This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This map service summarizes racial and ethnic diversity in the United States in 2012.

The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). Diversity in the U.S. population is increasing. The diversity score for the entire United States in 2012 is 61.

The data shown is from Esri's 2012 Updated Demographics. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. This map shows Esri's 2012 estimates using Census 2010 geographies.

The percent chance that two people picked at random within an area will be of a different race/ethnicity. This number does not reflect which race/ethnicity is predominant within an area. The higher the value, the more racially and ethnically diverse an area. Source: U.S. Bureau of the Census, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2017-2021, 2018-2022, 2019-2023

This map shows a comparison of diversity and median household income in the US by tract. Esri's Diversity Index measures the likelihood that two persons, chosen at random from the same area, belong to different races or ethnic groups. In theory, the index ranges from 0 (no diversity) to 100 (complete diversity). If an area's entire population is divided evenly into two race groups and one ethnic group, then the diversity index equals 50. As more race groups are evenly represented in the population, the diversity index increases. Minorities accounted for 30.9 percent of the population in 2000 and are expected to make up 42.3 percent of the population by 2023. Vintage of data: 2023Areas in a darker orange are less diverse than light blue areas with higher diversity. Median household income is symbolized by size. The national median household income is $58,100 and any household below the national value has the smallest symbol size. The largest size has a median household income over $100,000 per year. Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years.Esri Updated Demographics DocumentationMethodologyUnderstanding Esri’s Updated Demographics portfolioEssential Esri Demographics vocabularyThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

Community level metrics for the Chicago site.

Summary of landscape metrics.

This EnviroAtlas dataset contains species richness metrics based on habitat models generated by the U.S. Geological Survey (USGS) National Gap Analysis Project (GAP). Ecosystem services, i.e., services provided to humans from ecological systems have become a key issue of this century in resource management, conservation planning, and environmental decision analysis. Mapping and quantifying ecosystem services have become strategic national interests for integrating ecology with economics to help understand the effects of human policies and actions and their subsequent impacts on both ecosystem function and human well-being. Some aspects of biodiversity are valued by humans in varied ways, and thus are important to include in any assessment that seeks to identify and quantify the benefits of ecosystems to humans. Some biodiversity metrics clearly reflect ecosystem services (e.g., abundance and diversity of harvestable species), whereas others may reflect indirect and difficult to quantify relationships to services (e.g., relevance of species diversity to ecosystem resilience, cultural and aesthetic values). Wildlife habitat has been modeled at broad spatial scales and can be used to map a number of biodiversity metrics. We map 24 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for terrestrial vertebrate species. Metrics include all species richness, taxa specific species richness and other lists identifying species of conservation concern, climate vulnerabilities, etc. This dataset was produced by a joint effort of New Mexico State University, US EPA, and USGS to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

GIS Web Map Application of the 10 City Council Voter Districts

Aim

To understand the representativeness and accuracy of expert range maps, and explore alternate methods for accurately mapping species distributions.

Location

Global

Time period

Contemporary

Major taxa studied

Terrestrial vertebrates, and Odonata

Methods

We analyzed the biases in 50,768 animal IUCN, GARD and BirdLife species maps, assessed the links between these maps and existing political and various non-ecological boundaries to assess their accuracy for certain types of analysis. We cross-referenced each species map with data from GBIF to assess if maps captured the whole range of a species, and what percentage of occurrence points fall within the species’ assessed ranges. In addition, we use a number of alternate methods to map diversity patterns and compare these to high resolution models of distribution patterns.

Results

On average 20-30% of species’ non-coastal range boundaries overlapped with administrative national boundaries. In total, 60% of areas with the highest spatial turnover in species (high densities of species range boundaries marking high levels of shift in the community of species present) occurred at political boundaries, especially commonly in Southeast Asia. Different biases existed for different taxa, with gridded analysis in reptiles, river-basins in Odonata (except the Americas) and county-boundaries for Amphibians in the US. On average, up to half (25-46%) species recorded range points fall outside their mapped distributions. Filtered Minimum-convex polygons performed better than expert range maps in reproducing modeled diversity patterns.

