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% o...

This data is superseded by the MoBI 2024 data which can be found here.This map displays protection-weighted range-size rarity of vertebrates in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States. Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts summed protection-weighted range-size rarity for Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., full species listed as Endangered or Threatened under the Endangered Species Act) vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 309 species).High values identify areas where more unprotected, restricted-range vertebrates are likely to occur. These areas are of interest to conservationists due to both the restricted range sizes and need for protection from threats such as habitat loss.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Range-size rarity for each species is the inverse of the total area mapped as suitable habitat (using the 990-m raster). Protection-weighted range-size rarity (PWRSR) maps combine information on both range-size rarity and the degree to which habitat for the species is protected. Protected habitat was defined as that occurring within protected areas managed for biodiversity (i.e., Gap Status 1 and 2 lands in the USGS Protected Areas Database; PAD-US 2.0). Each species was assigned a PWRSR score equal to the product of range-size rarity and the percent of habitat that is unprotected. The PWRSR raster sums these scores for all species with habitat that overlaps a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 3 species were added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

This map displays the summed range-size rarity of vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes) in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,493 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts summed range-size rarity for Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) vertebrate species.High values identify where species with very small ranges (and thus fewer places where they can be conserved) are likely to occur; the presence of multiple imperiled species contributes to higher scores.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from Global Biodiversity Information Facility, and other publicly available sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30-m (most species) or 330-m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 330-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Range-size rarity for each species in the inverse of the total area mapped as habitat (using the 330-m raster). Summed range-size rarity is the sum of the range-size rarity values for all species with habitat that overlaps a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534Note that the above citation is based on the MoBI 2020 product and does not reflect the most current information. Please contact NatureServe for more information.This data supersedes the MoBI 2020 data which can be found here. A summary of changes between MoBI 2020 and 2024:Species included: MoBI 2024 includes 2,493 species, compared to 2,216 in MoBI 2024. Due to a combination of taxonomic updates and global rank/ESA status changes, 177 species from the 2020 product were removed while 454 species were added to this 2020 product. All taxonomic groups included in MoBI 2020 are included in the 2024 product, with the addition of several solitary bee genera.Scale changes: We increased the resolution from 990-m to 330-m for all MoBI products. Due to this resolution increase, we recommend caution conducting direct comparisons between the MoBI 2020 and MoBI 2024 products.To download data as a layer package, navigate here.

Results of the conditional autoregressive models fitted to explain the β–diversity of terrestrial vertebrates in Mexico.

This EnviroAtlas dataset was produced by a joint effort of New Mexico State University, US EPA, and the US Geological Survey (USGS) to support research and online mapping activities related to EnviroAtlas. 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, or 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 14 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for all vertebrate species except fish. Metrics include species richness for all vertebrates, specific taxon groups, harvestable species (i.e., waterfowl, furbearers, small game, and big game), threatened and endangered species, and state-designated species of greatest conservation need, as well as a metric for ecosystem (i.e., land cover) diversity. The 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).

Invertebrates constitute the majority of animal species and are critical for ecosystem functioning and services. Nonetheless, global invertebrate biodiversity patterns and their congruences with vertebrates remain largely unknown. We resolve the first high-resolution (~20-km) global diversity map for a major invertebrate clade, ants, using biodiversity informatics, range modeling, and machine learning to synthesize existing knowledge and predict the distribution of undiscovered diversity. We find that ants and different vertebrate groups have distinct features in their patterns of richness and rarity, underscoring the need to consider a diversity of taxa in conservation. However, despite their phylogenetic and physiological divergence, ant distributions are not highly anomalous relative to variation among vertebrate clades. Furthermore, our models predict rarity centers largely overlap (78%), suggesting that general forces shape endemism patterns across taxa. This raises confidence that conservation of areas important for small-ranged vertebrates will benefit invertebrates while providing a "treasure map" to guide future discovery.

This map displays numbers of vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes) in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,493 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) vertebrate species.High values identify areas where more imperiled species are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from Global Biodiversity Information Facility, and other publicly available sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30-m (most species) or 330-m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 330-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534Note that the above citation is based on the MoBI 2020 product and does not reflect the most current information. Please contact NatureServe for more information.This data supersedes the MoBI 2020 data which can be found here. A summary of changes between MoBI 2020 and 2024:Species included: MoBI 2024 includes 2,493 species, compared to 2,216 in MoBI 2024. Due to a combination of taxonomic updates and global rank/ESA status changes, 177 species from the 2020 product were removed while 454 species were added to this 2020 product. All taxonomic groups included in MoBI 2020 are included in the 2024 product, with the addition of several solitary bee genera.Scale changes: We increased the resolution from 990-m to 330-m for all MoBI products. Due to this resolution increase, we recommend caution conducting direct comparisons between the MoBI 2020 and MoBI 2024 products.To download data as a layer package, navigate here.

