Demographic modelling reveals a history of divergence with gene flow for a glacially tied stonefly in a changing post‐Pleistocene landscape

Abstract

Aim Climate warming is causing extensive loss of glaciers in mountainous regions, yet our understanding of how glacial recession influences evolutionary processes and genetic diversity is limited. Linking genetic structure with the influences shaping it can improve understanding of how species respond to environmental change. Here, we used genome‐scale data and demographic modelling to resolve the evolutionary history of Lednia tumana, a rare, aquatic insect endemic to alpine streams. We also employed a range of widely used data filtering approaches to quantify how they influenced population structure results. Location Alpine streams in the Rocky Mountains of Glacier National Park, Montana, USA. Taxon Lednia tumana, a stonefly (Order Plecoptera) in the family Nemouridae. Methods We generated single nucleotide polymorphism data through restriction‐site associated DNA sequencing to assess contemporary patterns of genetic structure for 11 L. tumana populations. Using identified clusters, we assessed demographic history through model selection and parameter estimation in a coalescent framework. During population structure analyses, we filtered our data to assess the influence of singletons, missing data and total number of markers on results. Results Contemporary patterns of population structure indicate that L. tumana exhibits a pattern of isolation‐by‐distance among populations within three genetic clusters that align with geography. Mean pairwise genetic differentiation (FST) among populations was 0.033. Coalescent‐based demographic modelling supported divergence with gene flow among genetic clusters since the end of the Pleistocene (~13‐17 kya), likely reflecting the south‐to‐north recession of ice sheets that accumulated during the Wisconsin glaciation. Main conclusions We identified a link between glacial retreat, evolutionary history and patterns of genetic diversity for a range‐restricted stonefly imperiled by climate change. This finding included a history of divergence with gene flow, an unexpected conclusion for a mountaintop species. Beyond L. tumana, this study demonstrates the complexity of assessing genetic structure for weakly differentiated species, shows the degree to which rare alleles and missing data may influence results, and highlights the usefulness of genome‐scale data to extend population genetic inquiry in non‐model species.

Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed byRADseq, in a highly dispersive marine invertebrate (Parastichopus californicus)

“…A distinct genetic cluster composed of individuals from Rennell Sound (REN) on the west coast of Haida Gwaii that was well separated from the other sites in the northern regional group was detected by DAPC, but not by the analysis of individual ancestry (ADMIXTURE). This outcome is in agreement with previous studies showing that DAPC is more efficient than other clustering methods at detecting genetic subdivisions when overall genetic differentiation is weak (Benestan et al., ; Hotaling et al., ; Jombart et al., ). Using hierarchical clustering of pairwise F ST , we showed that REN was significantly differentiated from other sampling locations, thus further corroborating the DAPC results, as were the two sites sampled on the west coast of Vancouver Island (TOF and QUA).…”

Congruent population genetic structure but differing depths of divergence for three alpine stoneflies with similar ecology and geographic distributions

“…before the last glacial maximum in the Rocky Mountains ~20,000 years ago, Carrara, 1987). This timeline is supported by estimates of divergence timing among L. tumana genetic clusters in GNP, which placed intraspecies splits as occurring in the last ~18,000 years (Hotaling et al, 2018). Conversely, Z. glacier may have more recently invaded headwaters, possibly due to range contraction into higher elevations to follow the retreat of glaciers (e.g.…”

A global analysis of bats using automated comparative phylogeography uncovers a surprising impact of Pleistocene glaciation

“…Automated big data phylogeography represents an important direction for the field. While single species studies, particularly those that utilize multilocus data (e.g., Hotaling et al., ), can compare a broad range of detailed models, and comparative studies into a single system enable a more nuanced investigation of how changes in the landscape have influenced evolutionary history (e.g., Mather, Hanson, Pope, & Riginos, ), neither has the potential to uncover generalized patterns on continental or global scales. Automated studies can incorporate organismal trait data in a manner that is not possible in more limited studies, which offers an important connection between evolutionary and ecological processes (Rissler, ).…”