Prioritizing marine aquaculture

Determining which West Coast Exclusive Economic Zones are best suited for developing marine aquaculture
R
Quarto
MEDS
Geospatial
Author
Affiliation

Josephine Cardelle

MEDS

Published

December 7, 2024

More content available at the github repository

About

Marine aquaculture has the potential to be an important part of global food supply as a more sustainable option than land-based meat production (Gentry et al.). In this project we will use data surrounding bathymetry, sea surface temperature (SST), and Exclusive Economic Zones (EEZ) to determine west coast areas suitable for marine aquaculture. We will specifically explore areas suitable for oysters and spiny lobsters.

Setup

Show the code 
# Load libraries
library(sf) # vector handling
library(terra) # raster handling
library(tidyverse)
library(tmap) # map making
library(spData) # spatial data
library(spDataLarge) # spatial data
library(here)
library(tmaptools)
library(kableExtra)

Prepare Data

Show the code 
# Read in data
sst_2008 <- rast(here("data", "average_annual_sst_2008.tif"))
sst_2009 <- rast(here("data", "average_annual_sst_2009.tif"))
sst_2010 <- rast(here("data", "average_annual_sst_2010.tif"))
sst_2011 <- rast(here("data", "average_annual_sst_2011.tif"))
sst_2012 <- rast(here("data", "average_annual_sst_2012.tif"))

depth <- rast(here("data", "depth.tif"))

eez <- st_read(here("data","wc_regions_clean.shp"))
Show the code 
# Create raster stack for sst
sst <- c(sst_2008, sst_2009, sst_2010, sst_2011, sst_2012)
# check if coordinate reference systems match and update if not
if(crs(sst) == crs(depth) & crs(sst) == crs(eez) & crs(depth) == crs(eez)) {
  print("Coordinate reference systems match")
} else{
  warning("Updating coordinate reference systems to match")
  sst <- sst %>% 
  project("EPSG:4326")
depth <- depth %>% 
  project("EPSG:4326")
eez <- eez %>% 
  st_set_crs("EPSG:4326")
}

Process Data

Show the code 
# Find the mean SST from 2008-2012
sst_mean <- mean(sst)

# Convert average SST from Kelvin to Celsius
sst_celsius <- (sst_mean - 273.15)

# Crop depth raster to match the extent of the SST raster
# Resample the depth data to match the resolution of the SST data using the nearest neighbor approach
depth_res <- resample(crop(depth, sst_celsius), sst_celsius, method = "near")


# Check that the depth and SST match in resolution, extent, and coordinate reference system
stacked <- c(depth_res, sst_celsius)

Determine suitable locations for Oysters

For optimal growth, oysters should be in areas with sea surface temperature of 11 - 30° C and at a depth of 0 - 70 meters below sea level.

Show the code 
# Create reclassification matrix for sst
categories <- c("Unsuitable", "Suitable")

sst_rcl <- matrix(c(-Inf, 11, 0,
                    11, 30, 1,
                    30, Inf, 0),
                    ncol = 3, byrow = TRUE)

# Use reclassification matrix to reclassify SST raster
sst_reclassified <- classify(sst_celsius, rcl = sst_rcl)

# Create reclassification matrix for depth
depth_rcl <- matrix(c(-Inf, -70, 0,
                    -70, 0, 1,
                    0, Inf, 0),
                    ncol = 3, byrow = TRUE)

# Use reclassification matrix to reclassify depth raster
depth_reclassified <- classify(depth_res, rcl = depth_rcl)

# Find locations that satisfy both SST and depth conditions
oyster_sst_depth <- lapp(c(sst_reclassified, depth_reclassified), function(x, y) x * y)
Show the code 
# Rasterize EEZ
wc_rasterized <- rasterize(eez, oyster_sst_depth, field = 'rgn')
  
# Mask and calculate total area
suitable_cells <- mask(cellSize(oyster_sst_depth, unit = "km"), oyster_sst_depth, maskvalue = 0)
suitable_area <- terra::extract(suitable_cells, eez, fun = sum, na.rm = TRUE)
names(suitable_area)[2] <- "area_km2"
  

# Join eez and suitable areas
eez_suitable_area <- left_join(eez, suitable_area, by = c("rgn_id" = "ID")) %>%
    rename(suitable_area = area_km2.y)

# Make table
kable(eez_suitable_area %>%
  st_drop_geometry() %>%
  select(Region = rgn,
         "Total Suitable Area(km<sup>2</sup>)" = suitable_area), caption = "Total suitable area of West Coast EEZs for oysters")
Total suitable area of West Coast EEZs for oysters
Region Total Suitable Area(km2)
Oregon 2272.2987
Northern California 915.7615
Central California 5333.4679
Southern California 4275.7045
Washington 5409.1635
Show the code 
# Map results
tmap_mode("view")
area_map <- tm_shape(eez_suitable_area) +
  tm_fill(col = "suitable_area",
         title = "Total suitable area(km<sup>2</sup>) for oysters",
          palette = "Greens") +
  tm_borders() +
  tm_legend(legend.outside = TRUE) +
  tm_text("rgn", size = 0.5) +
  tm_basemap("CartoDB.PositronNoLabels") +
  tm_layout(
    main.title = "Suitable EEZs for Oysters", 
    main.title.position = "center") +
  tm_scale_bar() +
  tm_compass()


area_map

According to our data, Washington has the most total area in Exclusive Economic Zones that are suitable for oysters on the West Coast. This is followed by Central California, Southern California, Oregon and Northern California, respectively.