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).

Mapping global parasite diversity is crucial to identify geographical hotspots of emerging disease, and guide public health and conservation efforts. In principle, assuming a bottom-up coupling between the diversity of resources and consumers, the geographical distribution of parasite diversity should match that of host diversity. We test the expected spatial congruence between host and parasite diversity for helminth parasites of vertebrate hosts, across grid cells of a global map. Using high-resolution databases on host species distributions and newly-compiled data on the geographical distribution of parasite species discovery, we found positive covariation between host species richness and the number of parasite species discovered, for all vertebrate groups, regardless of the analytical method used, spatial autocorrelation, and spatial resolution. However, all associations were very weak, indicating a poor match between host species richness and parasite species discovery. The research deficit in parasite discovery peaks in areas corresponding to hotspots of host diversity, where disproportionately fewer new parasites are discovered than expected based on local host richness. This spatially biased research effort prevents a full inventory of parasite biodiversity, and impedes predictions of where new diseases may emerge. The host taxon-specific maps we produced, however, can guide future efforts to uncover parasite biodiversity.

This EnviroAtlas dataset was produced by a joint effort of New Mexico State University, US EPA, and the US Geological Survey (USGS) to support research and online mapping activities related to EnviroAtlas. 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, or 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 15 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for all vertebrate species except fish. Metrics include species richness for all vertebrates, specific taxon groups, harvestable species (i.e., waterfowl, furbearers, small game, and big game), threatened and endangered species, and state-designated species of greatest conservation need, as well as a metric for ecosystem (i.e., land cover) diversity. The 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).

Aim: Biodiversity hotspots often span international borders, thus conservation efforts must as well. China is one of the most biodiverse countries and the length of its international land borders is the longest in the world; thus, there is a strong need for transboundary conservation. We identify China’s transboundary conservation hotspots and analyze the potential effects of the Belt and Road Initiative (BRI) on them to provide recommendations for conservation actions. Location: China, Asia Methods: We compiled a species list of terrestrial vertebrates that span China’s borders. Using their distribution, we extracted the top 30% of the area with the highest richness value weighted by Red List category and considered these transboundary hotspots for conservation priority. Then we analyzed protected area (PA) coverage and connectivity to identify conservation gaps. To measure the potential impact of the BRI, we counted the species whose distribution range is traversed by the BRI and calculated the aggregation index, proportion of natural land, and night light index along its routes. Results: We identified 1,964 terrestrial vertebrate species living in the border region. We identified four transboundary hotspots and found insufficient PA coverage and low connectivity in three of them. The BRI routes intersected all four hotspots and traversed 82.4% (1,619/1,964) of the transboundary species, half of which (918) are sensitive to the potential risks brought by the BRI. Night light index increased generally along the BRI. However, the proportion of natural land and the aggregation index near the BRI showed different trends in hotspots. Main conclusions: There is an urgent need for conservation action in China’s transboundary region. The BRI should put biodiversity conservation at the core of its development strategy. Furthermore, we suggest using the planned BRI as a platform for dialogue and consultation, knowledge and data sharing, and joint planning to promote transboundary conservation. Methods Data summary: This is the dataset used in the Diversity and Distributions contribution article "Transboundary conservation hotspots in China and potential impacts of the Belt and Road Initiative". The dataset includes heat maps of the transboundary distribution of terrestrial vertebrates in China drawn by the authors, as well as selected hotspots in the top 30% by value. In addition, a rasterized 0-1 protected area layer for the study area is provided for research reproduction. The heatmap and hotspots of transboundary species distribution were created as follows: We compiled a list of transboundary terrestrial vertebrates in China from the International Union for Conservation of Nature (IUCN) Red List database (https://www.iucnredlist.org/). We downloaded data of all species of mammals, birds, amphibians and reptiles from the database and filtered those living in terrestrial ecosystems. We then filtered these species based on their geographic ranges, to retain species living both in China and other neighboring countries. Furthermore, we filtered the species by their distribution codes and retained those with codes of “Extant”, “Possibly Extant”, “Native”, and for birds we excluded “Passage”. The retained species were classified as transboundary terrestrial vertebrates in China. We downloaded distribution maps of transboundary species from the IUCN Red List (IUCN, 2021) and BirdLife International and the Handbook of the Birds of the World (BirdLife International, 2018). We then refined the distribution range (R 4.1.0, terra package)(Hijmans, 2022) for each species according to its suitable habitat types (i.e., land cover types) and elevation range, which were obtained from the IUCN Red List. Land cover data were obtained from (Jung et al., 2020), which is consistent with the IUCN habitat classification, and elevation data were obtained from WorldClim (https://worldclim.org/) (Fick and Hijmans, 2017). All raster layers were rescaled to a spatial resolution of 1 km and were under spatial reference coordinate system of WGS1984. We created 10 km, 50 km and 100 km buffer zones on both sides of China's border as border region (made in ArcGIS 10.2.2). We used this border region to crop the distribution maps of transboundary terrestrial species in China. Within the border region, each specie has a distribution layer with a value of 0 or 1 in each 1-km2 cell, where 1 represents presence and 0 represents absence. All species were then weighted by their Red List category, assuming Least Concern (LC) as 1, Near Threatened (NT) as 2, Vulnerable (VU) as 3, Endangered (EN) as 4 and Critically Endangered (CR) as 5 (Balaguru et al., 2006). We valued DD as 3 because DD species are often considered potentially at risk of extinction (Jaric et al., 2016). However, excluding the 65 DD species did not affect the main results. The weighted distribution layers were stacked to obtain a weighted-richness map. Finally, we extracted the top 30% of cells with highest values in the weighted-richness map as conservation hotspots. The 30% was chosen as the threshold because according to the 2030 action target 3 of the 15th meeting of the Conference of the Parties to the Convention on Biological Diversity (COP15)(Convention on Biological Diversity, 2020), it is necessary to protect 30% of land and sea globally by 2030. The raster layer of protected area was created as follows: We obtained map layers of PAs in China’s neighboring countries from the World Database on Protected Areas (UNEP-WCMC, 2017) and supplemented China’s PAs from Yang et al. (Yang et al., 2018). For some PAs which are point data in the WDPA dataset, we constructed circles around the points with areas equal to the sizes listed in the attribute table. We rasterized this map and reassignment the value to 0(without PAs) and 1(with PAs). Finally, we used border regions to crop the raster map. References

