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 distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets.

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 (Lasiurus borealis, L. cinereus, and Lasionycteris noctivagans) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 41.6%, 21.3%, 2.2% of all individuals of L. borealis, L. cinereus, and L. noctivagans, respectively, had evidence of movement. In contrast, in Maryland 77.3%, 37.1%, and 27.3% of these same species were classified as migrants. Our results suggest bats killed at a given wind facility are likely derived from migratory as well as resident populations. Finally, there is variation in the proportion of migrants killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that the impact of wind-energy facilities on bat populations occurs across a large geographic extent, with the proportion of migrants impacted likely to vary across species and sites. Similar studies should be conducted across a broader geographic scale to understand the impacts on bat populations from wind-energy facilities.

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). We estimate genetic distances, population genetic structure, and historical demography using data from each of these datasets and compare the results across markers. As anticipated, the results exhibit differences across marker types, particularly in terms of the resolution offered by different analyses. A cost-benefit analysis indicates that SNPs collected using a GBS protocol are the most cost-effective molecular marker, with inferences that mirror those collected from the whole genome data at a fraction of the cost per sample.

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. Here we quantify nucleotide diversity, a common metric of intraspecific genetic diversity, in more than 38,000 plant and animal species using georeferenced DNA sequences from millions of samples. Results demonstrate that tropical species contain significantly more intraspecific genetic diversity than nontropical species. To explore potential evolutionary processes that may have contributed to this pattern, we calculated summary statistics that measure population demographic change and detected significant correlations between these statistics and latitude. We find that nontropical species are more likely to deviate from neutral expectations, indicating that they have historically experienced dramatic fluctuations in population size likely associated with Pleistocene glacial cycles. By analyzing the most comprehensive data set to date, our results imply that Quaternary climate perturbations may be more important as a process driving the latitudinal gradient in species richness than previously appreciated.

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 that 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.

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. We aimed to evaluate the influence of the formation of the BCP on the genetic structure of B. armata using nuclear microsatellites and chloroplast markers (cpDNA) to compare patterns of genetic diversity and structure with previous published studies. Because gene flow through seeds is usually more limited compared to pollen flow, we expect to find stronger genetic structure at (cpDNA) than at nuclear markers. Moreover, larger genetic structure might also be explained by the smaller effective population size of cpDNA. We analyzed six microsatellite markers and two cpDNA regions. The main results indicated high levels of genetic differentiation among isolated populations located in the BCP, while low genetic differentiation was found between southern populations of the BCP and Sonora, suggesting long distance gene flow. In contrast, chloroplast markers indicated high levels of genetic structure between BCP and Sonora populations, suggesting asymmetrical gene flow between pollen (measured by nuclear microsatellites) and seed (cpDNA markers). This study provides valuable information on genetic diversity of B. armata that can be relevant for conservation and management; and develops microsatellites markers that can be transferred to other Brahea species.

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 2 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; 4 individuals and 6,820 to 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.

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. 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.

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. Using single nucleotide polymorphism (SNP) data from these species, we assessed population genetic structure and used a machine-learning approach to compare models with and without gene flow between populations upon secondary contact after the last glacial maximum (LGM). Finally, we estimated divergence times and rates of gene flow between populations. SDMs suggest that there was less suitable habitat in the North Cascades and Northern Rocky Mountains during glacial compared to interglacial periods, resulting in reduced habitat suitability and increased habitat fragmentation during the LGM. Our genomic data identify population structure in all taxa, and support gene flow upon secondary contact in five of the seven taxa. Parameter estimates suggest that population divergences date to the later Pleistocene for most populations. Our results support a role of refugial dynamics in driving intraspecific divergence in the Cascades Range. In these invertebrates, population structure often does not correspond to current biogeographic or environmental barriers. Rather, population structure may reflect refugial lineages that have since expanded their ranges, often leading to secondary contact between once isolated lineages.

Giant tree frogs exemplify the promise of integrating multiple types of data in phylogeographic investigations.

