This data set comprises the Environmental Sensitivity Index (ESI) and Reach Sensitivity Index (RSI) data for Puerto Rico. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. Most rivers and streams can be readily subdivided into clear-cut segments, or reaches (RSI), that have very distinct and uniform characteristics within that reach of the stream. The definition of reach type is usually based on whatever the intended use of the reach classification might be. In this project, stream reaches are defined as those segments where similar spill-response modes and potential ecological and/or socioeconomic impacts from the spill are to be anticipated. However defined, the boundary of the reach is usually marked by an abrupt change in the morphology of the stream, a change commonly, but not always, brought about by an alteration in the stream's gradient. This data set contains sensitive biological resource polygonal data for fish.

This data set comprises the Environmental Sensitivity Index (ESI) and Reach Sensitivity Index (RSI) data for Puerto Rico. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. Most rivers and streams can be readily subdivided into clear-cut segments, or reaches (RSI), that have very distinct and uniform characteristics within that reach of the stream. The definition of reach type is usually based on whatever the intended use of the reach classification might be. In this project, stream reaches are defined as those segments where similar spill-response modes and potential ecological and/or socioeconomic impacts from the spill are to be anticipated. However defined, the boundary of the reach is usually marked by an abrupt change in the morphology of the stream, a change commonly, but not always, brought about by an alteration in the stream's gradient. This data set contains sensitive biological resource line data for fish.

Historical data are often one of the only resources for documenting and assessing causes of environmental change, particularly in developing regions where funding for ecological studies is limited. In this study, previously unpublished data from a 1977 year-long study of the fish community of the Espiritu Santo estuary are presented. This dataset is among the oldest and most extensive surveys of a Caribbean island estuarine fish community. A comparison of these historical data with data collected in June and July 2004 using identical sampling methods allowed description of potential long-term changes in the fish community, identification of vulnerable species, and assessment of potential drivers of change. Results strongly suggest a decline in species richness and abundance in the Espiritu Santo estuarine fish community, with greater declines in freshwater-tolerant than marine or euryhaline species. Declines in freshwater inflow to the estuary, due to large-scale upstream water abstractions for municipal use, have increased since the initial 1977 survey. This is the first study to examine long-term change in the fish community of a tropical island estuary. Additional research and conservation efforts are needed to understand mechanisms of change and to protect Caribbean island estuarine fish communities. Chapter 2 (isotope and gut content data): The contribution of riverine-derived organisms and organic matter to four fishes along the salinity gradient in two Puerto Rican estuaries, the Espiritu Santo and Mameyes, was examined via stable isotope and gut content analyses. Stable isotope analyses indicated that riverine organic matter potentially contributed as much as 69% of the diet of one (caitipa mojarra, Diapterus rhombeus) of four fishes sampled. In contrast, riverine organic matter was of little direct importance to the three other fishes, tarpon snook (Centropomus pectinatus), ground croaker (Bairdiella ronchus), and white mullet (Mugil curema) contributing less than a third of their assimilated material even in the estuaries’ upper reaches. Gut content analysis of estuarine fishes demonstrated that several species of pelagic or omnivorous fish consume riverine-derived organisms, specifically juvenile migratory freshwater shrimps, during their residence in the estuary. Freshwater shrimps were frequently encountered (in 37 and 39% of guts examined) and composed an average of 18 and 22% of gut content material of omnivorous fishes sampled in the Espiritu Santo and Mameyes estuaries, respectively. To our knowledge, this is the first study to examine the contribution of riverine subsidies to a Caribbean island estuary. Given increasing demand for water resources on tropical islands and the importance of diadromy in these systems, there is a need for additional research on this topic to better inform water management decisions. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

