Inferring the geographic origin of a range expansion: Latitudinal and longitudinal coordinates inferred from genomic data in an ABC framework with the program x‐origin

Abstract

Climatic or environmental change is not only driving distributional shifts in species today, but it has also caused distributions to expand and contract in the past. Inferences about the geographic locations of past populations especially regions that served as refugia (i.e., source populations) and migratory routes are a challenging endeavour. Refugial areas may be evidenced from fossil records or regions of temporal stability inferred from ecological niche models. Genomic data offer an alternative and broadly applicable source of information about the locality of refugial areas, especially relative to fossil data, which are either unavailable or incomplete for most species. Here, we present a pipeline we developed (called x-origin) for statistically inferring the geographic origin of range expansion using a spatially explicit coalescent model and an approximate Bayesian computation testing framework. In addition to assessing the probability of specific latitudinal and longitudinal coordinates of refugial or source populations, such inferences can also be made accounting for the effects of temporal and spatial environmental heterogeneity, which may impact migration routes. We demonstrate x-origin with an analysis of genomic data collected in the Collared pika that underwent postglacial expansion across Alaska, as well as present an assessment of its accuracy under a known model of expansion to validate the approach.

Population history provides foundational knowledge for utilizing and developing native plant restoration materials

“…Determining the location of a refugium is an inherently difficult task (e.g., He, Prado, & Knowles, 2017). Paleovegetation and prehistoric climate data compiled from sites across the West suggest cooler climatic conditions during the Last Glacial Maximum (Grayson, 2006;Ray & Adams, 2001;Thompson & Anderson, 2000), especially in the Great Basin (Waltari & Guralnick, 2009) Figure S3), a pattern that may be attributed to low habitat suitability across the Bonneville Salt Flats in north-central Utah.…”

Genomic evidence of a widespread southern distribution during the Last Glacial Maximum for two eastern North American hickory species

“…In addition, application of demographical and coalescent modelling techniques would greatly enhance our understanding of the phylogeographical histories of ENA taxa. Such techniques would allow statistical tests of hypotheses regarding levels of genetic connectivity among populations, changes in population size over time (Barthe et al, 2017), locations of source populations from which postglacial recolonization occurred (He, Prado, & Knowles, 2017), and which ecological factors have most strongly impacted historical migration patterns (Bemmels, Title, Ortego, & Knowles, 2016).…”

Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil

“…predictions of the niche) to inform simulations for ABC-based biogeographic inference (He et al 2013, Brown et al 2016, He et al 2017. predictions of the niche) to inform simulations for ABC-based biogeographic inference (He et al 2013, Brown et al 2016, He et al 2017.…”

“…Scaling of population sizes in the demographic simulations may help to some degree (He et al 2017). More complex models, as we have outlined, will only increase computational demands and thus will require efficient algorithms, potentially exploiting computer architectures such as graphical processing units for highly parallel problems.…”

“…Recently, investigators proposed using ENM output (i.e. predictions of the niche) to inform simulations for ABC-based biogeographic inference (He et al 2013, Brown et al 2016, He et al 2017. Specifically, in these simulations, ENM output was used to define habitat quality.…”