This layer represents the composite count overlap of six polygon source data sets that consider ecosystem structure, function, and composition in order to estimate relative ecological integrity across the High Divide region. We define and estimate ecological integrity by assembling publicly available spatial data that describe “elements of composition, structure, function, and ecological processes” (after Parrish et al. 2003; Wurtzebach and Schultz 2016) as described below.

CDFW BIOS GIS Dataset, Contact: Marcus Beck, Description: Stream management goals for biological integrity may be difficult to achieve in developed landscapes where channel modification and other factors impose constraints on in-stream conditions. To evaluate potential constraints on biological integrity, we developed a statewide landscape model for California that estimates ranges of likely scores for a macroinvertebrate-based index that are typical at a site for the observed level of landscape alteration.

Forest Landscape Integrity - Nature Communications. https://www.nature.com/articles/s41467-020-19493-3#Sec13Many global environmental agendas, including halting biodiversity loss, reversing land degradation, and limiting climate change, depend upon retaining forests with high ecological integrity, yet the scale and degree of forest modification remain poorly quantified and mapped. By integrating data on observed and inferred human pressures and an index of lost connectivity, we generate a globally consistent, continuous index of forest condition as determined by the degree of anthropogenic modification. Globally, only 17.4 million km2 of forest (40.5%) has high landscape-level integrity (mostly found in Canada, Russia, the Amazon, Central Africa, and New Guinea) and only 27% of this area is found in nationally designated protected areas. Of the forest inside protected areas, only 56% has high landscape-level integrity. Ambitious policies that prioritize the retention of forest integrity, especially in the most intact areas, are now urgently needed alongside current efforts aimed at halting deforestation and restoring the integrity of forests globally.

Integrity layer:This layer represents the composite count overlap of six polygon source data sets that consider ecosystem structure, function, and composition in order to estimate relative ecological integrity across the High Divide region. We define and estimate ecological integrity by assembling publicly available spatial data that describe “elements of composition, structure, function, and ecological processes” (after Parrish et al. 2003; Wurtzebach and Schultz 2016) as described below.Connectivity layer:From Belote et al. 2022, we used the middle tolerance scenario with a 150 m moving window and reclassified raster based on the mean value (.727). Everything above the mean was considered "suitable" connectivity. The layer was clipped to the analysis area and converted into a polygon. Dreiss et al. (2022) extracted raw data values on connectivity and climate flow for areas that were IDed as climate-informed corridors based on categorical connectivity and climate flow dataset (TNC 2020). The remaining values were rescaled to fall between 0 and 1. A second climate corridor dataset (Carroll et al. 2018) was similarly rescaled. These two datasets were combined and locations in the 80th percentile of the distribution of combined values were analyzed. Higher values in the dataset indicate more optimal climate corridors. From Dreiss et al. 2022, here we took the upper 66% of values from the climate-informed wildlife corridors, as the top 33% and 50% were both insufficient to show data in the region given the dataset's national scale. The layer was clipped to the analysis area and converted into a polygon.These two layers were combined using the Count Overlap tool.

This dataset represents the Scenic Integrity Objectives for the Tongass National Forest. The SIO layer is derived from a combination of Distance Zones (DZs) and LUDs, as described in the table on page 4-57 of the 2008 Forest Plan titled, “Adopted Scenery Objectives for Each Land Use Designation.”

Watershed Integrity MethodologySee also: https://www.cmap.illinois.gov/2050/maps/watershed

See https://wa-stateparks.maps.arcgis.com/home/item.html?id=be76cf1a59dc40c3b655ca746aee5820 for project report and metadata.

These two datasets represent a normalized least-cost corridor mosaic (see WHCWG 2010 and McRae and Kavanagh 2011) calculated using (1) temperature gradients and a landscape integrity resistance raster, or (2) temperature gradients only, following the climate gradient linkage-modeling methods outlined in Nuñez (2011), using an adapted version of the Linkage Mapper software (McRae and Kavanagh 2011). This GIS dataset is one of several climate connectivity analyses produced by Tristan Nuñez for a Master's thesis (Nuñez 2011). The dataset was produced in part to assist the Climate Change Subgroup of the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The core areas in the map lie in Washington State and neighboring areas in British Columbia, Idaho, and Oregon.This connectivity analysis should be displayed in conjunction with vector layer of Landscape Integrity Core Areas developed by the WHCWG (WHCWG 2010).

