Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species’ geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes. We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype-environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RA..., Raw sequence data is available at the European Nucleotide Archive (ENA): Myotis escalerai and M. crypticus (PRJEB29086), and the NCBI Short Read Archive (SRA): Afrixalus fornasini – (SRP150605). Input data (processed genomic data and spatial-environmental data prior to running the toolbox) available as part of this repository. Methods: see methods text of manuscript and tutorials: Setup and running the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette, Full tutorials for setup and running the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette This software is intended for HPC use. Please make sure the software below is installed and functional in your HPC environment before proceeding:

Life on the edge data and scripts (also available here: https://github.com/cd-barratt/Life_on_the_edge)

Singularity (3.5) and bioconductor container with correct R version:Â https://cloud.sylabs.io/library/sinwood/bioconductor/bioconductor_3.14

R (4.1.3). Dependencies for toolbox installed within R version in singularity container upon setup (you specify your R libraries in the script where annotated) Julia (1.7.2)

Additionally you need to download the following and place in the correct directories to be sure the toolbox will function properly: * Environmental predictor data - please download and place environmental layers used for SDMs, GEAs etc in separate folders for current and future environmental conditions. These f..., # Life on the edge: a new toolbox for population-level climate change vulnerability assessments

Dataset contains input files needed to run Life on the edge for an example dataset (Afrixalus fornasini) You may run data for your focal species following the structure and content of the example files provided

First you need to download the following and place in the correct directories to be sure the toolbox will function properly:

• Environmental predictor data (e.g. Worldclim2/CHELSA, land cover, see below)
• A working plink and maxent version (see below)
• Country border data (e.g. Natural Earth data, see below)

Tutorials for initial setup and running the toolbox

Full setup and how to run the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette

Description of the data and file structure

• Params.tsv is a tab separated file that contains all parameters for running each species ...

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82449. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=115 Webpage with information and links to data files for download

These are gridded yield shocks based on the meta-analysis described in Moore et al (in press) derived from a re-analysis of the database of yield studies reported in Challinor et al (2015).Files are provided for maize, rice, soy and wheat. Grids are R rasters (requires the installation of the raster library in R) compiled into a list and are provided for the main effect, and upper and lower bounds of the 95% confidence intervals.Each file contains a list of length 3 named "yieldchange_errorbars" where each element of the list is also a list of length 3. Data are organized first by quantile and then by temperature change. For example yieldchange_errorbars[[1]][[3]] would be the lower bound (2.5th quantile) of the yield changes as three degrees of warming. yieldchange_errorbars[[2]][[1]] would be the central estimate (50th quantile) of yield changes at one degree of warming.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found:http://hdl.handle.net/10217/82146. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=105 Webpage with information and links to data files for download

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82446. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=174 Webpage with information and links to data files for download