Balaguru, B., Britto, S. J., Nagamurugan, N., Natarajan, D. and Soosairaj, S. (2006) 'Identifying conservation priority zones for effective management of tropical forests in Eastern Ghats of India', Biodiversity and Conservation, 15(4), pp. 1529-1543. BirdLife International (2018) 'BirdLife International and handbook of the birds of the world (2018) Bird species distribution maps of the world. Version 2018.1. Available at http://datazone.birdlife.org/.'. Convention on Biological Diversity (2020) 'Update of the zero draft of the post‐2020 global biodiversity framework'. Fick, S. E. and Hijmans, R. J. (2017) 'WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas', International Journal of Climatology, 37(12), pp. 4302-4315. Hijmans, R. J. (2022) 'terra: Spatial Data Analysis'. IUCN (2021) 'The IUCN Red List of Threatened Species. 2021-3. https://www.iucnredlist.org. Downloaded on 20 june 2022.'. Jaric, I., Courchamp, F., Gessner, J. and Roberts, D. L. (2016) 'Potentially threatened: a Data Deficient flag for conservation management', Biodiversity and Conservation, 25(10), pp. 1995-2000. Jung, M., Dahal, P. R., Butchart, S. H. M., Donald, P. F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C. and Visconti, P. (2020) 'A global map of terrestrial habitat types', Scientific Data, 7(1), pp. 256. UNEP-WCMC (2017) 'World Database on Protected Areas User Manual 1.5. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual'. Yang, L., Chen, M. H., Challender, D. W. S., Waterman, C., Zhang, C., Huo, Z. M., Liu, H. W. and Luan, X. F. (2018) 'Historical data for conservation: reconstructing range changes of Chinese pangolin (Manis pentadactyla) in eastern China (1970-2016)', Proceedings of the Royal Society B-Biological Sciences, 285(1885).

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

This EnviroAtlas dataset was produced by a joint effort of New Mexico State University, US Environmental Protection Agency (US EPA,) and the U.S. Geological Survey (USGS) to support research and online mapping activities related to EnviroAtlas. 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 15 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for all vertebrate species except fish. Metrics include species richness for all vertebrates, specific taxon groups, harvestable species (i.e., upland game, waterfowl, furbearers, small game, and big game), threatened and endangered species, and state-designated species of greatest conservation need, and also a metric for ecosystem (i.e., land cover) diversity. This dataset contains information on Reptile Species Richness, the number of reptile species per 12-digit Hydrologic Unit (HUC). The 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).

