Biology subjects

Carstens, B. C.

Publications and source records attributed to Carstens, B. C..

14 recordsLinked to original sources

bioRxiv2026

What shapes intraspecific genetic diversity in amphibians and reptiles? A comparative macrogenetic study across six biogeographic regions

Aim: Given that much of Earth's biodiversity is contained within species, inferring intraspecific genetic diversity and identifying its ecological correlates is essential for understanding evolutionary processes and informing conservation. Here, we investigate broad-scale variation in species-level mitochondrial genetic diversity and evaluate the relative importance of alternative ecological hypotheses proposed to explain it. Location: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics. Taxon: Amphibians and reptiles Methods: We assembled a comprehensive dataset by integrating and manually curating mitochondrial sequences, occurrence records, environmental data and species traits from multiple open-access sources. We used multiple mitochondrial genes to estimate mean nucleotide diversity for each study species and quantified regional variation in genetic diversity. We then used random forests models to evaluate the relative importance of selected variables representing hypotheses related to water-energy availability, topography, historical climatic stability, land-cover characteristics, species geographic characteristics, life-history traits, evolutionary history and conservation status. Results: The final dataset comprised 247 amphibians and 295 reptiles, for which 43 and 52 explanatory variables were evaluated, respectively. Overall, the strongest support was found for hypotheses related to life-history traits, topographic heterogeneity and water-energy availability. In amphibians, higher genetic diversity was associated with smaller body size and greater environmental heterogeneity, particularly broader elevational and precipitation gradients. In reptiles, genetic diversity was primarily associated with life-history traits, with species of smaller body size and lower reproductive output exhibiting higher diversity. Long-term climatic stability showed comparatively little explanatory power for both taxa. Main conclusions: Our comprehensive study demonstrates that integrating extensive manual data curation with comparative macrogenetic analyses provides a robust framework for identifying broad-scale ecological correlates of mitochondrial genetic diversity. These findings improve our understanding of the factors associated with contemporary patterns of genetic diversity and provide a foundation for future conservation assessments in data-limited systems.

evolutionary biology↗

bioRxiv2026

Overcoming data limitations in macrogenetics: assessing and accounting for spatial heterogeneity in sequence data

At large scales, our ability to compare genetic diversity across species is often challenged by the difficulty of extracting range-wide sequence data from online databases. Using mitochondrial DNA from amphibians and reptiles spanning across six regions: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics, we develop and implement a framework to overcome common limitations in macrogenetics. We compiled and manually curated a spatially explicit genetic dataset from public repositories, quantified the spatial representativeness of genetic sampling across species' ranges and developed a novel interpolation framework to estimate and map species-specific patterns of intraspecific genetic diversity (ISD) while accounting for heterogeneous spatial sampling. We estimated mean nucleotide diversity across species to produce regional maps for each genetic marker. Our final dataset comprised 182 amphibian and 239 reptile species, encompassing 184 genera from 54 families. Range representativeness of genetic sampling was generally high, despite some variation across study regions. Spatial patterns of ISD revealed geographic heterogeneity, and were consistent across the best-sampled mitochondrial markers. By combining automated and manual georeferencing, quantifying range representativeness, and accounting for bias in spatial interpolations, our pipeline is marker-agnostic and directly applicable to nuclear and genomic data as they become available.

evolutionary biology↗

bioRxiv2024

Predicting genetic biodiversity in salamanders using geographic, climatic, and life history traits

The geographic distribution of genetic variation within a species reveals information about its evolutionary history, including responses to historical climate change and dispersal ability across various habitat types. We combine genetic data from salamander species with geographic, climatic, and life history data collected from open-source online repositories to develop a machine learning model designed to identify the traits that are most predictive of unrecognized genetic lineages. We find evidence of hidden diversity distributed throughout the clade Caudata that is largely the result of variation in climatic variables. We highlight some of the difficulties in using machine-learning models on open-source data that are often messy and potentially taxonomically and geographically biased.

evolutionary biology↗

bioRxiv2024

Bats of a feather: Range characteristics and wing morphology predict phylogeographic breaks in volant vertebrates

Intraspecific genetic variation and phylogeographic structure can be influenced by factors such as landscape features, environmental gradients, historical biogeography, and organismal traits such as dispersal ability. Since deep genetic structure is often considered a precursor to speciation, identifying the factors that are associated with genetic structure can contribute to a greater understanding about diversification. Here, we use repurposed data to perform a global analysis of volant vertebrates (i.e., bats and birds) to estimate where intraspecific phylogeographic breaks occur and identify the factors that are important predictors of these breaks. We estimate phylogeographic breaks using Monmoniers maximum difference barrier algorithm and conduct a Random Forests analysis using the presence of a phylogeographic break as a response variable. In bats, phylogeographic breaks are concentrated in biodiversity hotspots while breaks estimated in bird species are more widespread across temperate and tropical zones. However, for both clades geographical features such as maximum latitude, measures of wing morphology, and organismal traits associated with feeding ecology were found to be important predictors of phylogeographic breaks. Our analysis identifies geographical areas as wells as suites of organismal traits that could serve as a starting point for more detailed studies of biodiversity processes.

