic architecture of cumulative flowering, a fitness component, in second-generation hybrids between Silene dioica and S. latifolia transplanted into the natural habitat of each species. C_LIO_LIWe used reduced-representation sequencing and Bayesian Sparse Linear Mixed Models (BSLMMs) to analyze the genetic control of cumulative flowering in each habitat. C_LIO_LIOur results point to a polygenic architecture of cumulative flowering. Allelic effects were mostly beneficial or deleterious in one habitat and neutral in the other. The direction of allelic effects was associated with allele frequency differences between the species: positive-effect alleles were often derived from the native species, whereas negative-effect alleles, at other loci, tended to originate from the non-native species. C_LIO_LIWe conclude that ecological differentiation is governed and maintained by many loci with small, conditionally neutral effects. Conditional neutrality may result from differences in selection targets in the two habitats and provides hidden variation upon which selection can act. Polygenic architectures of adaptive differentiation are expected to be transient during lineage divergence and may therefore be unrelated to high genetic differentiation at the underlying loci. C_LI

evolutionary biology↗
bioRxiv2021

Ecological outcomes of hybridization vary extensively in Catostomus fishes

Hybridization has been studied extensively to learn about speciation and mechanisms of reproductive isolation, but increasingly we recognize that hybridization outcomes vary geo-graphically and can depend on the environment. At the same time, hybridization can reshape biotic interactions in an ecosystem, leading to ecological shifts where hybridization occurs. Identifying how hybrid individuals function ecologically would link evolutionary outcomes of hybridization to ecological consequences, but relatively few studies have focused on ecological traits of hybrids. We described the feeding ecology of several Catostomus fish species and their hybrids by using stable isotopes ({delta}13C and{delta} 15N) as a proxy for diet and habitat use, and compared two native species, an introduced species, and three interspecific hybrid crosses. We replicated this comparison spatially, including hybrids and parental species from seven rivers in the Upper Colorado River basin where parental species co-occur and the opportunity for hybridization exists. Although relationships between native species in isotopic space varied, individual native species did not fully overlap in isotopic space in any river sampled, suggesting little overlap of resource use between these historically sympatric species. The introduced species overlapped with one or both native species in every river, suggesting similar resource use and potential for competition. Hybrids occupied intermediate, matching, or more extreme (transgressive) isotopic space relative to parental species, and were isotopically variable within and among rivers. We suggest that ecological outcomes of hybridization, like genomic outcomes of hybridization, are likely to vary across locations where a pair of species hybridizes. This variation implies that hybridization might have large unpredictable, idiosyncratic ecological effects on fish assemblages where hybrids occur. Although we found little evidence that hybrids are at a disadvantage ecologically--there were no significant declines in body condition relative to parental species--it is nevertheless possible that abiotic or biotic attributes of a river might constrain the range of interspecific hybrids that are successful, thus contributing to variation in hybridization outcomes across rivers.

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

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↗
bioRxiv2019

Whole-genome duplication and host genotype affect rhizosphere microbial communities in Arabidopsis thaliana

The composition of complex microbial communities found in association with plants is influenced in part by host phenotype. Yet, the salient genetic architecture is often unknown. Genome duplication events are common in the evolutionary history of plants, influence many important plant traits, and may affect associated microbial communities. Using experimentally induced whole genome duplication (WGD), we tested the effect of WGD on rhizosphere bacterial communities in Arabidopsis thaliana. Specifically, we performed 16S rRNA amplicon sequencing to characterize differences between microbiomes associated with specific host genotypes (Columbia vs. Landsberg) and ploidy levels (diploid vs. tetraploid). We modeled abundances of individual bacterial taxa by utilizing a hierarchical Bayesian framework, based on the Dirichlet and multinomial distributions. We found that host genotype and host ploidy level affected rhizosphere community composition, for instance, the microbiome of the tetraploid Columbia genotype differed from that of other host genotypes. We then tested to what extent microbiomes derived from a given host genotype or ploidy level affected plant performance by inoculating sterile seedlings of each genotype with microbial communities harvested from a prior generation. We found a negative effect of the tetraploid Columbia microbiome on growth of all four plant genotypes. The findings suggest that while both host genotype and ploidy affect microbial community assembly, bacterial communities found in association with only some host genotypes may affect growth of subsequent plant generations. ImportancePlants influence the composition of their associated microbial communities; yet the underlying host genetic factors are often unknown. Genome duplication events are common in the evolutionary history of plants and affect many plant traits, including the quality and quantity of compounds exuded into the root zone, which can affect root-bound microbes. In Arabidopsis thaliana, we characterized how whole-genome duplication affected the composition of rhizosphere bacterial communities, and how bacterial communities associated with two host plant genotypes and ploidy levels affected subsequent plant growth. We observed an interaction in which ploidy level within one host genotype affected both bacterial community composition and function. This research reveals how genome duplication, a widespread genetic feature of both wild and crop plant species, influences the coexistence of bacterial taxa and affects plant growth.

