The RESOLVE Ecoregions dataset, updated in 2017, offers a depiction of the 846 terrestrial ecoregions that represent our living planet. View the stylized map at https://ecoregions2017.appspot.com/ or in Earth Engine. Ecoregions, in the simplest definition, are ecosystems of regional extent. Specifically, ecoregions represent distinct assemblages of biodiversity-all taxa, not just …

Ecoregions, in the simplest definition, are ecosystems of regional extent. Specifically, ecoregions represent distinct assemblages of biodiversity―all taxa, not just vegetation―whose boundaries include the space required to sustain ecological processes. Ecoregions provide a useful basemap for conservation planning in particular because they draw on natural, rather than political, boundaries, define distinct biogeographic assemblages and ecological habitats within biomes, and assist in representation of Earth’s biodiversity.This dataset is based on recent advances in biogeography - the science concerning the distribution of plants and animals. The original ecoregions dataset has been widely used since its introduction in 2001, underpinning the most recent analyses of the effects of global climate change on nature by ecologists to the distribution of the world's beetles to modern conservation planning.The 846 terrestrial ecoregions are grouped into 14 biomes and 8 realms. Six of these biomes are forest biomes and remaining eight are non-forest biomes. For the forest biomes, the geographic boundaries of the ecoregions (Dinerstein et al., 2017) and protected areas (UNEP-WCMC 2016) were intersected with the Global Forest Change data (Hansen et al. 2013) for the years 2000 to 2015, to calculate percent of habitat in protected areas and percent of remaining habitat outside protected areas. Likewise, the boundaries of the non-forest ecoregions and protected areas (UNEP-WCMC 2016) were intersected with Anthropogenic Biomes data (Anthromes v2) for the year 2000 (Ellis et al., 2010) to identify remaining habitats inside and outside the protected areas. Each ecoregion has a unique ID, area (sq. degrees), and NNH (Nature Needs Half) categories 1-4. NNH categories are based on percent of habitat in protected areas and percent of remaining habitat outside protected areas.Half Protected: More than 50% of the total ecoregion area is already protected.Nature Could Reach Half: Less than 50% of the total ecoregion area is protected but the amount of remaining unprotected natural habitat could bring protection to over 50% if new conservation areas are added to the system.Nature Could Recover: The amount of protected and unprotected natural habitat remaining is less than 50% but more than 20%. Ecoregions in this category would require restoration to reach Half Protected.Nature Imperilled: The amount of protected and unprotected natural habitat remaining is less than or equal to 20%. Achieving half protected is not possible in the short term and efforts should focus on conserving remaining, native habitat fragments.The updated Ecoregions 2017 is the most-up-to-date (as of February 2018) dataset on remaining habitat in each terrestrial ecoregion. It was released to chart progress towards achieving the visionary goal of Nature Needs Half, to protect half of all the land on Earth to save a living terrestrial biosphere.Note - a number of ecoregions are very complex polygons with over a million vertices, such as Rock & Ice.

Ecoregions, in the simplest definition, are ecosystems of regional extent. Specifically, ecoregions represent distinct assemblages of biodiversity―all taxa, not just vegetation―whose boundaries include the space required to sustain ecological processes. Ecoregions provide a useful basemap for conservation planning in particular because they draw on natural, rather than political, boundaries, define distinct biogeographic assemblages and ecological habitats within biomes, and assist in representation of Earth’s biodiversity.This dataset is based on recent advances in biogeography - the science concerning the distribution of plants and animals. The original ecoregions dataset has been widely used since its introduction in 2001, underpinning the most recent analyses of the effects of global climate change on nature by ecologists to the distribution of the world's beetles to modern conservation planning.The 846 terrestrial ecoregions are grouped into 14 biomes and 8 realms. Six of these biomes are forest biomes and remaining eight are non-forest biomes. For the forest biomes, the geographic boundaries of the ecoregions (Dinerstein et al., 2017) and protected areas (UNEP-WCMC 2016) were intersected with the Global Forest Change data (Hansen et al. 2013) for the years 2000 to 2015, to calculate percent of habitat in protected areas and percent of remaining habitat outside protected areas. Likewise, the boundaries of the non-forest ecoregions and protected areas (UNEP-WCMC 2016) were intersected with Anthropogenic Biomes data (Anthromes v2) for the year 2000 (Ellis et al., 2010) to identify remaining habitats inside and outside the protected areas. Each ecoregion has a unique ID, area (sq. degrees), and NNH (Nature Needs Half) categories 1-4. NNH categories are based on percent of habitat in protected areas and percent of remaining habitat outside protected areas.Half Protected: More than 50% of the total ecoregion area is already protected.Nature Could Reach Half: Less than 50% of the total ecoregion area is protected but the amount of remaining unprotected natural habitat could bring protection to over 50% if new conservation areas are added to the system.Nature Could Recover: The amount of protected and unprotected natural habitat remaining is less than 50% but more than 20%. Ecoregions in this category would require restoration to reach Half Protected.Nature Imperilled: The amount of protected and unprotected natural habitat remaining is less than or equal to 20%. Achieving half protected is not possible in the short term and efforts should focus on conserving remaining, native habitat fragments.The updated Ecoregions 2017 is the most-up-to-date (as of February 2018) dataset on remaining habitat in each terrestrial ecoregion. It was released to chart progress towards achieving the visionary goal of Nature Needs Half, to protect half of all the land on Earth to save a living terrestrial biosphere.Note - a number of ecoregions are very complex polygons with over a million vertices, such as Rock & Ice.

