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Supplementary material from: Alpine extremophytes in evolutionary turmoil: Complex diversification patterns and demographic responses of a Halophilic grass in a Central Asian biodiversity hotspot

Cite this dataset

Wróbel, Anna; Klichowska, Ewelina; Nowak, Arkadiusz; Nobis, Marcin (2024). Supplementary material from: Alpine extremophytes in evolutionary turmoil: Complex diversification patterns and demographic responses of a Halophilic grass in a Central Asian biodiversity hotspot [Dataset]. Dryad. https://doi.org/10.5061/dryad.sn02v6x85

Abstract

Diversification and demographic responses are key processes shaping species evolutionary history. Yet we still lack a full understanding of ecological mechanisms that shape genetic diversity at different spatial scales upon rapid environmental changes. In this study, we examined genetic differentiation in an extremophilic grass Puccinellia pamirica and factors affecting its population dynamics among the occupied hypersaline alpine wetlands on the arid Pamir Plateau in Central Asia. Using genomic data, we found evidence of fine-scale population structure and gene flow among the localities established across the high-elevation plateau as well as fingerprints of historical demographic expansion. We showed that an increase in the effective population size could coincide with the Last Glacial Period, which was followed by the species demographic decline during the Holocene. Geographic distance plays a vital role in shaping the spatial genetic structure of P. pamirica alongside with isolation-by-environment and habitat fragmentation. Our results highlight a complex history of divergence and gene flow in this species-poor alpine region during the Late Quaternary. We demonstrate that regional climate specificity and a shortage of nonclimate data largely impede predictions of future range changes of the alpine extremophile using ecological niche modeling. This study emphasizes the importance of fine-scale environmental heterogeneity for population dynamics and species distribution shifts.

README

https://doi.org/10.5061/dryad.sn02v6x85

This README file was generated on 2024-01-03 by Anna Wrobel.

Article: Alpine Extremophytes in Evolutionary Turmoil: Complex Diversification Patterns and Demographic Responses of a Halophilic Grass in a Central Asian Biodiversity Hotspot. Systematic Biology, 2023. https://doi.org/10.1093/sysbio/syad073

Authors: Anna Wrobel, Ewelina Klichowska, Arkadiusz Nowak, Marcin Nobis

Description: This README file describes the data accompanying the above mentioned publication.

###

GENERAL INFORMATION

  1. Title of dataset

    Supplementary Material associated with the article "Alpine Extremophytes in Evolutionary Turmoil: Complex Diversification Patterns and Demographic Responses of a Halophilic Grass in a Central Asian Biodiversity Hotspot"

  2. Author Information

    A. Principal Investigator Contact Information
    Name: Anna Wrobel
    ORCID: 0000-0002-6713-7867
    Institution: Institute of Botany, Faculty of Biology, Jagiellonian University
    Address: Gronostajowa 3, 30-387 Krakow, Poland
    Email: anna.wrobel@doctoral.uj.edu.pl
    ResearchGate profile: https://www.researchgate.net/profile/Anna-Wrobel-4

    B. Co-investigator Contact Information
    Name: Marcin Nobis
    ORCID: 0000-0002-1594-2418
    Institution: Institute of Botany, Faculty of Biology, Jagiellonian University
    Address: Gronostajowa 3, 30-387 Krakow, Poland
    Email: m.nobis@uj.edu.pl
    ResearchGate profile: https://www.researchgate.net/profile/Marcin-Nobis

  3. Date of material collection: 2016-2019

  4. Date of genetic data acquisition: 2020-12-10

  5. Geographic location of data collection: Pamir Mountains, Central Asia (Tajikistan)

  6. Examined species: Puccinellia pamirica (Roshev.) V.I.Krecz. ex Ovcz. & Czukav (Poaceae)

  7. Funding sources that supported the collection of the data

    A. Core funding
    Polish Ministry of Science and Higher Education via the Diamond Grant programme (grant number 0207/DIA/2018/47 to Anna Wrobel)

    B. Additional funding for the field research
    National Science Centre, Poland (grant number 2018/29/B/NZ9/00313 to Marcin Nobis)