Hugall et al. (2022) is one of the seminal publications from the single locus era of phylogeographic research. These authors were among the first to argue that genetic data are ideally suited to test hypotheses that are ultimately derived from other sources of information. While the testing of predictions from the fossil record has long been important to molecular systematics (e.g., Donoghue et al., 1989), phylogeographic investigations into the more recent evolutionary past lack a fossil record in most focal taxa. In lieu of fossils, which were not available for the small snails that served as the focal taxon, Hugall et al. (2002) applied the (then) new technique of environmental modelling to identify regions within the species range with habitat that was predicted to be stable throughout the Holocene. They then present data that suggests that these regions correspond to the areas with high genetic diversity. Apart from the inferences about snail evolutionary history, the core argument of Hugall et al. (2002) is that consilience (i.e., agreement between inferences drawn from different sources of data) is an important goal for phylogeographic investigation. Consilience in the inferences drawn from independent types of data has a multiplicative effect; when present the researcher is likely to have more confidence in their inference than would be possible to have in an inference from any one source of data. The manuscript by Jaynes et al. (2022) is a splendid illustration of this principle.

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.g., the absence of population genetic structure), and the naïve analysis of data collected from these systems may lead to spurious results. To address these issues, we introduce P2C2M.Skyline, an R package designed to assess model adequacy for BSPs using posterior predictive simulation. P2C2M.Skyline uses a phylogenetic tree and the log file output from Bayesian Skyline analyses to simulate posterior predictive datasets and then compares this null distribution to statistics calculated from the empirical data to check for model violations. P2C2M.Skyline was able to correctly identify model violations when simulated datasets were generated assuming genetic structure, which is a clear violation of BSP model assumptions. Conversely, P2C2M.Skyline showed low rates of false positives when models were simulated under the BSP model. We also evaluate the P2C2M.Skyline performance in empirical systems, where we detected model violations when DNA sequences from multiple populations were lumped together. P2C2M.Skyline represents a user-friendly and computationally efficient resource for researchers aiming to make inferences from BSP.

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. 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-analyse 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 reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources.

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., the Linnean shortfall) hampers research across the biological sciences. To explore whether the Linnean shortfall results from poor taxonomic practice or not enough taxonomic effort, we applied machine-learning techniques to build a predictive model to identify named species that are likely to contain hidden diversity. Results indicate that small-bodied species with large, climatically variable ranges are most likely to contain hidden species. These attributes generally match those identified in the taxonomic literature, indicating that the Linnean shortfall is caused by societal underinvestment in taxonomy rather than poor taxonomic practice.

Genomic evidence of an ancient inland temperate rainforest in the Pacific Northwest of North America.

The disjunct temperate rainforests of the Pacific Northwest of North America (PNW) are characterized by late-successional dominant tree species Thuja plicata (western redcedar) and Tsuga heterophylla (western hemlock). The demographic histories of these species, along with the PNW rainforest ecosystem in its entirety, have been heavily impacted by geological and climatic changes the PNW has experienced over the last 5 million years, including mountain orogeny and repeated Pleistocene glaciations. These environmental events have ultimately shaped the history of these species, with inland populations potentially being extirpated during the Pleistocene glaciations. Here, we collect genomic data for both species across their ranges to test multiple demographic models, each reflecting a different phylogeographical hypothesis on how the ecosystem-dominating species may have responded to dramatic climatic change. Our results indicate that inland and coastal populations in both species diverged ~2.5 million years ago in the early Pleistocene and experienced decreases in population size during glacial cycles, with subsequent population expansion. Importantly, we found evidence for gene flow between coastal and inland populations during the mid-Holocene. It is likely that intermittent migration in these species during this time has prevented allopatric speciation via genetic drift alone. In conclusion, our results from combining genomic data and demographic inference procedures establish that populations of the ecosystem dominants Thuja plicata and Tsuga heterophylla persisted in refugia located in both the coastal and inland regions of the PNW throughout the Pleistocene, with populations expanding and contracting in response to glacial cycles with occasional gene flow.

