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Data from: Dispersal barriers and opportunities drive multiple levels of phylogeographic concordance in the Southern Alps of New Zealand

Cite this dataset

Marske, Katharine; Thomaz, Andrea; Knowles, L Lacey (2020). Data from: Dispersal barriers and opportunities drive multiple levels of phylogeographic concordance in the Southern Alps of New Zealand [Dataset]. Dryad.


Phylogeographic concordance, or the sharing of phylogeographic patterns among co-distributed species, suggests similar responses to topography or climatic history. While the orientation and timing of breaks between lineages are routinely compared, spatial dynamics within regions occupied by individual lineages provide a second opportunity for comparing responses to past events. In environments with complex topography and glacial history, such as New Zealand’s South Island, geographically nested comparisons can identify the processes leading to phylogeographic concordance between and within regional genomic clusters. Here, we used single nucleotide polymorphisms (obtained via ddRADseq) for two co-distributed forest beetle species, Agyrtodes labralis (Leiodidae) and Brachynopus scutellaris (Staphylinidae), to evaluate the role of climate change and topography in shaping phylogeographic concordance at two, nested spatial scales: do species diverge over the same geographic barriers, with similar divergence times? And within regions delimited by these breaks, do species share similar spatial dynamics of directional expansion or isolation-by-distance? We found greater congruence of phylogeographic breaks between regions divided by the strongest dispersal barriers (i.e., the Southern Alps). However, these shared breaks were not indicative of shared spatial dynamics within the regions they delimit, and the most similar spatial dynamics between species occurred within regions with the strongest gradients in historical climatic stability. Our results indicate that lack of concordance as traditionally detected by lineage turnover does not rule out the possibility of shared histories, and variation in the presence and type of concordance may provide insights into the different processes shaping phylogeographic patterns across geologically dynamic regions.    

Usage notes

localities_accessions files:

A .csv file for each species includes extraction codes, region and population assignments for the Structure and Directionality Index analyses, respectively, geographic coordinates, BioSample accession numbers for raw reads, and Genbank accession numbers for corresponding mitochondrial sequences from Marske et al, 2012 (doi:10.1111/j.1558-5646.2011.01538.x).

Structure (zipped directory):

Files include a Structure input file allowing maximum 25% missing data, a parameters file, and zipped results from Structure Harvestor. For Agyrtodes labralis, inputs are given for a global analysis. For Brachynopus scutellaris, inputs are given for the global and two subStructure analyses.

Procrustes (zipped directory):

Files include a Structure-formatted input file allowing maximum 25% missing data, locality data for sequenced individuals, and locality data for populations. For each species, a .stru file and two .csv files are given for global and regional analyses.

Directionality Index (zipped directory):

Files include a SNAPP-formatted input file allowing maximum 25% missing data and locality data for each merged population (.txt).

FastSimcoal (zipped directory):

Separate folders with input files allowing maximum 25% missing dataand Python or R codes are given for calculating the site frequency spectrum (01_CalculateSFS), deriving the point estimates for divergence dates and other parameters (02_PointEstimates) and bootstrapping the model parameters for the divergence with migration model (03_Migration_model_Bootstrap).