###

SHARING/ACCESS INFORMATION

  1. Link to publication that uses the data

Wrobel A., Klichowska E., Nowak A., Nobis M. (2023). Alpine Extremophytes in Evolutionary Turmoil: Complex Diversification Patterns and Demographic Responses of a Halophilic Grass in a Central Asian Biodiversity Hotspot. Systematic Biology. https://doi.org/10.1093/sysbio/syad073

  1. Recommended citation for this dataset

Wrobel A., Klichowska E., Nowak A., Nobis M. (2023). Supplementary Material associated with the article "Alpine Extremophytes in Evolutionary Turmoil: Complex Diversification Patterns and Demographic Responses of a Halophilic Grass in a Central Asian Biodiversity Hotspot". Dryad Digital Repository. https://doi.org/10.5061/dryad.sn02v6x85

We would appreciate it if you could also cite the article associated with this dataset when appropriate.

###

DATA & FILE OVERVIEW

  1. File List

     1) Appendix_S1_Research_area_Model_System_Sampling.docx (Zenodo - Supplemental)
     2) Appendix_S2_DArT_SNP_marker_discovery.docx (Zenodo - Supplemental)
     3) Appendix_S3_PCoA_Population_Structure_Fixed_and_Private_alleles.docx (Zenodo - Supplemental)
     4) Appendix_S4_Treemix.docx (Zenodo - Supplemental)
     5) Appendix_S5_StairwayPlot_DIYABCRF.docx (Zenodo - Supplemental)
     6) Appendix_S6_Maxent_Ecological_Niche_Modelling.docx (Zenodo - Supplemental)
     7) Appendix_S7_Isolation_Factors_Linnear_Mixed_Effect_Models.docx (Zenodo - Supplemental)
     8) Puccinellia_pamirica_metafile_73_ind_15_pop.rdata
     9) Puccinellia_pamirica_metafile_80_ind_1_pop.rdata
     10) Puccinellia_pamirica_metafile_80_ind_2_pop.rdata
     11) Puccinellia_pamirica_metafile_80_ind_21_pop.rdata
     12) Puccinellia_pamirica_metafile_80_ind_80_pop.rdata
     13) Central_Asia_water_polygonst.cpg
     14) Central_Asia_water_polygonst.dbf
     15) Central_Asia_water_polygonst.prj
     16) Central_Asia_water_polygonst.sbn
     17) Central_Asia_water_polygonst.sbx
     18) Central_Asia_water_polygonst.shp
     19) Central_Asia_water_polygonst.shp.xml
     20) Central_Asia_water_polygonst.shx
     21) 1_DistWater_cur_30sec.asc
     22) 1_embergerQ_cur_30sec.asc
     23) 1_maxTempColdest_cur_30sec.asc
     24) 1_PETDriestQuarter_cur_30sec.asc
     25) 1_PETWettestQuarter_cur_30sec.asc
     26) 1_TopoWetness_cur_30sec.asc
     27) 1_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_30sec.asc
     28) 1_Pucpamirica_avg_WaterDist_TopoWetness_current_30sec.asc
     29) 2_DistWater_cur_2_5min.asc
     30) 2_embergerQ_cur_2_5min.asc
     31) 2_maxTempColdest_cur_2_5min.asc
     32) 2_PETDriestQuarter_cur_2_5min.asc
     33) 2_PETWettestQuarter_cur_2_5min.asc
     34) 2_TopoWetness_cur_2_5min.asc
     35) 2_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_2_5min.asc
     36) 3_embergerQ_cur_2_5min.asc
     37) 3_maxTempColdest_cur_2_5min.asc
     38) 3_PETDriestQuarter_cur_2_5min.asc
     39) 3_PETWettestQuarter_cur_2_5min.asc
     40) 3_embergerQ_LGM_2_5min.asc
     41) 3_maxTempColdest_LGM_2_5min.asc