These are the discrete sampling locations brought out of the Water Quality Sampling Data dataset [https://data.austintexas.gov/Environmental/Water-Quality-Sampling-Data/5tye-7ray] for ease of mapping. SampleSiteNo in this table maps to SAMPLE_SITE_NO in the larger dataset. Note that not all samples in the larger dataset have a match in this table ... this table only contains sampling locations with valid latitude/longitude values. Reasons for samples not having a valid physical location: the data represents a non-spatial object like a product or a lab standard or blank; the data was collected at a protected karst feature; the data was collected prior to GIS or GPS and the information never existed or was lost.

This GIS dataset is part of a suite of wildlife habitat connectivity data produced by the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). This dataset quantifies current wildlife habitat connectivity patterns for the Columbia Plateau Ecoregion in Washington, Oregon, and Idaho. Available WHCWG raster data include model base layers, resistance, habitat, cost-weighted distance, and landscape integrity. Grid cell size is 90 m x 90 m. Habitat concentration areas, core areas, and linkage maps reside in raster and vector format.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This data represents the NJDEP Fish Index of Biotic Integrity Monitoring Network active sample point locations for the years 2000 to 2011. A FIBI is an index that measures the health of a stream based on multiple attributes of the resident fish assemblage. Each site sampled is scored based on its deviation from reference conditions (i.e., what would be found in an unimpacted stream) and classified as "poor", "fair", "good" or "excellent".

description: This GIS dataset is part of a suite of wildlife habitat connectivity data produced by the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). The statewide analysis quantifies current connectivity patterns for Washington State and adjacent areas in British Columbia, Idaho, Oregon and a small portion of Montana. Available WHCWG raster data include model base layers, resistance, cost-weighted distance, landscape integrity networks, focal species networks, and focal species guild networks. Grid cell size is 100meters x 100meters. Habitat concentration areas, landscape integrity core areas, and linkage maps reside in raster and vector format. Project background can be found in the report: Washington Wildlife Habitat Connectivity Working Group (WHCWG). 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Online linkage: http://www.waconnected.org; abstract: This GIS dataset is part of a suite of wildlife habitat connectivity data produced by the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). The statewide analysis quantifies current connectivity patterns for Washington State and adjacent areas in British Columbia, Idaho, Oregon and a small portion of Montana. Available WHCWG raster data include model base layers, resistance, cost-weighted distance, landscape integrity networks, focal species networks, and focal species guild networks. Grid cell size is 100meters x 100meters. Habitat concentration areas, landscape integrity core areas, and linkage maps reside in raster and vector format. Project background can be found in the report: Washington Wildlife Habitat Connectivity Working Group (WHCWG). 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Online linkage: http://www.waconnected.org

Fish IBI - Index of Biological Integrity scores in and around Strategic Habitat Units (SHU) in Alabama. These fish IBI's were compiled from member organizations of the Alabama Rivers and Streams Network. This does not represent all IBI's done in the state. This dataset is displayed on the Alabama Rivers and Streams Network Strategic Habitat Unit Mapper Experience page Dashboard. The information available from this dataset includes:SiteLatitude/LongitudeDateIBI ScoreIBI RankingThe Index of Biotic Integrity (IBI) is a well-known indexing procedure commonly used by academia, agencies, and groups to assess watershed condition. This index has been used in throughout the United States and many countries internationally, and has proven to be a reliable means of assessing the effect of human disturbance on streams and watersheds.