evolutionary biology↗

bioRxiv2023

Environmental factors and cultural drift influence song evolution in New World Sparrows

Variation in bird song is often assumed to be determined by sexual selection, rather than natural selection. However, most investigations to date have drawn their conclusions from a handful of species due to the challenges with manually processing sound data. Here, we use deep machine learning to investigate nearly all species of New World Sparrows. We leverage existing data to identify the processes that structure variation in bird song and to determine how this variation corresponds to patterns in genes and traits. Song variation in ~40% of species can be explained by environment, geography, and time. Across a community and global scale, the action of natural selection on the evolution of song is at least as impactful as it is on other genetically-determined traits.

evolutionary biology↗

bioRxiv2023

Dialect differences correlate with environment in migratory coastal White-crowned Sparrows

Vocalization behavior in birds, especially songs, strongly affects reproduction, but it is also highly impacted by geographic distance, climate, and time. For this reason, phenotypic differences in vocalizations between different bird populations are often interpreted as evidence of lineage divergence. Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. However, it is unknown whether the divergence in their songs correlates to environmental or geographical factors. Previous work has been hindered by time-consuming traditional methods to study bird songs that rely on the manual annotation of song spectrograms into individual syllables. Here we explore the performance of automated machine learning methods of song annotation, which can process large datasets more efficiently, paying attention to the question of subspecies differences. We utilize a recently published artificial neural network to automatically annotate hundreds of White-crowned Sparrow vocalizations across two subspecies. By analyzing differences in syllable usage and composition, we find that Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in syllables in the White-crowned Sparrow repertoire. However, the large sample size enabled by the AI approach allows us to demonstrate that divergence in song is correlated with environmental difference and migratory status, but not with geographical distance. Our findings support the hypothesis that the evolution of vocalization behavior is affected by environment, in addition to population structure. LAY SUMMARYO_LIBirdsong is an important behavior because it is important in bird communication and reproduction. C_LIO_LIWhite-crowned Sparrows in western North America are known to use different songs along their range, but it is unknown if those songs vary due to the environment. C_LIO_LIWe used machine learning to analyze these songs and found that populations of White-crowned Sparrows can be differentiated based on their songs. C_LIO_LIEnvironmental factors during the breeding season exert a greater influence on song evolution in migratory subspecies. C_LI

evolutionary biology↗

bioRxiv2022

The Translation Initiation Factor Homolog, eif4e1c, Regulates Cardiomyocyte Metabolism and Proliferation During Heart Regeneration

The eIF4E family of translation initiation factors bind 5 methylated caps and act as the limiting-step for mRNA translation. The canonical eIF4E1A is required for cell viability, yet other related eIF4E families exist and are utilized in specific contexts or tissues. Here, we describe a family called Eif4e1c for which we find roles during heart development and regeneration in zebrafish. The Eif4e1c family is present in all aquatic vertebrates but is lost in all terrestrial species. A core group of amino acids shared over 500 million years of evolution forms an interface along the protein surface, suggesting Eif4e1c functions in a novel pathway. Deletion of eif4e1c in zebrafish caused growth deficits and impaired survival in juveniles. Mutants surviving to adulthood had fewer cardiomyocytes and reduced proliferative responses to cardiac injury. Ribosome profiling of mutant hearts demonstrated changes in translation efficiency of mRNA for genes known to regulate cardiomyocyte proliferation. Although eif4e1c is broadly expressed, its disruption had most notable impact on the heart and at juvenile stages. Our findings reveal context-dependent requirements for translation initiation regulators during heart regeneration.

developmental biology↗

bioRxiv2022

Predictors of genomic diversity within North American squamates

Comparisons of intraspecific genetic diversity across species can reveal the roles of geography, ecology, and life history in shaping biodiversity. The wide availability of mitochondrial DNA (mtDNA) sequences in open-access databases makes this marker practical for conducting analyses across several species in a common framework, but patterns may not be representative of overall species diversity. Here, we gather new and existing mtDNA sequences and genome-wide nuclear data (genotyping-by-sequencing; GBS) for 30 North American squamate species sampled in the Southeastern and Southwestern United States. We estimated mtDNA nucleotide diversity for two mtDNA genes, COI (22 species alignments; average 16 sequences) and cytb (22 species; average 58 sequences), as well as nuclear heterozygosity and nucleotide diversity from GBS data for 118 individuals (30 species; four individuals and 6,820-44,309 loci per species). We showed that nuclear genomic diversity estimates were highly consistent across individuals for some species, while other species showed large differences depending on the locality sampled. Range size was positively correlated with both cytb diversity (Phylogenetically Independent Contrasts: R2 = 0.31, p = 0.007) and GBS diversity (R2 = 0.21; p = 0.006), while other predictors differed across the top models for each dataset. Mitochondrial and nuclear diversity estimates were not correlated within species, although sampling differences in the data available made these datasets difficult to compare. Further study of mtDNA and nuclear diversity sampled across species ranges is needed to evaluate the roles of geography and life history in structuring diversity across a variety of taxonomic groups.