     42) 3_PETDriestQuarter_LGM_2_5min.asc
     43) 3_PETWettestQuarter_LGM_2_5min.asc
     44) 3_Pucpamirica_avg_envirem4_current_2_5min.asc
     45) 3_Pucpamirica_avg_envirem4_LGM_2_5min.asc
     46) 4_bio10_cur_30sec.asc
     47) 4_bio16_cur_30sec.asc
     48) 4_bio18_cur_30sec.asc
     49) 4_TopoWetness_cur_30sec.asc
     50) 4_WaterDist_cur_30sec.asc
     51) 4_Pucpamirica_avg_worldclim3_WaterDist_TopoWetness_current_30sec.asc
     52) 5_bio10_cur_2_5min.asc
     53) 5_bio16_cur_2_5min.asc
     54) 5_bio18_cur_2_5min.asc
     55) 5_Pucpamirica_avg_worldclim3_current_2_5min.asc
     56) 6_bio10_future_ccsm_rcp4_5_30sec.asc
     57) 6_bio16_future_ccsm_rcp4_5_30sec.asc
     58) 6_bio18_future_ccsm_rcp4_5_30sec.asc
     59) 6_Pucpamirica_avg_worldclim3_TopoWetness_future_CCSM_RCP4_5.asc
     60) 7_bio10_future_ccsm_rcp8_5_30sec.asc
     61) 7_bio16_future_ccsm_rcp8_5_30sec.asc
     62) 7_bio18_future_ccsm_rcp8_5_30sec.asc
     63) 7_Pucpamirica_avg_worldclim3_future_CCSM_RCP8_5.asc
     64) 7_Pucpamirica_avg_worldclim3_TopoWetness_future_CCSM_RCP8_5.asc
     65) 8_bio10_future_miroc_rcp4_5_30sec.asc
     66) 8_bio16_future_miroc_rcp4_5_30sec.asc
     67) 8_bio18_future_miroc_rcp4_5_30sec.asc
     68) 8_Pucpamirica_avg_worldclim3_TopoWetness_future_MIROC_RCP4_5.asc
     69) 9_bio10_future_miroc_rcp8_5_30sec.asc
     70) 9_bio16_future_miroc_rcp8_5_30sec.asc
     71) 9_bio18_future_miroc_rcp8_5_30sec.asc
     72) 9_Pucpamirica_avg_worldclim3_future_MIROC_RCP8_5.asc
     73) 9_Pucpamirica_avg_worldclim3_TopoWetness_future_MIROC_RCP8_5.asc
     74) 10_bio10_future_mpi_rcp4_5_30sec.asc
     75) 10_bio16_future_mpi_rcp4_5_30sec.asc
     76) 10_bio18_future_mpi_rcp4_5_30sec.asc
     77) 10_Pucpamirica_avg_worldclim3_TopoWetness_future_MPI_RCP4_5.asc
     78) 11_bio10_future_mpi_rcp8_5_30sec.asc
     79) 11_bio16_future_mpi_rcp8_5_30sec.asc
     80) 11_bio18_future_mpi_rcp8_5_30sec.asc
     81) 11_Pucpamirica_avg_worldclim3_future_MPI_RCP8_5.asc
     82) 11_Pucpamirica_avg_worldclim3_TopoWetness_future_MPI_RCP8_5.asc
     83) PROJ_bio10_cur_30sec.asc
     84) PROJ_bio16_cur_30sec.asc
     85) PROJ_bio18_cur_30sec.asc
     86) PROJ_TopoWetness_cur_30sec.asc
     87) LME_models_input_dataset.csv
    
  2. Description of the data and file structure

    A. Supplementary appendices - these files uploaded to Zenodo as Supplemental Materials

     1) Appendix_S1_Research_area_Model_System_Sampling.docx
     2) Appendix_S2_DArT_SNP_marker_discovery.docx
     3) Appendix_S3_PCoA_Population_Structure_Fixed_and_Private_alleles.docx
     4) Appendix_S4_Treemix.docx
     5) Appendix_S5_StairwayPlot_DIYABCRF.docx
     6) Appendix_S6_Maxent_Ecological_Niche_Modelling.docx
     7) Appendix_S7_Isolation_Factors_Linnear_Mixed_Effect_Models.docx
    

    Description: These files include the additional information related to the accompanying article. All necessary details are presented within the appendices.

    Usage notes: Files can be viewed in Microsoft Word.