Main conclusions

Expert range maps showed high bias at administrative borders in all taxa, but this was highest at the transition from tropical to subtropical regions. Methods used were inconsistent across space, time and taxa, and ranges mapped did not match species distribution data. Alternate approaches can better reconstruct patterns of distribution than expert maps, and data driven approaches are needed to provide reliable alternatives to better understand species distributions.

Methods Materials and methods

We use a combination of approaches to explore the relationship between species range maps and geopolitical boundaries and a subset of geographic features. In some cases we used the density of species range boundaries to explore the relationship between these and various features (i.e. administrative boundaries, river basin boundaries etc.). Additionally, species richness and spatial turnover are used to explore changes in richness over short geographic distances. Analyses were conducted in R statistical software unless noted otherwise. All code scripts are available at https://github.com/qiaohj/iucn_fix. Workflows are shown in Figure S1a-c with associated scripts listed.

Species ranges and boundary density maps

ERMs (Expert range maps) were downloaded from the IUCN RedList website for mammals (5,709 species), odonates (2,239 species) and amphibians (6,684 species; https://www.iucnredlist.org/resources/grid/spatial-data). Shapefile maps for birds were downloaded from BirdLife (10,423 species, http://datazone.birdlife.org/species/requestdis), and for reptiles from the Global Assessment of Reptile Distributions (GARD) (10,064 species; Roll et al., 2017). Each species’ polygon boundaries were converted to a polylines to show the boundary of each species range (Figure S1a-II; codes are lines 7 – 18 in line2raster_xxxx.r ; xxxx varies based on the taxa). The associated shapefile was then split to produce independent polyline files for each species within each taxon (see Figure S1a-I, codes are lines 29 to 83 in the same file above.).

To generate species boundary density maps, species range boundaries were rasterized at 1km spatial resolution with an equal area projection (Eckert-IV), and stacked to form a single raster for each taxon (at the level of amphibians, odonates, etc.). This represented the number of species in each group and their overlapping range boundaries (Figure S1b-II, codes are in line2raster_all.r). Each cell value indicated the number of species whose distribution boundaries overlapped with each cell, enabling us to overlay this rasterized information with other features (i.e. administrative boundaries) so that the overlaps between them can be calculated in R. These species boundary density maps underlie most subsequent analyses. R code and caveats are given in the supplements, links are provided in text and Figure S1.

Geographic boundaries

Spatial exploration of species range boundaries in ArcGIS suggested that numerous geographic datasets (i.e. political and in few cases geographic features such as river basins) were used to delineate the species ranges for different regions and taxa (this is sometimes part of the methodology in developing ERMs as detailed by Ficetola et al., 2014). Thus in addition to analyzing the administrative bias and the percentage of occurrence records within each species’ ERM for all taxa, additional analyses were conducted when other biases were evident in any given taxa or region (detailed later in methods on a case-by-case basis).

For all taxa, we assessed the percentage of overlap between species range boundaries and national and provincial boundaries by digitizing each to 1km (equivalent to buffering thie polyline by 500m), both with and without coastal boundaries. An international map was used because international (Western) assessors use them, and does not necessarily denote agreed country boundaries (https://gadm.org/). The different buffers (500m, 1000m, 2500m, 5000m) were added to these administrative boundaries in ArcMap to account for potential, insignificant deviations from political boundaries (Figure S1b). An R script for the same function is provided in “country_line_buffer.r”.

To establish where multiple species shared range boundaries we reclassified the species range boundary density rasters for each taxa into richness classes using the ArcMap quartile function (Figure S1). From these ten classes the percentage of the top-two, and top-three quartiles of range densities within different buffers (500m, 1000m, 2500m, 5000m) was calculated per country to determine what percentage of highest range boundary density approximately followed administrative borders. This was done because people drawing ERMs may use detailed administrative maps or generalize near political borders, or may use political shapefiles that deviate slightly. It is consequently useful to include varying distances from administrative features to assess how range boundary densities vary in relation to administrative boundaries. Analyses of relationships between individual species range boundaries and administrative boundaries (coastal, non-coastal) were made in R and scripts provided (quantile_country_buffer_overlap.r).