genomics↗

bioRxiv2022

The impacts of transfer learning, phylogenetic distance, and sample size on big-data bioacoustics

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

evolutionary biology↗

bioRxiv2021

phylogatR: Phylogeographic data aggregation and repurposing

Patterns of genetic diversity within species contain information about the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta-analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time-consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready-to-analyze sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR, large meta-analyses of thousands of species that can address classic questions in evolutionary biology and ecology and student- or citizen-science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web-based tools that enable these data to be recycled and reanalyzed and increase accessibility to big data for research labs and classroom instructors with limited computational expertise and resources.

bioinformatics↗

bioRxiv2021

Genomic resources for the North American water vole (Microtus richardsoni) and the montane vole (Microtus montanus)

BackgroundVoles of the genus Microtus are important research organisms, yet genomic resources in the genus are lacking. Providing such resources would benefit future studies of immunology, phylogeography, cryptic diversity, and more. FindingsWe sequenced and assembled nuclear genomes from two subspecies of water vole (Microtus richardsoni) and from the montane vole (Microtus montanus). The water vole genomes were sequenced with Illumina and 10X Chromium plus Illumina sequencing, resulting in assemblies with ~1,600,000 and ~30,000 scaffolds respectively. The montane vole was assembled into ~13,000 scaffolds using Illumina sequencing also. In addition to the nuclear assemblies, mitochondrial genome assemblies were also performed for both species. We conducted a structural and functional annotation for the best water vole nuclear genome, which resulted in ~24,500 annotated genes, with 83% of these receiving functional annotations. Finally, we find that assembly quality statistics for our nuclear assemblies fall within the range of genomes previously published in the genus Microtus, making the water vole and montane vole genomes useful additions to currently available genomic resources.

genomics↗

bioRxiv2020

Phylogeographic model selection using convolutional neural networks

The field of phylogeography has evolved rapidly in terms of the analytical toolkit to analyze the ever-increasing amounts of genomic data. Despite substantial advances, researchers have not fully explored all potential analytical tools to tackle the challenge posed by the huge size of genomic datasets. For example, deep learning techniques, such as convolutional neural networks (CNNs), widely employed in image and video classification, are largely unexplored for phylogeographic model selection. In non-model organisms, the lack of information about their ecology, natural history, and evolution can lead to uncertainty about which set of demographic models should be considered. Here we investigate the utility of CNNs for assessing a large number of competing phylogeographic models using South American lizards as an example, and approximate Bayesian computation (ABC) to contrast the performance of CNNs. First, we evaluated three demographic scenarios (constant, expansion, and bottleneck) for each of four recovered lineages and found that the overall model accuracy was higher than 98% for all lineages. Next, we evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among these lineages and recovered an overall accuracy of 87%. In contrast, ABC was unable to single out a best fit model among 26 competing models. Finally, we used the CNN model to investigate the evolutionary history of two South American lizards. Our results indicate the presence of hidden genetic diversity, gene flow between non-sister populations, and changes in effective population sizes through time, likely in response to Pleistocene climatic oscillations. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographers toolkit.

evolutionary biology↗

bioRxiv2020

Predicting intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic datasets, in combination with machine learning frameworks, offer great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What determines intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we combined repurposed data with machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this dataset, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contain lower genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful datasets with greater utility for understanding global biodiversity.

evolutionary biology↗

bioRxiv2018

Disentangling the process of speciation using machine learning

Historically, investigations into the processes driving speciation have largely been isolated from systematic investigations into species limits. Recent advances in sequencing technology have led to a rapid increase in the availability of genomic data, and this, in turn, has led to the introduction of many novel methods for species delimitation. However, these methods have been limited to divergence-only scenarios and have not attempted to evaluate complex modes of speciation, such as those that include gene flow during early stages of divergence (sympatric speciation) or population size changes (founder effect speciation). To address this shortcoming, we introduce delimitR, an approach that enables biologists to infer species boundaries and evaluate the demographic processes that may have led to speciation. delimitR uses the binned multidimensional Site Frequency Spectrum and a machine-learning algorithm (Random Forests) to compare speciation models. We use simulations to evaluate the accuracy of delimitR. When comparing models that include lineage divergence and gene flow for three populations, error rates are near zero with recent divergence times (<100,000 generations) and a modest number of Single Nucleotide Polymorphisms (SNPs; 1,500). When applied to a more complex model set (including divergence, gene flow, and population size changes), error rates are moderate (~0.15 with 10,000 SNPs), and misclassifications are generally between highly similar models. We also evaluate the utility of delimitR using three previously published datasets and find results that corroborate previous findings. Our analyses indicate that delimitR can serve as an important conceptual bridge uniting various investigations into the process of speciation.

evolutionary biology↗