    B. Genetic datasets

     8) Puccinellia_pamirica_metafile_73_ind_15_pop.rdata
     9) Puccinellia_pamirica_metafile_80_ind_1_pop.rdata
     10) Puccinellia_pamirica_metafile_80_ind_2_pop.rdata
     11) Puccinellia_pamirica_metafile_80_ind_21_pop.rdata
     12) Puccinellia_pamirica_metafile_80_ind_80_pop.rdata
    

    Description: These files store genetic information on the single nucleotide polymorphism markers (SNPs) in Puccinellia pamirica. The datasets were generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism.

    Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle these files. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse these data files as objects of a class genlight (dartR) in the R environment. You may also handle these files as objects of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.

    C. Customised map of water bodies in the Pamir Mountains

     13) Central_Asia_water_polygonst.cpg - optional file
     14) Central_Asia_water_polygonst.dbf - mandatory file - attribute format
     15) Central_Asia_water_polygonst.prj - optional file
     16) Central_Asia_water_polygonst.sbn - optional file
     17) Central_Asia_water_polygonst.sbx - optional file
     18) Central_Asia_water_polygonst.shp - mandatory file (main file) - shape format
     19) Central_Asia_water_polygonst.shp.xml - optional file
     20) Central_Asia_water_polygonst.shx - mandatory file - shape index format
    

    Description: These files represent a customised map of water bodies and wetlands in the Pamir Mountains. We used OpenStreetMap waterway polygon layer of the studied area [https://www.openstreetmap.org; accessed via https://data.humdata.org/dataset/ with a keyword “Waterways” and country names] to assemble a layer of water bodies and wetlands in the studied area. We used files: hotosm_tjk_waterways_polygons_shp.zip (Tajikistan), hotosm_kgz_waterways_polygons_shp.zip (Kyrgyzstan), hotosm_kaz_waterways_polygons_shp.zip (Kazakhstan), hotosm_chn_west1_waterways_polygons_shp.zip (north-western China), hotosm_tkm_waterways_polygons_shp.zip (Turkmenistan), hotosm_uzb_waterways_polygons_shp.zip (Uzbekistan), hotosm_afg_waterways_polygons_shp.zip (Afghanistan), and hotosm_pak_waterways_polygons_shp.zip (Pakistan). We created the dataset in Esri ArcMap 10.8. More information about how we used this dataset in our analyses can be found in Appendix_S6_Maxent_Ecological_Niche_Modelling.docx.

    Usage notes: This dataset is in Esri shapefile format for geographic information system (GIS) software. Shapefile is composed of mandatory files (extension: SHP, SHX and DBF) and optional files (extension: PRJ, XML, SBN, CPG and SBX). At least mandatory files should be downloaded and placed in one folder to access data stored in the main file (extension: SHP). The shapefile may be opened by means of the Esri software (ArcGIS, ArcMap) or may also be imported and visualised in the R environment. For example, we used the rgdal R-package (version 1.6-2) with the sp R-package (version 1.5-1) to visualise data using commands:
    shp<-rgdal::readOGR("D:/Path_to_folder/Central_Asia_water_polygonst.shp")
    plot(shp)

    D. Ecological niche modelling - Environmental leyers and model outputs

     Files 21-86 with .asc extension
    

    Description: The input raster layers and results (outputs) of the ecological niche (species distribution/habitat suitability) modelling under current, Last Glacial Maximum ~20-25 ka or future (~2070) conditions. We performed niche modelling using MaxEnt (version 3.4.4). See Appendix_S6_Maxent_Ecological_Niche_Modelling.docx to learn more about variables, variable selection procedure, model settings and model interpretation.

    Usage notes: The files are in the ASCII format (extension: .asc) that could be handled by the Esri software or in the R environment. For example, we used the raster R-package (version 3.6-11) with the sp R-package (version 1.5-1) to visualise data using commands:
    data<-raster("File_name.asc")
    plot(data)

    E. Input dataset to Linear Mixed-Effects models (Landscape Genetics)

     87) LME_models_input_dataset.csv
    
     Description: This is the input file including all the variables used for the Linear Mixed-Effects models. See Supplementary Appendix S8 for more details regarding LME models.
    
