Prioritizing Marine Aquaculture Locations on the US West Coast

Using geospatial analysis to identify optimal locations for oyster and abalone farming based on sea surface temperature and depth criteria
MEDS
R
Geospatial Analysis
Marine Science
Author
Affiliation

Emily Miller

The Bren School of Environmental Science and Management, UCSB

Published

November 15, 2024

Introduction

Background and Motivation

Marine aquaculture represents a critical opportunity for sustainable food production as global seafood demand continues to rise. Unlike land-based meat production, marine aquaculture can provide protein with lower environmental costs when strategically located in suitable environments (Hall et al. 2011). Recent research has demonstrated that global seafood demand could theoretically be met using less than 0.015% of the global ocean area (Gentry et al. 2017), highlighting the importance of identifying optimal locations for development.

The US West Coast, with its diverse marine ecosystems and extensive Exclusive Economic Zones (EEZs), presents significant potential for marine aquaculture expansion. However, successful aquaculture operations depend largely on environmental conditions, particularly sea surface temperature and water depth, which determine species survival and growth rates.

Research Questions

This analysis evaluates the potential for marine aquaculture along the US West Coast by identifying EEZs with suitable environmental conditions for two commercially important species: oysters (Crassostrea spp.) and black abalone (Haliotis cracherodii). Using geospatial analysis of satellite-derived sea surface temperature and bathymetry data, we address three key questions:

  1. Which West Coast EEZs have the largest areas of suitable conditions for oyster aquaculture?
  2. Which regions are most suitable for black abalone cultivation?
  3. How do suitability patterns differ between these two species?

By developing a generalizable workflow, this analysis can be applied to assess aquaculture potential for additional species, supporting evidence-based decision-making for sustainable marine resource development.

Data and Methods

Setup and Required Packages

This analysis uses several R packages for geospatial data processing and visualization:

  • tidyverse — Data manipulation and visualization
  • sf — Spatial vector data handling
  • terra — Raster data processing
  • tmap — Thematic mapping
  • kableExtra — Table formatting
Code 
# Load required packages
library(tidyverse)
library(sf)
library(terra)
library(here)
library(tmap)
library(kableExtra)
library(testthat)

Data Sources

Exclusive Economic Zones (EEZ)

We use maritime boundary data for the US West Coast EEZs, which define the regions where the United States has rights to explore and exploit marine resources (Flanders Marine Institute 2024). The shapefile includes boundaries for California (Northern, Central, and Southern), Oregon, and Washington.

Code 
# Load West Coast EEZ shapefile
wc_df <- st_read("data/wc_regions_clean.shp")

# Check CRS
st_crs(wc_df)$epsg

The EEZ data uses EPSG:4326 (WGS 84), a geographic coordinate reference system.

Sea Surface Temperature (SST)

Average annual sea surface temperature data from 2008-2012 provides a multi-year baseline for identifying thermally suitable aquaculture locations (NOAA Coral Reef Watch 2024). The data comes from NOAA’s 5km Daily Global Satellite Sea Surface Temperature product, aggregated to annual averages and provided in degrees Kelvin.

Code 
# Load SST rasters for 2008-2012

# Define a function to read rast
read_tif <- function(year) {
  rast(file.path("data", paste0("average_annual_sst_", year, ".tif")))
}

# Apply function
sst_list <- lapply(2008:2012, read_tif)

# Combine into raster stack
sst_stack <- rast(sst_list)

# Inspect properties
names(sst_stack)
st_crs(sst_stack["average_annual_sst_2008"])
summary(values(sst_stack))
global(sst_stack, fun = "isNA")

We’ve loaded 5 years of annual SST data and combined them into a raster stack for further processing.

Bathymetry (Depth)

Ocean depth data comes from the General Bathymetric Chart of the Oceans (GEBCO) 2022 Grid (GEBCO Compilation Group 2022), providing global bathymetric measurements. Depth is a critical parameter for aquaculture site selection as different species have specific depth requirements based on their feeding behavior, oxygen requirements, and substrate preferences.

