EEZs for Aquaculture

Author

Kylie Newcomer

Published

December 12, 2025

Background

Marine aquaculture is a growing source of food in many areas around the world, yet remains wildly under-utilized in the United States. The U.S. imports between 70% and 85% of its seafood, with more than half of that comes from aquaculture1. By increasing production of aquaculture in the U.S., we can find economic benefit and meet growing seafood demands. It has the capability of producing more sustainable protein options than land-based meat2. A study by Gentry et al. mapped the potential for marine aquaculture globally based on multiple constraints, including ship traffic, dissolved oxygen, and bottom depth. They found that global seafood demand could be met using less than 0.015% of the global ocean area3.

In this project, I aim to visualize suitable areas for aquaculture of Oysters and Red Abalone along the west coast. Additionally, I created a function that will produce a suitability map for any species with habitat depth and temperature ranges. This project can help provide data to support future aquaculture along the west coast.

Data

Suitable growing conditions

Information for both Oysters and Red Abalone temperature and depth ranges come from SeaLifeBase4.

Sea Surface Temperature

To determine the average seat surface temperature across the west coast an average from 2008-2012 data was calculated. Rasters were created by NOAA using satellite data from Coral Reef Watch5.

Bathymetry

Depth data comes from General Bathymetric Chart of the Oceans (GEBCO)6.

Exclusive Economic Zones

The West Coast Exclusive Economic Zone data represents designated coastal areas designating state control over the natural resources. These maritime boundaries come from marineregions.org7.

Data Processing

Cleaning

Because I am working with geospatial data, there is a lot data wrangling and transforming that needs to be done. I have to ensure that the raster data is compatible to be stacked. This includes matching the projections, resolution and extent. Additionally, the non-raster data needs to transformed so it can be used with the temperature and depth rasters. All temperature rasters from 2008 to 2012 are stacked. The mean of all temperature rasters is taken to determine the average sea surface temperature. The temperature is converted from Kelvins to degrees Celsius.

Find suitable locations

In order to determine suitable areas of habitat, reclassification matrices are created to assign 1’s to areas within the suitable temperature and depth ranges for Oysters. Areas without suitable depth and temperature ranges are assigned a 0. The CRS, extent, and resolution of the reclassified depth raster are updated to match the reclassified temperature raster. The updated depth and temperature rasters are multiplied together to determine which areas are suitable in both temperature and depth requirements. Any cell with a 1 in both temperature and depth will remain 1, while any cell without suitable depth or temperature ranges, or both, will become 0.

Code 
# Reclassify temperature data
rcl_temp <- matrix(c(-Inf, 11, 0,
         11, 30, 1,
         30, Inf, 0),
       ncol = 3, byrow = TRUE)


temp_reclass <- classify(mean_sst, rcl = rcl_temp)
Code 
# Reclassify depth data
rcl_depth <- matrix(c(-Inf, -70, 0,
         -70, 0, 1,
         0, Inf, 0),
       ncol = 3, byrow = TRUE)

depth_reclass <- classify(depth, rcl = rcl_depth)
Code 
# If-else statement to check/update CRS
if (crs(depth_reclass) == crs(temp_reclass)){
  print("Projections match!")
}else {
  crs(depth_reclass) <- crs(temp_reclass)
  warning("Updating depth CRS")
}
# Match resolutions of rasters
depth_resample <- resample(depth_reclass, temp_reclass, method = "near")

# Match extents of depth and temp rasters
cropped_depth <- crop(depth_resample, temp_reclass)

# Stack temperature and depth rasters
temp_depth <- c(temp_reclass, cropped_depth)
Code 
# Multiply temperature and depth rasters together
ocean <- temp_reclass * cropped_depth

Map Suitable Habitat

Once the suitable cells are found, it can be visualized along the west coast.

Code 
# Looking at suitable habitat based on new raster
tm_shape(ocean) +
  tm_raster(col.scale = tm_scale(
    breaks = c(0,1,Inf),
    values = c("lightblue2", "black"),
    labels = c("Not Suitable", "Suitable")),
    col.legend = tm_legend(
     title = "Suitable habitat",
     position = tm_pos_out("right", "center")
    )) +
  tm_grid(alpha = 0.4)

Find Suitable habitat in EEZ’s

While I found the cells within the raster that have suitable habitat, raster data is in a cell by cell format, which does not give an actual size of suitable habitat. I want to see the bigger picture of these suitable cells and how that reflects in the Exclusive Economic Zones (EEZ) on the coast. This can be done through combining the EEZ data with the suitability raster and calculating the number of suitable cells in each region. Once the amount of cells per region is quantified, I can calculate the total area of suitable habitat by finding individual cell size in km^2 and multiplying that by the number of cells in each region.

Suitable habitat by EEZ region
Region Number of cells with suitable habitat Area of suitable habitat (sq km)
Central California 238 3296.1331
Southern California 211 2922.2021
Washington 162 2243.5864
Oregon 71 983.3002
Northern California 11 152.3423

Map suitable Area by West Coast EEZ’s

Now that I have a calculated area of suitable habitat for each region, I can visualize it within the EEZ’s. My results show that the Central California region, followed by Southern California are the most suitable for Oyster aquaculture.

Code 
# Map the results
tm_shape(ocean) +                                      
  tm_raster(col.legend = tm_legend(show = FALSE)) +
tm_shape(suitable_habitat) +
  tm_polygons(fill = "suitable_habitat", 
              fill.scale = tm_scale(values =  "viridis"),
                                    #breaks = c(0,200,1000,2500,3000, Inf)),
              fill.legend = tm_legend(title  = "Suitable habitat (sq km)")) + 
  tm_text(text = "rgn", # Add labels for each region
          col = "white",
          size = 0.6,
          xmod = 4,
          ymod = 1,
          bgcol = "black",
          bgcol_alpha = 0.75)+
tm_grid(alpha = 0.5) +
tm_title("Suitable Aquaculture Zones for Oysters")

Create Function

After successfully determining aquaculture habitats for Oysters, I can generalize my code into a function, which will produce a habitat suitability map for any species with specified depth and temperature ranges.

Code 
aquaculture_zones <- function(species, min_depth, max_depth, min_temp, max_temp){
  # Temp reclassification
  rcl_temp <- matrix(c(-Inf, min_temp, 0,
         min_temp, max_temp, 1,
         max_temp, Inf, 0),
       ncol = 3, byrow = TRUE)
    
  temp_reclass <- classify(mean_sst, rcl = rcl_temp)
  
  # Depth reclassification
  rcl_depth <- matrix(c(-Inf, min_depth, 0,
         min_depth, max_depth, 1,
         max_depth, Inf, 0),
       ncol = 3, byrow = TRUE)
  depth_reclass <- classify(depth, rcl = rcl_depth)

  # Adjust CRS to match
  crs(depth_reclass) <- crs(temp_reclass)
 
  # Match resolutions of rasters
  depth_resample <- resample(depth_reclass, temp_reclass, method = "near")

  # Match extents of depth and temp rasters
  cropped_depth <- crop(depth_resample, temp_reclass)

  # Create raster denoting suitable depth AND temperature ranges
  ocean <- cropped_depth * temp_reclass
  
  # Change EEZ CRS
  st_crs(eez) <- crs(ocean)
  
  # Mask ocean data outside of EEZ regions
  ocean_mask <- mask(ocean, eez)

  # Rasterize eez data based on ocean mask
  eez_raster <- rasterize(eez, ocean_mask,
                          field = "rgn")

  # Add up number of suitable cells per region
  suitable_cells <- zonal(ocean_mask, eez_raster, fun = "sum", na.rm = TRUE)

  # Determine cell size
  cell_area <- cellSize(ocean_mask, unit="km")[1]

  # Calulcate area of suiable habitat based on number of suitable cells and cell size
  suitable_cell_area <- suitable_cells %>%
    rename(cells = depth) %>%
    mutate(cell_area = cell_area$area,
          suitable_habitat = cells * cell_area) %>% 
    select(-cell_area)
  
  # Join EEZ regions and suitable area
  suitable_habitat <- left_join(eez, suitable_cell_area, by = "rgn")
  
  # Create map
  tm_shape(ocean) +                                      
    tm_raster(col.legend = tm_legend(show = FALSE)) +
    tm_shape(suitable_habitat) +
    tm_polygons(fill = "suitable_habitat", 
                fill.scale = tm_scale(values =  "viridis"),
                                    #breaks = c(0,200,1000,2500,3000, Inf)),
                fill.legend = tm_legend(title  = "Suitable habitat (sq km)")) + 
    tm_text(text = "rgn",
            col = "white",
            size = 0.6,
            xmod = 4,
            ymod = 1,
            bgcol = "black",
            bgcol_alpha = 0.75)+
    tm_grid(alpha = 0.5) +
    tm_title(paste("Suitable Aquaculture Zones for", species))
}

Suitable Habitat for Red Abalone

After streamlining my code and creating a function, I can use it to determine sutiable habitat for a new species of interest.

Red Abalone were a historically popular fishery across the west coast, however the fishery closed in 2018 due to declining populations. The only source of Red Abalone on the west coast is currently through aquaculture. Because this species was a popular fishery and still has demand across the west coast, utilizing aquaculture zones along the coast would increase supply for this sought after mollusk. The depth range for Red Abalone is 0-24 m and the temperature range is 8-18 ºC.

Code 
aquaculture_zones(species = "Red Abalone", min_depth = -24, max_depth = 0, min_temp =  8, max_temp = 18)

Conclusion

There are some limitations to the map. While it does provide areas of suitable habitat, it does not factor in species natural habitat, as my results show that Washington has the most suitable habitat for Red Abalone but are not part of Red Abalone’s natural range. It should also be pointed out that these are a subtidal species, and the temperature data used was sea surface temperature. This means that the temperature data may not be representative of true habitat ranges for species of interest, especially those that live at deeper depths.

Footnotes

  1. National Oceanic and Atmospheric Administration. (2022). U.S. Aquaculture. NOAA Fisheries. https://www.fisheries.noaa.gov/national/aquaculture/us-aquaculture. [Accessed Dec. 6, 2025]↩︎

  2. Hall, S. J., Delaporte, A., Phillips, M. J., Beveridge, M. & O’Keefe, M. Blue Frontiers: Managing the Environmental Costs of Aquaculture (The WorldFish Center, Penang, Malaysia, 2011).↩︎

  3. Gentry, R. R., Froehlich, H. E., Grimm, D., Kareiva, P., Parke, M., Rust, M., Gaines, S. D., & Halpern, B. S. Mapping the global potential for marine aquaculture. Nature Ecology & Evolution, 1, 1317-1324 (2017).↩︎

  4. Palomares, M.L.D. and D. Pauly. Editors. 2025. SeaLifeBase. World Wide Web electronic publication. www.sealifebase.org, version (04/2025). [Accessed Novemeber 29, 2025]↩︎

  5. NOAA Coral Reef Watch. 2019, updated daily. NOAA Coral Reef Watch Version 3.1 Daily 5km Satellite Regional Virtual Station Time Series Data for Southeast Florida, Mar. 12, 2013-Mar. 11, 2014. College Park, Maryland, USA: NOAA Coral Reef Watch. Data set accessed 2020-02-05 at https://coralreefwatch.noaa.gov/product/vs/data.php.↩︎

  6. Bathymetry data was obtained through open access on November 29, 2025 from the GEBCO Grid↩︎

  7. Flanders Marine Institute (2025): MarineRegions.org. Available online at www.marineregions.org. Consulted on 2025-11-29.↩︎