A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, “medium” storminess and “high” storminess based on storm climatology data) and 4 sea-level scenarios (that is, a “low” increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, “medium” storminess and “high” storminess based on storm climatology data) and 4 sea-level scenarios (that is, a “low” increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea level (Enwright and others, 2020). This USGS data release contains five habitat model predictions from the aforementioned modeling effort. These include: (1) the contemporary period (that is, 2015); (2) with action Year 0 (that is, hypothetically, predicted habitat coverage in 2128 based on our sea-level change rate); (3) with action Year 10 (that is, predicted habitat coverage after ten years of morphodynamic modeling with simulated storms); (4) without action Year 0; and (5) without action Year 10. Additionally, this data release includes change maps that highlight changes over the decadal simulation (that is, Year 0 to Year 10) with and without action, respectively, along with the difference between Year 10 for the with and without the action simulation. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020) and Enwright and others (in review).

A spatially explicit oyster habitat suitability index (HSI) model was developed for the Alabama barrier island restoration assessment at Dauphin Island. Based on previous oyster habitat suitability studies, seven water quality variables were selected and their relationships with habitat suitability were developed and incorporated into the oyster HSI model for Dauphin Island restoration assessment: 1) mean salinity, 2) minimum monthly mean salinity, 3) annual mean salinity, 4) annual mean dissolved oxygen, 5) annual mean total suspended solids, 6) annual mean water depth, and 7) annual mean water temperature. The final HSI score was calculated using the weighted geometric mean of the suitability scores of these individual variables. The oyster HSI model was calibrated and validated using field data on oyster density from the Alabama Department of Conservation and Natural Resources (ADCNR) Marine Resources Division (MRD and continuous water quality data from the Mobile Bay National Estuary Program. Then, the oyster HSI model was used to assess oyster habitat suitability changes with and without restoration under future storminess and sea level (SL) conditions. The barrier island restoration actions being assessed include beach and dune restoration, marsh restoration, and placement of sand in the littoral zone. The storminess bins included realizations with a “medium” storminess, which included 1 to 3 storms over a 10-year period (that is, ST2) and a “high” storminess, which included 4 to 5 storms over an equal period (that is, ST3). The two future sea levels included a SL of 0.3 m (that is, SL1) and a SL of 1.0 m (that is, SL3) above the contemporary SL. Specifically, the medium storminess was paired with the 0.3 m above the contemporary SL (that is, ST2SL1) and the “high” storminess bin was paired with the 1.0 m above the contemporary SL (that is, ST3SL3). To account for intertidal marsh vertical accretion as a component of marsh morphology evolution, two scenarios were included in modeling: the U.S. Army Corps of Engineers (USACE) high and intermediate SLR curves in which marsh kept pace with SLR through accretion (1 cm/yr) through 2022 under high SLR curve whereas marsh kept pace with SLR by accretion for the entirety of the USACE intermediate curve. Inputs of water quality conditions under future storminess and sea level conditions were provided by the CE-QUAL-ICM model that was coupled with a geomorphology model and a hydrodynamic model. This data release includes simulation results and metadata of oyster habitat suitability scores at each spatial unit (grid cell) across the study domain: estuarine waters near Dauphin Island.