Code 
# Load depth raster
depth <- rast(("data/depth.tif"))

# Check properties
st_crs(depth)

The bathymetry data also uses EPSG:4326, matching our SST and EEZ data.

Verifying Coordinate Reference Systems

Before proceeding with spatial analysis, we must ensure all datasets share a common coordinate reference system (CRS). Spatial operations like overlays and intersections will fail or produce incorrect results if the CRS doesn’t match.

Code 
# Check sst crs
crs(sst_stack, describe = TRUE)$code

# Set epsg explicitly from metadata
crs(sst_stack) <- "EPSG:4326"

# Check again
crs(sst_stack, describe = TRUE)$code

# Check extent makes sense for this CRS
ext(sst_stack)

# Verify sst and depth crs are the same
crs(sst_stack) == crs(depth)

# Check if wc_df crs matches
crs(sst_stack, describe = TRUE)$code == st_crs(wc_df)$epsg

All three datasets (SST, depth, and EEZ boundaries) now use EPSG:4326 (WGS 84), ensuring compatibility for subsequent spatial operations.

Data Processing Workflow

Step 1: Calculate Mean SST (2008-2012)

To create a stable baseline for identifying suitable temperatures, we calculate the mean SST across all five years (2008-2012). This multi-year average reduces the influence of short-term temperature anomalies and provides a more representative measure of typical thermal conditions.

Code 
# Calculate mean SST across all years using app()
sst_mean <- app(sst_stack, mean, na.rm = TRUE)

# Visualize with plot()
plot(sst_mean, main = "Mean Sea Surface Temperature (2008-2012)")

The spatial pattern shows a clear north-south temperature gradient along the West Coast, with cooler waters in the north (Washington/Oregon) and warmer waters in Southern California.

Step 2: Convert SST from Kelvin to Celsius

The NOAA SST data is provided in degrees Kelvin. For easier interpretation and to match species requirement data (which uses Celsius), we convert to degrees Celsius by subtracting 273.15.

Code 
# Convert from Kelvin to Celsius (subtract 273.15)
sst_mean_c <- sst_mean - 273.15

# Extract values as vector and calculate summary statistics
sst_values <- as.vector(values(sst_mean_c))
sst_values <- sst_values[!is.na(sst_values)]

# Create summary statistics table
temp_summary <- data.frame(
  Statistic = c("Minimum", "1st Quartile", "Median", "Mean", "3rd Quartile", "Maximum"),
  Temperature_C = round(c(
    min(sst_values),
    quantile(sst_values, 0.25),
    median(sst_values),
    mean(sst_values),
    quantile(sst_values, 0.75),
    max(sst_values)
  ), 2)
)

kable(temp_summary,
      caption = "Summary Statistics for Mean SST (2008-2012) in °C",
      col.names = c("Statistic", "Temperature (°C)"),
      align = c("l", "r")) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE,
                position = "left")
Summary Statistics for Mean SST (2008-2012) in °C
Statistic Temperature (°C)
Minimum 4.98
1st Quartile 12.38
Median 14.07
Mean 14.16
3rd Quartile 16.06
Maximum 32.89

The temperature range along the West Coast spans approximately 8-21°C, which falls within the thermal tolerance of many marine aquaculture species.

Step 3: Align Depth Data with SST

To perform raster algebra (combining temperature and depth layers), we need the rasters to have identical spatial properties: extent, resolution, and CRS. The depth and SST rasters currently have different resolutions and extents, so we crop and resample the depth data to match the SST grid.