This fish and benthic composition database is the result of a multifaceted effort described below. The intent of this work is five fold: 1) To spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrates (conch, lobster, Diadema); 2) To relate this information to in-situ data collected on associated benthic composition parameters; 3) To use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting; 4) To establish the efficacy of those management decisions; and 5) To work with the National Coral Reef Monitoring Program to develop data collection standards and easily implemented methodologies for transference to other agencies and to work toward standardizing data collection throughout the US states and territories. Toward this end, the Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) has been conducting research in Puerto Rico and the US Virgin Islands since 2000 and 2001, respectively. It is critical, with recent changes in management at both locations (e.g. implementation of MPAs) as well as proposed changes (e.g. zoning to manage multiple human uses) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas. It is also important that BB work closely with the individuals responsible for recommending and implementing these management strategies. Recognizing this, BB has been collaborating with partners at the University of Puerto Rico, National Park Service, US Geological Survey and the Virgin Islands Department of Planning and Natural Resources.To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are stratified to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef or seagrass area. At each site, fish, macro-invertebrates, and benthic composition information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, BB is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between locations. Armed with the knowledge of where "hot spots" of species richness and diversity are likely to occur in the seascape, the BB is in a unique position to answer questions about the efficacy of marine zoning strategies (e.g. placement of no fishing, anchoring, or snorkeling locations), and what locations are most suitable for establishing MPAs. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

Chapter 1 (fish community data): Historical data are often one of the only resources for documenting and assessing causes of environmental change, particularly in developing regions where funding for ecological studies is limited. In this study, previously unpublished data from a 1977 year-long study of the fish community of the Espiritu Santo estuary are presented. This dataset is among the oldest and most extensive surveys of a Caribbean island estuarine fish community. A comparison of these historical data with data collected in June and July 2004 using identical sampling methods allowed description of potential long-term changes in the fish community, identification of vulnerable species, and assessment of potential drivers of change. Results strongly suggest a decline in species richness and abundance in the Espiritu Santo estuarine fish community, with greater declines in freshwater-tolerant than marine or euryhaline species. Declines in freshwater inflow to the estuary, due to large-scale upstream water abstractions for municipal use, have increased since the initial 1977 survey. This is the first study to examine long-term change in the fish community of a tropical island estuary. Additional research and conservation efforts are needed to understand mechanisms of change and to protect Caribbean island estuarine fish communities. Chapter 2 (isotope and gut content data): The contribution of riverine-derived organisms and organic matter to four fishes along the salinity gradient in two Puerto Rican estuaries, the Espiritu Santo and Mameyes, was examined via stable isotope and gut content analyses. Stable isotope analyses indicated that riverine organic matter potentially contributed as much as 69% of the diet of one (caitipa mojarra, Diapterus rhombeus) of four fishes sampled. In contrast, riverine organic matter was of little direct importance to the three other fishes, tarpon snook (Centropomus pectinatus), ground croaker (Bairdiella ronchus), and white mullet (Mugil curema) contributing less than a third of their assimilated material even in the estuaries’ upper reaches. Gut content analysis of estuarine fishes demonstrated that several species of pelagic or omnivorous fish consume riverine-derived organisms, specifically juvenile migratory freshwater shrimps, during their residence in the estuary. Freshwater shrimps were frequently encountered (in 37 and 39% of guts examined) and composed an average of 18 and 22% of gut content material of omnivorous fishes sampled in the Espiritu Santo and Mameyes estuaries, respectively. To our knowledge, this is the first study to examine the contribution of riverine subsidies to a Caribbean island estuary. Given increasing demand for water resources on tropical islands and the importance of diadromy in these systems, there is a need for additional research on this topic to better inform water management decisions.

This project ensures that the existing data from previous fishery independent surveys can be combined with data collected under new methodology, ensuring preservation of long-time series reef fish datasets in the US Caribbean. Specifically, this archived dataset are the site level fish abundance and size observations collected simultaneously by one transect diver and a pair of stationary point count divers at survey sites in Puerto Rico and the U.S. Virgin Islands. These collection methods represent the pre- National Coral Reef Monitoring Program (NCRMP) belt-transect survey methodology and the currently used Reef Visual Census (RVC) survey methodology, respectively. More information about the methodologies can be found (Jeffrey 2019 & CRCP 2022). In the U.S. Caribbean, the National Coral Reef Monitoring Program began in 2012. Initially, the belt transect method was used for fish surveys, but then transitioned to the RVC method in all the U.S. Caribbean by 2016. This dataset is the field samples collected in order to properly calibrate belt transect data to RVC data.