Spatial turnover and administrative boundaries

Heatmaps of species richness were generated by summing entire sets of compiled species ranges for each taxon in polygonal form (Figure 1; Figure S1b-I). To assess abrupt diversity changes, standard deviations for 10km blocks were calculated using the block statistics function in ArcMap. Abrupt changes in diversity were signified by high standard deviations based on the cell statistics function in ArcGIS, which represented rapid changes in the number of species present. Maps were then classified into ten categories using the quartile function. Given the high variation in maximum diversity and taxonomic representation, only the top two –three richness categories were retained per taxon. This was then extracted using 1km buffers of national administrative boundaries to assess percentages of administrative boundaries overlapping turnover hotspots by assessing what proportion of political boundaries were covered by these turnover hotspots.

Taxon-specific analyses

Data exploration and mapping exposed taxon and regional-specific biases requiring additional analysis. Where other biases and irregularities were clear from visual inspection of the range boundary density maps for each taxa, the possible causes of biases were assessed by comparing range boundary density maps to high-resolution imagery and administrative maps via the ArcGIS server (AGOL). Standardized overlay of the taxon boundary sets with administrative or geophysical features from the image-server revealed three types of bias which were either spatially or taxonomically limited between: 1) amphibians with county borders in the United States, 2) dragonflies and river basins globally and 3) gridding of distributions of reptiles. In these cases, species boundary density maps were used as a basis to identify potential biases which were then explored empirically using appropriate methods.

For amphibians, counties in the United States (US) were digitized using a county map from the US (https://gadm.org/), then buffered by with 2.5km either side. Amphibian species range boundary density maps were reclassified showing where species range boundaries existed (with other non-range boundary areas reclassified as “no data,”) and all species boundaries numerically indicated (i.e. values of 1 indicates one species range boundary, values of 10 indicates ten species range boundaries). Percentages of species boundary areas falling on county and in the buffers, in addition to species range boundaries which did not overlap with county boundaries were calculated to give measures of what percentage of the species boundaries fell within 2.5km of county boundaries.

For Odonata, many species were mapped to river basin borders. We used river basins of levels 6-8 (sub-basin to basin) in the river hierarchy (https://hydrosheds.org) to assess the relationship between Odonata boundaries and river boundaries. Two IUCN datasets exist for Odonata; the IUCN Odonata specialist group spatial dataset

These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.