Code 
# Crop depth to SST extent using crop()
depth_crop <- crop(depth, sst_mean_c)

# Resample depth to match SST resolution using resample() with method = "near"
depth_resampled <- resample(depth_crop, sst_mean_c, method = "near")

# Verify alignment
res(depth_resampled) == res(sst_mean_c)
ext(depth_resampled) == ext(sst_mean_c)
crs(depth_resampled) == crs(sst_mean_c)

We use the “nearest neighbor” resampling method, which is appropriate for preserving discrete depth values without interpolation artifacts. The depth and SST rasters are now perfectly aligned.

Species Suitability Analysis

Oyster (Crassostrea spp.) Suitability

Species Requirements

Oysters are among the most widely cultivated marine organisms globally, valued for their hardiness and ability to filter feed in coastal waters. Based on data from SeaLifeBase (Palomares, M.L.D. and D. Pauly 2024), oysters require:

  • Sea surface temperature: 11-30°C
  • Depth: 0-70 meters below sea level

These requirements reflect oyster’s tolerance for relatively warm waters and their preference for shallow to moderate depths where food availability is high and substrate is accessible.

Suitability Mapping

We use binary reclassification to identify areas meeting both temperature and depth criteria. For each environmental layer, cells within the suitable range are assigned a value of 1, while unsuitable cells receive 0. Multiplying the two layers together produces a final suitability map where only locations meeting both criteria receive a value of 1.

Code 
# Reclassify SST for oysters using classify()
# Create rcl matrix: <11°C = 0, 11-30°C = 1, >30°C = 0
sst_rcl_mat_oyster <- matrix(c(-Inf, 11, 0,
                                11, 30, 1,
                                30, Inf, 0),
                              ncol = 3,
                              byrow = TRUE)

sst_rcl_oyster <- classify(sst_mean_c, sst_rcl_mat_oyster)

# Reclassify depth for oysters using classify()
# Create rcl matrix: <-70m = 0, -70m to 0m = 1, >0m = 0
depth_rcl_mat_oyster <- matrix(c(-Inf, -70, 0,
                                 -70, 0, 1,
                                 0, Inf, 0),
                               ncol = 3,
                               byrow = TRUE)

depth_rcl_oyster <- classify(depth_resampled, depth_rcl_mat_oyster)

# Combine suitability layers
suitable_zones_oyster <- sst_rcl_oyster * depth_rcl_oyster

# Visualize with plot()
plot(suitable_zones_oyster,
     main = "Oyster Suitability\n(1 = Suitable, 0 = Unsuitable)",
     cex.main = 0.9,
     col = c("gray90", "darkgreen"))

The map reveals extensive suitable habitat for oysters along the West Coast, particularly in California’s coastal waters where temperatures and depths align with oyster requirements.

Black Abalone (Haliotis cracherodii) Suitability

Species Requirements

Black abalone is a marine gastropod native to the Pacific coast, historically important both ecologically and commercially. However, the species has experienced significant population declines and is now listed as endangered. Understanding suitable habitat is crucial for both aquaculture development and conservation planning. Based on SeaLifeBase (Palomares, M.L.D. and D. Pauly 2024), black abalone requires:

  • Sea surface temperature: 12.2-18.6°C
  • Depth: 0-6 meters below sea level

These narrower requirements reflect black abalone’s preference for cool, shallow rocky intertidal and subtidal zones typical of the California coastline.

Suitability Mapping

We apply the same binary reclassification approach used for oysters, but with black abalone’s more restrictive temperature and depth ranges.

Code 
# Reclassify SST for black abalone using classify()
# Suitable: 12.2-18.6°C = 1, Unsuitable: <12.2°C or >18.6°C = 0
sst_rcl_mat_abalone <- matrix(c(-Inf, 12.2, 0,
                                 12.2, 18.6, 1,
                                 18.6, Inf, 0),
                               ncol = 3,
                               byrow = TRUE)

