Caribbean Beach Habitat (Southeast Blueprint Indicator)

Reason for Selection Beaches in Puerto Rico and the U.S. Virgin Islands support a wide array of shorebirds, colonial seabirds, and sea turtles (ACJV et al. 2015, USFWS 2022). However, their limited spatial extent makes beaches an ecosystem of special concern for conservation (ACJV et al. 2015). In addition, coastal dunes and beaches are some of the Caribbean ecosystems facing the greatest threat from disturbance and development (ACJV et al. 2015). This indicator focuses on a suite of bird and sea turtle species that nest on beaches, though it includes beach habitat used for other activities like foraging and breeding, in addition to nesting. Input Data * Puerto Rico Gap Analysis Project predicted vertebrate species distributions: data provided by Dr. Bill Gould with the Caribbean Climate Hub on 4-4-2022 (contact [william.a.gould@usda.gov] for more information); [read the final report] In Puerto Rico, we used the following GAP species models: * Wilson’s plover * [Puerto Rico Gap Analysis Project landcover]; [download the data]; [read the final report] * U.S. Virgin Islands Gap Analysis Project predicted vertebrate species distributions and landcover; data and report appendices provided by Dr. Bill Gould with the Caribbean Climate Hub on 2-6-2023 (contact [william.a.gould@usda.gov] for more information); [read the final report] In the USVI, we used the following GAP species models: * Wilson’s plover * American oystercatcher * Hawksbill sea turtle * Leatherback sea turtle * Green sea turtle * [State of the World’s Sea Turtles] (SWOT) nest locations for hawksbill, leatherback, green, and loggerhead sea turtles; download the data using the download icon on the left side of [the SWOT mapping application]. Note: loggerhead was only observed in USVI, while the other species were observed in both PR and USVI. * [OpenStreetMap data], accessed 6-28-2023 A polygon from this dataset is considered a beach if the value in the “nature” attribute is beach. OpenStreetMap describes natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format using R code. OpenStreetMap[®] is open data, licensed under the [Open Data Commons Open Database License] (ODbL) by the [OpenStreetMap Foundation] (OSMF). Additional credit to OSM contributors. Read more on [the OSM copyright page]. * U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact [jdwyer@lynker.com] for more information) * [Southeast Blueprint 2023 subregions]: Caribbean * [Southeast Blueprint 2023 extent] Mapping Steps * Buffer by 15 m the beach polygons from OpenStreetMap and the USVI beaches layer. This is consistent with the methodology used in the urban park size indicator to avoid the loss of narrow beaches when converting to raster. Project and convert to raster. * Extract from GAP landcover the relevant beach classes and resample to a 30 m pixel. In Puerto Rico, the beaches include two classes: “Gravel beaches and stony shoreline” and “Fine to coarse sandy beaches, mixed sand and gravel beaches”. In the U.S. Virgin Islands, the beaches include three classes: “Fine to Medium Grained Sandy Beaches,” “Gravel Beaches” and “Mixed Sand and Gravel Beaches”. * Extract the predicted habitat class from the GAP predicted habitat rasters for species that nest on beaches. These include Wilson’s plover, American oystercatcher, and hawksbill, leatherback, and green sea turtles. Note: Only Wilson’s plover was predicted in Puerto Rico. Project and resample the rasters to 30 m. * Extract from the SWOT data nest all point locations for hawksbill, leatherback, green and loggerhead sea turtles and convert them to 30 m rasters. * Merge together the SWOT and GAP predicted habitat rasters for each species and identify each pixel that contains at least one species. Then clip the resulting raster to the beach extent. * To define individual beaches, run a region group on beach extent. * Run the Zonal Statistics “MAX” function to apply species presence to the entirety of each beach. * Reclassify to 0 the beach extent layer created above. * To create the final indicator values seen below, mosaic together three rasters: the beaches containing at least one species, the beach extent, and the Caribbean Blueprint 2023 extent. This adds back in a 0 value for areas not identified as beaches and a 1 value for beaches that did not contain any species predictions or observations. Zero values better represent the extent of the source data and make the indicator perform better in online tools. * 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 2025 Data Download] or [Caribbean-only Southeast Blueprint 2025 Data Download] under > 6_Code. Final indicator values Indicator values are assigned as follows: 2 = Beach with 1+ nesting species predicted or observed 1 = Other beach 0 = Not identified as a beach Known Issues * This indicator likely underprioritizes beaches in Puerto Rico due to disparities in both beach extent and species data coverage between Puerto Rico and the U.S. Virgin Islands. USVI has a comprehensive hand-digitized