📄 Dataset Description This dataset contains selected ecoregion shapefiles related to the Amazon biome, extracted from the official RESOLVE Ecoregions 2017 dataset available through Google Earth Engine (GEE). The original dataset was developed by The Nature Conservancy, the World Wildlife Fund (WWF), and other partners, and provides a scientifically-based global map of terrestrial ecoregions.

The specific ecoregions included here are:

Southwest Amazon Moist Forests

Amazon-Orinoco-Southern Caribbean Mangroves

These shapefiles were exported directly from GEE for the purpose of geospatial analysis in the context of detecting potential archaeological or ecological sites using machine learning.

🔗 Source Dataset name: RESOLVE/ECOREGIONS/2017

Provider: World Wildlife Fund (WWF) and partners

Accessed via: Google Earth Engine Dataset Catalog

Citation: Dinerstein et al. (2017). "An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm." BioScience, 67(6), 534–545.

📦 File Format The dataset is provided in the ESRI Shapefile format and includes:

.shp — main geometry file

.shx — shape index format

.dbf — attribute data

.prj — projection information

Each ecoregion is stored as a FeatureCollection of polygon geometries with associated metadata such as ecoregion name, biome type, realm, and code.

⚠️ Disclaimer This dataset was extracted and re-shared for academic and research purposes only. All original credit goes to the dataset authors and providers. Please cite the source if used in your work.

Das 2017 aktualisierte RESOLVE-Dataset zu Ökoregionen bietet eine Darstellung der 846 terrestrischen Ökoregionen, die unseren lebenden Planeten repräsentieren. Die stilisierte Karte ist unter https://ecoregions2017.appspot.com/ oder in Earth Engine verfügbar. Ökoregionen sind in ihrer einfachsten Definition Ökosysteme von regionaler Ausdehnung. Konkret stellen Ökoregionen unterschiedliche Ansammlungen von Biodiversität dar – alle Taxa, nicht nur …

