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.

Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.

While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversity. However, even as both disciplines have come to utilize SNP data over the last decades, analytical approaches have remained for the most part focused on either broad-scale inferences of historical processes (phylogeography) or on more localized inferences about environmental and/or landscape features (landscape genetics). Here we demonstrate that an artificial intelligence model-based analytical framework can consider both deeper historical factors and landscape-level processes in an integrated analysis.

Genetic structure and landscape effects on gene flow in the Neotropical lizard Norops brasiliensis (Squamata: Dactyloidae).

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g.

Geographic source of bats killed at wind-energy facilities in the eastern United States.

Bats subject to high rates of fatalities at wind-energy facilities are of high conservation concern due to the long-term, cumulative effects they have, but the impact on broader bat populations can be difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute migrants or more local individuals. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the most likely summer geographic origins of three different bat species (, , and ) killed at wind-energy facilities in Ohio and Maryland in the eastern United States.

Reduced representation approaches produce similar results to whole genome sequencing for some common phylogeographic analyses.

When designing phylogeographic investigations researchers can choose to collect many different types of molecular markers, including mitochondrial genes or genomes, SNPs from reduced representation protocols, large sequence capture data sets, and even whole genomes. Given that the statistical power and accuracy of various analyses are expected to differ depending on both the type of marker and the amount of data collected, an exploration of the variance across methodological results as a function of marker type should provide valuable information to researchers. Here we collect mitochondrial Cytochrome b sequences, whole mitochondrial genomes, single nucleotide polymorphisms (SNP)s isolated using a genotype by sequencing (GBS) protocol, sequences from ultraconserved elements, and low-coverage nuclear genomes from the North American water vole (Microtus richardsoni).

Pleistocene glaciations caused the latitudinal gradient of within-species genetic diversity.

Intraspecific genetic diversity is a key aspect of biodiversity. Quaternary climatic change and glaciation influenced intraspecific genetic diversity by promoting range shifts and population size change. However, the extent to which glaciation affected genetic diversity on a global scale is not well established.

Geographic isolation and long-distance gene flow influence the genetic structure of the blue fan palm Brahea armata (Arecaceae).

The formation of the Baja California Peninsula (BCP) has impacted the microevolutionary dynamics of different species in ways that depend on biological traits such as dispersal capacity. Plants with relatively low levels of vagility have exhibited high genetic divergence between the BCP and Continental mainland. Brahea armata (Arecaceae) is a palm species inhabiting the northern part of the BCP and Sonora; its distribution occurs in isolated oases of vegetation.

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.

The impacts of fine-tuning, 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.

Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.

Bayesian skyline plots (BSPs) are a useful tool for making inferences about demographic history. For example, researchers typically apply BSPs to test hypotheses regarding how climate changes have influenced intraspecific genetic diversity over time. Like any method, BSP has assumptions that may be violated in some empirical systems (e.

The role of multiple Pleistocene refugia in promoting diversification in the Pacific Northwest.

Pleistocene glacial cycles drastically changed the distributions of taxa endemic to temperate rainforests in the Pacific Northwest, with many experiencing reduced habitat suitability during glacial periods. In this study, we investigate whether glacial cycles promoted intraspecific divergence and whether subsequent range changes led to secondary contact and gene flow. For seven invertebrate species endemic to the PNW, we estimated species distribution models (SDMs) and projected them onto current and historical climate conditions to assess how habitat suitability changed during glacial cycles.

phylogatR: Phylogeographic data aggregation and repurposing.

Patterns of genetic diversity within species contain information 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.

Analysis of biodiversity data suggests that mammal species are hidden in predictable places.

SignificanceOnly an estimated 1 to 10% of Earth's species have been formally described. This discrepancy between the number of species with a formal taxonomic description and actual number of species (i.e.

Assessing model adequacy leads to more robust phylogeographic inference.