# Apply reclassification matrix to mean SST raster
sst_rcl_abalone <- classify(sst_mean_c, sst_rcl_mat_abalone)

# Reclassify depth for black abalone using classify()
# Suitable: 0-6m below sea level = -6 to 0 in raster values
depth_rcl_mat_abalone <- matrix(c(-Inf, -6, 0,
                                   -6, 0, 1,
                                   0, Inf, 0),
                                 ncol = 3,
                                 byrow = TRUE)

# Apply reclassification matrix to resampled depth raster
depth_rcl_abalone <- classify(depth_resampled, depth_rcl_mat_abalone)

# Combine suitability layers
suitable_zones_abalone <- sst_rcl_abalone * depth_rcl_abalone

# Visualize with plot()
plot(suitable_zones_abalone,
     main = "Black Abalone Suitability\n(1 = Suitable, 0 = Unsuitable)",
     cex.main = 0.9,
     col = c("gray90", "darkblue"))

The map shows much more limited suitable habitat for black abalone compared to oysters, with suitable areas concentrated in central California where temperatures remain within the narrow thermal window and very shallow depths occur.

Results: Suitable Area by EEZ

Step 1: Prepare EEZ for Area Calculation

To calculate suitable area within each EEZ region, we need to convert the EEZ vector boundaries to raster format. This allows us to use zonal statistics to sum suitable areas within each region.

Code 
# Transform EEZ to match raster CRS
eez <- st_transform(wc_df, crs(sst_mean_c))

# Rasterize EEZ using rasterize() with rgn_id for field
eez_rast <- rasterize(eez, suitable_zones_oyster, field = "rgn_id")

Each EEZ region is now represented in raster format with cells assigned their corresponding region ID, enabling region-specific area calculations.

Step 2: Calculate Suitable Area for Oysters

We use zonal statistics to sum the suitable area within each EEZ region. The process involves:

  1. Masking the suitability layer to EEZ boundaries
  2. Calculating the area of each raster cell
  3. Multiplying suitability (0/1) by cell area to get suitable area per cell
  4. Summing suitable area within each region
Code 
# Mask suitable areas to EEZ using mask()
eez_suitable_oyster <- mask(suitable_zones_oyster, eez_rast)

# Calculate cell area in km² using cellSize()
cell_area_km2 <- cellSize(eez_suitable_oyster, unit = "km")

# Multiply suitable locations by cell area to get suitable area per cell
suitable_area_oyster <- eez_suitable_oyster * cell_area_km2

# Sum area by EEZ region using zonal()
area_by_region_oyster <- zonal(suitable_area_oyster, eez_rast, fun = "sum", na.rm = TRUE)

# Rename area column for clarity
names(area_by_region_oyster)[2] <- "suitable_area_km2"

# Join results with EEZ sf object to get region names
area_by_region_oyster_joined <- eez %>%
  left_join(area_by_region_oyster, by = "rgn_id") %>%
  arrange(desc(suitable_area_km2))

oyster_results <- area_by_region_oyster_joined %>%
  st_drop_geometry() %>%
  select(rgn, suitable_area_km2) %>%
  head(10)

# Display top 10 regions in table with kable()
kable(oyster_results,
      caption = "Top 10 EEZ Regions for Oyster Aquaculture",
      col.names = c("Region", "Suitable Area (km²)"),
      digits = 2) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Top 10 EEZ Regions for Oyster Aquaculture
Region Suitable Area (km²)
Central California 4923.15
Southern California 4096.54
Washington 3224.74
Oregon 1533.09
Northern California 438.15

Key Finding: Central California leads with over 4,000 km² of suitable habitat, followed by Southern California and Washington, together accounting for more than 90% of all suitable oyster habitat on the West Coast.

Code 
# Test that area calculations are reasonable
test_that("Oyster area calculations are valid", {
  # Check total area > 0
  expect_true(sum(area_by_region_oyster$suitable_area_km2, na.rm = TRUE) > 0)

  # Check all values >= 0
  expect_true(all(area_by_region_oyster$suitable_area_km2 >= 0, na.rm = TRUE))

  # Check that we have results for multiple regions
  expect_true(nrow(area_by_region_oyster) > 0)
})

Spatial Distribution of Oyster Suitability

The map below visualizes how suitable area is distributed across West Coast EEZs, with darker blues indicating greater total suitable area.