Generalize workflow

Show the code 
depth_fun <- rast(here("data", "depth.tif"))

eez_fun <- st_read(here("data","wc_regions_clean.shp"))

suitable_eez <- function(species, min_depth, max_depth, min_temp, max_temp) {
# Create raster stack for sst
sst_fun <- c(sst_2008, sst_2009, sst_2010, sst_2011, sst_2012)

# check if coordinate reference systems match and update if not
if(crs(sst_fun) == crs(depth_fun) & crs(sst_fun) == crs(eez_fun) & crs(depth_fun) == crs(eez_fun)) {
  print("Coordinate reference systems match")
} else{
  warning("Updating coordinate reference systems to match")
  sst_fun <- sst_fun %>% 
  project("EPSG:4326")
depth_fun <- depth_fun %>% 
  project("EPSG:4326")
eez_fun <- eez_fun %>% 
  st_set_crs("EPSG:4326")
}

# Find the mean SST from 2008-2012
sst_mean_fun <- mean(sst_fun)

# Convert average SST from Kelvin to Celsius
sst_celsius_fun <- (sst_mean_fun - 273.15)

# Crop depth raster to match the extent of the SST raster
# Resample the depth data to match the resolution of the SST data using the nearest neighbor approach
depth_res_fun <- resample(crop(depth_fun, sst_celsius_fun), sst_celsius_fun, method = "near")

# Reclassify sst data

sst_reclassified_fun <- classify(sst_celsius_fun, rcl = matrix(c(-Inf, min_temp, 0, min_temp, max_temp, 1, max_temp, Inf, 0), ncol = 3, byrow = TRUE))
  depth_reclassified_fun <- classify(depth_res_fun, rcl = matrix(c(-Inf, -max_depth, 0, -max_depth, -min_depth, 1, -min_depth, Inf, 0), ncol = 3, byrow = TRUE))
  
# Combine suitability
sst_depth_fun <- lapp(c(sst_reclassified_fun, depth_reclassified_fun), function(x, y) x * y)

# Rasterize EEZ
wc_rasterized_fun <- rasterize(eez_fun, sst_depth_fun, field = 'rgn')
  
# Mask and calculate area
suitable_cells <- mask(cellSize(sst_depth_fun, unit = "km"), sst_depth_fun, maskvalue = 0)
suitable_area_fun <- terra::extract(suitable_cells, eez_fun, fun = sum, na.rm = TRUE)
names(suitable_area_fun)[2] <- "area_km2"
  

# Join eez and suitable areas
eez_suitable_area_fun <- left_join(eez_fun, suitable_area_fun, by = c("rgn_id" = "ID")) %>%
    rename(suitable_area = area_km2.y)



# Map results
tmap_mode("view")
area_map_fun <- tm_shape(eez_suitable_area_fun) +
  tm_fill(col = "suitable_area",
         title = paste("Total suitable area(km<sup>2</sup>) for ", species),
          palette = "Greens") +
  tm_borders() +
  tm_legend(legend.outside = TRUE) +
  tm_text("rgn", size = 0.5) +
  tm_basemap("CartoDB.PositronNoLabels") +
    tm_layout(
    main.title = paste("Suitable EEZs for", species), 
    main.title.position = "center") +
  tm_scale_bar() +
  tm_compass(position = c("bottomleft"))

return(area_map_fun)
}

Determine suitable locations for Spiny Lobster

For optimal growth, oysters should be in areas with sea surface temperature of 23.7 - 28° C and at a depth of 0 - 90 meters below sea level.

Show the code 
# Use function for Spiny Lobster
suitable_eez(species = "Spiny Lobster", min_depth = 0 , max_depth = 90, min_temp = 23.7, max_temp = 28)

According to our data, Washington has the most total area in Exclusive Economic Zones that are suitable for Spiny Lobsters on the West Coast. This is followed by Central California, Oregon, Southern California, and Northern California, respectively.

Citations

Data Citation Link
Sea Surface Temperaature National Oceanic and Atmospheric Administration. “NOAA Coral Reef Watch 5-km Satellite Sea Surface Temperature Anomaly Product.” Accessed Nov. 11, 2024. https://coralreefwatch.noaa.gov/product/5km/index_5km_ssta.php
Bathymetry General Bathymetric Chart of the Oceans (GEBCO). “Gridded Bathymetry Data.” Accessed Nov. 11, 2024. https://www.gebco.net/data_and_products/gridded_bathymetry_data/#area
Exclusive Economic Zones Marine Regions. “Exclusive Economic Zones (EEZ).” Accessed Nov. 11, 2024. https://www.marineregions.org/eez.php
Spiny Lobster SeaLifeBase. “Search Species.” Accessed Nov. 26, 2024. https://www.sealifebase.ca/search.php
Mapping the global potential for marine aquaculture. 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) https://www.nature.com/articles/s41559-017-0257-9