Tereza Jezkova

The opposing forces of gene flow and isolation are two major processes shaping genetic diversity. Understanding how these vary across space and time is necessary to identify the environmental features that promote diversification. The detection of considerable geographic structure in taxa from the arid Nearctic has prompted research into the drivers of isolation in the region. Several geographic features have been proposed as barriers to gene flow, including the Colorado River, Western Continental Divide (WCD), and a hypothetical Mid-Peninsular Seaway in Baja California. However, recent studies suggest that the role of barriers in genetic differentiation may have been overestimated when compared to other mechanisms of divergence. In this study, we infer historical and spatial patterns of connectivity and isolation in Desert Spiny Lizards (Sceloporus magister) and Baja Spiny Lizards (Sceloporus zosteromus), which together form a species complex composed of parapatric lineages with wide distributions in arid western North America. Our analyses incorporate mitochondrial sequences, genomic-scale data, and past and present climatic data to evaluate the nature and strength of barriers to gene flow in the region. Our approach relies on estimates of migration under the multispecies coalescent to understand the history of lineage divergence in the face of gene flow. Results show that the S. magister complex is geographically structured, but we also detect instances of gene flow. The WCD is a strong barrier to gene flow, while the Colorado River is more permeable. Analyses yield conflicting results for the catalyst of differentiation of peninsular lineages in S. zosteromus. Our study shows how large-scale genomic data for thoroughly sampled species can shed new light on biogeography. Furthermore, our approach highlights the need for the combined analysis of multiple sources of evidence to adequately characterize the drivers of divergence.

Hybridization facilitates recombination between divergent genetic lineages and can be shaped by both neutral and selective processes. Upon hybridization, loci with no net fitness effects introgress randomly from parental species into the genomes of hybrid individuals. Conversely, alleles from one parental species at some loci may provide a selective advantage to hybrids, resulting in patterns of introgression that do not conform to random expectations. We investigated genomic patterns of differential introgression in natural hybrids of two species of Caribbean anoles, Anolis pulchellus and A. krugi in Puerto Rico. Hybrids exhibit A. pulchellus phenotypes but possess A. krugi mitochondrial DNA, originated from multiple, independent hybridization events, and appear to have replaced pure A. pulchellus across a large area in western Puerto Rico. Combining genome‐wide SNP datasets with bioinformatic methods to identify signals of differential introgression in hybrids, we demonstrate that the genomes of hybrids are dominated by pulchellus ‐derived alleles and show only 10%–20% A. krugi ancestry. The majority of A. krugi loci in hybrids exhibit a signal of non‐random differential introgression and include loci linked to genes involved in development and immune function. Three of these genes (delta like canonical notch ligand 1, jagged1 and notch receptor 1) affect cell differentiation and growth and interact with mitochondrial function. Our results suggest that differential non‐random introgression for a subset of loci may be driven by selection favouring the inheritance of compatible mitochondrial and nuclear‐encoded genes in hybrids.

Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency and (2) the performance of models built using selected‐historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real‐world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected‐historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in area under the curve performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected‐historical occurrences did not notably improve performance over all‐historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential trade‐offs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity versus specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.