Download: Density of indicative threatened ecological community distributions (arcgis.com)Web service: species/ec_density (ImageServer)The density of indicative threatened ecological community distributions is derived from the Department's ecological communities of national environmental significance data. Threatened Ecological Communities (TEC) distributions contain three categories to indicate where their habitat is known, likely or may occur across Australia. The spatial input data was filtered using the following criteria: 1. Distributions for EPBC Act (1999) listed TECs that are Matters of National Environmental Significance (critically endangered or endangered).2. Contains ‘known’ and/or ‘likely to occur’ habitat categories. 3. Marine TECs are includedThe number of overlaps for each distribution in the selected feature set were counted and gridded to a 0.01 decimal degree (~1km) cell size. Note projecting the data will alter the cell size. The source distribution for each TEC is determined independently of others and is indicative in nature. As such, a count higher than one may indicate:• TECs have been mapped in the same habitat or • TECs are mapped adjacent within the same 1km grid cell or • TECs distributions have been mapped at different scales or levels of detail Given the indicative nature of the source data which includes data of a range of quality and currency, this output should be used as a guide to the location of TECs across the country.The selection of TEC distributions for inclusion in the count is based on the EPBC Act list of TECs and spatial data in the Department enterprise GIS as at the revision date in the metadata. Current EPBC Act listed TECs are described in the Species Profiles and Threats application (SPRAT: https://www.environment.gov.au/cgi-bin/sprat/public/sprat.pl).

These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Original data can be downloaded from here. Another online version of the data can be found HERE.This version presented and hosted by CPAWS-NL allows for data extraction and analysis within ArcGIS Online Map Viewer."Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbor-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass." (DOI: 10.17632/dtk86rjm86.2)

Essential fish habitat (EFH) for Gulf of Mexico waters and/or substrate areas in the Gulf of Mexico, which may include estuaries and open water from the US/Mexico border to the boundary between the areas covered by the Gulf of Mexico Fishery Management Council (GMFMC) and the South Atlantic Fishery Management Council (SAFMC) from estuarine waters out to depths of 100 fathoms. Essential fish habitat (EFH) consists of areas of higher species density, based on the NOAA Atlas (NOAA 1985) and the functional relationships analysis in the EIS (GMFMC 2004).

This data set comes from the National Institute of Forests (INAB) in Guatemala. Through various joint efforts and in coordination with the Inter-institutional Group for Forest Monitoring and the GIZ, INAB obtained 308 high resolution RapidEye (RE) images to cover the entire country. These images, with a spatial resolution of 5 meters multispectral, were used to detail 16 classes of forest, 21 subtypes of forest, and 16 subtypes of forest by density. For broadleaf, coniferous, and mixed forest, detailed densities (sparse and dense) were differentiated for the first time in Guatemala.Mangroves were identified at the species level thanks to the database of Project Mangrove, 2012 MARN-CATHALAC, which has registers of four species. For the purposes of this map, un-forested zones were simply designated “No Forest”."

The escalating urbanization and human-induced land use changes have precipitated a global biodiversity crisis, imperiling indigenous ecosystems and cultural heritage. Nanaimo Regional District (NRD) in British Columbia, with its rich biodiversity and Indigenous lands, is facing the challenge of balancing urban development and species conservation. To provide better insights for urban planners and indigenous communities on protecting local biodiversity, the study aims to visualize the biodiversity of the identified species and evaluate the land protection levels by species richness and Land Cover Species Importance Score (LCSIS). The study assigns value to two criteria by importance level to gain the SIS of the identified species at risk and of cultural value. Integrating species occurrence data with land parcels and land cover data to illustrate spatial patterns of species richness and importance level. It reveals that larger rural parcels have higher species richness, primarily located in the eastern part of NRD. Smaller parcels around urban areas, particularly east coastal regions, have higher species richness density. LCSIS value varies across different land cover types. By reclassifying and combining the species richness and LCSIS, the spatial distribution of identified Protection Areas (PAs) is mapped and classified into three classes, high, median, and low. The study also explores the proximity of different classes of PAs to urban areas, to assess if further urban expansion would impact the identified PA. Implications for urban planning are profound. By delineating priority conservation areas and integrating them into land use plans, planners can mitigate the adverse impacts of urban expansion on biodiversity and cultural heritage. This proactive approach fosters sustainable development, preserves vital ecosystems, and honors Indigenous traditions.

Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This data set contains biological information from important coastal mangrove areas situated at the border of, or near the targeted seascapes of the Caribbean Marine Biodiversity Program (CMBP). The data covers: raw species density, diameter at breast height, canopy cover and average heights; GIS data on all locations where data was collected. Targeted seascapes and corresponding countries include: 1) The Mangroves of Bajo Yuna and Los Corozos - Samana Bay - Dominican Republic 2) The Mangroves of Protected Area of Three Bays - Haiti This file reports density data on mangroves of the Protected Area of Three Bays, Haiti. The two datasets, "data" and "density" together describe each area.

CDFW BIOS GIS Dataset, Contact: Patrick Huber, Description: Potential corridors were identified using a modified version of the least cost corridor ArcMap tool. This tool identifies a connectivity surface rather than single line - we then selected the highest rated raster cells from the resulting surfaces and converted them to polygons. The model included current land cover and management, road density, urban area density, natural area density, waterway density, and a surface of three broad suites of species - forest, open/shrub, and aquatic/riparian.

The LandScan data set is a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other data sets. LandScan has been developed as part of the Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk. The LandScan files are available via the internet in ESRI grid format by continent and for the world. You can access the data files after user registration through the data links. For an overview of the methods used to develop LandScan, please read the documentation and FAQs.