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. However, comparative studies across different spatial, temporal and biological scales on DOF species are still meagre. Here, we combine phylogeographical model selection and machine learning predictive frameworks to investigate the influence of Pleistocene climatic changes on several plant and animal species from the DOF. We assembled mitochondrial/chloroplastic DNA sequences in public repositories and inferred the demographic responses of 44 species, comprising 70 intraspecific lineages of plants, lizards, frogs, spiders and insects. We then built a random forest model using biotic and abiotic information to identify the best predictors of demographic responses in the Pleistocene. Finally, we assessed the temporal synchrony of species demographic responses with hierarchical approximate Bayesian computation. Biotic variables related to population connectivity, gene flow and habitat preferences largely predicted how species responded to Pleistocene climatic changes, and demographic changes were synchronous primarily during the Middle Pleistocene. Although 22 (~31%) lineages underwent demographic expansion, presumably associated with the spread of aridity during the glacial Pleistocene periods, our findings suggest that nine lineages (~13%) exhibited the opposite response due to taxon-specific attributes.

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 Anguispira kochi occidentalis and Anguispira nimapuna. These two taxa are disjunct from the remaining species of the genus, which achieves its greatest diversity in eastern North America. Given the disjunct nature of A. k. occidentalis and A. nimapuna, we here present a mtDNA phylogeny of the genus that includes both eastern and western species to assess the phylogenetic position of A. k. occidentalis and A. nimapuna. We then reconstruct the demographic history of A. k. occidentalis and A. nimapuna by analysing current patterns of genetic variation and interpreting the results considering the historical biogeography of the region. Both A. k. occidentalis and A. nimapuna represent unique taxa that are genetically and geographically distinct from their congeners. The current distribution and genetic structure of A. k. occidentalis has been shaped by both historical isolation in refugia and more recent northward shifts, whereas A. nimapuna is represented by two populations with shallow divergence in an area of long-term habitat stability.

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. This hypothesis postulates that temporal asynchrony of wet and dry seasons over short distances causes parapatric populations to become isolated by time. After determining genetic structure, inferring past distributions, ranking demographic models, and estimating the power of monthly climatic variables, our results identified two populations that are not associated with geographic barriers, biome gradients, or historical refugia. Instead, they are predicted by spatial variation in monthly rainfall and minimum temperature, consistent with the asynchrony of seasons hypothesis, supported also by our comparative framework using multiple matrix regression and linear mixed effects modeling. Due to the toad's life history, climate likely mediates gene flow directly, with genetic differentiation being provoked by neutral mechanisms related to climate driven population isolation, and/or by natural selection against migrants from populations with different breeding times. The asynchrony of seasons hypothesis is seldom considered in phylogeographic studies, but our results indicate that it should be tested in systems where breeding is tightly coupled with climate.

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. Here we describe seasonally resolved distributions for the three species that are most impacted by wind farms (Lasiurus borealis (eastern red bat), L. cinereus (hoary bat) and Lasionycteris noctivagans (silver-haired bat)) and use these distributions to infer their most likely migratory pathways. To accomplish this, we collected 2,880 occurrence points from the Global Biodiversity Information Facility over five decades in North America to model species distributions on a seasonal basis and used an ensemble approach for modeling distributions. This dataset included 1,129 data points for L. borealis, 917 for L. cinereus and 834 for L. noctivagans. The results suggest that all three species exhibit variation in distributions from north to south depending on season, with each species showing potential migratory pathways during the fall migration that follow linear features. Finally, we describe proposed migratory pathways for these three species that can be used to identify stop-over sites, assess small-scale migration and highlight areas that should be prioritized for actions to reduce the effects of wind farm mortality.

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. In non-model organisms, the lack of information about their ecology and evolution can lead to uncertainty about which demographic models are appropriate. Here, we assess the utility of convolutional neural networks (CNNs) for assessing demographic models in South American lizards in the genus Norops. Three demographic scenarios (constant, expansion, and bottleneck) were considered for each of four inferred population-level lineages, and we found that the overall model accuracy was higher than 98% for all lineages. We then evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among the four lineages, identifying a single model with an estimated overall accuracy of 87% when using CNNs. The inferred demography of the lizard system suggests that gene flow between non-sister populations and changes in effective population sizes through time, probably in response to Pleistocene climatic oscillations, have shaped genetic diversity in this system. Approximate Bayesian computation (ABC) was applied to provide a comparison to the performance of CNNs. ABC was unable to identify a single model among the larger set of 26 models in the subsequent analysis. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer's toolkit.