DNA sequences offer powerful tools for describing the members and interactions of natural communities. In this study, we establish the to-date most comprehensive library of DNA barcodes for a terrestrial site, including all known macroscopic animals and vascular plants of an intensively studied area of the High Arctic, the Zackenberg Valley in Northeast Greenland. To demonstrate its utility, we apply the library to identify nearly 20 000 arthropod individuals from two Malaise traps, each operated for two summers. Drawing on this material, we estimate the coverage of previous morphology-based species inventories, derive a snapshot of faunal turnover in space and time and describe the abundance and phenology of species in the rapidly changing arctic environment. Overall, 403 terrestrial animal and 160 vascular plant species were recorded by morphology-based techniques. DNA barcodes (CO1) offered high resolution in discriminating among the local animal taxa, with 92% of morphologically distinguishable taxa assigned to unique Barcode Index Numbers (BINs) and 93% to monophyletic clusters. For vascular plants, resolution was lower, with 54% of species forming monophyletic clusters based on barcode regions rbcLa and ITS2. Malaise catches revealed 122 BINs not detected by previous sampling and DNA barcoding. The insect community was dominated by a few highly abundant taxa. Even closely related taxa differed in phenology, emphasizing the need for species-level resolution when describing ongoing shifts in arctic communities and ecosystems. The DNA barcode library now established for Zackenberg offers new scope for such explorations, and for the detailed dissection of interspecific interactions throughout the community. DS-ZACKANIM intrasp divergence and distance to NN raw dataRaw data on the intraspecific divergence and distance to nearest neighbour for the dataset DS-ZACKANIM (dx.doi.org/10.5883/DS-ZACKANIM) and the DNA barcode region CO1DS-ZACKPLAN ITS2 intrasp divergence and distance to NN raw dataRaw data on intraspecific divergence and distance to nearest neighbour for the dataset DS-ZACKPLAN (dx.doi.org/10.5883/DS-ZACKPLAN) and the DNA barcode region ITS2DS-ZACKPLAN rbcLa intrasp divergence and distance to NN raw dataRaw data on intraspecific divergence and distance to nearest neighbour for the dataset DS-ZACKPLAN (dx.doi.org/10.5883/DS-ZACKPLAN) and the DNA barcode region rbcLa

Data for Figure TS.17 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure TS.17 shows an overview of physical and biogeochemical feedbacks in the climate system.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.

Figure subpanels

The figure has three panels with data provided for all panels in one single directory

List of data provided

This dataset contains:

• Synthesis of physical, biogeophysical and non-carbon dioxide (CO2) biogeochemical feedbacks
• Estimated values of individual biogeophysical and non-CO2 biogeochemical feedbacks.
• Carbon-cycle feedbacks as simulated by models participating in the C4MIP of the Coupled Model Intercomparison Project Phase 6 (CMIP6).

Data provided in relation to figure

Panel a: - Data file: data_panel_a.csv: first column feedback name, second column bar length, third column minimum of the very likely range, fourth column maximum of the very likely range.

Panel b: - Data file: data_panel_b.csv: first column feedback name, second column bar length, third column minimum of the 5-95% range, fourth column maximum of the 5-95% range.

Panel c: - Data file: data_panel_c.csv: first column feedback name, second column bar length, third column minimum of the 5-95% range, fourth column maximum of the 5-95% range.

Notes on reproducing the figure from the provided data

Script to reproduce the figure provided in this dataset (Plotting_Script.txt). R library extra font only required to set font type to Arial Narrow. Code executes without this library, but positioning of labels may be off.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Technical Summary) - Link to the code for the figure, archived on Zenodo.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83446. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=122 Webpage with information and links to data files for download

This app is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsDroughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different temporal periods (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

This data set was acquired with a Sippican MK21 Expendable BathyThermograph during R/V Laurence M. Gould expedition LMG1502 conducted in 2015 (Chief Scientist: Dr. Richard Aronson, Investigator: Dr. Richard Aronson). These data files are of Sippican MK21 Export Data File format and include Temperature data that have not been processed. Data were acquired as part of the project(s): Collaborative Research: Climate Change and Predatory Invasion of the Antarctic Benthos. Funding was provided by NSF award(s): ANT11-41877.

Reference journal article:

Cos, P., Marcos-Matamoros, R., Donat, M., Mahmood, R., & Doblas-Reyes, F. J. (2024). Near-Term Mediterranean Summer Temperature Climate Projections: A Comparison of Constraining Methods. Journal of Climate, 37(17), 4367-4388. https://doi.org/10.1175/JCLI-D-23-0494.1

Example of code to read the YAML files:

#######################################
################## R ##################
#######################################

# install.packages("yaml")
library(yaml)
file <- 'RankedMembers_accumyears9_constraintregionGlobal_ensemble209.yaml'
member_list <- yaml.load_file(file)
print(member_list[['s1970']][1:30]) # to get the best 30 members for the start date 1970

#######################################
################ Python ###############
#######################################

