Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).

This dataset shows the results of mapping the connectivity of key values (natural heritage, indigenous heritage, social and historic and economic) of the Great Barrier Reef with its neighbouring regions (Torres Strait, Coral Sea and Great Sandy Strait). The purpose of this mapping process was to identify values that need joint management across multiple regions. It contains a spreadsheet containing the connection information obtained from expert elicitation, all maps derived from this information and all GIS files needed to recreate these maps. This dataset contains the connection strength for 59 attributes of the values between 7 regions (GBR Far Northern, GBR Cairns-Cooktown, GBR Whitsunday-Townsville, GBR Mackay-Capricorn, Torres Strait, Coral Sea and Great Sandy Strait) based on expert opinion. Each connection is assessed based on its strength, mechanism and confidence. Where a connection was known to not exist between two regions then this was also explicitly recorded. A video tutorial on this dataset and its maps is available from https://vimeo.com/335053846.

Methods:

The information for the connectivity maps was gathered from experts (~30) during a 3-day workshop in August 2017. Experts were provided with a template containing a map of Queensland and the neighbouring seas, with an overlay of the regions of interest to assess the connectivity. These were Torres Strait, GBR:Far North Queensland, GBR:Cairns to Cooktown, GBC: Townsville to Whitsundays, GBR: Mackay to Capricorn Bunkers and Great Sandy Strait (which includes Hervey bay). A range of reference maps showing locations of the values were provided, where this information could be obtained. As well as the map the template provided 7x7 table for filling in the connectivity strength and connection type between all combinations of these regions. The experts self-organised into groups to discuss and complete the template for each attribute to be mapped. Each expert was asked to estimate the strength of connection between each region as well as the connection mechanism and their confidence in the information. Due to the limited workshop time the experts were asked to focus on initially recording the connections between the GBR and its neighbouring regions and not to worry about the internal connections in the GBR, or long-distance connections along the Queensland coast. In the second half of the workshop the experts were asked to review the maps created and expand on the connections to include those internal to the GBR. After the workshop an initial set of maps were produced and reviewed by the project team and a range of issues were identified and resolved. Additional connectivity maps for some attributes were prepared after the workshop by the subject experts within the project team. The data gathered from these templates was translated into a spreadsheet, then processing into the graphic maps using QGIS to present the connectivity information. The following are the value attributes where their connectivity was mapped: Seagrass meadows: pan-regional species (e.g. Halophila spp. and Halodule spp.) Seagrass meadows: tropical/sub-tropical (Cymodocea serrulata, Syringodium isoetifolium) Seagrass meadows: tropical (Thalassia, Cymodocea, Thalassodendron, Enhalus, Rotundata) Seagrass meadows: Zostera muelleri Mangroves & saltmarsh Hard corals Crustose coralline algae Macroalgae Crown of thorns starfish larval flow Acropora larval flow Casuarina equisetifolia & Pandanus tectorius Argusia argentia Pisonia grandis: cay vegetation Inter-reef gardens (sponges + gorgonians) (Incomplete) Halimeda Upwellings Pelagic foraging seabirds Inshore and offshore foraging seabirds Migratory shorebirds Ornate rock lobster Yellowfin tuna Black marlin Spanish mackerel Tiger shark Grey nurse shark Humpback whales Dugongs Green turtles Hawksbill turtles Loggerhead turtles Flatback turtles Longfin & Shortfin Eels Red-spot king prawn Brown tiger prawn Eastern king prawns Great White Shark Sandfish (H. scabra) Black teatfish (H. whitmaei) Location of sea country Tangible cultural resources Location of place attachment Location of historic shipwrecks Location of places of social significance Location of commercial fishing activity Location of recreational use Location of tourism destinations Australian blacktip shark (C. tilstoni) Barramundi Common black tip shark (C. limbatus) Dogtooth tuna Grey mackerel Mud crab Coral trout (Plectropomus laevis) Coral trout (Plectropomus leopardus) Red throat emperor Reef manta Saucer scallop (Ylistrum balloti) Bull shark Grey reef shark