The National Coral Reef Monitoring Plan (NCRMP) is a framework for conducting sustained observations of biological, climate, and socioeconomic indicators at 10 priority coral reefs across the U.S. and its territories. This integrated approach will consolidate monitoring of coral reefs under a uniform method in the Pacific, Atlantic, Caribbean, and the Gulf of Mexico for the first time. NCRMP is funded by the NOAA Coral Reef Conservation Program (CRCP) and supported by NOAA National Centers for Coastal Ocean Science (NCCOS) and many other partners. NCCOS is co-leading biological monitoring missions with NOAA s Southeast Fisheries Science Center (SEFSC) to gather data on fish populations and coral reef communities in the U.S. Caribbean, Florida, and the Gulf of Mexico. Each year, our scientists work closely with CRCP and local partners to collect biological data from thousands of strategically selected sites. We then use innovative analysis techniques to develop products that give fellow scientists, managers, decision makers and the public a better understanding of a region s resources and how they are changing over time. The biological component of NCRMP provides a biennial ecological characterization at a broad spatial scale of general reef condition for reef fishes, corals and benthic habitat (i.e., fish species composition/density/size, benthic cover, and coral density/size/condition). Data collection occurs at stratified random sites where the sampling domain for each region is partitioned by habitat type and depth, sub-regional location (e.g., along-shelf position) and management zone. Data provided in this data set were collected around Puerto Rico as part of an NCCOS-led mission in collaboration with partners at U.S. Fish and Wildlife Service, University of Puerto Rico Department of Marine Sciences, HJR Reefscaping, and University of the Virgin Islands.

This fish and benthic composition database is the result of a multifaceted effort described below.The National Oceanic and Atmospheric Administration's (NOAA) Biogeography Branch, in consultation with NOAA's Office of Response and Restoration (ORandR) and other local and regional experts, is conducting an ecological characterization of the marine ecosystem around Vieques Island, Puerto Rico. The assessment will support effective management and conservation of marine resources in Vieques as a whole. To date a spatially comprehensive assessment of coral reef and hardbottom habitat around Vieques has been lacking. To fill this gap, the Biogeography Branch is expanding long term monitoring efforts to Vieques to collect detailed information about the benthic habitats, fish, and invertebrate communities. Spatially comprehensive information on reefs and hardbottom is vital to future management of the marine resources around Vieques. The collected data will be used to quantify the abundance and spatial distribution of fish, corals, and benthic invertebrates on hardbottom habitats around Vieques. Further, with regular monitoring, changes in the composition and condition of Vieques reefs over time can be detected.Data is collected using established protocols and monitoring efforts consistent with NOAA's National Coral Reef Monitoring Program and the Biogeography Branch's long-term monitoring efforts in southwest Puerto Rico and the US Virgin Islands since 2000 and 2001, respectively. The intent of this work is: 1) To spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrates (conch, lobster, Diadema); 2) To relate this information to in-situ data collected on associated benthic composition parameters; and 3) To use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting. It is critical, with recent changes in land and maritime use in Vieques (i.e., transfer of former Navy lands to the Fish and Wildlife Service, the Municipality in Vieques, and the Puerto Rico Conservation Trust) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas.To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are used to select reef/hardbottom habitat that is further stratified by proximity to former land use and geographic side of the island (i.e., north vs. south) to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef. At each site, fish, macro-invertebrates, and benthic composition information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, the Biogeography Branch is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between Vieques and the other locations. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

This dataset contains (pre-project, post-project and monitoring) aerial imagery of each of the ecological restoration sites captured at an altitude of 30 m with an RTK GPS enabled UAV. Tracks in the sand are color coded in order to be able to calculate percent coverage of the beach use patterns on each image. Human tracks = yellow; Dog tracks = green, Invasive green iguana tracks = blue; Bike tracks = brown yellow, Horse tracks = brown and Bird tracks = Violet. This analysis is very important for monitoring and for planning the implementation of ecological restoration prescriptions at every site.