# pip install pyyaml
import yaml
file = 'RankedMembers_accumyears9_constraintregionGlobal_ensemble209.yaml'
with open(file, "r") as f:
 member_list = yaml.safe_load(f)
print(member_list['s1970'][0:30]) # to get the best 30 members for the start date 1970

Data files and R code to replicate the econometric analysis in the journal article: B Chen, BM Gramig and SD Yun. “Conservation Tillage Mitigates Drought Induced Soybean Yield Losses in the US Corn Belt.” Q Open. https://doi.org/10.1093/qopen/qoab007

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

File List The zipped folder STI_package.zip -- contains the STI R-language package: STI_1.0_source.tar.gz -- Folder containing the source code of the package STI_1.0_win.zip -- package compiled for Windows STI_1.0_mac.tar.gz -- package compiled for Mac OS X Description The R-language STI package performs two-way ANOVA to test space–time interaction in the absence of replication. This Supplement contains the package STI (source code), an R-language library for the analysis of the main factors space and time, and the interaction, in space–time studies, using permutation tests. The functions correspond to the six models presented in Fig. 1 of the paper. The downloadable files comprise the source code as well as packages compiled for Windows and Mac OS X.

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

This dataset integrates remotely sensed estimates of sagebrush (Artemisia spp.) cover from the RCMAP product (https://www.mrlc.gov/data-services-page), areas that received post-fire seeding, identified using the Land Treatment Digital Library (LTDL; https://ltdl.wr.usgs.gov/), and GridMet surface meteorological data (https://www.climatologylab.org/gridmet.html) to describe the impacts of weather on sagebrush recovery following restoration treatments. We identified observations from the LTDL in which at least one Artemisia species had been seeded following fire, within the extent covered by the RCMAP (NLCD back-in-time sagebrush cover) that burned between 1980 and 2005 and that were subsequently seeded. We then removed all areas that burned or were seeded multiple times between 1980 and 2015. We then selected all RCMAP pixels that overlapped these burned, seeded areas and extracted sagebrush cover for all years of the record for each pixel. Data was processed in chunks, due to the large number of pixels included in analysis. In order to reduce the data dimensions and redundancy, we next clustered the pixel data using the algorithm spatially contiguous multivariate clustering in ArcGIS. The number of clusters was set at 1/1000 of the initial number of pixels and the spatial constraint was set to contiguity edges only. The analysis fields (data attributes upon which the algorithm was run to decide on cluster membership) were elevation, TWI, heatload, Level 3 ecoregion (coded as a dummy variable), and slope. If the algorithm failed with the initial number of clusters, the number of clusters was increased by 10% until the algorithm would run. We did allow for spatial non-contiguous clusters in the case that the algorithm was not solvable with contiguous clusters only. Post-processing of the clusters included checking to make sure the relative standard error for elevation was less than 20% within a cluster and to screen for multiple fires being combined into one cluster. If multiple fires were combined into a cluster initially, they were separated into different clusters. We also assessed whether dividing the data into chunks significantly influenced the clustering process. Comparisons of the data chunks suggested that each chunk had a similar distribution of relative standard deviations for elevation, slope, heatload, and TWI among the clusters contained within it. In R, using the extract function in the raster package (Hijmans & van Etten, 2012), we extracted sagebrush cover for each year following fire and the following GridMet variables using the centerpoint of each RCMAP pixel as the point to extract to: daily precipitation, minimum temperature, and maximum temperature for the February-April in the first four years after fire, 30-year climate means, and monthly SPEI for the two years before and the four years after fire (calculated from the SPEI package in R). We extracted additional covariates for each pixel, which included elevation, TWI, heatload, Level 3 ecoregion, and slope. We then described the mean characteristics of each cluster for each of these variables. For each climate variable, we calculated each year's deviation (mean - year's observation) from the long-term (30 year) mean. This process resulted in the dataset entitled “longtermsage.csv”. For the autoregressive model (Question 3 in associated manuscript), we formatted the data to allow for statistical modeling of annual changes in sagebrush cover, in a second dataset entitled “growthannualsage.csv”. Specific variable names are described in the README file. Interannual variation, especially weather, is an often-cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. In the analysis associated with this dataset, we examined post-fire treatments within sagebrush-steppe ecosystems to ask: 1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and 2) Does the management-relevance of weather differ across space and with time since treatment? This dataset integrates remotely sensed estimates of sagebrush (Artemisia spp.) cover from the RCMAP product (https://www.mrlc.gov/data-services-page), areas that received post-fire seeding, identified using the Land Treatment Digital Library (https://ltdl.wr.usgs.gov/), and GridMet surface meteorological data (https://www.climatologylab.org/gridmet.html) to describe the impacts of weather on sagebrush recovery following restoration treatments. All analyses associated with this dataset can be found at: https://github.com/absimler/restoration-weather Please see ReadME file for more information about variable names and sources.