Limitations of the data:

The connectivity information in this dataset is only rough in nature, capturing the interconnections between 7 regions. The connectivity data is based on expert elicitation and so is limited by the knowledge of the experts that were available for the workshop. In most cases the experts had sufficient knowledge to create robust maps. There were however some cases where the knowledge of the participants was limited, or the available scientific knowledge on the topic was limited (particularly for the ‘inter-reefal gardens’ attribute) or the exact meaning of the value attribute was poorly understood or could not be agreed up on (particularly for the social and indigenous heritage maps). This information was noted with the maps. These connectivity maps should be considered as an initial assessment of the connections between each of the regions and should not be used as authoritative maps without consulting with additional sources of information. Each of the connectivity links between regions was recorded with a level of confidence, however these were self-reported, and each assessment was performed relatively quickly, with little time for reflection or review of all the available evidence. It is likely that in many cases the experts tended to have a bias to mark links with strong confidence. During subsequent revisions of some maps there were substantial corrections and adjustments even for connections with a strong confidence, indicating that there could be significant errors in the maps where the experts were not available for subsequent revisions. Each of the maps were reviewed by several project team members with broad general knowledge. Not all connection combinations were captured in this process due to the limited expert time available. A focus was made on capturing the connections between the GBR and its neighbouring regions. Where additional time was available the connections within 4 regions in the GBR was also captured. The connectivity maps only show connections between immediately neighbouring regions, not far connections such as between Torres Strait and Great Sandy Strait. In some cases the connection information for longer distances was recorded from the experts but not used in the mapping process. The coastline polygon and the region boundaries in the maps are not spatially accurate. They were simplified to make the maps more diagrammatic. This was done to reduce the chance of misinterpreting the connection arrows on the map as being spatially explicit.

Format:

This dataset is made up of a spreadsheet that contains all the connectivity information recorded from the expert elicitation and all the GIS files needed to recreate the generated maps.

original/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_Master_v2018-09-05.xlsx: ‘Values connectivity’: This sheet contains the raw connectivity codes transcribed from the templates produced prepared by the subject experts. This is the master copy of the connection information. Subsequent sheets in the spreadsheet are derived using formulas from this table. 1-Vertical-data: This is a transformation of the ‘Values connectivity’ sheet so that each source and destination connection is represented as a single row. This also has the connection mechanism codes split into individual columns to allow easier processing in the map generation. This sheet pulls in the spatial information for the arrows on the maps (‘LinkGeom’ attribute) or crosses that represent no connections (‘NoLinkGeom’) using lookup tables from the ‘Arrow-Geom-LUT’ and ‘NoConnection-Geom-LUT’ sheets. 2.Point-extract: This contains all the ‘no connection’ points from the ‘Values connectivity’ dataset. This was saved as working/ GBR_NESP-TWQ-3-3-3_Seascape-connectivity_no-con-pt.csv and used by the QGIS maps to draw all the crosses on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘NoLinkGeom’ attribute is used to filter out all line features, by unchecking blank rows in the ‘NoLinkGeom’ filter. 2.Line-extract: This contains all the ‘connections’ between regions from the ‘Values connectivity’ dataset. This was saved as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_arrows.csv and used by the QGIS maps to draw all the arrows on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘LinkGeom’ attribute is used to filter out all point features, by unchecking blank rows in the ‘LinkGeom’ filter. Map-Atlas-Settings: This contains the metadata for each of the maps generated by QGIS. This sheet was exported as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_map-atlas-settings.csv and used by QGIS to drive its Atlas feature to generate one map per row of this table. The AttribID is used to enable and disable the appropriate connections on the map being generated. The WKT attribute (Well Known Text) determines the bounding box of the map to be generated and the other attributes are used to display text on the map. map-image-metadata: This table contains metadata descriptions for each of the value attribute maps. This metadata was exported as a CSV and saved into the final generated JPEG maps using the eAtlas Image Metadata Editor Application