The Washington Wildlife Habitat Connectivity Working Group (https://waconnected.org/) conducted a Cascades to Coast Analysis to model habitat connectivity for 5 focal species (American Beaver, Cougar, Fisher, Mountain Beaver, and Western Gray Squirrel), as well as for existing protected areas (i.e., naturalness, landscape integrity). These data were combined into a synthesis analysis to identify important connectivity corridors for the region and to identify priority wildlife crossing areas across major highways. Data from the Cascades to Coast Analysis, as well as a technical report summarizing the project are available at https://waconnected.org/coastal-washington-analysis/.This layer was derived from the Landscape Integrity core areas and least-cost corridors data from the Washington Wildlife Habitat Connectivity Working Group's Cascades to Coast Analysis. Hexagon grid cells were symbolized based on their proportion of overlap with the core areas and least-cost corridors data. Grid cells with a proportion of core area overlap equal to or greater than 0.5 were assigned the Habitat Concentration Area classification. The least-cost corridor width assigned to each grid cell was based on the majority least-cost corridor width overlapping each grid cell.

The survey contains data on 17,500 properties in eligible historic districts across the six Hardest-Hit Fund districts. Volunteer surveyors answered three main questions about each property: its architectural integrity ("Architectu"), how in-keeping it was with neighborhood character ("Building_w"), and how intact its block was ("Block_Inta"). These three questions were aggregated into a Historic Preservation Score (HP_SCORE) aimed to help distill the survey data for decision-makers.

GIS data digitised from 2 DigitalGlobe images at a scale of 1:1000.
The features were digitised using ArcGIS Pro and were created within a topology to ensure the spatial integrity of the data. Line data include coastlines, ice fronts and grounding lines. Polygon data include continent, island, ice tongue and rock features.

The images and data are of the Bølingen Islands and surrounding area, in the Prydz Bay region of Antarctica.
(18FEB23042505-P2AS-017311657010_01_P001.TIL; 18FEB23042504-M2AS-017311657010_01_P001.TIL)
(24MAR16035205-P2AS-017311660010_01_P001.TIL; 24MAR16035205-M2AS-017311660010_01_P001.TIL)
Copyright 2024 DigitalGlobe Incorporated, Longmont CO USA 80503-6493

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The New Jersey Department of Environmental Protection (NJDEP) Bureau of Freshwater and Biological Monitoring (BFBM) performs monitoring on non-tidal freshwater streams and rivers throughout the state using fish as biological indicators of stream health. This data is used for a wide variety of purposes, including the evaluation of aquatic life use assessment for the federally required NJ Integrated Water Quality Assessment Report and the designation of Category One antidegradation classification based on exceptional ecological significance. BFBM has established fish bioassessment protocols for three different stream types in New Jersey. The Bureau initiated Fish Index of Biotic Integrity (IBI) monitoring in 2000 following the development of the Northern Fish IBI by U.S. EPA Region 2 which was based on the EPA’s Rapid Bioassessment Protocols (RBP; USEPA 1999). This, the longest fish monitoring program in the NJDEP Division of Water Monitoring and Standards (DWMS), monitors resident fish assemblages in wadable streams larger than 4-square miles in drainage area. The Southern Fish IBI was developed by BFBM in 2012 for low gradient streams in the Inner Coastal Plain eco-region of NJ. Lastly, after several years of research and analysis by the Philadelphia Academy of Natural Sciences of Drexel University and BFBM, the Headwaters IBI was completed in 2014. This program is used to monitor small first and second order streams less than 4 square miles in drainage area within the same eco-regions of Northern New Jersey as the Northern Fish IBI. The two northern programs differ not only in the size of stream monitored, but also in the assemblages monitored. The Northern Fish IBI is solely a fish-based index, whereas the Headwaters IBI uses fish, crayfish, and streamside amphibians as bio-indicators.

State Nature Preserves are dedicated areas that have been recognized for their natural significance and protected by law for scientific and educational purposes. Dedicated state nature preserves are established solely to protect and preserve rare species, the natural environment, or exceptional natural scenery or environmental education opportunities. Public hiking trails are available when ecologically appropriate, but they are closely regulated to protect the natural integrity of the preserves. The State Nature Preserves available in this layer are open to the public for outdoor recreation.