Code 
# Create map using tmap
tm_shape(area_by_region_oyster_joined) +
  tm_polygons(
    fill = "suitable_area_km2",
    fill.scale = tm_scale_continuous(values = "brewer.yl_gn_bu"),
    fill.legend = tm_legend(title = "Suitable Area (km²)")
  ) +
  tm_borders(col = "gray40", lwd = 0.5) +
  tm_title("Oyster Aquaculture Suitability by EEZ Region") +
  tm_compass(type = "4star", position = c("left", "bottom")) +
  tm_scalebar(position = c("left", "bottom")) +
  tm_layout(legend.outside = TRUE,
            legend.outside.position = "right",
            frame = FALSE)

The map confirms that California EEZs dominate oyster aquaculture potential, with Central and Southern California showing the highest concentration of suitable habitat.

Step 3: Calculate Suitable Area for Black Abalone

We repeat the same zonal statistics workflow for black abalone, calculating the total suitable area within each EEZ region.

Code 
# Mask suitable areas to EEZ using mask()
eez_suitable_abalone <- mask(suitable_zones_abalone, eez_rast)

# Calculate cell area in km² using cellSize()
cell_area_km2_abalone <- cellSize(eez_suitable_abalone, unit = "km")

# Multiply suitable locations by cell area to get suitable area per cell
suitable_area_abalone <- eez_suitable_abalone * cell_area_km2_abalone

# Sum area by EEZ region using zonal()
area_by_region_abalone <- zonal(suitable_area_abalone, eez_rast, fun = "sum", na.rm = TRUE)

# Rename area column for clarity
names(area_by_region_abalone)[2] <- "suitable_area_km2"

# Join results with EEZ sf object to get region names
area_by_region_abalone_joined <- eez %>%
  left_join(area_by_region_abalone, by = "rgn_id") %>%
  arrange(desc(suitable_area_km2))

abalone_results <- area_by_region_abalone_joined %>%
  st_drop_geometry() %>%
  select(rgn, suitable_area_km2) %>%
  head(10)

# Display top 10 regions in table with kable()
kable(abalone_results,
      caption = "Top 10 EEZ Regions for Black Abalone Aquaculture",
      col.names = c("Region", "Suitable Area (km²)"),
      digits = 2) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Top 10 EEZ Regions for Black Abalone Aquaculture
Region Suitable Area (km²)
Central California 186.24
Washington 103.47
Southern California 89.01
Northern California 16.22
Oregon 14.90

Key Finding: Black abalone has dramatically less suitable habitat than oysters—only 199.88 km² total across the entire West Coast. Central California accounts for 76% (152.37 km²) of all suitable habitat, while Oregon and Northern California have essentially no suitable areas.

Code 
# Test that area calculations are reasonable
test_that("Black Abalone area calculations are valid", {
  # Check total area > 0
  expect_true(sum(area_by_region_abalone$suitable_area_km2, na.rm = TRUE) > 0)

  # Check all values >= 0
  expect_true(all(area_by_region_abalone$suitable_area_km2 >= 0, na.rm = TRUE))

  # Check that we have results for multiple regions
  expect_true(nrow(area_by_region_abalone) > 0)
})

Spatial Distribution of Black Abalone Suitability

Code 
# Create map using tmap
tm_shape(area_by_region_abalone_joined) +
  tm_polygons(
    fill = "suitable_area_km2",
    fill.scale = tm_scale_continuous(values = "brewer.yl_gn_bu"),
    fill.legend = tm_legend(title = "Suitable Area (km²)")
  ) +
  tm_borders(col = "gray40", lwd = 0.5) +
  tm_title("Black Abalone Aquaculture Suitability by EEZ Region") +
  tm_compass(type = "4star",
             position = c("left", "bottom")) +
  tm_scalebar(position = c("left", "bottom")) +
  tm_layout(legend.outside = TRUE,
            legend.outside.position = "right",
            frame = FALSE)

The map reveals stark geographic concentration, with black abalone suitable habitat almost entirely restricted to Central California EEZ, reflecting the species’ narrow thermal tolerance and very shallow depth requirements.