     Usage notes: The dataset is a comma separated file (.csv) and could be viewed e.g. in Microsoft Excel or in Notepad++. We performed Linear Mixed-Effects models using the lme4 (version 1.1-33), lmerTest (version 3.1-3), MuMIn (version 1.47.5) and partR2 (version 0.9.1.9000) R-packages.
    
    

#########################################################################

DATA-SPECIFIC INFORMATION FOR: Genetic Datasets

  1. General information:
    Total number of individuals: 80
    Total number of populations: 21
    Number of populations with at least 3 individuals sampled: 15
    Number of individuals in populations with at least 3 individuals sampled: 73

  2. Individual names:
    pamP030
    pam1400_4
    pam1406_6
    pamP061
    pamA121
    pamA156
    pamA185
    pamA219
    pamA243
    pamP031
    pam1400_5
    pam1404_2
    pam1406_7
    pamA123
    pamA157
    pamA187
    pamA220
    pamA244
    pam1399_2
    pam1400_8
    pam1404_5
    pam1411_3
    pamA074
    pamA125
    pamA158
    pamA188
    pamA221
    pamA061
    pam1403_2
    pam1404_6
    pam1411_4
    pamA126
    pamA161
    pamA190
    pamA249
    pam1395_2
    pam1399_6
    pamP053
    pam1403_4
    pam1405_5
    pamA064
    pamA127
    pamA162
    pamA186
    pamA235
    pam1395_3
    pam1399_7
    pamP054
    pam1403_5
    pam1406_3
    pamA065
    pamA067
    pamA105
    pamA134
    pamA163
    pamA194.1
    pamA237
    pam1395_4
    pam1399_8
    pamP055
    pam1403_6
    pam1406_4
    pamA068
    pamA116
    pamA142
    pamA180
    pamA239
    pam1395_5
    pam1400_1
    pam1403_7
    pam1406_5
    pamP056
    pamA154
    pamA183
    pamA208
    pamA242
    pam1395_7
    pam1400_2
    pam1404_3
    pam1411_8

  3. Population names:
    Akbaital_valley
    Alichur
    Alichur_1
    Bulunkul
    Chukurkul
    Chukurkul_1
    Karabumier
    Karabumier_1
    Karakul
    Karakul_1
    KGZ_TJK
    Khargush
    Langar
    Markansu
    Murghab
    Pamir_River
    Pamir_River_1
    Rangkul
    Sasykkul
    Sasykkul_1
    Shorkul

  4. Notes: More information on particular individuals can be found in Appendix_S1_Research_area_Model_System_Sampling.docx.

  5. Raw Metafile List:
    A) Puccinellia_pamirica_metafile_73_ind_15_pop.rdata
    B) Puccinellia_pamirica_metafile_80_ind_1_pop.rdata
    C) Puccinellia_pamirica_metafile_80_ind_21_pop.rdata
    D) Puccinellia_pamirica_metafile_80_ind_80_pop.rdata
    E) Puccinellia_pamirica_metafile_80_ind_2_pop.rdata

  6. DATA-SPECIFIC INFORMATION FOR: Puccinellia_pamirica_metafile_73_ind_15_pop.rdata
    Number of individuals: 73
    Number of populations: 15
    Number of loci: 156066
    Comment: This file includes 15 populations that have at least 3 individuals sampled. Population coding - in the slot gl_pam_73@pop you will find names of 15 localities where individuals were collected.

  7. DATA-SPECIFIC INFORMATION FOR: Puccinellia_pamirica_metafile_80_ind_1_pop.rdata
    Number of individuals: 80
    Number of populations: 1
    Number of loci: 156305
    Comment: This file includes all 80 individuals sampled. Population coding - in the slot gl_pam_80@pop you will find a name "pam" (acronym of Puccinellia pamirica) assigned to all individuals.

  8. DATA-SPECIFIC INFORMATION FOR: Puccinellia_pamirica_metafile_80_ind_21_pop.rdata
    Number of individuals: 80
    Number of populations: 21
    Number of loci: 156305
    Comment: This file includes all 80 individuals sampled. Population coding - in the slot gl_pam_80pop@pop you will find names of 21 localities where individuals were collected.