OverviewThis layer shows global species richness and rarity patterns for select terrestrial vertebrate and plant groups, based on data availability. Species richness is the number of species ranges estimated to occur in each grid cell. Here richness is expressed as percentile rank values from 1 (low) to 100 (high). Species rarity is the average range-restrictedness of all species expected to occur in each cell. Here rarity is expressed as percentile rank values from 1 (low) to 100 (high). These values are derived from quantile binning as shown on the Half-Earth Map website.Species range maps were used for six terrestrial species groups: amphibians, birds, cacti, conifers, mammals, and reptiles (Wilson et al 2009, Jetz et al 2012, Leslie 2014, Barthlott et al 2015, IUCN 2017, Roll et al 2017). The 'all' taxa attributes gives the richness and rarity patterns of these six species groups combined. The species groups that are included in this layer were determined based on data available based on complete spatial and taxonomic coverage. Vertebrate patterns (amphibians, birds, mammals, and reptiles) are derived from range maps refined by known habitat associations and preferences; cacti and conifers are based on unrefined range maps.The global grid used in this dataset has a grid cell area of ~777 km^2 (approximately 27.75km x 27.75km or 0.25˚ x 0.25˚ at 30˚ latitudes). The grid cells have been clipped to GADM 3.6 political boundaries (Hijmans et al 2018).Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.CitationWhen citing this dataset, please use: Jetz, W., McPherson, J. M., and Guralnick, R. P. (2012). Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology and Evolution 27:151-159. DOI:10.1016/j.tree.2011.09.007.ReferencesBarthlott, W., Burstedde, K., Geffert, J.L., Ibisch, P.I., Korotkoba, N., Miebach, A., Rafiqpoor, M.D., Stein, A., & J. Mutke (2015). Biogeography and biodiversity of cacti. Schumannia, 7.Hijmans, R., Garcia, N., & J. Wieczorek (2018). Global Administrative Areas Database (GADM) Version 3.6. UN: New York, NY, USA.Hurlbert, A.H. & W. Jetz (2007). Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. PNAS, 104(33), 13384-13389.IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed January 2017.Jetz, W., Thomas, G.H., Joy, J.B., Hartmann, K., & A.O. Mooers (2012). The global diversity of birds in space and time. Nature, 491, 444-448.Leslie, A. (2014). Conifers of the World. Unpublished MS.Powers, R.P. & W. Jetz (2019). Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nature Climate Change, 9(4), 323-329.Roll, U., Feldman, A., Novosolov, M., Allison, A., Bauer, A.M., Bernard, R., Bohm, M., Castro-Herrera, F., Chirio, L., Collen, B., & G.R. Colli (2017). The global distribution of tetrapods reveals a need for targeted reptile conservation. Nature Ecology & Evolution, 1(11), 1677-1682.Wilson, D.E., Lacher Jr, T.E., & R.A. Mittermeier eds (2009). Handbook of the mammals of the world, Vols. 1-9. Barcelona: Lynx Edicions.

Beta-diversity, the change in species composition between places, is a critical but poorly understood component of biological diversity. Patterns of beta-diversity provide information central to many ecological and evolutionary questions, as well as to conservation planning. Yet beta-diversity is rarely studied across large extents, and the degree of similarity of patterns among taxa at such scales remains untested. To our knowledge, this is the first broad-scale analysis of cross-taxon congruence in beta-diversity, and introduces a new method to map beta-diversity continuously across regions. Congruence between amphibian, bird, and mammal beta-diversity in the Western Hemisphere varies with both geographic location and spatial extent. We demonstrate that areas of high beta-diversity for the three taxa largely coincide, but areas of low beta-diversity exhibit little overlap. These findings suggest that similar processes lead to high levels of differentiation in amphibian, bird, and mammal assemblages, while the ecological and biogeographic factors influencing homogeneity in vertebrate assemblages vary. Knowledge of beta-diversity congruence can help formulate hypotheses about the mechanisms governing regional diversity patterns and should inform conservation, especially as threat from global climate change increases.