RESOLVE 生態區域資料集於2017 年更新，描繪出代表我們所居住星球的846 個陸地生態區域。如要查看經過樣式化的地圖，請前往https://ecoregions2017.appspot.com/ 或 Earth Engine。 簡單來說，生態區域就是區域範圍的生態系統。具體來說，生態區域代表不同的生物多樣性組合(所有分類單元，而不只是植被)，其邊界包含維持生態程序所需的空間。生態區域以自然而非政治界線為依據，定義生物群系內不同的生物地理組合和生態棲地，並協助呈現地球的生物多樣性，因此特別適合做為保育規劃的實用底圖。 這項資料集是以生物地理學的最新進展為基礎，生物地理學是研究動植物分布的科學。自 2001 年推出以來，原始的生態區域資料集已廣泛使用，為生態學家對全球氣候變遷對自然影響的最新分析提供基礎，包括全球甲蟲的分布情形，以及現代保育規劃。 846 個陸地生態區域分為14 個生物群落和8 個領域。其中六個是森林生物群落，其餘八個則是非森林生物群落。就森林生物群系而言，生態區域的地理界線(Dinerstein 等人，2017 年) 和保護區(UNEP-WCMC 2016 年) 與 2000 年至2015 年的全球森林變化資料(Hansen 等人，2013 年) 相交，以計算保護區內的棲息地百分比，以及保護區外剩餘棲息地的百分比。同樣地，非森林生態區和保護區的界線(UNEP-WCMC 2016) 與 2000 年的人為生物群系資料(Anthromes v2) 相交 (Ellis 等人，2010 年) 找出保護區內外的剩餘棲息地。每個生態區域都有專屬ID、面積 (平方度) 和 NNH (Nature Needs Half) 類別 1 到 4。NNH 類別的依據是保護區內的棲息地百分比，以及保護區外的剩餘棲息地百分比。 半保護：超過 50% 的生態區域總面積已受到保護。 自然保護區可望達到一半：總生態區域面積中，有不到50% 受到保護，但如果系統新增保護區，剩餘未受保護的自然棲息地可望將保護比例提升至50% 以上。 自然環境可望復原：剩餘的自然棲息地(包括受保護和未受保護的棲息地) 比例不到50%，但超過20%。這類生態區域需要復育，才能達到「半受保護」狀態。 自然環境危在旦夕：剩餘的受保護和未受保護自然棲息地數量小於或等於20%。短期內無法達成保護一半土地的目標，因此應將重點放在保護剩餘的本土棲地碎片。 更新後的2017 年生態區域資料集是目前最新的資料集(2018 年 2 月)，內容涵蓋每個陸地生態區域的剩餘棲地。這份報告旨在記錄「自然需要一半」願景目標的進展，也就是保護地球上的一半土地，拯救陸地生物圈。 注意 - 許多生態區域都是非常複雜的多邊形，有超過一百萬個頂點，例如岩石和冰。必要時，這些生態區域會分割，但 Eco_ID 等屬性會保留。如要查看所有已分割的生態區域，請執行這項指令碼。

Overview

This dataset was developed to provide a representative sample of Earth’s terrestrial land surface and near-shore ecosystems while optimizing for coverage across space, time, and availability of data sources. Our sampling strategy prioritizes coverage of locations where we have geocoded text information by first taking a gridded sample that covers point locations for geotagged features from Wikipedia and GBIF species observations (1). To cover the remaining land surface, we use the 2017 RESOLVE Ecoregions dataset ("RESOLVE/ECOREGIONS/2017" in the Earth Engine Data Catalog) (2) to draw an additional random stratified sample by ecoregion ID. We supplement our initial RESOLVE sample, which largely targets terrestrial ecosystems, with additional stratified samples from the Allen Coral Atlas (3) and Global Intertidal Zones datasets (4) to improve representation of near-shore ecosystems. We sample 4,141 locations from the Allen Coral Atlas ("ACA/reef_habitat/v2_0") and 2,968 from the Murray Global Intertidal dataset ("UQ/murray/Intertidal/v1_1/global_intertidal"). We ensure a minimum distance of 1.28 km between sampled locations, and we sample two year-long periods for each location. After culling locations with insufficient image availability, the final dataset hosted here includes 8,412,511 unique (x, y, t_start, t_end) rows that can be used to query imagery from publicly available image collections.

License

Copyright 2025 Google LLC

All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0 All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.

This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile.

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

Wildfire is globally an important ecological disturbance affecting biochemical cycles, and vegetation composition, but also puts people and their homes at risk. Suppressing wildfires has detrimental ecological effects and can promote larger and more intense wildfires when fuels accumulate, which increases the threat to buildings in the Wildland Urban Interface (WUI). Yet, when wildfires occur, typically only a small proportion of the buildings within the fire perimeter are lost, and the question is what determines which buildings burn. Our goal was to examine which factors are related to building loss when a wildfire occurs throughout the United States. We were particularly interested in the relative roles of vegetation, topography, and the spatial arrangement of buildings, and how their respective roles vary among ecoregions. We analyzed all fires that occurred within the conterminous U.S. from 2000 to 2010 and digitized which buildings were lost and which survived according to Google Earth historical imagery. We modeled the occurrence as well as the percentage of buildings lost within clusters using logistic and linear regression. Overall, variables related to topography and the spatial arrangement of buildings were more frequently present in the best 20 regression models than vegetation-related variables. In other words, specific locations in the landscape have a higher fire risk, and certain development patterns can exacerbate that risk. Fire policies and prevention efforts focused on vegetation management are important, but insufficient to solve current wildfire problems. Furthermore, the factors associated with building loss varied considerably among ecoregions suggesting that fire policy applied uniformly across the US will not work equally well in all regions and that efforts to adapt communities to wildfires must be regionally tailored.