  9. DATA-SPECIFIC INFORMATION FOR: Puccinellia_pamirica_metafile_80_ind_80_pop.rdata
    Number of individuals: 80
    Number of populations: 80
    Number of loci: 156305
    Comment: This file includes all 80 individuals sampled. Population coding - in the slot gl_pam_80ind@pop each individual was coded as a separate population using an individual name.

  10. DATA-SPECIFIC INFORMATION FOR: Puccinellia_pamirica_metafile_80_ind_2_pop.rdata
    Number of individuals: 80
    Number of populations: 2
    Number of loci: 156305
    Comment: This file includes all 80 individuals sampled. Population coding - in the slot gl_pam_80pop2@pop each individual was assigned to two main geographic and genetic groups: southern (labelled as "south") and northern (labelled as "north").

#########################################################################

DATA-SPECIFIC INFORMATION FOR: Ecological niche modelling - Environmental leyers and model outputs

  1. Data structure

     1)raster layers for the model of current habitat suitability in 30 arcseconds raster resolution:
            CURRENT raster layers:
                1_DistWater_cur_30sec.asc
                1_embergerQ_cur_30sec.asc
                1_maxTempColdest_cur_30sec.asc
                1_PETDriestQuarter_cur_30sec.asc
                1_PETWettestQuarter_cur_30sec.asc
                1_TopoWetness_cur_30sec.asc
    
            OUTPUTS derived from these layers:
                1_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_30sec.asc
                1_Pucpamirica_avg_WaterDist_TopoWetness_current_30sec.asc
    
     2)raster layers for the model of current habitat suitability in 2.5 arcminutes raster resolution:
            CURRENT raster layers:
                2_DistWater_cur_2_5min.asc
                2_embergerQ_cur_2_5min.asc
                2_maxTempColdest_cur_2_5min.asc
                2_PETDriestQuarter_cur_2_5min.asc
                2_PETWettestQuarter_cur_2_5min.as
                2_TopoWetness_cur_2_5min.asc
    
            OUTPUT derived from these layers:
                2_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_2_5min.asc
    
     3)raster layers for the LGM projection using only bioclimatic variables in 2.5 arcminutes raster resolution:
            CURRENT raster layers:
                3_embergerQ_cur_2_5min.asc
                3_maxTempColdest_cur_2_5min.asc
                3_PETDriestQuarter_cur_2_5min.asc
                3_PETWettestQuarter_cur_2_5min.asc
            LGM raster layers:
                3_embergerQ_LGM_2_5min.asc
                3_maxTempColdest_LGM_2_5min.asc
                3_PETDriestQuarter_LGM_2_5min.asc
                3_PETWettestQuarter_LGM_2_5min.asc
    
            OUTPUTS derived from these layers:
                3_Pucpamirica_avg_envirem4_LGM_2_5min.asc
                3_Pucpamirica_avg_envirem4_current_2_5min.asc
    
     4)raster layers for the model of current habitat suitability in 30 arcseconds raster resolution:   
            CURRENT raster layers:
                4_bio10_cur_30sec.asc
                4_bio16_cur_30sec.asc
                4_bio18_cur_30sec.asc
                4_TopoWetness_cur_30sec.asc
                4_WaterDist_cur_30sec.asc
    
            OUTPUT derived from these layers:
                4_Pucpamirica_avg_worldclim3_WaterDist_TopoWetness_current_30sec.asc
    
     5)raster layers for the model of current habitat suitability using only bioclimatic variables in 2.5 arcminutes raster resolution:     
            CURRENT raster layers:
                5_bio10_cur_2_5min.asc
                5_bio16_cur_2_5min.asc
                5_bio18_cur_2_5min.asc
    
            OUTPUT derived from these layers:
                5_Pucpamirica_avg_worldclim3_current_2_5min.asc
    
