Biology subjects

Gehara, M.

Publications and source records attributed to Gehara, M..

3 recordsLinked to original sources

bioRxiv2021

Genomic architecture controls spatial structuring in Amazonian birds

Large rivers are ubiquitously invoked to explain the distributional limits and speciation of the Amazon Basins mega-diversity. However, inferences on the spatial and temporal origins of Amazonian species have narrowly focused on evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known to produce heterogeneity in differentiation across the genome. To test how genomic architecture impacts our ability to reconstruct patterns of spatial diversification across multiple taxa, we sequenced whole genomes for populations of bird species that co-occur in southeastern Amazonian. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with the species tree topology weight. Gene flow was less pervasive in regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection skewing genome-wide estimates of effective population sizes and gene flow between populations towards lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from the neutral processes to elucidate how speciation hypotheses and biogeographic patterns are sensitive to genomic architecture.

genomics↗

bioRxiv2020

PipeMaster: inferring population divergence and demographic history with approximate Bayesian computation and supervised machine-learning in R

Understanding population divergence involves testing diversification scenarios and estimating historical parameters, such as divergence time, population size and migration rate. There is, however, an immense space of possible highly parameterized scenarios that are difsficult or impossible to solve analytically. To overcome this problem researchers have used alternative simulation-based approaches, such as approximate Bayesian computation (ABC) and supervised machine learning (SML), to approximate posterior probabilities of hypotheses. In this study we demonstrate the utility of our newly developed R-package to simulate summary statistics to perform ABC and SML inferences. We compare the power of both ABC and SML methods and the influence of the number of loci in the accuracy of inferences; and we show three empirical examples: (i) the Mullers termite frog genomic data from Southamerica; (ii) the cottonmouth and (iii) and the copperhead snakes sanger data from Northamerica. We found that SML is more efficient than ABC. It is generally more accurate and needs fewer simulations to perform an inference. We found support for a divergence model without migration, with a recent bottleneck for one of the populations of the southamerican frog. For the cottonmouth we found support for divergence with migration and recent expansion and for the copperhead we found support for a model of divergence with migration and recent bottleneck. Interestingly, by using an SML method it was possible to achieve high accuracy in model selection even when several models were compared in a single inference. We also found a higher accuracy when inferring parameters with SML.

evolutionary biology↗

bioRxiv2020

Resolving spatial complexities of hybridization in the context of the gray zone of speciation in North American ratsnakes (Pantherophis obsoletus complex)

Inferring the history of divergence between species in a framework that permits the presence of gene flow has been crucial for characterizing the gray zone of speciation, which is the period of time where lineages have diverged but have not yet achieved strict reproductive isolation. However, estimates of both divergence times and rates gene flow often ignore spatial information, for example the formation and shape of hybrid zones. Using population genomic data from the eastern ratsnake complex (Pantherophis obsoletus), we infer phylogeographic groups, gene flow, changes in demography, the timing of divergence, and hybrid zone widths. We examine the spatial context of diversification by linking migration and timing of divergence to the location and widths of hybrid zones. Artificial neural network approaches are applied to understand how landscape features and past climate have influenced population genetic structure among these lineages prior to hybridization. Rates of migration between lineages are associated with the width and shape of hybrid zones. Timing of divergence is not related to migration rate across species pairs and is therefore a poor proxy for inferring position in the gray zone. However, timing of divergence is related to the number of loci weakly introgressing through hybrid zones.

evolutionary biology↗