Aim Mining is increasingly pressuring areas of critical importance for biodiversity conservation, such as the Brazilian Amazon. Biodiversity data are limited in the tropics, restricting the scope for risks to be appropriately estimated before mineral licencing decisions are made. As the distributions and range sizes of other taxa differ markedly from those of vertebrates – the common proxy for analysis of risk to biodiversity from mining – whether mining threatens lesser-studied taxonomic groups differentially at a regional scale is unclear. Location Brazilian Amazon Methods We assess risks to several facets of biodiversity from industrial mining by comparing mining areas (within 70km of an active mining lease) and areas unaffected by mining, employing species richness, species endemism, phylogenetic diversity, and phylogenetic endemism metrics calculated for angiosperms, arthropods, and vertebrates. Results Mining areas contained higher densities of species occurrence records than the unaffected landscape, and we accounted for this sampling bias in our analyses. None of the four biodiversity metrics differed between mining and non-mining areas for vertebrates. For arthropods, species endemism was greater in mined areas. Mined areas also had greater angiosperm species richness, phylogenetic diversity, and phylogenetic endemism, although lower species endemism than unmined areas. Main Conclusions Unlike for vertebrates, facets of angiosperm and arthropod diversity are relatively higher in areas of mining activity, underscoring the need to consider multiple taxonomic groups and biodiversity facets when assessing risk and evaluating management options for mining threats. Particularly concerning is the proximity of mining to areas supporting deep evolutionary history, which may be impossible to recover or replace. As pressures to expand mining in the Amazon grow, impact assessments with broader taxonomic reach and metric focus will be vital to conserving biodiversity in mining regions. Methods Database Assembly Mapping Mining Areas We obtained spatial information on mineral prospecting and mineral mining leases within the Brazilian Amazon from SIGMINE (Sistema de Informações Geográficas da Mineração; DNPM, 2012). This database catalogues all registered legal mining activities within Brazil, detailing the extent of each activity, dates of operation, and mined commodities. To map ‘mining leases’ of industrial-scale mineral mines, we selected records greater than 100 hectares in area and classified as mining concessions (Concessão de Lavra) and omitted leases extracting water or those classified as small-scale artisanal operations (Lavra Garimpeira). This resulted in 411 polygons (including active leases and adjacent extensions of such leases) of 15,750 km2 in total area, with mining start dates ranging from 1944 to 2017 (mean = 1978, sd = 11.9; Fig. 1). To map ‘mining areas,’ which include the direct (i.e., immediate land-use change resulting from mineral extraction) and indirect (i.e., extensive land-use change associated with mineral extraction, processing, and transportation) impacts of mining on forests (Sonter et al., 2017), we created a 70 km buffer surrounding each mining lease. ‘Non-mining areas’ (i.e., areas unaffected by industrial mining) were mapped by extracting our mapped ‘mining areas’ and an additional layer representing all other legal mining leases excluded from our analyses (i.e., inactive leases, those targeting water, or operations smaller than 100 hectares in area; shown in white in Fig. 1) from the Brazilian Amazon (Fig. 1). For interpolation analyses, hexagons are the most logical sampling unit shape as their centroids are equidistant, the distance of points from the edges to the centroid is the closest, and sampling biases are reduced due to their lower perimeter-area ratio compared to squares or triangles (Birch et al., 2007). Hexagons of approximately 0.5° with equal area were assigned to one of two study areas – mining areas or non-mining areas – based on where their centroid was located (Fig. 1). Hexagons were omitted from our analyses if they contained fewer than 20 occurrence records per taxonomic group or their centroid was located outside the Brazilian Amazon. We used 0.5° hexagon sampling units as sensitivity analyses conducted in previous studies utilising the same dataset indicated reduced variation in results for hexagon areas of 0.5° and above (Oliveira et al., 2017a; Strand et al., 2018) and so any fine-scale georeferencing inaccuracies remaining in the dataset after filtering are minimised (Oliveira et al., 2017b). This sampling unit area also ensured sufficient sample sizes would be assigned within and among mining-induced deforestation-affected areas to enable robust comparisons across the study area for all taxonomic groups, particularly arthropods, while reducing the amount of area hexagon interpolations may sample from outside their respective