This fish and benthic composition database is the result of a multifaceted effort described below.The intent of this work is five fold: 1) To spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrates (conch, lobster, Diadema); 2) To relate this information to in-situ data collected on associated benthic composition parameters; 3) To use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting; 4) To establish the efficacy of those management decisions; and 5) To work with the National Coral Reef Monitoring Program to develop data collection standards and easily implemented methodologies for transference to other agencies and to work toward standardizing data collection throughout the US states and territories. Toward this end, the Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) has been conducting research in Puerto Rico and the US Virgin Islands since 2000 and 2001, respectively. It is critical, with recent changes in management at both locations (e.g. implementation of MPAs) as well as proposed changes (e.g. zoning to manage multiple human uses) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas. It is also important that BB work closely with the individuals responsible for recommending and implementing these management strategies. Recognizing this, BB has been collaborating with partners at the University of Puerto Rico, National Park Service, US Geological Survey and the Virgin Islands Department of Planning and Natural Resources.To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are stratified to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef or seagrass area. At each site, fish, macro-invertebrates, and benthic composition information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, BB is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between locations. Armed with the knowledge of where "hot spots" of species richness and diversity are likely to occur in the seascape, the BB is in a unique position to answer questions about the efficacy of marine zoning strategies (e.g. placement of no fishing, anchoring, or snorkeling locations), and what locations are most suitable for establishing MPAs. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

This fish and benthic composition database is the result of a multifaceted effort described below. The intent of this work is five fold: 1) To spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrate (conch, lobster, Diadema); 2) To relate this information to in-situ data collected on associated benthic composition parameters; 3) To use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting; 4) To establish the efficacy of those management decisions; and 5) To work with the National Coral Reef Monitoring Program to develop data collection standards and easily implemented methodologies for transference to other agencies and to work toward standardizing data collection throughout the US states and territories. Toward this end, the Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) has been conducting research in Puerto Rico and the US Virgin Islands since 2000 and 2001, respectively. It is critical, with recent changes in management at both locations (e.g. implementation of MPAs) as well as proposed changes (e.g. zoning to manage multiple human uses) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas. It is also important that BB work closely with the individuals responsible for recommending and implementing these management strategies. Recognizing this, BB has been collaborating with partners at the University of Puerto Rico, National Park Service, US Geological Survey and the Virgin Islands Department of Planning and Natural Resources.To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are stratified to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef or seagrass area. At each site, fish, macro-invertebrates, and benthic composition information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, BB is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between locations. Armed with the knowledge of where "hot spots" of species richness and diversity are likely to occur in the seascape, the BB is in a unique position to answer questions about the efficacy of marine zoning strategies (e.g. placement of no fishing, anchoring, or snorkeling locations), and what locations are most suitable for establishing MPAs. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

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

This fish and benthic composition database is the result of a multifaceted effort described below. The intent of this work is five fold: 1) To spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrates (conch, lobster, Diadema); 2) To relate this information to in-situ data collected on associated benthic composition parameters; 3) To use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting; 4) To establish the efficacy of those management decisions; and 5) To work with the National Coral Reef Monitoring Program to develop data collection standards and easily implemented methodologies for transference to other agencies and to work toward standardizing data collection throughout the US states and territories. Toward this end, the Center for Coastal Monitoring and Assessment's Biogeography Team (BT) has been conducting research in Puerto Rico and the US Virgin Islands since 2000 and 2001, respectively. It is critical, with recent changes in management at both locations (e.g. implementation of MPAs) as well as proposed changes (e.g. zoning to manage multiple human uses) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas. It is also important that BT work closely with the individuals responsible for recommending and implementing these management strategies. Recognizing this, BT has been collaborating with partners at the University of Puerto Rico, National Park Service, US Geological Survey and the Virgin Islands Department of Planning and Natural Resources. To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are stratified to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef or seagrass area. At each site, fish, macro-invertebrates, and benthic composition information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, BT is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between locations. Armed with the knowledge of where "hot spots" of species richness and diversity are likely to occur in the seascape, the BT is in a unique position to answer questions about the efficacy of marine zoning strategies (e.g. placement of no fishing, anchoring, or snorkeling locations), and what locations are most suitable for establishing MPAs. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