Conservation planning in the Great Plains often depends on understanding the degree of fragmentation of the various types of grasslands and savannas that historically occurred in this region. To define ecological subregions of the Great Plains, we used a revised version of Kuchler’s (1964) map of the potential natural vegetation of the United States. The map was digitized from the 1979 physiographic regions map produced by the Bureau of Land Management, which added 10 physiognomic types. All analyses are based on data sources specific to the United States; hence, we only analyze the portion of the Great Plains occurring in the United States.We sought to quantify the current amount of rangeland in the US Great Plains converted due to 1) woody plant encroachment; 2) urban, exurban, and other forms of development (e.g., energy infrastructure); and 3) cultivation of cropland. At the time of this analysis, the most contemporary measure of land cover across the United States was the 2011 NLCD (Homer et al. 2015). One limitation of the NLCD is that some grasslands with high rates of productivity, such as herbaceous wetlands or grasslands along riparian zones, are misclassified as cropland. A second limitation is the inability to capture cropland conversion occurring after 2011 (Lark et al. 2015). Beginning in 2009 (and retroactively for 2008), the US Department of Agriculture - NASS has annually produced a Cropland Data Layer (CDL) for the United States from satellite imagery, which maps individual crop types at a 30-m spatial resolution. We used the annual CDLs from 2011 to 2017 to map the distribution of cropland in the Great Plains. We merged this map with the 2011 NLCD to evaluate the degree of fragmentation of grasslands and savannas in the Great Plains as a result of conversion to urban land, cropland, or woodland. We produced two maps of fragmentation (best case and worst case scenarios) that quantify this fragmentation at a 30 x 30 m pixel resolution across the US Great Plains, and make them available for download here. Resources in this dataset: Resource title: Data Dictionary for Figure 2 derived land cover of the US portion of the North American Great Plains File name: Figure2_Key for landcover classes.csv Resource title: Figure 1. Potential natural vegetation of US portion of the North American Great Plains, adapted from Kuchler (1964). File name: Figure1_Kuchler_GPRangelands.zip Resource description: Extracted grassland, shrubland, savanna, and forest communities in the US Great Plains from the revised Kuchler natural vegetation map Resource title: Figure 2. Derived land cover of the US portion of the North American Great Plains. File name: Figure2_Key for landcover classes.zip Resource description: The fNLCD-CDL product estimates that 43.7% of the Great Plains still consists of grasslands and shrublands, with the remainder consisting of 40.6% cropland, 4.4% forests, 3.0% UGC, 3.0% developed open space, 2.9% improved pasture or hay fields, 1.2% developed land, 1.0% water, and 0.2% barren land, with important regional and subregional variation in the extent of rangeland loss to cropland, forests, and developed land. Resource title: Figure 3. Variation in the degree of fragmentation of Great Plains measured in terms of distance to cropland, forest, or developed lands. File name: Figure3_bestcase_disttofrag.zip Resource description: This map depicts a “best case” scenario in which 1) croplands are mapped based only on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017), 2) all grass-dominated cover types including hay fields and improved pasture are considered rangelands, and 3) developed open space (as defined by the National Land Cover Database) are assumed to not be a fragmenting land cover type. Resource title: Figure 4. Variation in the degree of fragmentation of Great Plains measured in terms of distances to cropland, forest, or developed lands. File name: Figure4_worstcase_disttofrag.zip Resource description: This map depicts a ‘worst case’ scenario in which 1) croplands are mapped based on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017) and the 2011 National Land Cover Database (NLCD), 2) hay fields and improved pasture are not included as rangelands, and 3) developed open space (as defined by NLCD) is included as a fragmenting land cover type.

This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.

This map shows the diversity index of the population in the USA in 2010 by block group. "The diversity index summarizes racial and ethnic diversity. The index shows the likelihood that two people, chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). For example, a diversity index of 59 means there is a 59 percent probability that two people randomly chosen would belong to different race or ethnic groups." -Esri DemographicsIt calls to the 2010 Census service with attributes related to race and ethnicity. The field PctNonWhite calculates the total percentage of non-white population by subtracting the Total white population from the reported population total. This yields the total non-white population (Field "TotNonWhite"). This number was then divided by the total reported population and multipled by 100 to yield a percetage of the population that is non-white (Field "PctNonWhite"). Original data sourced from: https://tpc.maps.arcgis.com/home/item.html?id=04a8fbbf59aa48ebbc646ba2bc8d9b1c

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.

Terrestrial and Freshwater Data
• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries.

• The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity.
Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership.

•The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such,
boundaries represented within it may not align with other data sources.

• Numerous datasets representing designated boundaries for entities such as
National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas,
and others, were downloaded from publicly available sources, typically
hosted by the managing agency.

Methodology
1.CPAD and CCED represent the most accurate location and ownership information for
parcels in California which contribute to the preservation of open space
and cultural and biological resources.

2. Superunits are collections of parcels (Holdings) within CPAD which share a name,
manager, and access policy. Most Superunits are also managed with a
generally consistent strategy for biodiversity conservation. Examples of
Superunits include Yosemite National Park, Giant Sequoia National
Monument, and Anza-Borrego Desert State Park.

3. Some Superunits, such as those owned and managed by the Bureau of Land
Management, U.S. Forest Service, or National Park Service , are
intersected by one or more designations, each of which may have a
distinct management emphasis with regards to biodiversity. Examples of
such designations are Wilderness Areas, Wild and Scenic Rivers, or
National Monuments.

4. CPAD Superunits and CCED easements were
intersected with all designation boundary files to create the operative
spatial units for conservation analysis, henceforth 'Conservation
Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit.