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

Accurate coastal wave and hydrodynamic modelling relies on quality bathymetric input. Many national scale modelling studies, hindcast and forecast products, have, or are currently using a 2009 digital elevation model (DEM), which does not include recently available bathymetric surveys and is now out of date. There are immediate needs for an updated national product, preceding the delivery of the AusSeabed program’s Global Multi-Resolution Topography for Australian coastal and ocean models. There are also challenges in stitching coarse resolution DEMs, which are often too shallow where they meet high-resolution information (e.g. LiDAR surveys) and require supervised/manual modifications (e.g. NSW, Perth, and Portland VIC bathymetries). This report updates the 2009 topography and bathymetry with a selection of nearshore surveys and demonstrates where the 2009 dataset and nearshore bathymetries do not matchup. Lineage: All of the datasets listed in Table 1 (see supporting files) were used in previous CSIRO internal projects or download from online data portals and processed using QGIS and R’s ‘raster’ package. The Perth LiDAR surveys were provided as points and gridded in R using raster::rasterFromXYZ(). The Macquarie Harbour contour lines were regridded in QGIS using the TIN interpolator. Each dataset was mapped with an accompanying Type Identifier (TID) following the conventions of the GEBCO dataset. The mapping went through several iterations, at each iteration the blending was checked for inconstancy, i.e., where the GA250m DEM was too shallow when it met the high-resolution LiDAR surveys. QGIS v3.16.4 was used to draw masks over inconstant blending and GA250 values falling within the mask and between two depths were assigned NA (no-data). LiDAR datasets were projected to +proj=longlat +datum=WGS84 +no_defs using raster::projectRaster(), resampled to the GA250 grid using raster::resample() and then merged with raster::merge(). Nearest neighbour resampling was performed for all datasets except for GEBCO ~500m product, which used the bilinear method. The order of the mapping overlay is sequential from TID = 1 being the base, through to 107, where 0 is the gap filled values.

Permissions are required for all code and internal datasets (Contact Julian OGrady).

Data acquisitionOccurrence data for bee species were downloaded from ALA60 using ALA4R version 1.8.064 in R version 3.6.265.Floral visitation data were obtained from ALA60, Museums Victoria, the Western Australian Museum66,67, and publications (Tables S1 and S2). Floral visitation records were checked for errors and synonymies using the Australian Plant Name Index68. Life-history traits for bee species were sourced, in most cases, from the most recent taxonomic descriptions, or other publications (Tables S1 and S2). A one-hectare resolution Major Vegetation Subgroup (MVS) map was sourced from Geoscience Australia’s National Mapping Division (NMD)61. Fire frequency data from 1988 to 2016 were downloaded from the Department of Environment and Energy (DEE)69, 2019–20 wildfire occurrence data (National Indicative Aggregated Fire Extent Dataset — NIAFED — version 20200623) were sourced from the Department of Agriculture, Water and the Environment (DAWE)36, and 2019–20 wildfire intensity data (Google Earth Engine Burnt Area Map — GEEBAM) were sourced from the Department of Planning, Industry and Environment (DPIE)62. All raster data sources were matched in resolution to the one-hectare MVS map. These GIS data sources may vary in spatial uncertainty or resolution and their caveats can be found at their respective locations online.Data filtering and analysesOccurrence data from ALA were filtered to include only reliable (“preserved specimens”, “machine observations” — e.g., malaise traps, — and data from published datasets) and “present” (compared to “absent”) records. Records without geographic locations or that did not align with base maps were excluded from GIS analyses. Species were then filtered for minimum sample size (n = 30) and minimum number of unique localities (n = 5). However, if there were 15 or more unique localities and a sample size of less than 30, the species was included.The MVS map was reprojected to a world geodetic system (WGS 1984, EPSG:4326) and clipped to the 2019–20 wildfire map in QGIS version 3.1270. The NIAFED and GEEBAM maps were aligned and matched to the resolution of the MVS map using the package raster version 3.0-1271 in R version 3.6.265. Major vegetation subgroups61, 2019–20 wildfire status36, and fire frequency69 were extracted for each ALA record using raster. The proportion of each MVS burnt was calculated by clipping MVS maps with the 2019–20 burn map in ArcMap Version 10.6.172. All map files used in our analyses are available at (html location to be confirmed upon acceptance) for use with our R script.We complemented species distributional data (ALA60 point data) with spatial information on their associated habitat (MVS61), to avoid reliance on the limited data for some species. To determine the potential distribution of each species we buffered the latitudinal and longitudinal extents of the raster datasets (MVS, fire frequency, NIAFED, and GEEBAM) by 20% in each direction. For geographically-restricted species with latitudinal or longitudinal ranges less than one degree (~111 km), we buffered their extent by one degree in each direction along that axis or axes. These values were chosen as conservative estimates of species distributional extents, but we recognize that this treatment may over-inflate the distribution of some species with highly-localized ranges. These data are broken into four files:Map_data — hosts all of the map files used in the analysesBee-plant_point_data — hosts the ALA download data, combined bee dataset, and the life history and plant data spreadsheetWard_comparison_data — hosts some of the data used for the Ward co-analysis using our methodAll_other_R_data — hosts many of the runfiles from our main analysis