beach layer that is not available in Puerto Rico. GAP models only one beach-nesting species in Puerto Rico, compared to five in the U.S. Virgin Islands (though the additional species are known to occur in Puerto Rico as well). In addition, the SWOT sea turtle observations better aligned with the beach polygons in USVI. We will explore additional datasets and methods for addressing these disparities in future revisions. To help mitigate this issue for this year, we set the maximum species richness as 1+ rather than using the full range of species richness values, since Puerto Rico had a maximum species richness of 3 in the available data, compared to a maximum value of 6 in USVI. * This indicator includes beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a beach) or incorrect tags (e.g., labelling an area as a beach that is not actually a beach). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future. * This indicator may exclude some small beaches that aren’t captured in the source data. We encourage interested partners and citizens to add any missing beaches to OpenStreetMap so we can better capture them in future updates. * This indicator does not account for other factors that influence the quality of beach habitat, such as distance to roads, light pollution, and vulnerability to erosion and sea-level rise. Other Things to Keep in Mind * The species chosen for this indicator are birds and sea turtles that nest on beaches. However, the indicator also includes beach habitat used for activities other than nesting, like foraging and breeding. * This indicator does not always align with Caribbean coastal shoreline condition. Some areas identified as important beach habitat in this indicator, especially those coming from the GAP Wilson’s plover model, are scored as armored in coastal shoreline condition (e.g., the Hyatt Regency Grand Hotel in Río Grande, Puerto Rico). This often occurs where riprap is present along narrow beaches, or occasionally near bulkheads. There is often a section of beach present behind the riprap or bulkhead that could still provide habitat, or the riprap is sporadically placed on a long stretch of beach to protect inland structures. In these cases, the mismatch reflects the different intent of these complementary indicators. In some cases, hardened structures may be actually misclassified as beach. Inconsistencies in alignment and classification likely result from the older age and coarser resolution of the GAP data (10 m raster based on 2001 landcover) compared to the more recent and fine-scale CUSP shorelines (vectors dating primarily from 2014-2021) and challenges in distinguishing the unique remote sensing signature of beach vs. riprap and other hardened structures. Because of the 30 m resolution of the Blueprint and underlying data, a single pixel may contain a mix of beach habitat and hardened structures and be reflected differently in each of these two indicators due to their different functions. 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 Atlantic Coast Joint Venture, Caribbean Landscape Conservation Cooperative, and U.S. Fish and Wildlife Service. Avian Conservation Planning Priorities for Puerto Rico and the U.S. Virgin Islands (BCR 69). February 2015. [[https://acjv.org/documents/PRUSVI_plan.pdf]]. Gould, William A.; Alarcón, Caryl; Fevold, Brick; Jiménez, Michael E.; Martinuzzi, Sebastián; Potts, Gary; Quiñones, Maya; Solórzano, Mariano; Ventosa, Eduardo. 2008. The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. [[https://data.fs.usda.gov/research/pubs/iitf/iitf_gtr39.pdf]]. Gould WA, Solórzano MC, Potts GS, Quiñones M, Castro-Prieto J, Yntema LD. 2013. U.S. Virgin Islands Gap Analysis Project – Final Report. USGS, Moscow ID and the USDA FS International Institute of Tropical Forestry, Río Piedras, PR. 163 pages and 5 appendices. [[https://www.thinkamap.com/share/IndividualGISdata/PDFs/USVI_FINAL_REPORT.pdf]]. Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography 22(2):104-115. [[https://tos.org/oceanography/assets/docs/22-2_halpin.pdf]]. Kot, C.Y., E. Fujioka, A.D. DiMatteo, B.P. Wallace, B.J. Hutchinson, J. Cleary, P.N. Halpin and R.B. Mast. 2021. The State of the World’s Sea Turtles Online Database: Data provided by the SWOT Team and hosted on OBIS-SEAMAP. Oceanic Society, Conservation International, IUCN Marine Turtle Specialist Group (MTSG), and Marine Geospatial Ecology Lab, Duke University. [[https://seamap.env.duke.edu/swot]]. OpenStreetMap. Natural. Accessed 6-28-2023. Data extracted through Geofabrik downloads. [[https://wiki.openstreetmap.org/wiki/Key:natural]]. Puerto Rico GAP Analysis Project. 2006. PRGAP Landcover. USDA Forest Service, International Institute of Tropical Forestry. U.S. Fish and Wildlife Service Caribbean Ecological Services Field Office. July 2022. Conservation Measures for sea turtles in Puerto Rico and the U.S. Virgin Islands. [[https://www.fws.gov/sites/default/files/documents/Conservation%20Measures%20for%20sea%20turtles%20in%20Puerto%20Rico%20and%20the%20U.S.%20Virgin%20Islands.pdf]].