Just like its climate, Earth’s land cover varies widely between regions. Some regions are characterized by deserts, while in others wetlands predominate. Boreal forests, also called taiga, cover much of the planet’s northern latitudes, while tropical forests are a common feature in equatorial countries. These diverse types of land cover can be further broken down into “ecoregions”—large expanses of land, each with a distinct biological and environmental character.Mapping land cover often involves defining a set of ecoregions and determining which part or parts of Earth’s surface match the criteria for each ecoregion. To define a set of ecoregions, scientists may supplement existing work, such as maps of species distribution and vegetation types, with new insights and data gathered from regional experts. The land cover types included in this map layer are based on biogeographic research (sources listed here), a framework last updated in 2017 that defines more than 846 land-based ecoregions within about a dozen biomes or habitat types. This map layer represents those broader categories, like deserts and tropical forests. A couple tips for navigating this layer: 1) If a region is shaded entirely in the color representing a particular biome, it indicates that that biome is the predominant one, but there may be characteristics of other biomes present as well. 2) The actual borders between biomes are often large regions unto themselves rather than precise lines. There’s even a name for these transition areas: ecotones!This map layer from RESOLVE Biodiversity and Wildlife Solutions includes the following biomes:Boreal Forests/Taiga: widespread in northern Russia and Canada, boreal forests are typically home to lots of conifers, mosses, and lichensDeserts and Xeric Shrubland: the evaporation rate may be greater than the rate of precipitation in these dry regions exemplified by the Sahara and GobiFlooded Grasslands and Savannas: like mangroves, this biome is waterlogged land that may support grasses, shrubs, and trees; the Everglades of South Florida are an exampleMangroves: the mangrove tree dominates these coastal regions, which frequently lie within intertidal zonesMediterranean Forests, Woodlands, and Scrub: these wooded regions are known for their hot, dry summers and cool, wet wintersMontane Grasslands and Shrublands: this biome, which features waxy, hairy plants, defines the Tibetan Plateau and parts of the Andes Bare Earth: occurring largely in Earth’s polar regions, bare earth includes tundra, a type of cold desert with sparse vegetationTemperate Broadleaf and Mixed Forests: this biome may include oak, beech, and maple trees; in contrast to tropical forests, biodiversity here is usually concentrated near the forest floorTemperate Coniferous Forests: this biome has warm summers and cool winters with a wide variety of plant life including either needleleaf or broadleaf evergreen treesTemperate Grasslands, Savannas, and Shrubland: trees are less common in this biome, which goes by many names—such as prairie, pampas, and veldTropical and Subtropical Coniferous Forests: located mostly in North and Central American regions with low precipitation and moderate temperature variability making it ideal for needleleaf conifers to growTropical and Subtropical Dry Broadleaf Forests: this biome is characterized by year-round warm temperatures but seasonal precipitation that results in a long dry periods and feature drought-deciduous trees, for example the forests of southern Mexico or central India Tropical and Subtropical Grasslands, Savannas, and Shrublands: prominent in East Africa, these regions are often too dry to support much tree growthTropical and Subtropical Moist Broadleaf Forests: common in the region between the Tropics of Cancer and Capricorn, this biome has steady temperatures year round and high precipitation allowing for evergreen and semi-evergreen treesTundra: found near the poles, this biome is characterized by a cold desert, dark winters and sunny summers with low growing vegetation

R script for clustering specimens based on measurement dataScript for performing UPGMA hierarchical cluster analysis on the R platform.R script for clustering.pdfR script for Principal Component Analysis (PCA): specimens on a morphometric ordination spaceScript for performing Principal Component Analysis (PCA) on the R platform.R script for PCA.pdfScript for mapping the distribution of Stigmatomma species in Madagascar and SeychellesR code for making species distribution maps. Note: Our maps use the ecoregion outlines of Madagascar, which were based on the vector data disclosed by the Terrestrial Ecoregions of the World (available at the WWF website). However, the original outlines were slightly mismatching the relief of Madagascar. To solve this, we combined the original ecoregion data with data from the Remaining Primary Vegetation of Madagascar (available at the Kew Royal Botanic Gardens website), which has more natural outlines.Linear morphometry of Stigmatomma species in the Malaga...