The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica and Greenland) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).

A map of GRIT segments labelled with OSM river names is available here: Map with names

Report bugs and feedback

Your feedback and bug reports are welcome here: GRIT bug report form

The feedback may be used to improve and validate GRIT in future versions.

Regions

Vector files are provided in 7 regions with the following codes:

• AF - Africa
• AS - Asia (excl. Siberia)
• EU - Europe
• NA - North America
• SA - South America
• SI - Siberia
• SP - South Pacific/Australia

The domain polygons (GRITv06_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).

Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).

Change log

• v0.6 - 2024-05-30
  • Rivers/streams outside of the GRWL mask forced by all OSM water lines (not only those with waterway=river/canal)
  • Some manual directions in the Irrawaddy delta and fixed erronous sink in the Volga delta
• v0.5 - 2024-02-14
  • Cyclicity and discontinuities resolved through improved algorithms, bug fixes, more sophisticated cycle solving algorithms and some manually forced directions. Only insignificant cycles (non-sinks, less than 50) were removed.
  • Added segment and reach attributes
  • Computational domain fixes
  • Segments include OSM river names
  • Asia domain split into Siberia and rest of Asia
  • Vector files available in EPSG:8857 and EPSG:4326
• v0.4 - 2023-03-11
  
  • First globally complete dataset published

Network segments

Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.

Attribute description of lines layer

Attribute description of nodes layer

Network reaches

Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.

Attribute description of lines layer

Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars

Overview:

This repository contains the map-projected HiRISE Digital Elevation Models (DEMs) and the map-projected HiRISE image for each DEM and for each site in the study. Also contained in the repository is a GeoPackage file (beds_2019_08_28_09_29.gpkg) that contains the dip corrected bed thickness measurements, longitude and latitude positions, and error information for each bed measured in the study. GeoPackage files supersede shapefiles as a standard geospatial data format and can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS. For more information about GeoPackage files, please use https://www.geopackage.org/ as a resource. A more detailed description of columns in the beds_2019_08_28_09_29.gpkg file is described below in a dedicated section. Table S1 from the supplementary is also included as an excel spreadsheet file (table_s1.xlsx).

HiRISE DEMs and Images:

Each HiRISE DEM, and corresponding map-projected image used in the study are included in this repository as GeoTiff files (ending with .tif). The file names correspond to the combination of the HiRISE Image IDs listed in Table 1 that were used to produce the DEM for the site, with the image with the smallest emission angle (most-nadir) listed first. Files ending with “_align_1-DEM-adj.tif” are the DEM files containing the 1 meter per pixel elevation values, and files ending with “_align_1-DRG.tif” are the corresponding map-projected HiRISE (left) image. Table 1 Image Pairs correspond to filenames in this repository in the following way: In Table 1, Sera Crater corresponds to HiRISE Image Pair: PSP_001902_1890/PSP_002047_1890, which corresponds to files: “PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif” for the DEM file and “PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif” for the map-projected image file. Each site is listed below with the DEM and map-projected image filenames that correspond to the site as listed in Table 1. The DEM and Image files can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