Phylogeographic studies base inferences on large data sets and complex demographic models, but these models are applied in ways that could mislead researchers and compromise their inference. Researchers face three challenges associated with the use of models: (i) 'model selection', or the identification of an appropriate model for analysis; (ii) 'evaluation of analytical results', or the interpretation of the biological significance of the resulting parameter estimates, delimitations, and topologies; and (iii) 'model evaluation', or the use of statistical approaches to assess the fit of the model to the data. The field collectively invests most of its energy in point (ii) without considering the other points; we argue that attention to points (i) and (iii) is essential to phylogeographic inference.

Comparative and predictive phylogeography in the South American diagonal of open formations: Unravelling the biological and environmental influences on multitaxon demography.

Phylogeography investigates historical drivers of the geographical distribution of intraspecific lineages. Particular attention has been given to ecological, climatic and geological processes in the diversification of the Neotropical biota. Several species sampled across the South American diagonal of open formations (DOF), comprising the Caatinga, Cerrado and Chaco biomes, experienced range shifts coincident with Quaternary climatic changes.

A role of asynchrony of seasons in explaining genetic differentiation in a Neotropical toad.

The process of diversification can be studied at the phylogeographic level by attempting to identify the environmental features that promote and maintain population divergence. Here we investigate diversification in Rhinella granulosa, a Neotropical toad from northeastern Brazil, by testing a range of hypotheses that encompass different putative mechanisms reducing gene flow among populations. We sequenced single nucleotide polymorphisms and examined individual predictions related to the role of geographic barriers (rivers), ecological gradients, historical habitat stability, and spatial variation in climate seasonality, also known as the asynchrony of seasons hypothesis.

Comparative phylogeography of two Northern Rocky Mountain endemics: the widespread Anguispira kochi occidentalis and the narrow-range Anguispira nimapuna (Gastropoda: Discidae).

The Northern Rocky Mountain ecosystem supports rich biological diversity with many endemic and rare species. Extant endemics display two biogeographic patterns: widespread species with fragmented populations, and narrow-range endemics. These distributions are shown by the congeneric snails and .

Phylogeographic model selection using convolutional neural networks.

The discipline of phylogeography has evolved rapidly in terms of the analytical toolkit used to analyse large genomic data sets. Despite substantial advances, analytical tools that could potentially address the challenges posed by increased model complexity have not been fully explored. For example, deep learning techniques are underutilized for phylogeographic model selection.

Predicting migration routes for three species of migratory bats using species distribution models.

Understanding seasonal variation in the distribution and movement patterns of migratory species is essential to monitoring and conservation efforts. While there are many species of migratory bats in North America, little is known about their seasonal movements. In terms of conservation, this is important because the bat fatalities from wind energy turbines are significant and may fluctuate seasonally.

Genomic Resources for the North American Water Vole () and the Montane Vole ().

Voles of the genus are important research organisms, yet genomic resources are lacking. Such resources would benefit future studies of immunology, phylogeography, cryptic diversity, and more. We sequenced and assembled nuclear genomes from two subspecies of water vole () and from the montane vole ().

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

The growing availability of genetic data sets, in combination with machine learning frameworks, offers great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine 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.

Using trace elements to identify the geographic origin of migratory bats.

The expansion of the wind energy industry has had benefits in terms of increased renewable energy production but has also led to increased mortality of migratory bats due to interactions with wind turbines. A key question that could guide bat-related management activities is identifying the geographic origin of bats killed at wind-energy facilities. Generating this information requires developing new methods for identifying the geographic sources of individual bats.

Population genetic structure and demographic history of the lone star tick, Amblyomma americanum (Ixodida: Ixodidae): New evidence supporting old records.

Range expansions are a potential outcome of changes in habitat suitability, which commonly result as a consequence of climate change. Hypotheses on such changes in the geographic distribution of a certain species can be evaluated using population genetic structure and demography. In this study we explore the population genetic structure, genetic variability, demographic history of, and habitat suitability for Amblyomma americanum, a North American tick species that is a known vector of several pathogenic microorganisms.

Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP.

Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.