This data set was used to assess the climate resilience of Themeda triandra, a foundational species and the most widespread plant in Australia, by assessing the relative contributions of spatial, environmental, and ploidy factors to contemporary genomic variation. Reduced-representation genome sequencing on 472 samples from 52 locations was used to test how the distribution of genomic variation, including ploidy polymorphism, supports adaptation to hotter and drier climates. For reduced-representation library preparation and sequencing, genomic DNA from each individual was isolated from approximately 25 mg of silica-dried leaf tissue using the Stratec Invisorb DNA Plant HTS 96 kit (Invitek, Berlin, Germany). Libraries for each individual were created similarly to Ahrens et al. (2017). Briefly, extracted DNA was digested with PstI for genome complexity reduction, and ligated with a uniquely barcoded sequencing adapter pair. We then amplified each sample individually by PCR. Amplicons between 350 and 600 bp were selected from an agarose gel. The final library pool was sequenced on three Illumina NextSeq400 lanes using a 75 bp paired-end protocol on a high output flowcell at the Biomolecular Resources Facility at the Australian National University, generating approximately 864 million read pairs.

We checked the quality of the raw short-read sequencing reads with FastQC v0.10.1 (Andrews, 2010), then demultiplexed the raw reads associated with each sample’s unique combinatorial barcode using AXE v0.2.6 (Murray & Borevitz, 2018). During this step we were unable to assign 19% of the reads. Each sequence was trimmed to 64 basepairs while removing the barcodes. Read quality was assessed with trimmomatic v 0.38 (Bolger, Lohse, & Usadel, 2014) using a sliding window of 4 basepairs (the number of bases used to average quality) and a quality score of 15 (the average quality required among the sliding window), and if the average quality dropped below 15, the sequences were cut. Long-reads were indexed (Figure S2 for distribution of length and number of reads sequenced) using the BWA software and the index argument. Short-reads were aligned to the long-reads for more accurate SNP calling compared to a de novo pipeline. Short-reads were aligned using BWA-mem v 0.7.17-r1198 (Li, 2013), as paired reads, with 82.5% of reads successfully mapped. Samtools v 1.9 (Li et al., 2009) was used to transform the SAM files to BAM files for use within STACKS v 2.41 (Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). The argument gstacks and populations were used in that order on the BAM files to create a VCF file, minimum thresholds (minor allele frequency = 0.01; one random SNP per read was retained) were set here for further filtering in R (R core development team 2019).

The minimum missing data threshold was set to 50% per locus and individual which resulted in an average of 30% missing data from the whole SNP dataframe. Minor allele frequency was set to 0.05 to avoid identifying patterns of population structure that may be due to locally shared alleles.

Usage notes:

lfmm file for snmf analysis - 012 file represents count of minor allele. missing values = 9 |||| the individual key file is: themea_52_012v2_indkey.key

genpop file provided for input as genind object. missing values = 0000