5. Each Conservation Unit was intersected with the PAD-US dataset in order to
determine the management emphasis with respect to biodiversity, i.e.,
the GAP code. Because PAD-US is national in scope and not specifically
parcel aligned with California assessors' surveys, a direct spatial
extraction of GAP codes from PAD-US would leave tens of thousands of GAP
code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single
GAP code was uniformly assigned that code. Additionally, the total area
of GAP codes 1 and 2 were summed for the remaining uncoded Conservation
Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2.

6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded,
either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset.

7.These uncoded Conservation Units were then broken down into their
constituent, finer resolution Holdings, which were then analyzed
according to the above workflow.

8. Areas remaining uncoded following the two-step process of coding at the Superunit and
then Holding levels were assigned a GAP code of 4. This is consistent
with the definition of GAP Code 4: areas unknown to have a biodiversity
management focus.

9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved
Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.

10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved.

Tracking Conserved Areas
The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing
improvements in our understanding of existing biodiversity conservation
efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data.

CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

This layer summarizes racial and ethnic diversity in the United States. The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity).The data shown is from Esri's 2020 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2020/2025) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Act 174 requires plans to identify potential areas for the development and siting of renewable energy resources and areas that are unsuitable for siting those resources or particular categories or sizes of those resources. It furthermore requires that the standards address the potential generation from the potential siting areas. Lastly, it requires that � in order to receive an affirmative determination � regional plans allow for the siting in the region of all types of renewable generation technologies.

The Vermont Conservation Design Forest Block components identify areas of forest blocks that would be considered a possible constraint for renewable energy siting projects under ACT 174. �The full set of physical landscape diversity also includes surface waters and riparian areas, but those are excluded here. �

This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets. Why was this layer created? To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.Develop a national context inventory of riparian areas and their condition within national forests and rangelands.How was this layer created? Using freely available data.Develop cost effective modeling approach & technique.Multi-scale (national, regional, & local).Promote technology transfer to train/reach out to our partners.Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMapData Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Biodiversity-ecosystem functioning (BEF) experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types. Here, we analyzed effects of landscape-level diversity, measured as the number of land-cover types (different ecosystems) per 250 × 250 m, across all of North America. We find that this metric is positively related to landscape-wide remotely-sensed primary production, and that a higher number of land-cover types also is associated with greater temporal stability of productivity, and with accelerated 20-year greening trends, in particular at high latitudes. Species diversity was correlated with landscape-level productivity, but the effect of species diversity and landscape diversity were independent. This indicates that diversity-functioning patterns resembling the ones at smaller scales also exist at higher levels of biological organization., Data was collected by processing satellite-remote sensing products collected with the MODIS instrument, at 250m pixel resolution. Land-cover type information was extracted at 30-m spatial resolution from the Commission for Environmental Cooperation’s North American Land Monitoring System’s map (CEC map, based on Landsat-7 satellite imagery), and from the global GlobeLand30 map (GLC map, based on Landsat-5 and China Environmental Disaster Alleviation Satellite (HJ-1) imagery). We focused on the land covers forest, grassland, shrubland, agriculture, wetland and urban, combining the different forest types distinguished in the CEC map. Study plots were selected across North America to form a quasi-experimental study design with 3x6° latutude x longitude tiles that were further divided into 16 ecoregions. Within each tile x ecoregion combination, parallel experimental sub-designs spanning gradients in land-cover type richness were formed. Plots were selected so that land-cover type richness ..., , # Landscape diversity is correlated with satellite-sensed primary productivity in North America

https://doi.org/10.5061/dryad.v41ns1s3p

CHANGELOG

This dataset consists of the following four files:

ecoregions.csv

This files provides a textual description of the ecoregion codes used in the other files

data_CEC.csv

Abbreviations used:

• LC = land cover (A=agriculture, F=forest, G=grassland, S=shrubland, U=urban, W=wetland)
• EVI = enhanced MODIS vegetation index, normalized for each ecoregion
• NE = net biodiversity effect
• invCV = inverse coefficient of variation

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