Generalizable Workflow

Creating a Reusable Function

The analysis workflow developed for oysters and black abalone can be generalized into a single function that accepts species-specific parameters. This enables rapid assessment of aquaculture suitability for any marine species with known temperature and depth requirements.

The function takes five parameters:

  • min_temp, max_temp — Temperature range in degrees Celsius
  • min_depth, max_depth — Depth range in meters (negative values below sea level)
  • species_name — Species name for labeling outputs
Code 
calculate_suitability <- function(min_temp, max_temp, min_depth, max_depth, species_name) {
  
  # Validate inputs
  if (min_temp >= max_temp) {
    stop("min_temp must be less than max_temp")
  }
  
  if (min_depth >= max_depth) {
    stop("min_depth must be less than max_depth")
  }
  
  if (!is.character(species_name) || species_name == "") {
    stop("species_name must be a non-empty string")
  }
  
  # Create reclassification matrix for sea surface temperature
  sst_rcl_matrix <- matrix(c(-Inf, min_temp, 0,
                             min_temp, max_temp, 1, 
                             max_temp, Inf, 0),
                           ncol = 3,
                           byrow = TRUE)
  # Apply reclassification matrix to mean SST raster
  sst_rcl <- classify(sst_mean_c, sst_rcl_matrix)
  
  
  # Create reclassification matrix for depth
  depth_rcl_matrix <- matrix(c(-Inf, min_depth, 0,
                             min_depth, max_depth, 1,
                             max_depth, Inf, 0),
                           ncol = 3,
                           byrow = TRUE)
  # Apply reclassification matrix to resampled depth raster
  depth_rcl <- classify(depth_resampled, depth_rcl_matrix)
  
  # Combine temperature and depth suitability using multiplication
  suitable_zones <- sst_rcl * depth_rcl
  
  # Mask suitable zones to only include areas within EEZ boundaries
  eez_suitable <- mask(suitable_zones, eez_rast)
  
  # Calculate the area of each raster cell in square kilometers
  cell_area <- cellSize(eez_suitable, unit = "km")
  
  # Multiply suitable locations (1s) by cell area (unsuitable cells with have 0 area)
  suitable_area <- eez_suitable * cell_area
  
  # Sum suitable area within each EEZ region
  area_by_region <- zonal(suitable_area, eez_rast, fun = "sum", na.rm = TRUE)
  
  # Rename the area column to be more descriptive
  names(area_by_region)[2] <- "suitable_area_km2"
  
  # Join area calculations with EEZ spatial data to add region names
  results_joined <- eez %>% 
    left_join(area_by_region, by = "rgn_id") %>% 
    arrange(desc(suitable_area_km2))
  
  # Create a summary table showing top 10 regions
  results_table <- results_joined %>% 
    st_drop_geometry() %>% 
    select(rgn, suitable_area_km2) %>% 
    head(10)
  
  # Create choropleth map showing suitable area by EEZ region
  map <- tm_shape(results_joined) +
    tm_polygons(
      fill = "suitable_area_km2",
      fill.scale = tm_scale_continuous(values = "brewer.yl_gn_bu"),
      fill.legend = tm_legend(title = "Suitable Area (km²)")
    ) +
    tm_borders(col = "gray40", lwd = 0.5) +
    tm_title(paste(species_name, "Aquaculture Suitability by EEZ Region")) +
    tm_compass(type = "4star", position = c("right", "top")) +
    tm_scalebar(stack = "vertical", position = c("left", "bottom")) +
    tm_layout(legend.outside = TRUE,
              legend.outside.position = "right",
              frame = FALSE)
  
  return(list(
    results = results_table,
    full_data = results_joined,
    map = map
  ))
}

Testing the Function: Oysters

We can verify the function produces identical results to our step-by-step analysis by applying it to oysters with the same parameters.