study area polygons. Assembling Biodiversity Data Data on species occurrences were obtained from (Oliveira et al., 2017a) and (Oliveira et al., 2019a) and represent the most comprehensive dataset of species occurrences in Brazil to date. These data were assembled from online databases spanning GBIF (gbif.org); CRIA (specieslink.net); Birdlife International (birdlife.org), Herpnet (herpnet.org), Nature Serve (natureserve.org); and Orthoptera Species File (orthoptera.speciesfile.org). These data were also supplemented with occurrence records obtained from taxonomic literature and biodiversity inventories (Oliveira et al., 2017a; Oliveira et al., 2019a). All species occurrence records were filtered to determine if they lacked geographic coordinates or exhibited location errors using a map of Brazilian municipalities (mapas.ibge.gov.br; Oliveira et al., 2017a; Oliveira et al., 2019a). Taxonomic validity for all occurrence records was confirmed using taxon-specific catalogues and expert reviews for each taxonomic group (Oliveira et al., 2017a; Oliveira et al., 2019a). After filtering for geographic and taxonomic accuracy, the final dataset comprised 113,790 occurrence records for all the Brazilian Amazon. The dataset contained 44,660 records of angiosperms (6899 species of families Asteraceae, Bromeliaceae, Fabaceae, Melastomataceae, Myrtaceae, Orchidaceae, Poaceae, and Rubiaceae), 24,374 records of arthropods (4630 species of bees, spiders, millipedes, Orthoptera, dragonflies, moths and Diptera), and 44,756 records of vertebrates (1584 species of birds, mammals, and anurans). Spatial distributions of occurrence record densities for each taxonomic group are provided in the supporting information (Fig. S1). Phylogenetic trees were constructed from published figures into Newick code with TreeSnatcherPlus (Laubach & Von Haeseler, 2007) and supplemented with data from empirical phylogenetic studies synthesised by The Open Tree of Life project (Hinchliff et al., 2015). As branch lengths, when available, are not directly comparable between trees, all branch lengths were considered equal to one (Oliveira et al., 2017a; Oliveira et al., 2019a). Phylogenetic trees were compiled into a supertree using matrix representation with parsimony (Baum, 1992) and pruned to represent species restricted to Brazil. Our dataset represents the most extensive collection of species occurrence records and phylogenetic trees compiled in Brazil for this purpose to date (Oliveira et al., 2017a). However, data collected for environmental impact assessments that are not published online will inevitably be missing from our database, and rare, threatened, or range-restricted organisms may also not be included due to limited sampling. Calculation of Biodiversity Facets Sampling Effort We first intersected mining lease and mining area polygons with species occurrence records to provide a coarse estimate of the proportion of occurrence records within mining leases and their more expansive impact areas from the total contained in our database. An equal area measure was calculated through the ‘Sampling Effort’ functor of the BioDinamica plug-in (Oliveira et al., 2019b) of Dinamica EGO (Ferreira et al., 2019), which was set with a 10 km search radius due to limited and sporadic biodiversity sampling in the Brazilian Amazon (Oliveira et al., 2016; Oliveira et al., 2017a). We then converted the output raster to points and summed the mean sample effort index values across 0.5° radius hexagon sampling units (Fig. 1). The ‘Sampling Effort’ functor in BioDinamica employs a Gaussian kernel density index function. For all analyses using BioDinamica, 0.5° hexagon sampling units were only created where ≥ 20 species occurrence records existed. Biodiversity Metrics We calculated four sampling-effort-corrected biodiversity metrics for each of the three taxonomic groups: species richness, species endemism, phylogenetic diversity, and phylogenetic endemism, since measuring biodiversity with species richness alone does not capture values pertinent to conservation at the landscape scale, such as endemism or evolutionary history (Faith, 1992; Faith et al., 2004). Indeed, the loss of species is not equivalent to the loss of evolutionary history (Vane-Wright et al., 1991), and conservation priority areas can differ when using species richness and phylogenetic diversity (Rodrigues et al., 2005; Forest et al., 2007). Furthermore, phylogenetic measures may capture the quantity and distribution of diversity better than species-based measures, especially when data are limited, but both are representative of different diversity components (Rosauer & Mooers, 2013; Tucker et al., 2017). Thus, here we employ a variety of biodiversity metrics for comparison between mining and non-mining areas in the Brazilian Amazon. Species-based Metrics Species richness (per unit area) is the most sensitive biodiversity measure to variation in sampling effort (Oliveira