The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.

The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:

• id unique feature identifier
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• area area of the polygon in squared kilometres
• geom the geometry of the features in geographical coordinates WGS84

The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from Ecoregions 2017 ^{© Resolve} (Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).

This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:

• id unique feature identifier
• id_hcluster unique feature identifier
• list_of_commodities a comma-separated list of commodities
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• ecoregion ecoregion name
• biome biome name
• year the year
• area_forest_loss_XXX_YYY

The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).

• XXX can take one of:
  • 000: total tree cover loss independently from the initial tree cover
  • 025: tree cover loss on pixels with initial tree cover between 0 and 25%
  • 050: tree cover loss on pixels with initial tree cover between 25 and 50%
  • 075: tree cover loss on pixels with initial tree cover between 50 and 75%
  • 100: tree cover loss on pixels with initial tree cover between 75 and 100%
• YYY can take one of:
  • la: tree cover loss within strict nature reserve
  • Ib: tree cover loss within wilderness area
  • II: tree cover loss within national park
  • III: tree cover loss within natural monument or feature
  • IV: tree cover loss within habitat/species management area
  • V: tree cover loss within protected landscape/seascape
  • VI: tree cover loss within PA with sustainable use of natural resources
  • p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported
  • none: when YYY is omitted, total tree cover loss within the polygon

For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The id can be used to link polygons to forest loss data.

Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National Fire Plan, Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.Data Summary:The Fire Regime Condition Class (FRCC) data layer categorizes departure between current vegetation conditions and reference vegetation conditions according to the methods outlined in the Interagency Fire Regime Condition Class Guidebook (Hann and others 2004). For the full product description, please refer to Rollins and others 2007, "Developing the LANDFIRE Fire Regime Data Products" available at www.landfire.gov.Scope and Applications:The LANDFIRE Project charter states: "LANDFIRE is a landscape-scale fire, ecosystem, and fuel assessment mapping project designed to generate comprehensive maps of vegetation, fire and fuel characteristics nationally and identify and develop a set of tools to create and distribute data to users." This statement must always frame any discussion about the data products developed by LANDFIRE. Within this context, the summary and reporting of LANDFIRE fire regime data products nationally should include reporting by state and entire bureau/agency ownership. Any characterization or use of the data below that level is the responsibility of the user. Several analysis tools for working with LANDFIRE data products at finer spatial scales have been developed by the National Interagency Fuel Technology Team and are provided as part of the LANDFIRE deliverables (see http://frames.nbii.gov/niftt/).Inherent limitations of LANDFIRE fire regime data products include but are not limited to:1) Establishing break points to simplify and clarify data display. In other words, FRCC is mapped in three distinct categories rather than continuously.2) Data verification is based on field-referenced data that are not nationally consistent in quality or quantity.3) The Landsat imagery used in the data characterization process is several years old (much is from calendar year 2000 and earlier). As a result, recent disturbance and management activities are not represented nor are changes in ecosystems with relatively rapid vegetation succession cycles.4) Edge effects are present due to independent map unit development, relative scale of ecological classification, and limited time available to resolve edge effect issues given the timeframe of the project.Summarization at the national and state levels does not change the relevance of LANDFIRE data that are available to support management decisions at the unit level. The advantages of a nationally consistent data set and repeatable methodology preclude any short comings of the LANDFIRE data products when used at the local level. The information included in this section is derived from an Interagency Fuel Coordination Group memorandum dated April 12, 2007, available at www.landfire.gov.Technical Methods:Ecological Subsections (Cleland and others 2005) are used within LANDFIRE mapping zones to stratify the calculation of vegetation departure. Within each biophysical setting (BpS) in each subsection, we compare the reference percentage of each succession class (SClass) to the current percentage, and the smaller of the two is summed to determine the similarity index for the BpS. This value is then subtracted from 100 to determine the departure index. This departure index is represented using a 0 to 100 percent scale, with 100 representing maximum departure. The departure index is then classified into three condition classes. It is important to note that the LANDFIRE FRCC approach differs from that outlined in the Interagency Fire Regime Condition Class Guidebook (Hann and others 2004) as follows: LANDFIRE FRCC is based on departure of current vegetation conditions from reference vegetation conditions only, whereas the Guidebook approach also includes departure of current fire regimes from those of the reference period.The reference conditions are derived from simulations using the vegetation and disturbance dynamics model LANDSUM (Keane and others 2002; Keane and others 2003; Keane and others 2005; Pratt and others 2006). LANDSUM simulates fire dynamics as a function of vegetation dynamics, topography, and spatial context in addition to variability introduced by dynamic wind direction and speed, frequency of extremely dry years, and landscape-level fire size characteristics. The reference conditions are intended to describe one component of simulated historical fire regimes and vegetation dynamics in the context of the broader historical time period represented by the LANDFIRE Biophysical Settings layer and LANDFIRE Vegetation Dynamics Models. The proportion of the landscape in each SClass in each BpS unit is reported every 20 years during a 10,000-year simulation period. It is important to note that this simulation period represents 10,000 years of stochastic modeling by LANDSUM rather than a depiction of the last 10,000 years of history. These data are prepared for use in the FRCC calculation by first deriving a median value for each SClass across its respective time series and then normalizing the median values to ensure that they sum to 100 percent of the area in the BpS.The current conditions are derived from the LANDFIRE Succession Class data layer; please refer to the product description page at www.landfire.gov for more information. The proportion of the landscape occupied by each SClass in each BpS unit in each subsection is used to represent the current condition of that SClass in the FRCC calculation. The areas currently mapped to agriculture, urban, water, barren, or sparsely vegetated BpS units are not included in the FRCC calculation; thus, FRCC is based entirely on the remaining area of each BpS unit that is occupied by valid SClasses.The fire regime condition classes are defined as follows:Condition Class I: vegetation departure index of 0 to 33Condition Class II: vegetation departure index of 34 to 66Condition Class III: vegetation departure index of 67 to 100Additional data layer values were included to represent Water (111), Snow / Ice (112), Urban (120), Barren (131), Sparsely Vegetated BpS (132), and Agriculture (180).References:Cleland, D.T.; Freeouf, J. A.; Keys, J.E.; Nowacki, G.J.; Carpenter, C.A.; McNab, W.H. 2005. Ecological Subregions: Sections and Subsections for the conterminous United States. (A.M. Sloan, technical editor). Washington, D.C.: USDA Forest Service.Hann, W.; Shlisky, A.; Havlina, D.; Schon, K.; Barrett, S.; DeMeo, T.; Pohl, K.; Menakis, J.; Hamilton, D.; Jones, J.; Levesque, M. 2004. Interagency Fire Regime Condition Class Guidebook. Interagency and The Nature Conservancy Fire Regime Condition Class website. USDA Forest Service, U.S. Department of the Interior, The Nature Conservancy, and Systems for Environmental Management. Available online: www.frcc.gov.Keane, R.E.; Parsons, R.; Hessburg, P. 2002. Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach. Ecological Modeling 151: 29-49.Keane, R.E.; Cary, G.J.; Parsons, R. 2003. Using simulation to map fire regimes: an evaluation of approaches, strategies, and limitations. International Journal of Wildland Fire 12: 309-322.Keane, R.E.; Holsinger, L.; Pratt, S. 2006. Simulating historical landscape dynamics using the landscape fire succession model LANDSUM version 4.0. Gen. Tech. Rep. RMRS-GTR-171CD. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory.Pratt, S.D.; Holsinger, L.; Keane, R.E. 2006. Using simulation modeling to assess historical reference conditions for vegetation and fire regimes for the LANDFIRE Prototype Project. Chapter 10 in: The LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data for wildland fire management. Rollins, M.G. and Frame, C.K., tech. eds. Gen. Tech Rep. RMRS-GTR-175. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. 277-314.