     6)raster layers for the model of future niche projection based on CCSM4 atmospheric circulation model under RCP 4.5 scenario in 30 arcseconds raster resolution:       
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                6_bio10_future_ccsm_rcp4_5_30sec.asc
                6_bio16_future_ccsm_rcp4_5_30sec.asc
                6_bio18_future_ccsm_rcp4_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc              
    
            OUTPUT derived from these layers:
                6_Pucpamirica_avg_worldclim3_TopoWetness_future_CCSM_RCP4_5.asc
    
     7)raster layers for the model of future niche projection based on CCSM4 atmospheric circulation model under RCP 8.5 scenario in 30 arcseconds raster resolution:       
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                7_bio10_future_ccsm_rcp8_5_30sec.asc
                7_bio16_future_ccsm_rcp8_5_30sec.asc
                7_bio18_future_ccsm_rcp8_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            OUTPUTS derived from these layers:
                7_Pucpamirica_avg_worldclim3_future_CCSM_RCP8_5.asc
                7_Pucpamirica_avg_worldclim3_TopoWetness_future_CCSM_RCP8_5.asc
    
     8)raster layers for the model of future niche projection based on MIROC atmospheric circulation model under RCP 4.5 scenario in 30 arcseconds raster resolution:       
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                8_bio10_future_miroc_rcp4_5_30sec.asc
                8_bio16_future_miroc_rcp4_5_30sec.asc
                8_bio18_future_miroc_rcp4_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            OUTPUT derived from these layers:
                8_Pucpamirica_avg_worldclim3_TopoWetness_future_MIROC_RCP4_5.asc                
    
     9)raster layers for the model of future niche projection based on MIROC atmospheric circulation model under RCP 8.5 scenario in 30 arcseconds raster resolution:       
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                9_bio10_future_miroc_rcp8_5_30sec.asc
                9_bio16_future_miroc_rcp8_5_30sec.asc
                9_bio18_future_miroc_rcp8_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            OUTPUTS derived from these layers:                  
                9_Pucpamirica_avg_worldclim3_future_MIROC_RCP8_5.asc
                9_Pucpamirica_avg_worldclim3_TopoWetness_future_MIROC_RCP8_5.asc
    
     10)raster layers for the model of future niche projection based on MPI atmospheric circulation model under RCP 4.5 scenario in 30 arcseconds raster resolution:        
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                10_bio10_future_mpi_rcp4_5_30sec.asc
                10_bio16_future_mpi_rcp4_5_30sec.asc
                10_bio18_future_mpi_rcp4_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc              
    
            OUTPUT derived from these layers:                       
                10_Pucpamirica_avg_worldclim3_TopoWetness_future_MPI_RCP4_5.asc
    
     11)raster layers for the model of future niche projection based on MPI atmospheric circulation model under RCP 8.5 scenario in 30 arcseconds raster resolution:        
            CURRENT raster layers:
                PROJ_bio10_cur_30sec.asc
                PROJ_bio16_cur_30sec.asc
                PROJ_bio18_cur_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc
    
            FUTURE raster layers:
                11_bio10_future_mpi_rcp8_5_30sec.asc
                11_bio16_future_mpi_rcp8_5_30sec.asc
                11_bio18_future_mpi_rcp8_5_30sec.asc
                PROJ_TopoWetness_cur_30sec.asc              
    
            OUTPUTS derived from these layers:              
                11_Pucpamirica_avg_worldclim3_future_MPI_RCP8_5.asc
                11_Pucpamirica_avg_worldclim3_TopoWetness_future_MPI_RCP8_5.asc
    
    
  2. Abbreviations

     WaterDist - raster Euclidean distance to water calculated in the ARCGIS 10.8 based on the selected water bodies from the customised OpenStreetMap waterway polygon layer of the studied area [https://www.openstreetmap.org; accessed via https://data.humdata.org/dataset/ with a keyword 'Waterways' and country names]
    
     TopoWetness - Topographic Wetness Index from the ENVIREM database
    
     envirem4 - 4 most informative bioclimatic variables from the ENVIREM database: 
        embergerQ - Emberger's pluviothermic quotient
        PETDriestQuarter - mean monthly potential evapotranspiration of driest quarter
        maxTempColdestMonth - maximum temperature of the coldest month
        PETWettestQuarter - mean monthly potential evapotranspiration of wettest quarter
    
     worldclim3 - 3 most informative bioclimatic variables from the WorldClim 2.1 database:
        bio10 - Mean Temperature of Warmest Quarter
        bio16 - Precipitation of Wettest Quarter
        bio18 - Precipitation of Warmest Quarter
    