plant biology↗
bioRxiv2019

Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modeled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM--Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

bioinformatics↗
bioRxiv2019

A suite of rare microbes interacts with a dominant, heritable, fungal endophyte to influence plant trait expression

Endophytes are microbes that live, for at least a portion of their life history, within plant tissues. Endophyte assemblages are often composed of a few abundant taxa and many infrequently-observed, rare taxa. The ways in which most endophytes affect host phenotype are unknown; however, certain dominant endophytes can influence plants in ecologically meaningful ways-including by affecting growth and contributing to immune responses. In contrast, the effects of rare endophytes have been unexplored, and how rare and common endophytes might interact is also unknown. Here, we manipulate both the suite of rare foliar endophytes (including both fungi and bacteria) and Alternaria fulva-a dominant, vertically- transmitted fungus-within the fabaceous forb Astragalus lentiginosus. We report that rare, low-biomass endophytes affected host size and foliar %N, but only when the dominant fungal endophyte (A. fulva) was not present. A. fulva also reduced plant size and %N, but these deleterious effects on the host could be offset by a striking antagonism we observed between this heritable fungus and a foliar pathogen. These results are unusual in that they are derived from experimental manipulation in a non-crop or non-grass system and demonstrate that interactions among taxa determine the net effect of endophytic assemblages on their hosts. We suggest that the myriad infrequently-observed endophytes within plant leaves may be more than a collection of uninfluential, commensal organisms, but instead have meaningful ecological roles.

ecology↗
bioRxiv2018

Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits

The genomic architecture of functionally important traits is key to understanding the maintenance of reproductive barriers and trait differences when divergent populations or species hybridize. We conducted a Genome-Wide Association Study (GWAS) to study trait architecture in natural hybrids of two ecologically divergent Populus species. We genotyped 472 seedlings from a natural hybrid zone of Populus alba and P. tremula for genome-wide markers from reduced representation sequencing, phenotyped the plants in common gardens for 46 phytochemical (phenylpropanoid), morphological, and growth traits, and used a Bayesian polygenic model for mapping. We detected three classes of genomic architectures: (1) traits with finite, detectable associations of genetic loci with phenotypic variation in addition to highly polygenic heritability, (2) traits with indications for polygenic heritability only, (3) traits with no detectable heritability. For class (1), we identified genome regions with plausible candidate genes for phenylpropanoid biosynthesis or its regulation, including MYB transcription factors and glycosyl transferases. GWAS in natural, recombinant hybrids represents a promising step towards resolving the genomic architecture of phenotypic traits in long-lived species. This facilitates the fine-mapping and subsequent functional characterization of genes and networks causing differences in hybrid performance and fitness.

evolutionary biology↗
bioRxiv2018

Caterpillars on a phytochemical landscape

Modern metabolomic approaches that generate more comprehensive phytochemical profiles than were previously available are providing new opportunities for understanding plant-animal interactions. Specifically, we can characterize the phytochemical landscape by asking how a larger number of individual compounds affect herbivores and how compounds covary among plants. Here we use the recent colonization of alfalfa (Medicago sativa) by the Melissa blue butterfly (Lycaeides melissa) to quantify plant metabolites and the performance of caterpillars as affected by both individual compounds and suites of covarying phytochemicals. We find that survival, development time and adult weight are all associated with variation in nutrition and toxicity, including biomolecules associated with plant cell function as well as putative anti-herbivore action. The plant-insect interface is complex, with clusters of covarying compounds in many cases encompassing divergent effects on different aspects of caterpillar performance. Individual compounds with the strongest associations are largely specialized metabolites, including alkaloids, phenolic glycosides and saponins. The saponins are represented in our data by more than 25 individual compounds with beneficial and detrimental effects on L. melissa caterpillars, which highlights the value of metabolomic data as opposed to approaches that rely on total concentrations within broad defensive classes.

ecology↗