DOI: 10.5061/dryad.ng3bv
title: Data from: Contrasting support for alternative models of genomic variation based on microhabitat preference: species-specific effects of climate change in alpine sedges

description: Deterministic processes may uniquely affect codistributed species’
 phylogeographic patterns such that discordant genetic variation among taxa
 is predicted. Yet, explicitly testing expectations of genomic discordance
 in a statistical framework remains challenging. Here, we construct
 spatially and temporally dynamic models to investigate the hypothesized
 effect of microhabitat preferences on the permeability of glaciated
 regions to gene flow in two closely related montane species. Utilizing
 environmental niche models from the Last Glacial Maximum and the present
 to inform demographic models of changes in habitat suitability over time,
 we evaluate the relative probabilities of two alternative models using
 approximate Bayesian computation (ABC) in which glaciated regions are
 either (i) permeable or (ii) a barrier to gene flow. Results based on the
 fit of the empirical data to data sets simulated using a spatially
 explicit coalescent under alternative models indicate that genomic data
 are consistent with predictions about the hypothesized role of
 microhabitat in generating discordant patterns of genetic variation among
 the taxa. Specifically, a model in which glaciated areas acted as a
 barrier was much more probable based on patterns of genomic variation in
 Carex nova, a wet-adapted species. However, in the dry-adapted Carex
 chalciolepis, the permeable model was more probable, although the
 difference in the support of the models was small. This work highlights
 how statistical inferences can be used to distinguish deterministic
 processes that are expected to result in discordant genomic patterns among
 species, including species-specific responses to climate change.
description: chal_empirical_dataEmpirical SNP dataset for Carex chalciolepis. These data were used to create summary statistics in arlsumstat for comparison with modeled data in the ABC procedure. They also informed the sampling of the modeled data across the landscape (so that the modeled data had the same pattern of missing data as the empirical data). This file can be viewed and manipulated in a text editor or Excel. It was generated by converting a vcf output file from the populations program in Stacks using PGDSpider.chal_1227_cut.strunova_empirical_dataEmpirical SNP dataset for Carex nova. These data were used to create summary statistics in arlsumstat for comparison with modeled data in the ABC procedure. They also informed the sampling of the modeled data across the landscape (so that the modeled data had the same pattern of missing data as the empirical data). This file can be viewed and manipulated in a text editor or Excel. It was generated by converting a vcf output file from the populations program in Stacks using PGDSpider.nova_1186_cut.struchangeArpAfterSimThis python script is integrated into the modeling simulations (see 'chalciolepis_ABCsampler_input_file'). After each coalescent simulation in Splatch, this script changes the resulting modeled dataset to have the same pattern of missing data as the empirical data. A companion file is needed for the script to work properly (see 'chal_missing_example'). The script can be viewed and modified in a text editor and was created by myself and members of the Knowles lab.chal_missing_exampleThis is an example file that describes the pattern of missing data in an empirical dataset ('chal_empirical_data'). The file is utilized by the changeArpAfterSim.py script (see script in this data archive) to create modeled datasets with the same pattern of missing data as the empirical datasets. The file can be viewed in a text editor.chal_missing.txtcreate_maskThis is an R script that creates a text file describing the pattern of missing data in an empirical dataset (see example mask file 'chal_missing_example' created from 'chal_empirical_data'). The resulting mask file is used by the changeArpAfterSim script to change modeled datasets into modeled datasets with the same pattern of missing data as the empirical data. This script was generated by and modified by myself and members of the Knowles lab.chalciolepis_parameters_exampleThis file defines the parameters and the prior ranges/distributions from which values are pulled to update the simulations at the beginning of every Splatche simulation. See the ABCSampler documentation for more information about file format and options. The file can be viewed and modified in a text editor. The file is defined in the ABCSampler input file (see 'chalciolepis_ABCsampler_input_file').chal.estchalciolepis_ABCsampler_input_fileThis is an example of an input file used by the ABCsampler program. It defines the values that need to be updated at every new iteration (in capital letters - taken from the parameter file 'chalciolepis_parameters_file'), as well as the programs that need to be run (and the order in which those programs are executed). This file can be viewed and modified in a text editor. See the ABCSampler documentation for detailed information about the structure and options for this file.chal.inputchalciolepis_empirical_summary_statsThis is an example of the empirical summary statistics that must be included in the simulation folder. This file was created using arlsumstat program and the empirical dataset (see 'chal_empirical_data'). The file can be viewed and modified in a text editor. See the Arlequin/arlsumstat documentation for more information.chal.obssettings_chalciolepis_splatcheThis is an example of a settings file for a single Splatche run. The Splatche documentation describes all of the options within the file in detail. The file can be viewed and modified in a text editor.settings_chal_barrier.txtchalciolepis_distribution_ptsFile containing the distribution points used for distribution modeling in Maxent. These points come from verified herbarium records and personal collections.chal.txtnova_distribution_pointsFile containing the distribution points used for distribution modeling in Maxent. These points come from verified herbarium records and personal collections.nova.txt