Reason for Selection Impervious cover is easy to monitor and model and is widely used and understood by diverse partners. It is also strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. Impervious surface affects not only aquatic habitats and biodiversity, but also human communities. High levels of impervious surface cause more frequent flooding by increasing the volume of stormwater runoff, reduce the amount of available drinking water by preventing groundwater recharge, and pollute waterways where people swim and fish (Chesapeake 2023, USGS 2018, EPA 2018).

The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major, negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surface threshold (i.e., 5% impervious) has been documented to impact Piedmont fish tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae) and estuarine species blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus).

While most of these species do not occur in Puerto Rico and the U.S. Virgin Islands, we kept these thresholds in the Caribbean for consistency with the continental version of the indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean 
Southeast Blueprint 2023 extent
2012 National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) land cover files for the U.S. Virgin Islands (St. Thomas, St. John, and St. Croix are provided as separate rasters) accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
2010 NOAA C-CAP land cover files for Puerto Rico, accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
National Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the data

CatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics.

To learn more about catchments and how they’re defined, check out these resources:

An article from USGS explaining the differences between various NHD products
The glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key terms 


  NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)

Mapping Steps

NHDPlus HR catchments are currently only available for the islands of Puerto Rico, Vieques, Culebra, St. Croix, St. John, and St. Thomas. Because the catchments don’t cover many of the smaller islands, use CUSP to add islands larger than 900 sq m (the area of a 30 m pixel). Start by converting CUSP shoreline lines to polygons.
Dissolve interior waterbodies on islands to represent each island with only one polygon.
To eliminate alignment issues between the CUSP and catchment polygons, remove most island areas that overlap with or are near (<10 m from) the NHDPlus HR catchments, ensuring that all of Culebra is retained.
The original NHDPlus HR catchment data was missing coverage of a small area on the west coast of Puerto Rico (just east of Parcelas Aguas Claras). Create an additional catchment polygon for this missing area so that the indicator covers the entire island of Puerto Rico.The missing area is essentially outlined by extremely thin catchment polygons. To fill the gap, make a new rectangular feature class covering the missing area, then union it together with the original NHDPlus HR catchments. From that output, select the newly created polygon that fills in the hole. 
    The resulting polygon is a multipart feature, so use the explode tool to separate out just the missing catchment. Export it as a shapefile.
    Union together the missing catchment with the other NHDPlus HR catchments and use that combined output as the catchment layer for the rest of the mapping steps.


Remove islands created from the CUSP dataset that are less than 900 sq m.
Merge the remaining CUSP islands with the NHDPlus catchments to create a single set of polygons in which to calculate average permeable surface.
Convert the C-CAP land cover rasters for Puerto Rico (2 m resolution) and the U.S. Virgin Islands (separate downloads for St. Thomas, St. John, and St. Croix with 2.4 m resolution) from .img format to .tif using the Copy Raster function.
For each individual C-CAP layer, use the ArcPy Conditional function to make a binary raster assigning the impervious class a value of 100 (representing fully impervious) and all other classes a value of 0 (representing fully permeable). This mimics the data format of the 2019 National Land Cover Database used in the continental Southeast permeable surface indicator, which provides a continuous impervious surface value ranging from 0 to 100.
Using the ArcPy Mosaic to New Raster function, mosaic all 4 rasters into 1 raster. Reproject to match the Blueprint projection and the 2 m cell size of the original Puerto Rico C-CAP data.
Calculate the average percent of impervious surface for each NHDPlus catchment or CUSP island using the ArcPy Spatial Analyst Zonal Statistics “MEAN” function, assigning the average impervious surface value to each catchment or island.
Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast Blueprint indicators (where high values indicate better ecological condition).
Reclassify the above raster into 4 classes, seen in the final indicator values below.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 4 = >95% of catchment or small island permeable (likely high water quality and supporting most sensitive aquatic species) 3 = >90-95% of catchment or small island permeable (likely declining water quality and supporting most aquatic species) 2 = >70-90% of catchment or small island permeable (likely degraded water quality and not supporting many aquatic species) 1 = ≤70% of catchment or small island permeable (likely degraded instream flow, water quality, and aquatic species communities) Known Issues