This project will act as an integrating focus within the rainforest theme to strategically target research gaps and thereby increase our understanding of the drivers of rainforest biodiversity. We will generate high resolution maps and landscape scale estimates of temporal trends in the condition of biodiversity and environmental changes.

The project consists of four subprojects:

A. Monitoring: Tasks include a microsensor network, standardised vertebrate surveys, habitat structure monitoring and data harvesting from other projects.

A comprehensive review of regional literature followed by extensive stakeholder consultation identified long-term monitoring data as the most important knowledge gap in the region (Welbergen et al. 2011). This sub-project is aimed at maintaining and significantly improving a regional-scale, long-term environmental monitoring program that provides biodiversity and environmental data that has a demonstrated value to a wide range of users including the research community, regional/state/national management agencies and conservation policy development, and national / international bioinformatic infrastructure initiatives (e.g. ALA, TERN). Data collected and maintained here will provide the primary input for the other sub-projects described below with flow-on inputs to many of the other proposed projects across the rainforest node. These data will include but not be limited to: 1. Regional microclimate sensor network at more than 30 sites established under MTSRF that are strategically placed across elevational and latitudinal gradients in the region. o Replace and upgrade existing microclimate stations (now defunct/worn out) o Establish standardised microclimate logging stations in new sites in gaps in environmental coverage, identified climatic refugia, peripheral habitat isolates and increased coverage of the rainforest edge habitats (e.g. wet sclerophyll). Data: temperature (air, soil, microhabitats), humidity, soil moisture, cloud interception. 2. Standardised vertebrate surveys across all long-term sites (>30) including: o 2-4 complete surveys per year for three years with 6 replicated sampling points within each site and including standardised surveys of: birds, reptiles, spotlighting (mammals and other nocturnal fauna) and microhylid frogs, with potential to add specific other groups dependent on student projects. o These surveys follow well-established and extensively published methodologies within the CTBCC (e.g. Williams et al. Ecology 2010).
3. Habitat structure monitoring will be continued and improved at all monitoring sites both directly by this project and via site-based collaboration with other projects including rainforest dynamics project (Project 10 - Laurance) and plant genetics (Project 9 -Crayn). 4. Link to Project 14: potential monitoring of vegetation structure and thermal properties using UAV technology to capture aerial photos, multispectral remote sensing, Lidar vegetation structure, thermal imagery of habitat and fauna, cyclone damage and canopy condition. 5. Additional monitoring data will be harvested across the node for increased regional and taxonomic coverage and baseline data improvements via links and data exchange with Projects 4, 5, 7, 9, 10, 11, 15, 16, 18, 20 and 25.

B. Climate change vulnerability and adaptation: Includes the production of downscaled regional climate projections, projected changes in species distribution models, composite biodiversity maps, identification and mapping of climate refugia, predictive models of impacts on biodiversity including extreme events.

Climate change is arguably the single largest threat to biodiversity in Australia and the unique biodiversity of the Wet Tropics rainforests is recognised as one of the most threatened ecosystems globally (IPCC 4th AR). This subproject will build on previous and existing research to provide cutting-edge predictions on climate change impacts, vulnerability assessment and adaptation options for rainforest biodiversity. We will link closely with the National Climate Change Adaptation Research Network to ensure that outputs, tools and approaches are distributed across this network for maximum national and regional benefit and outcomes. Specific objectives and collaborative links include: 1. Produce and make available downscaled regional climate projections using eight Global Climate Models across multiple (at least 3) emission scenarios at 10 year time steps from 1970-2080 for more than 50 bioclimatic variables; 2. Projected changes, including uncertainty estimates, in species distribution models and composite biodiversity maps for the majority of rainforest vertebrates, 500+ species of invertebrates, major vegetation types and some key ecosystem processes (baseline data for these analyses were collected under MTSRF and ongoing projects within the CTBCC); 3. Identify and map climatic refugia (extension of previous MTSRF work that mapped landscape-scale temperature refugia by Shoo et al. 2010a, 2010b). This analysis will expand previous work to include finer scale microhabitat refugia and also increase the generality of the analyses by examining moisture refugia and dry season drought events that have been shown to have significant impacts on biodiversity (Williams & Middleton 2008, Middleton & Williams in review). 4. Produce predictive impact models on biodiversity that explicitely include a consideration of extreme events rather than just environmental means/averages. Project will link closely with the extreme climate events project (Project 16 ¿ Welbergen) to incorporate the impacts of changes in the frequency, intensity, duration and extent of extreme events, such as heat waves and droughts, as a major component of assessing relative vulnerability and adaptation actions; 5. Project will closely collaborate with Project 15 (Phillips, Llewelyn) examining the potential for useful local adaptation to climate changeextremes in isolated populations. This link explores the potential to utilise existing adaptive potential as a means to increase species resilience to climate change. 6. Other external links: ¿ NCCARF Refugia project (National) - proposed ¿ NCCARF Terrestrial Biodiversity Research Network ¿ Northern Biodiversity NERP Hub ¿ AEDA Hub ¿ Restoration project and Future Fellowship (Wintle) examining demographic modelling and climate change. 7. Incorporate IPCC 5th Assessment Report climate models and scenarios into all above analyses, once they become available.

