Search

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., (© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.)

Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine-learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine-learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies., (© 2019 John Wiley & Sons Ltd.)

Model selection approaches in phylogeography have allowed researchers to evaluate the support for competing demographic histories, which provides a mode of inference and a measure of uncertainty in understanding climatic and spatial influences on intraspecific diversity. Here, to rank all models in the comparison set and determine what proportion of the total support the top-ranked model garners, we conduct model selection using two analytical approaches-allele frequency-based, implemented in fastsimcoal2, and gene tree-based, implemented in phrapl. We then expand this model selection framework by including an assessment of absolute fit of the models to the data. For this, we utilize DNA isolated from existing natural history collections that span the distribution of red alder (Alnus rubra) in the Pacific Northwest of North America to generate genomic data for the evaluation of 13 demographic scenarios. The quality of DNA recovered from herbarium specimen leaf tissue was assessed for its utility and effectiveness in demographic model selection, specifically in the two approaches mentioned. We present strong support for the use of herbarium tissue in the generation of genomic DNA, albeit with the inclusion of additional quality control checks prior to library preparation and analyses with multiple approaches that incorporate various data. Analyses with allele frequency spectra and gene trees predominantly support A. rubra having experienced an ancient vicariance event with intermittent and frequent gene flow between the disjunct populations. Additionally, the data consistently fit the most frequently selected model, corroborating the model selection techniques. Finally, these results suggest that the A. rubra disjunct populations do not represent separate species., (© 2018 John Wiley & Sons Ltd.)

Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the "curse of dimensionality" and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated., (© 2017 John Wiley & Sons Ltd.)