· Sera

o DEM: PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif

o Image: PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif

· Banes

o DEM: ESP_013611_1910_ESP_014033_1910_align_1-DEM-adj.tif

o Image: ESP_013611_1910_ESP_014033_1910_align_1-DRG.tif

· Wulai 1

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Wulai 2

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Jiji

o DEM: ESP_016657_1890_ESP_017013_1890_align_1-DEM-adj.tif

o Image: ESP_016657_1890_ESP_017013_1890_align_1-DRG.tif

· Alofi

o DEM: ESP_051825_1900_ESP_051970_1900_align_1-DEM-adj.tif

o Image: ESP_051825_1900_ESP_051970_1900_align_1-DRG.tif

· Yelapa

o DEM: ESP_015958_1835_ESP_016235_1835_align_1-DEM-adj.tif

o Image: ESP_015958_1835_ESP_016235_1835_align_1-DRG.tif

· Danielson 1

o DEM: PSP_002733_1880_PSP_002878_1880_align_1-DEM-adj.tif

o Image: PSP_002733_1880_PSP_002878_1880_align_1-DRG.tif

· Danielson 2

o DEM: PSP_008205_1880_PSP_008930_1880_align_1-DEM-adj.tif

o Image: PSP_008205_1880_PSP_008930_1880_align_1-DRG.tif

· Firsoff

o DEM: ESP_047184_1820_ESP_039404_1820_align_1-DEM-adj.tif

o Image: ESP_047184_1820_ESP_039404_1820_align_1-DRG.tif

· Kaporo

o DEM: PSP_002363_1800_PSP_002508_1800_align_1-DEM-adj.tif

o Image: PSP_002363_1800_PSP_002508_1800_align_1-DRG.tif

Description of beds_2019_08_28_09_29.gpkg:

The GeoPackage file “beds_2019_08_28_09_29.gpkg” contains the dip corrected bed thickness measurements among other columns described below. The file can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

(Column_Name: Description)

sitewkn: Site name corresponding to the bed (i.e. Danielson 1)

section: Section ID of the bed (sections contain multiple beds)

meansl: The mean slope (dip) in degrees for the section

meanaz: The mean azimuth (dip-direction) in degrees for the section

ang_error: Angular error for a section derived from individual azimuths in the section

B_1: Plane coefficient 1 for the section

B_2: Plane coefficient 2 for the section

lon: Longitude of the centroid of the Bed

lat: Latitude of the centroid of the Bed

thickness: Thickness of the bed BEFORE dip correction

dipcor_thick: Dip-corrected bed thickness

lon1: Longitude of the centroid of the lower layer for the bed (each bed has a lower and upper layer)

lon2: Longitude of the centroid of the upper layer for the bed

lat1: Latitude of the centroid of the lower layer for the bed

lat2: Latitude of the centroid of the upper layer for the bed

meanc1: Mean stratigraphic position of the lower layer for the bed

meanc2: Mean stratigraphic position of the upper layer for the bed

uuid1: Universally unique identifier of the lower layer for the bed

uuid2: Universally unique identifier of the upper layer for the bed

stdc1: Standard deviation of the stratigraphic position of the lower layer for the bed

stdc2: Standard deviation of the stratigraphic position of the upper layer for the bed

sl1: Individual Slope (dip) of the lower layer for the bed

sl2: Individual Slope (dip) of the upper layer for the bed

az1: Individual Azimuth (dip-direction) of the lower layer for the bed

az2: Individual Azimuth (dip-direction) of the upper layer for the bed

meanz: Mean elevation of the bed

meanz1: Mean elevation of the lower layer for the bed

meanz2: Mean elevation of the upper layer for the bed

rperr1: Regression error for the plane fit of the lower layer for the bed

rperr2: Regression error for the plane fit of the upper layer for the bed

rpstdr1: Standard deviation of the residuals for the plane fit of the lower layer for the bed

rpstdr2: Standard deviation of the residuals for the plane fit of the upper layer for the bed