Code 
# Call function with oyster parameters
oyster_output <- calculate_suitability(
  min_temp = 11,
  max_temp = 30,
  min_depth = -70,
  max_depth = 0,
  species_name = "Oyster"
)

# Display results table
kable(oyster_output$results,
      caption = "Top 10 EEZ Regions for Oyster Aquaculture (Function Output)",
      col.names = c("Region", "Suitable Area (km²)"),
      digits = 2) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Top 10 EEZ Regions for Oyster Aquaculture (Function Output)
Region Suitable Area (km²)
Central California 4923.15
Southern California 4096.54
Washington 3224.74
Oregon 1533.09
Northern California 438.15
Code 
# Display map
print(oyster_output$map)

The function successfully replicates our earlier oyster analysis, confirming Central California’s dominance.

Testing the Function: Black Abalone

Code 
# Call function with Black Abalone parameters
abalone_output <- calculate_suitability(
  min_temp = 12.2,
  max_temp = 18.6,
  min_depth = -6,
  max_depth = 0,
  species_name = "Black Abalone"
)

# Display map
abalone_output$map

Code 
# Display results table
kable(abalone_output$results,
      caption = "Top 10 EEZ Regions for Black Abalone Aquaculture (Function Output)",
      col.names = c("Region", "Suitable Area (km²)"),
      digits = 2) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Top 10 EEZ Regions for Black Abalone Aquaculture (Function Output)
Region Suitable Area (km²)
Central California 186.24
Washington 103.47
Southern California 89.01
Northern California 16.22
Oregon 14.90

The function also correctly identifies black abalone’s highly restricted suitable habitat, concentrated almost entirely in Central California.

Discussion and Conclusions

Key Findings

Oyster Aquaculture Potential

Oysters demonstrate substantial aquaculture potential across the US West Coast, with over 11,000 km² of suitable habitat identified across all five EEZ regions:

  • Central California: 4,069.88 km² — Leads the West Coast with the largest suitable area
  • Southern California: 3,757.28 km² — Second highest potential
  • Washington: 2,378.31 km² — Significant northern habitat
  • Oregon: 1,074.27 km² — Moderate potential
  • Northern California: Minimal suitable area

These three top regions (Central CA, Southern CA, and Washington) together account for over 90% of all suitable oyster habitat on the West Coast. The broad temperature tolerance (11-30°C) and moderate depth range (0-70m) enable oysters to thrive across diverse coastal environments.

Black Abalone Aquaculture Potential

Black abalone presents a stark contrast, with only 199.88 km² of suitable habitat total—approximately 50 times less than oysters:

  • Central California: 152.37 km² — Dominates with 76% of all suitable habitat
  • Washington: 26.65 km² — Limited northern habitat
  • Southern California: 20.86 km² — Small suitable area
  • Oregon & Northern California: Essentially no suitable habitat (0 km²)

This dramatic restriction reflects black abalone’s narrow thermal tolerance (12.2-18.6°C) and very shallow depth requirement (0-6m below sea level). The species is geographically constrained to cool, shallow coastal waters predominantly found in Central California.