C. Synthesis, analysis and integration: Determinants of biodiversity: Includes mapping of almost all rainforest vertebrates and 200+ species of invertebrates, identify key monitoring locations, examining the relationships between biodiversity and vegetation and landscape structure.

An understanding of the drivers of biodiversity in the region is crucial to predicting impacts from a variety of threats and ensuring effective conservation planning and management that aims to maintain a resilient landscape. We will use data collected in subproject A in combination with our existing extensive vertebrate and invertebrate database to examine the drivers of biodiversity in the region and to provide the resources and knowledge to make this useful to stakeholders. Specific objectives will include, but not be limited to: 1. Mapping of almost all rainforest vertebrates and >500 species of invertebrates (distribution and abundance) with emphasis on threatened species; 2. Identify key locations and taxa where we have long-term count data and/or high frequency of repeat count surveys over time periods that have encompassed important environmental change. We will undertake statistical power analyses to evaluate condition and trends of species (e.g., range shifts, change in population size); 3. Analyses will also inform the design of our ongoing monitoring program (subproject A) to maximise the detection of change in a cost-effective manner. 4. Comprehensive habitat/vegetation type vulnerability assessment; 5. Examine a range of environmental and evolutionary drivers of biodiversity to provide the basic scientific underpinnings for evidence-based policy and management in the region including paleostability of habitat; seasonal habitat and climatic stability, relationships to ecosystem processes such as net primary productivity, habitat structure and heterogeneity, species and habitat compositional turnover and evolutionary biology. 6. Examine relationships between biodiversity and vegetation and landscape structure (vegetation type and structure, habitat extent, connectivity etc.)

D. Status, trends and future projections: Includes Producing a spatial and temporal resources tool that allows web-based query of all the above datasets.

No practical measure currently exists to evaluate trends in biodiversity values at the ¿whole-of-region¿ scale in near real-time on a regular, repeatable and affordable basis (WTMA Research Strategy 2010-2014). We will generate high resolution maps and landscape scale estimates of temporal trends in the condition of biodiversity and environmental changes. This will be the major vehicle for synthesizing, integrating and communicating data from all projects. This project will make use of extensive computing power represented by the collaboration between the CTBCC and the James Cook University eResearch group and High Performance Computing Facility. Specific objectives: 1. Produce and make publicly available a spatial and temporal resources tool that allows web-based query of all the above datasets based on a user-defined spatial area that will return all predicted and observed data within the query area for climate (past, current and future projections), habitat, species (predicted and observed), biodiversity values, terrain, ecosystem processes and, where available data is site-based, the tool could query the temporal patterns in the data (e.g. changes in abundance of a

The Napo Basin in Ecuador is an important drainage of the Amazon Basin, the most biodiverse ecosystem for freshwater species. At the same time, this basin has conspicuous information gaps on its biodiversity patterns and human threats. Here, we estimated the diversity distribution patterns of freshwater vertebrates and invertebrates in the Napo Basin, as a tool for present and future management and conservation efforts. Also, we assessed the spatial congruence of the diversity patterns observed between aquatic vertebrates and invertebrates. For this, we compiled occurrence records for 481 freshwater vertebrate species (amphibians, birds, mammals, reptiles, and fish), and 54 invertebrate families obtained across an altitudinal gradient of the basin (200–4500 m). Using these occurrence records and environmental variables, we modeled the distribution of each vertebrate species and invertebrate family. Then, we stacked these distributions to build species richness maps for vertebrates, and a family richness map for invertebrates. We found that the most diverse areas for vertebrate species are the lowlands (

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of vertebrates in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 309 species).High values identify areas where more imperiled vertebrates are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 6 species were added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

This application allows the viewer to spatially and graphically view the first leave spring index, first bloom spring index, a comparison of the two, fish habitat condition and disturbance summaries, protection status of ecological systems, most reported marine species per exclusive economic zone, and the protection status of terrestrial vertebrate species. The USFS describes the application as: "A new dataset of habitat distribution for terrestrial vertebrate species in the conterminous United States is now available from the USGS. These data represent foundational data for the USGS National Biogeographic Map. This project is designed to integrate and expand a wide range of biological data and analysis for use by land managers, decision makers, and others addressing biodiversity challenges. These species data are one of the foundational data sets in the Map."