[Summary provided by Oak Ridge National Laboratory]

This web map is used by Esri's Green Infrastructure Initiative to power the "Filter Your Intact Landscape Cores" app and the "Prioritize Your Intact Landscape Cores" app. It displays modeled Intact Habitat Cores, or minimally disturbed natural areas. This map represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores.

Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data.

The source data used to derive this attribution is as follows:

Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Reason for Selection As a keystone species, even at diminished population levels, sea turtles play an important role in ocean ecosystems by maintaining healthy seagrass beds and coral reefs, providing key habitat for other marine life, helping to balance marine food webs, and facilitating nutrient cycling from water to land (Wilson 2010). Sea turtles use large areas of the ocean for feeding and reproduction, making them a good indicator of ocean productivity and overall ocean health. For example, Kemp’s ridley sea turtles nesting in southern Texas consistently forage in areas near the Yucatán Peninsula, the Gulf coast of Florida, the Mississippi River Delta, and the Texas-Louisiana shelf (Gredzens and Shaver 2020).Input DataGulf of Mexico Marine Assessment Program for Protected Species (GoMMAPPS) -GoMMAPPS sea turtle spatial density model outputs (version 2.2)Based on ship-based and aerial line-transect surveys conducted in the U.S. waters of the Gulf of America between 2003 and 2019, the NOAA Southeast Fisheries Science Center developed spatial density models (SDMs) for cetacean and sea turtle species for the entire Gulf of America. SDMs were developed using a generalized additive modeling framework to determine the relationship between species abundance and environmental variables (monthly averaged oceanographic conditions during 2015-2019).Southeast Blueprint 2023 subregions– marine (combined Atlantic & Gulf of America)Southeast Blueprint 2023 extent2019 National Land Cover Database (NLCD)Mapping StepsReplace all values of -9999 with 0.Convert to monthly rasters for each species using the following fields: “Jan_n”, “Feb_n”, “Mar_n”, “Apr_n”, “May_n”, “Jun_n”, “Jul_n”, “Aug_n”, “Sep_n”, “Oct_n”, “Nov_n”, and “Dec_n”. Use the marine subregion for pixel size, snap, and extent.Use the loggerhead sea turtle data and the NLCD to create a mask to define the extent of the Zonation analysis. The loggerhead data represents the full sample area for the other species in GoMMAPPS. The area covered by the sea turtle models overlaps with land in a few areas. This mask removes from the analysis all landcover classes that are not open water (not a value of 11 in the NLCD) within the extent of NLCD. The resulting Zonation mask covers open water areas where there is both modeled data for sea turtles and NLCD data to remove land.To identify important areas for each species, use the core area algorithm (CAZMAX) in Zonation 5. Include each monthly density layer as a separate input and weight them equally.Reproject the Zonation results data to Albers Equal Area.Convert from a floating point raster with a range of 0-1 to an integer raster ranging from 0-100.Reclassify to produce the indicator values seen below so that 0-65 is 1, 66-70 is 2, 71-80 is 3, 81-90 is 4, and 91-100 is 5. The variation in values from Zonation below 65 was less helpful than the other higher classes so we classified all values from 65 and below as 1.Use the NLCD and the modeling extent of the source data to identify areas of land not used in the analysis and assign those pixels a value of 0, since they are outside the scope of this marine indicator.As a final step, clip to the spatial extent of Southeast Blueprint 2023.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:5 = >90th percentile of importance for sea turtle index species (across larger analysis area)4 = >80th-90th percentile of importance3 = >70th-80th percentile of importance2 = >65th-70th percentile of importance1 = ≤65th percentile of importance0 = LandKnown IssuesWhile this layer has a 30 m resolution, the source data was coarser than that. We downsampled hexagons with an area of 40 km2 to 30 m pixels.Other Things to Keep in MindWe ran the Zonation analysis across open water areas where there were both sea turtle models and NLCD data present to discriminate between land and water. We did this for multiple reasons. We didn’t run Zonation across the full area covered by the GoMMAPPS data because the full files were very large and required long processing times. We also anticipated that Zonation would not have been able to computationally handle the full area. We extended the Zonation run beyond U.S. waters to try to account for areas of high mammal density just south of the Blueprint’s Gulf of America subregion. As a result, the various classes within the indicator do not cover equal areas within the indicator’s extent, as you might expect with a percentile-based indicator—they cover equal areas within the full analysis area, and then are clipped down to produce the indicator.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedGredzens C, Shaver DJ. 2020. Satellite tracking can inform population-level dispersal to foraging grounds of post-nesting Kemp’s ridley sea turtles. Frontiers in Marine Science, section Marine Megafauna, Special Theme Issue Research Topic: Advances in Understanding Sea Turtle Use of the Gulf of Mexico. [https://doi.org/10.3389/fmars.2020.00559]. Litz J, Aichinger Dias L, Rappucci G, Martinez A, Soldevilla M, Garrison L, Mullin K, Barry K, Foster M. 2022. Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800). NOAA National Centers for Environmental Information. Dataset. [https://doi.org/10.25921/efv4-9z56]. Moilanen A, Lehtinen P, Kohonen I, Virtanen E, Jalkanen J, Kujala H. 2022.Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance. Methods in Ecology and Evolution. [https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13819]. Wilson, E.G., Miller, K.L., Allison, D. and Magliocca, M. 2010. Why Healthy Oceans Need Sea Turtles: The Importance of Sea Turtles to Marine Ecosystems. [https://oceana.org/wp-content/uploads/sites/18/Why_Healthy_Oceans_Need_Sea_Turtles_0.pdf].