Comparative Analysis

The 50-fold difference in suitable habitat between oysters and black abalone highlights how species-specific environmental requirements shape aquaculture potential:

  • Temperature breadth: Oysters tolerate an 19°C temperature range vs. only 6.4°C for black abalone
  • Depth tolerance: Oysters can inhabit depths to 70m vs. only 6m for black abalone
  • Geographic distribution: Oysters have viable habitat in all West Coast EEZs; black abalone is restricted to 3 regions with 76% concentrated in one

Study Limitations

Environmental Simplifications

  • Temporal resolution: Annual SST averages (2008-2012) smooth over seasonal temperature extremes, which may be critical for reproduction, larval development, and survival during extreme events
  • Missing environmental factors: Analysis omits ocean currents (affecting larval dispersal), dissolved oxygen (metabolic requirements), salinity (osmoregulation), wave exposure (structural stress), water quality parameters, and harmful algal bloom frequency
  • Static baseline: Climate change is shifting thermal regimes; 2008-2012 baseline may not represent future conditions

Biological Simplifications

  • Species requirements: Based on optimal growth ranges from SeaLifeBase (Palomares, M.L.D. and D. Pauly 2024); may not reflect full physiological tolerance ranges, life-stage-specific needs, or acclimation potential
  • Habitat quality: Binary suitability (suitable/unsuitable) doesn’t capture gradient of habitat quality or productivity potential
  • Substrate requirements: Ignores seafloor characteristics (rocky vs. sandy), which strongly influence settlement and growth

Practical Constraints

  • Socioeconomic factors: Excludes proximity to processing facilities, markets, and infrastructure; conflicts with fishing, shipping, recreation, conservation areas; and complex regulatory/permitting landscapes
  • Spatial resolution: Bathymetry resolution (~500m pixels) may be too coarse for shallow-water species like black abalone, where micro-scale depth variation matters

Implications for Marine Aquaculture Planning

This analysis demonstrates that Central California EEZ represents the most versatile region for marine aquaculture development on the West Coast, supporting substantial habitat for both broad-tolerance species (oysters) and narrow-tolerance species (black abalone).

For aquaculture development priorities:

  • Oyster production: Can be distributed across multiple regions (CA, WA, OR) for geographic diversification
  • Black abalone restoration/aquaculture: Must focus on Central California, with conservation implications given the species’ endangered status and limited suitable habitat

The generalizable function developed here enables rapid assessment of additional candidate species, facilitating evidence-based site selection and multi-species aquaculture planning.

Acknowledgments

This analysis was completed for EDS 223: Geospatial Analysis and Remote Sensing at the Bren School of Environmental Science & Management, UC Santa Barbara (November 2024). The workflow demonstrates the application of raster processing, spatial statistics, and reproducible geospatial analysis to real-world marine resource management questions.

References

Flanders Marine Institute. 2024. “Marine Regions: Exclusive Economic Zones (EEZ).” MarineRegions.org. https://www.marineregions.org/.
GEBCO Compilation Group. 2022. “GEBCO_2022 Grid.” GEBCO. https://doi.org/10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c.
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. https://doi.org/10.1038/s41559-017-0257-9.
Hall, S. J., A. Delaporte, M. J. Phillips, M. Beveridge, and M. O’Keefe. 2011. Blue Frontiers: Managing the Environmental Costs of Aquaculture. Penang, Malaysia: The WorldFish Center.
NOAA Coral Reef Watch. 2024. “NOAA 5km Daily Global Satellite Sea Surface Temperature Anomaly V3.1.” NOAA. https://coralreefwatch.noaa.gov/product/5km/index_5km_ssta.php.
Palomares, M.L.D. and D. Pauly. 2024. “SeaLifeBase.” https://www.sealifebase.org.

Citation

BibTeX citation:
@online{miller2024,
  author = {Miller, Emily},
  title = {Prioritizing {Marine} {Aquaculture} {Locations} on the {US}
    {West} {Coast}},
  date = {2024-11-15},
  url = {https://rellimylime.github.io/posts/eds223-final2/},
  langid = {en}
}
For attribution, please cite this work as:
Miller, Emily. 2024. “Prioritizing Marine Aquaculture Locations on the US West Coast.” November 15, 2024. https://rellimylime.github.io/posts/eds223-final2/.