This indicator may not account for differences in permeability between different types of soils and land uses.
The C-CAP impervious layer used in this indicator contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permeable surface in some catchments.
C-CAP dates from 2010 for Puerto Rico and 2012 for the U.S. Virgin Islands. As a result, this indicator likely overestimates permeable surface values in areas that have been developed since the data was collected. 
C-CAP landcover is not available for some islands over 900 sq m. While these islands exceeded the size threshold for inclusion in this indicator, they are therefore scored as NoData. This indicator only covers areas where C-CAP landcover is present, and either NHDPlus HR catchments or islands over 900 sq m that were generated using CUSP data are also present. 
NHDPlus HR contains multiple catchments that are very small. The reduced size of these catchments may result in exaggerating their values in the indicator. 

Other Things to Keep in Mind

The impervious surface in the C-CAP data has impervious surface as one class in the landcover, which differs from the 2019 NLCD percent developed impervious layer used in the continental Southeast version of the permeable surface indicator. NLCD 2019 is served up as a continuous raster ranging from 0-100% impervious.
We used the Caribbean island size and extent layer for this indicator and not others because landcover data was available for small islands that were not covered by catchments, which otherwise would have been excluded. This was not the case for other indicators. For example, while we use catchments in natural landcover in floodplains, the floodplains and flowlines did not occur on small islands, anyway, so we did not leave any data out by using the catchments only and not supplementing with the islands layer.

Disclaimer: Comparing with Older Indicator Versions There 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 Cited Chesapeake Bay Program. 2023. Stormwater Runoff. Accessed September 7, 2023. [https://www.chesapeakebay.net/issues/threats-to-the-bay/stormwater-runoff].

Environmental Protection Agency. EnviroAtlas. Data Fact Sheet. January 2018. Percent of Stream and Shoreline with 15% or More Impervious Cover within 30 Meters. Accessed September 7, 2023. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/Percstreamw15percentimperviousin30meters.pdf].

Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User’s guide for the national hydrography

Wetlands are areas where water is present at or near the surface of the soil during at least part of the year. Wetlands provide habitat for many species of plants and animals that are adapted to living in wet habitats. Wetlands form characteristic soils, absorb pollutants and excess nutrients from aquatic systems, help buffer the effects of high flows, and recharge groundwater. Data on the distribution and type of wetland play an important role in land use planning and several federal and state laws require that wetlands be considered during the planning process.The National Wetlands Inventory (NWI) was designed to assist land managers in wetland conservation efforts. The NWI is managed by the US Fish and Wildlife Service.Dataset SummaryPhenomenon Mapped: WetlandsCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands and the Northern Mariana IslandsVisible Scale: The data is visible at scales from 1:144,000 to 1:1,000Resolution/Tolerance: 0.0001 meters/0.001 metersNumber of Features: 34,954,623 diced, after applying a 50,000 vertex limit to an original set of 34,950,653 featuresFeature Limit: 10,000Source: U.S. Fish and Wildlife ServicePublication Date: September 29, 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/This layer was created from the September 29, 2020 version of the NWI. This layer includes attributes from the original dataset as well as attributes added by Esri for use in the default pop-up and to allow the user to query and filter the data.NWI derived attributes:Wetland Code - a code that identifies specific attributes of the wetlandWetland Type - one of 8 wetland typesArea - area of the wetland in acresEsri created attributes:System - code indicating the system and subsystem of the wetlandClass - code indicating the class and subclass of the wetlandModifier 1, Modifier 2, Modifier 3, Modifier 4 - these four fields contain letter codes for modifiers applied to the wetland descriptionSystem Name - the name of the system (Marine, Estuarine, Riverine, Lacustrine, or Palustrine)Subsystem Name - the name of the subsystemClass Name - the name of the classSubclass Name - the name of the subclassModifier 1 Name, Modifier 2 Name, Modifier 3 Name , Modifier 4 Name - these four fields contain names for modifiers applied to the wetland descriptionPopup Header - this field contains a text string that is used to create the header in the default pop-up System Text - this field contains a text string that is used to create the system description text in the default pop-upClass Text - this field contains a text string that is used to create the class description text in the default pop-upModifier Text - this field contains a text string that is used to create the modifier description text in the default pop-upSpecies Text - this field contains a text string that is used to create the species description text in the default pop-upCodes, names, and text fields were derived from the publication Classification of Wetlands and Deepwater Habitats of the United States.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for System Text = 'Palustrine' to create a map of palustrine wetlands only.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Reason for Selection Protected natural areas help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). However, parks are not equitably distributed within easy walking distance for everyone. It also complements the urban park size indicator by capturing the value of potential new parks. Input Data