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom and these associated communities provide important habitat structure for many invertebrate and fish species (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016). According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Human-created hardbottom (e.g., artificial reefs) is also known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019).Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomMapping and Geomorphic Characterization of the Vast Cold-Water Coral Mounds of the Blake Plateau; data provided prior to official release on 6-14-2023 by Dr. Derek Sowers with Ocean Exploration Trust (derek@oceanexplorationtrust.org); read the published journal article about this research; read more about the mapping expedition; read an abstract describing this work from the 2024 Ocean Sciences Meeting; read a white paper about the surveyThe Nature Conservancy’s (TNC) South Atlantic Bight Marine Assessment (SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 5-10-2024 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023National Oceanic and Atmospheric Administration (NOAA) Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic; read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data and draft final report entitled Assessment of Benthic Habitats for Fisheries Management provided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Shipwrecks & artificial reefsNOAA wrecks and obstructions layer (shapefile), accessed 5-1-2023 on the Marine CadastreNOAA artificial reefs, accessed 5-9-2024 on the Marine Cadastre, provided by the NOAA Office for Coastal ManagementFlorida Fish and Wildlife Conservation Commission (FWC): Artificial Reefs in Florida (.xlsx), accessed 5-9-2024Defining inland extent & split with GulfMarine Ecoregions Level IIIfrom the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Atlantic version of this indicator to separate it from the Gulf. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Atlantic and Gulf.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Atlantic and Gulf extent.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m, convert to raster, and assign them a value of 8. The buffer distance used here, and later for shipwrecks and artificial reefs, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).From the FL FWC coral and hardbottom data, convert to raster and assign the “coral reef” class a value of 8 and the “hardbottom” and “hardbottom with seagrass” classes a value of 7.From the TNC SABMA data, pull out observed hardbottom polygons that contain a value of “01. mapped hardbottom area” in the TEXT_DESC field. Convert to a raster and assign a value of 7.Combine the NOAA regional artificial reef dataset with the Florida FWC artificial reefs. Buffer the points by 150 m and convert to raster, assigning all buffered points a value of 6.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer by 150 m and convert to raster, assigning them a value of 5.From the Blake Plateau dataset, pull out peaks, ridges, and slopes from the landform data and assign them all a value of 4.Reclassify both NOAA hardbottom suitability datasets into 5 quantiles. Assign the top quantile a value of 3, the second-highest quantile a value of 2, and the middle quantile a value of 1. Assign the lower two quantiles a value of NoData. They are not used in the indicator due to the relatively low likelihood of hardbottom presence (<40% probability).Combine the two NOAA hardbottom suitability datasets and use the newer data from the “Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitat in the U.S. Southeast Atlantic” project where pixels overlap. Snap and project the result based on the Southeast Blueprint 2024 extent.Combine all the layers produced above using the cell statistics tool with the overlay statistic maximum.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:8 = Confirmed hardbottom-associated species (corals, sponges)7 = Confirmed natural hardbottom6 = Artificial reefs5 = Shipwrecks4 = Predicted cold-water coral mounds (Blake Plateau)3 = Highest probability of hardbottom (>80th percentile)2 = High probability of hardbottom (>60th-80th percentile)1 = Medium probability of hardbottom (>40th-60th percentile)0 = Not identified as hardbottomKnown IssuesThis indicator likely underpredicts hardbottom suitability in shallow waters. While this indicator includes new hardbottom suitability models based on recent hardbottom observations for deep waters (depths of 50 m or below), the underlying NOAA data available for shallow waters were developed in 2014.While this layer has a 30 m resolution, both NOAA hardbottom datasets were coarser than that. We downsampled 100 m pixels and 92 m pixels to 30 m.This indicator underestimates shallower hardbottom habitat (<200 m depth) north of the NC/VA state line because the study area of the shallower hardbottom suitability dataset was restricted only to the South Atlantic marine environment and did not cover the northern portion of the SECAS marine area. The indicator also underestimates deeper hardbottom habitat north of approximately 37.5°N latitude because the study area of the deeper hardbottom suitability dataset does not perfectly align with the SECAS marine area and leaves an area of NoData.This indicator likely overpredicts hardbottom in the “3 = highest probability of hardbottom” class because the sampling data used to generate these hardbottom probability models is not sufficient to finely resolve the relative likelihood of hardbottom within this bin. While there is a high probability of hardbottom within these areas, there is likely more variation than the model depicts (i.e., the probabilities are somewhat “smeared”).Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedCommission for Environmental Cooperation. 2009. Marine Ecoregions of North America, 2008. Vector digital data. Montréal, Québec, Canada. [https://www.cec.org/north-american-environmental-atlas/marine-ecoregions/].Conley, M.F., M.G.

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf Data Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster. Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among