A spatially explicit oyster habitat suitability index (HSI) model was developed for the Alabama barrier island restoration assessment at Dauphin Island. Based on previous oyster habitat suitability studies, seven water quality variables were selected and their relationships with habitat suitability were developed and incorporated into the oyster HSI model for Dauphin Island restoration assessment: 1) mean salinity, 2) minimum monthly mean salinity, 3) annual mean salinity, 4) annual mean dissolved oxygen, 5) annual mean total suspended solids, 6) annual mean water depth, and 7) annual mean water temperature. The final HSI score was calculated using the weighted geometric mean of the suitability scores of these individual variables. The oyster HSI model was calibrated and validated using field data on oyster density from the Alabama Department of Conservation and Natural Resources (ADCNR) Marine Resources Division (MRD and continuous water quality data from the Mobile Bay National Estuary Program. Then, the oyster HSI model was used to assess oyster habitat suitability changes with and without restoration under future storminess and sea level (SL) conditions. The barrier island restoration actions being assessed include beach and dune restoration, marsh restoration, and placement of sand in the littoral zone. The storminess bins included realizations with a “medium” storminess, which included 1 to 3 storms over a 10-year period (that is, ST2) and a “high” storminess, which included 4 to 5 storms over an equal period (that is, ST3). The two future sea levels included a SL of 0.3 m (that is, SL1) and a SL of 1.0 m (that is, SL3) above the contemporary SL. Specifically, the medium storminess was paired with the 0.3 m above the contemporary SL (that is, ST2SL1) and the “high” storminess bin was paired with the 1.0 m above the contemporary SL (that is, ST3SL3). To account for intertidal marsh vertical accretion as a component of marsh morphology evolution, two scenarios were included in modeling: the U.S. Army Corps of Engineers (USACE) high and intermediate SLR curves in which marsh kept pace with SLR through accretion (1 cm/yr) through 2022 under high SLR curve whereas marsh kept pace with SLR by accretion for the entirety of the USACE intermediate curve. Inputs of water quality conditions under future storminess and sea level conditions were provided by the CE-QUAL-ICM model that was coupled with a geomorphology model and a hydrodynamic model. This data release includes simulation results and metadata of oyster habitat suitability scores at each spatial unit (grid cell) across the study domain: estuarine waters near Dauphin Island.

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism (Barbier and others, 2011; Feagin and others, 2010). These islands tend to be dynamic due to their location along the estuarine-marine interface. Besides gradual changes caused by constant forces, such as currents and tides, barrier islands face numerous threats including hurricanes, accelerated sea-level rise, oil spills, and anthropogenic impacts (Pilkey and Cooper, 2014). These threats are likely to influence the future of barrier islands in the latter part of the 21st century, especially as climate-related threats to coastal areas are expected to increase in the future (Knutson and others, 2010; Hansen and others, 2016). As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Geomorphology regulates many abiotic factors that influence the performance of foundation plant species, including wave energy, salinity, inundation frequency, sea spray, Aeolian transport, and nutrient availability (Young and others, 2011). Researchers have established linkages between barrier island habitats and specific landscape position variables, such as distance from shoreline (Young and others, 2011) and elevation (Anderson and others, 2016; Foster and others, 2017; Halls and others, 2018; Young and others, 2011). Here, we built upon recent barrier island habitat model efforts by Foster and others (2017) and Halls and others (2018) to develop a machine learning-based habitat model for Dauphin Island, Alabama, USA. Our model incorporated elevation uncertainty for elevation-dependent habitat extraction and yields spatially explicit predictions of general barrier island habitats based on landscape position information, such as elevation, distance from shoreline, and relative topography. The habitats that were predicted in this model included: 1) Barrier flat; 2) Beach; 3) Dune; 4) Intertidal beach; 5) Intertidal flat; 6) Intertidal marsh; 7) Water-estuarine; 8) Water-fresh; 9) Water-marine; 10) Woody vegetation; and 11) Woody wetland. Models were developed for three tidal zones: 1) subtidal; 2) intertidal; and 3) supratidal/upland. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. This data release contains data used to develop and validate the machine learning-based habitat model including: 1) final contemporary habitat model results; 2) contemporary habitat model training data per tidal zone; 3) contemporary habitat model predictor variables per tidal zone; 4) contemporary habitat model validation data; 5) final hindcast habitat model results; 6) hindcast habitat predictor variables per tidal zone; and 7) hindcast habitat validation data. For more information, see Enwright and others (2019).