  The Trust for Public Land (TPL) ParkServe database, accessed 8-8-2021: Park priority areas (ParkServe_ParkPriorityAreas_08062021) 
    From the TPL ParkServe documentation:

The ParkServe database maintains an inventory of parks for every urban area in the U.S., including Puerto Rico. This includes all incorporated and Census-designated places that lie within any of the country’s 3,000+ census-designated urban areas. All populated areas in a city that fall outside of a 10-minute walk service area are assigned a level of park priority, based on a comprehensive index of six equally weighted demographic and environmental metrics:Population densityDensity of low-income households – which are defined as households with income less than 75 percent of the urban area median household incomeDensity of people of colorCommunity health – a combined index based on the rate of poor mental health and low physical activity from the 2020 CDC PLACES census tract datasetUrban heat islands – surface temperature at least 1.25o greater than city mean surface temperature from The Trust for Public Land, based on Landsat 8 satellite imageryPollution burden - Air toxics respiratory hazard index from 2020 EPA EJScreen The 10-minute walkFor each park, we create a 10-minute walkable service area using a nationwide walkable road network dataset provided by Esri. The analysis identifies physical barriers such as highways, train tracks, and rivers without bridges and chooses routes without barriers.

  CDC Social Vulnerability Index 2018: RPL_Themes 

Social vulnerability refers to the capacity for a person or group to “anticipate, cope with, resist and recover from the impact” of a natural or anthropogenic disaster such as extreme weather events, oil spills, earthquakes, and fires. Socially vulnerable populations are more likely to be disproportionately affected by emergencies (Wolkin et al. 2018).

In this indicator, we use the “RPL_THEMES” attribute from the Social Vulnerability Index, described here. “The Geospatial Research, Analysis, and Services Program (GRASP) at Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry developed the Social Vulnerability Index (SVI). The SVI is a dataset intended to help state, local, and tribal disaster management officials identify where the most socially vulnerable populations occur (Agency for Toxic Substances and Disease Registry [ATSDR] 2018)” (Flanagan et al. 2018).

“The SVI database is regularly updated and includes 15 census variables (ATSDR 2018). Each census variable was ranked from highest to lowest vulnerability across all census tracts in the nation with a nonzero population. A percentile rank was calculated for each census tract for each variable. The variables were then grouped among four themes.... A tract-level percentile rank was also calculated for each of the four themes. Finally, an overall percentile rank for each tract as the sum of all variable rankings was calculated. This process of percentile ranking was then repeated for the individual states” (Flanagan et al. 2018).

