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.
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GENERAL INFORMATION
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"
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-4B. 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-NobisDate of material collection: 2016-2019
Date of genetic data acquisition: 2020-12-10
Geographic location of data collection: Pamir Mountains, Central Asia (Tajikistan)
Examined species: Puccinellia pamirica (Roshev.) V.I.Krecz. ex Ovcz. & Czukav (Poaceae)
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)
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SHARING/ACCESS INFORMATION
- 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
- 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.
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DATA & FILE OVERVIEW
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.csvDescription 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.docxDescription: 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.rdataDescription: 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 formatDescription: 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 extensionDescription: 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.
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DATA-SPECIFIC INFORMATION FOR: Genetic Datasets
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: 73Individual 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_8Population 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
ShorkulNotes: More information on particular individuals can be found in Appendix_S1_Research_area_Model_System_Sampling.docx.
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.rdataDATA-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.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.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.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.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").
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DATA-SPECIFIC INFORMATION FOR: Ecological niche modelling - Environmental leyers and model outputs
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.ascAbbreviations
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 centuryNotes
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.
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DATA-SPECIFIC INFORMATION FOR: Input dataset to Linear Mixed-Effects models (Landscape Genetics)
File list:
LME_models_input_dataset.csvColumn 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 populationsNotes
For more details see Appendix_S7_Isolation_Factors_Linnear_Mixed_Effect_Models.docx.
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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