     Pucpamirica_avg - the average ecological niche model for wetland-dependent halophilic grass Puccinellia pamirica on the Pamir Plateau and its adjacent areas
    
     current / cur - current conditions
     LGM - Last Glacial Maximum conditions estimated at ~20-25 ka
     future - projection to 2070
    
     30sec - 30 arcseconds raster resolution
     2_5min - 2.5 arcminutes raster resolution
    
     .asc - ASCII format file extension
    
     PROJ_bio10_cur_30s.asc - this layer is compatible with all bio10 future projection layers; values in the original current bio10 layer were multiplied by 10 to ensure the same format as in the future projection layers, in which temperature values are multiplied by 10 
    
     PROJ_TopoWetness_current_30sec.asc - this layer was also used as a future projection layer under the assumption that the Topographic Wetness Index (general patterns of land relief) would not change much over XIX century 
    
  3. Notes

Past projection to LGM was conducted using the most informative ENVIREM (http://envirem.github.io/#varTable) climatic variables in 2.5 arcminutes resolution based on the climate simulation of the Model for Interdisciplinary Research on Climate version 3.2 (MIROC; https://ccsr.aori.u-tokyo.ac.jp/~hasumi/miroc_description.pdf).

For future projections we used bioclimatic variables projected under three different atmospheric circulation models (MIROC-ESM, CCSM4 MPI-ESM) within Coupled Model Intercomparison Project Phase 5 (CMIP5). The raster layers were derived from the WORLDCLIM repository in 30 arcseconds resolution [http://www.worldclim.com/cmip5_30s] for 2070 (average for 2061-2080) under representative concentration pathways (RCPs) of 4.5 (intermediate CO2 emission scenario) and 8.5 (rising CO2 emission scenario).

For more details see Appendix_S6_Maxent_Ecological_Niche_Modelling.docx.

#########################################################################

DATA-SPECIFIC INFORMATION FOR: Input dataset to Linear Mixed-Effects models (Landscape Genetics)

  1. File list:
    LME_models_input_dataset.csv

  2. Column headers (Variables):

     INDIVIDUAL_1 - individual of Puccinellia pamirica
     INDIVIDUAL_2 - individual of Puccinellia pamirica
     DSNP_JAC - pairwise Jaccard genetic distance between individuals
     DSNP_NEI - pairwise Nei genetic distance between individuals
     RHAB_worldclim30 - pairwise habitat-related resistance between localities modelled in CIRCUITSCAPE based on the MaxEnt output 4_Pucpamirica_avg_worldclim3_WaterDist_TopoWetness_current_30sec.asc
     RHAB_env25 - pairwise habitat-related resistance between localities modelled in CIRCUITSCAPE based on the MaxEnt output 2_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_2_5min.asc
     RHAB_env30 - pairwise habitat-related resistance between localities modelled in CIRCUITSCAPE based on the MaxEnt output 1_Pucpamirica_avg_envirem4_WaterDist_TopoWetness_current_30sec.asc
     DGEO - pairwise straight-line geographic distance between localities
     RGEO - pairwise geographic distance as a resistance factor between localities
     DENV - pairwise distance representing environmental heterogeneity between localities
     POP1_FACTOR - number of population where INDIVIDUAL_1 was sampled 
     POP2_FACTOR - number of population where INDIVIDUAL_2 was sampled 
     PAIRWISE_LOCALITY_FACTOR - each level of this factor represent comparisons between all individuals from a given pair of populations
    
    
  3. Notes

For more details see Appendix_S7_Isolation_Factors_Linnear_Mixed_Effect_Models.docx.

#########################################################################

Funding

Ministry of Science and Higher Education, Award: 0207/DIA/2018/47, Diamond Grant

National Science Center, Award: 2018/29/B/NZ9/00313, Grant OPUS