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Seagrass habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island

Dates

Publication Date
Time Period
2019

Citation

Wang, H., Enwright, N.M., Darnell, K.M., La Peyre, M.K., Cebrian, J., Kim, S.-C., Bunch, B., Stelly, S.J., Couvillion, B.R., Dalyander, P.S., Mickey, R.C., and Segura, M., 2020, Seagrass habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island: U.S. Geological Survey data release, https://doi.org/10.5066/P9B32VTE.

Summary

A barrier island seagrass habitat suitability index (HSI) model was developed for the Alabama barrier island restoration assessment at Dauphin Island. Shoal grass (Halodule wrightii) was selected as the representative species for seagrass community near Dauphin Island waters since H. wrightii is the dominant species (>62%) of seagrass communities in this area due to its rapid growth and tolerance to a wide range of salinity. Five water quality and morphological variables were selected and their relationships with habitat suitability were developed and incorporated into the seagrass HSI model for Dauphin Island restoration assessment: 1) mean salinity during the summer growing season, 2) mean temperature during the growing season, 3) [...]

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HSI_Seagrass_Modeling.zip 8.94 MB

Purpose

This data release provides the details on the calculations of seagrass habitat suitability index values across the model domain and final model outputs. The HSI model simulations include eight scenarios for each of the six restoration actions and the future-without action: ST2SL1 under USACE high SLR curve and intermediate curve for initial year (Yr = 0) and a period after 10 years (Yr = 10), and ST3SL3 under USACE high SLR curve and intermediate curve for initial year (Yr = 0) and a period after 10 years (Yr = 10). There is a total of 60 simulation runs. Values of each habitat suitability variable from water quality model monthly output and the relative wave exposure index (REI) for the cells were calculated using the Esri ArcMap 10.7.1 for each simulation run. Landscape position-based habitat model outputs were used to calculate the mean water depth. A Python script (Python 2.7) was developed to read in the value of each habitat suitability parameter in spatial layers of the habitat suitability variables, calculate the individual suitability index based on the habitat suitability curves and determine the total habitat suitability index using the weighted geometric mean method, classify the suitability scores (0 to 1) into groups, and generate spatial a distribution map of seagrass habitat suitability designations (that is, unsuitable, marginally suitable, suitable, and highly suitable).

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  • USGS Data Release Products
  • USGS Wetland and Aquatic Research Center

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DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9B32VTE

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