Base Blueprint 2022 extent
Southeast Blueprint 2023 extent

Mapping Steps

Convert the ParkServe park priority areas layer to a raster using the ParkRank field. Note: The ParkRank scores are calculated using metrics classified relative to each city. Each city contains park rank values that range from 1-3. For the purposes of this indicator, we chose to target potential park areas to improve equity. Because the ParkRank scores are relative for each city, a high score in one city is not necessarily comparable to a high score from another city. In an effort to try to bring more equity into this indicator, we also use the CDC Social Vulnerability Index to narrow down the results.
Reclassify the ParkServe raster to make NoData values 0. 
Convert the SVI layer from vector to raster based on the “RPL_Themes” field. 
To limit the ParkRank layer to areas with high SVI scores, first identify census tracts with an “RPL_Themes” field value >0.65. Make a new raster that assigns a value of 1 to census tracts that score >0.65, and a value of 0 to everything else. Take the resulting raster times the ParkRank layer.
Reclassify this raster into the 4 classes seen in the final indicator below.
Clip to the spatial extent of Base Blueprint 2022.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 3 = Very high priority for a new park that would create nearby equitable access

2 = High priority for a new park that would create nearby equitable access1 = Moderate priority for a new park that would create nearby equitable access 0 = Not identified as a priority for a new park that would create nearby equitable access (within urban areas) Known Issues

This indicator could overestimate park need in areas where existing parks are missing from the ParkServe database. TPL regularly updates ParkServe to incorporate the best available park data. If you notice missing parks or errors in the park boundaries or attributes, you can submit corrections through the ParkReviewer tool or by contacting TPL staff.
Within a given area of high park need, the number of people served by the creation of a new park depends on its size and how centrally located it is. This indicator does not account for this variability. Similarly, while creating a new park just outside an area of high park need would create access for some people on the edge, the indicator does not capture the benefits of new parks immediately adjacent to high-need areas. For a more granular analysis of new park benefits, ParkServe’s ParkEvaluator tool allows you to draw a new park, view its resulting 10-minute walk service area, and calculate who would benefit.
Beyond considering distance to a park and whether it is open to the public, this indicator does not account for other factors that might limit park access, such as park amenities or public safety. The TPL analysis excludes private or exclusive parks that restrict access to only certain individuals (e.g., parks in gated communities, fee-based sites). The TPL data includes a wide variety of parks, trails, and open space as long as there is no barrier to entry for any portion of the population.
The indicator does not incorporate inequities in access to larger versus smaller parks. In predicting where new parks would benefit nearby people who currently lack access, this indicator treats all existing parks equally.
This indicator identifies areas where parks are needed, but does not consider whether a site is available to become a park. We included areas of low intensity development in order to capture vacant lots, which can serve as new park opportunities. However, as a result, this indicator also captures some areas that are already used for another purpose (e.g., houses, cemeteries, and businesses) and are unlikely to become parks. In future updates, we would like to use spatial data depicting vacant lots to identify more feasible park opportunities.
This indicator underestimates places in rural areas where many people within a socially vulnerable census tract would benefit from a new park. ParkServe covers incorporated and Census-designated places within census-designated urban areas, which leaves out many rural areas. We acknowledge that there are still highly socially vulnerable communities in rural areas that would benefit from the development of new parks. However, based on the source data, we were not able to capture those places in this version of the indicator. 

Other Things to Keep in MindThe zero values in this indicator contain three distinct types of areas that we were unable to distinguish between in the legend: 1) Areas that are not in a community analyzed by ParkServe (ParkServe covers incorporated and Census-designated places within census-designated urban areas); 2) Areas in a community analyzed by ParkServe that were not identified as a priority; 3) Areas that ParkServe identifies as a priority but do not meet the SVI threshold used to represent areas in most need of improved equitable access.This indicator only includes park priority areas that fall within the 65th percentile or above from the Social Vulnerability Index. We did not perform outreach to community leaders or community-led organizations for feedback on this threshold. This indicator is intended to generally help identify potential parks that can increase equitable access but should not be solely used to inform the creation of new parks. As the social equity component relies on information summarized by census tract, it should only be used in conjunction with local knowledge and in discussion with local communities (NRPA 2021, Manuel-Navarete et al. 2004). Disclaimer: Comparing with Older Indicator Versions There 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 Cited Centers for