This dataset includes barrier island land cover types collected from mid-November 2015 to mid-December 2015 along randomly placed transects at seven sites throughout the east end of Dauphin Island. Specifically, this data collection included characterizing land cover types and measuring horizontal position and elevation. We characterized plant community composition and structure for a subset of these points (see Vegetation Survey Data Table). This work was conducted through a joint effort by the State of Alabama, the U.S. Geological Survey, and the U.S. Army Corps of Engineers to evaluate the feasibility of various restoration alternatives and how specific alternatives might increase the resiliency and sustainability of Dauphin Island. The overarching goal of the aforementioned effort is to preserve and enhance the ecological functions and values of the island. This product provides a powerful tool for tracking changes to barrier island habitats over time. This data release includes the following three components, which are included in the attached ZIP file: 1) Dauphin Island Habitat Map (Raster data) 2) Land Cover and Vegetation Field Data Points (Vector data) 3) Vegetation Survey Data (Tabular data)

This dataset includes barrier island land cover types collected from mid-November 2015 to mid-December 2015 along randomly placed transects at seven sites throughout the east end of Dauphin Island. Specifically, this data collection included characterizing land cover types and measuring horizontal position and elevation. We characterized plant community composition and structure for a subset of these points (see Vegetation Survey Data Table). This work was conducted through a joint effort by the State of Alabama, the U.S. Geological Survey, and the U.S. Army Corps of Engineers to evaluate the feasibility of various restoration alternatives and how specific alternatives might increase the resiliency and sustainability of Dauphin Island. The overarching goal of the aforementioned effort is to preserve and enhance the ecological functions and values of the island. This product provides a powerful tool for tracking changes to barrier island habitats over time. This data release includes the following three components, which are included in the attached ZIP file: 1) Dauphin Island Habitat Map (Raster data) 2) Land Cover and Vegetation Field Data Points (Vector data) 3) Vegetation Survey Data (Tabular data)

The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana’s Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). The U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. Previous efforts of this habitat mapping program included developing habitat maps for 2008 and 2015-2016 for the following BICM regions (Enwright and others, 2020): 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. Additionally, a habitat change analysis wasconducted comparing reaches mapped in 2008 and 2015-2016. The current effort of this habitat mapping program includes developing habitat maps for 2021 for the previously mentioned regions. A habitat change analysis will be conducted comparing reaches mapped 2015-2016 and 2021. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was developed specifically for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous BICM map products, we have crosswalked the detailed classes to general habitat classes previously used by Fearnley and others (2009). In other words, the general habitat classes included in these products were not directly interpreted using aerial imagery and lidar elevation data. Thus, we recommend only using these general habitat classes for analyses that include previous BICM habitat maps (1996-2005). For more information about the BICM program, see Kindinger and others (2013). For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (i.e., dataset and recommended symbology). For more information about the BICM program, see Kindinger and others (2013). References: Kindinger, J.L., Buster, N.A., Flocks, J.G., Bernier, J.C., and Kulp, M.A., 2013, Louisiana Barrier Island Comprehensive Monitoring (BICM) program summary report—Data and analyses 2006 through 2010: U.S. Geological Survey Open-File Report 2013–1083, 86 p., at https://pubs.usgs.gov/of/2013/1083/. Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana

The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana’s Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). The U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. Previous efforts of this habitat mapping program included developing habitat maps for 2008 and 2015-2016 for the following BICM regions (Enwright and others, 2020): 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. Additionally, a habitat change analysis was conducted comparing reaches mapped in 2008 and 2015-2016. The current effort of this habitat mapping program includes developing habitat maps for 2021 for the previously mentioned regions. A habitat change analysis will be conducted comparing reaches mapped 2015-2016 and 2021. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was developed specifically for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous BICM map products, we have crosswalked the detailed classes to general habitat classes previously used by Fearnley and others (2009). In other words, the general habitat classes included in these products were not directly interpreted using aerial imagery and lidar elevation data. Thus, we recommend only using these general habitat classes for analyses that include previous BICM habitat maps (1996-2005). For more information about the BICM program, see Kindinger and others (2013). For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (i.e., dataset and recommended symbology). For more information about the BICM program, see Kindinger and others (2013). References: Kindinger, J.L., Buster, N.A., Flocks, J.G., Bernier, J.C., and Kulp, M.A., 2013, Louisiana Barrier Island Comprehensive Monitoring (BICM) program summary report—Data and analyses 2006 through 2010: U.S. Geological Survey Open-File Report 2013–1083, 86 p., at https://pubs.usgs.gov/of/2013/1083/. Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana