Mapping West Coast EEZ Aquaculture Suitability

Identify optimal West Coast locations for oyster and California spiny lobster aquaculture based on sea surface temperature and depth data.
R
Geospatial Analysis
Marine Aquaculture
Sustainability
Author

Richard Montes Lemus

Published

December 6, 2025

View Repository on GitHub

Sustainable Protein Production Challenges

With the global population approaching 10 billion by 2050, protein demand has continuously risen. Terrestrial livestock has increased to meet this demand - degrading the environment in the process. Marine aquaculture, however, offers an attractive solution to this problem. It is associated with lower carbon emissions, reduced freshwater use, and minimal land use compared to terrestrial livestock (Gephart et al. 2021).

Implementing this alternative requires finding locations with specific temperature, depth, and other environmental factors needed for particular marine species. Finding these optimal zones with spatial analysis would encourage sustainable aquaculture within an Exclusive Economic Zone (EEZ) like the U.S’s West coast. Exclusive Economic Zones are maritime zones extending up to 200 nautical miles from a country’s coastline, a country has special rights to the exploration and use of marine resources in this zone.

Research Question

Where are the most suitable locations along the U.S. West Coast for cultivating oysters and California spiny lobsters?

This analysis uses sea surface temperature data and ocean depth to produce a map containing optimal environmental conditions for these two commercially valuable species.

Data & Methods

Data Sources

This analysis uses the following datasets:

  1. Sea Surface Temperature (SST): Average annual temperature raster tiff data (2008-2012) from NOAA Coral Reef Watch, providing a five-year baseline to capture climate variability
  2. Ocean Depth: Bathymetry raster data from the GEBCO 2022 Grid
  3. EEZ Boundaries: West Coast Exclusive Economic Zone boundary shapefiles from the Flanders Marine Institute

Species Requirements

Based on the species-specific biological research from SeaLifeBase (Palomares and Pauly 2025), I found the following optimal conditions for my species of interest.

Oysters:

  • Depth: 0 to -70 meters

  • Temperature: 11°C to 30°C

California Spiny Lobster:

  • Depth: 0 to -150 meters

  • Temperature: 14.8°C to 22.3°C

Analytical Approach

The analysis workflow involved:

  1. Data preparation: Transforming coordinate reference systems to match spatial extents and resolutions across all datasets. This will allow us to conduct zonal statistics between them and plot them on a map together.
  2. Temperature processing: Calculating mean sea surface temperature (SST) (2008-2012) and converting from Kelvin to Celsius. This ensures we have a robust SST informed by the historical temperatures of the area and is in a temperature people are familiar with.
  3. Finding suitable areas: Reclassifying temperature and depth rasters to identify suitable areas (binary: suitable = 1, unsuitable = 0). This returns a raster that only meets our suitability condition.
  4. Area calculation: Calculating suitable area (km²) within each EEZ region using zonal statistics. This allows us to quantify how suitable each EEZ is based on area of suitability.
  5. Create Function: Creating a function that takes in minimum sea surface temperature, maximum sea surface temperature, minimum sea depth, maximum sea depth, and species name to create a map that colors each exclusive economic zone on the US west coast by suitable area quantity in km^2. This makes the workflow generalizable.
Code 
# Load in necessary libraries
library(tidyverse)
library(terra)
library(here)
library(sf)
library(tmap)
library(knitr)
Code 
# Create raster objects for sea surface temperature
sst_2008 <- rast(here("posts", "west-coast-eez", "data", "average_annual_sst_2008.tif"))
sst_2009 <- rast(here("posts", "west-coast-eez", "data", "average_annual_sst_2009.tif"))
sst_2010 <- rast(here("posts", "west-coast-eez", "data", "average_annual_sst_2010.tif"))
sst_2011 <- rast(here("posts", "west-coast-eez", "data", "average_annual_sst_2011.tif"))
sst_2012 <- rast(here("posts", "west-coast-eez", "data", "average_annual_sst_2012.tif"))

# Create stacked raster
sst_all <- c(sst_2008, sst_2009, sst_2010, sst_2011, sst_2012)

# Create raster object for sea depth
depth <- rast(here("posts", "west-coast-eez", "data", "depth.tif"))

# Create shapefile for eez
eez <- st_read(here::here("posts", "west-coast-eez", "data", "wc_regions_clean","wc_regions_clean.shp"), quiet = TRUE)

# Reproject to match CRS
depth <- project(depth, sst_all)
eez <- st_transform(eez, crs = crs(sst_all))

# Find mean SST and convert to Celsius
sst_avg <- app(sst_all, fun = mean, na.rm = TRUE)
sst_avg_c <- sst_avg - 273.15

# Match extent and resolution
depth_cropped <- crop(depth, sst_avg_c)
depth_resample <- resample(depth_cropped, sst_avg_c, method = "near")
Code 
# Create matrix with oyster depth limits
depth_rcl <- matrix(c(-Inf, -70, 0,
                      -70, 0, 1, 
                      0, Inf, 0), 
                    ncol = 3, byrow = TRUE)

# Reclassify raster for suitable depth
depth_reclassified <- classify(depth_resample, rcl = depth_rcl)

# Create matrix with oyster temperature limits
sst_rcl <- matrix(c(-Inf, 11, 0,
                      11, 30, 1, 
                      30, Inf, 0), 
                    ncol = 3, byrow = TRUE)

# Reclassify raster for suitable temperature
sst_reclassified <- classify(sst_avg_c, rcl = sst_rcl)

# Combine suitability layers
suitable_oyster <- lapp(c(depth_reclassified, sst_reclassified), fun = function(x, y) x * y)

# Set zeros to NA and mask to EEZ
suitable_oyster[suitable_oyster == 0] <- NA
suitable_oyster_eez <- mask(suitable_oyster, eez)

# Calculate area
suitable_cell_area <- cellSize(suitable_oyster_eez, unit = "km")
suitable_oyster_area <- suitable_oyster_eez * suitable_cell_area

# Zonal statistics
eez_rast <- rasterize(eez, suitable_oyster_eez, field = "rgn")
suitable_oyster_region <- zonal(suitable_oyster_area, eez_rast, fun = "sum", na.rm = TRUE)
names(suitable_oyster_region)[2] <- "area_km_2"

# Join to EEZ shapefile
eez_oyster <- left_join(x = eez, y = suitable_oyster_region, by = "rgn")

Results

Oyster Aquaculture Suitability

The analysis finds the most suitable EEZs for oyster aquaculture across the U.S. West Coast:

Code 
kable(suitable_oyster_region, 
      col.names = c("EEZ Region", "Suitable Area (km²)"),
      caption = "Oyster Aquaculture Suitable Area per EEZ Region",
      format.args = list(big.mark = ","))
Oyster Aquaculture Suitable Area per EEZ Region
EEZ Region Suitable Area (km²)
Central California 4,373.5565
Northern California 323.9124
Oregon 1,395.7328
Southern California 3,329.9002
Washington 3,091.5158

Central California is found to be the most suitable region for oyster aquaculture since it contains the largest area of optimal conditions.

Code 
tm_shape(eez_oyster) +
  tm_polygons(fill = "area_km_2",
        fill.scale = tm_scale_continuous(values = "brewer.greens"), 
        fill.legend = tm_legend(title = "Suitable Area (km²)",
              position = c(0.6, 0.9))) + 
  tm_scalebar(position = c(0.25, 0.15)) + 
  tm_compass(position = c(0.3, 0.35)) + 
  tm_title(text = "Oyster Aquaculture Suitability") +
  tm_layout(asp = 1.8,
            legend.title.size = 0.7, 
            legend.text.size = 0.5,   
            legend.width = 13,       
            legend.height = 15,
            title.position = c(0.05, 1.08), 
            frame = FALSE)

West Coast EEZ regions colored by suitable oyster aquaculture area (km²). Darker green indicates larger suitable areas.

California Spiny Lobster Suitability

The analysis finds the most suitable EEZs for California spiny lobster aquaculture across the U.S. West Coast:

Code 
# Generalized function for species suitability
eez_map <- function(min_sst, max_sst, min_depth, max_depth, species_name){
  
  # Create depth classification matrix
  depth_rcl <- matrix(c(-Inf, max_depth, 0,
                        max_depth, min_depth, 1, 
                        min_depth, Inf, 0), 
                      ncol = 3, byrow = TRUE)
  
  depth_reclassified <- classify(depth_resample, rcl = depth_rcl)
  
  # Create temperature classification matrix
  sst_rcl <- matrix(c(-Inf, min_sst, 0,
                      min_sst, max_sst, 1, 
                      max_sst, Inf, 0), 
                    ncol = 3, byrow = TRUE)
  
  sst_reclassified <- classify(sst_avg_c, rcl = sst_rcl)
  
  # Combine layers
  suitable_sst_depth <- lapp(c(depth_reclassified, sst_reclassified), fun = function(x, y) x * y)
  suitable_sst_depth[suitable_sst_depth == 0] <- NA
  suitable_eez <- mask(suitable_sst_depth, eez)
  
  # Calculate area
  suitable_cell_area <- cellSize(suitable_eez, unit = "km")
  suitable_area <- suitable_eez * suitable_cell_area
  eez_rast <- rasterize(eez, suitable_eez, field = "rgn")
  suitable_region <- zonal(suitable_area, eez_rast, fun = "sum", na.rm = TRUE)
  names(suitable_region)[2] <- "area_km_2"
  eez_area <- left_join(x = eez, y = suitable_region, by = "rgn")
  
  # Create map
  map <- tm_shape(eez_area) +
    tm_polygons(fill = "area_km_2",
                fill.scale = tm_scale_continuous(values = "brewer.greens"), 
                fill.legend = tm_legend(title = "Suitable Area (km²)",
                                        position = c(0.6, 0.9))) + 
    tm_scalebar(position = c(0.25, 0.15)) + 
    tm_compass(position = c(0.3, 0.35)) + 
    tm_title(text = paste(species_name, "Aquaculture Suitability")) +
    tm_layout(asp = 1.8,
              legend.title.size = 0.7, 
              legend.text.size = 0.5,   
              legend.width = 13,       
              legend.height = 15,
              title.position = c(0.05, 1.08), 
              frame = FALSE)
  
  return(map)
}
Code 
eez_map(14.8, 22.3, 0, -150, "California Spiny Lobster")

West Coast EEZ regions colored by suitable California spiny lobster aquaculture area (km²). The warmer waters of Southern California provide optimal habitat.

Southern California is found to be the most suitable region for California spiny lobster aquaculture since it contains the largest area of optimal conditions - warmer waters and appropriate depth profiles.

Discussion & Implications

Key Findings

This spatial analysis maps potential aquaculture EEZs for Oyster and California spiny lobster.

  • Central California is optimal for oyster cultivation, with moderate temperatures and shallow coastal waters
  • Southern California is best suited for California spiny lobster, with warmer waters and appropriate depths

Limitations

The following limitation should be considered:

  1. Simplified environmental criteria: This analysis only considers temperature and depth. Additional factors should be considered in order to find more accurate optimal zones.

Conclusion

Given the increasing demand for protein and depleted wild fisheries, marine aquaculture offers a sustainable pathway (FAO 2020). Spatial tools like this analysis can help guide development in appropriate exclusive economic zones. (Gentry et al. 2017).

References

FAO. 2020. “The State of World Fisheries and Aquaculture 2020: Sustainability in Action.” Rome: Food; Agriculture Organization of the United Nations. https://www.fao.org/state-of-fisheries-aquaculture.
Gentry, Rebecca R, Halley E Froehlich, Dietmar Grimm, Peter Kareiva, Michael Parke, Michael Rust, Steven D Gaines, and Benjamin S Halpern. 2017. “Mapping the Global Potential for Marine Aquaculture.” Nature Ecology & Evolution 1 (9): 1317–24.
Gephart, Jessica A, Patrik JG Henriksson, Robert WR Parker, Alon Shepon, Kelvin D Gorospe, Kathryn Bergman, Gidon Eshel, et al. 2021. “Environmental Performance of Blue Foods.” Nature 597 (7876): 360–65.
Palomares, M. L. D., and D. Pauly. 2025. “SeaLifeBase.” World Wide Web electronic publication. www.sealifebase.org.

Data Sources

West Coast Exclusive Economic Zones
Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: United States West Coast Exclusive Economic Zones, version 12. Available online at https://www.marineregions.org/. https://doi.org/10.14284/632

Depth
GEBCO Compilation Group (2022) GEBCO_2022 Grid (doi:10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c).

Sea Surface Temperature
NOAA Coral Reef Watch. 2008-2012. NOAA Coral Reef Watch Average Annual Sea Surface Temperature Product. College Park, Maryland, USA: NOAA Coral Reef Watch. Data set accessed November 30, 2025 at https://coralreefwatch.noaa.gov.

Species Suitable Depth and Temperature
Palomares, M.L.D. and D. Pauly. Editors. 2025. SeaLifeBase. World Wide Web electronic publication. www.sealifebase.org, version (04/2025).