Ocean Acidification (OA) is progressing on the West Coast of North America at a rate 2x higher than the global average. However, with enhanced monitoring and evaluation, marine managers can be better prepared to understand how OA is changing regionally, and creating effects locally.

The Monitoring Inventory

The West Coast Ocean Acidification and Hypoxia (OAH) Monitoring Inventory is a joint project of the Pacific Coast Collaborative and the Interagency Working Group on Ocean Acidification (OA). It emerged as a project to identify and catalog all monitoring assets along the West Coast that are measuring metrics of OAH. The assets are owned and operated with different priorities by different institutions, but are brought together in an attempt to analyze how well we understand and are monitoring OA trends at a regional level.

Click on a monitoring asset to learn more

#load packages
library(tidyverse)
library(leaflet)
library(sp)
library(RColorBrewer)

#read in inventroy and transform to spatial points
oahfocus <- read_csv("/Users/rttaylorburnscom/github/resilienseas.github.io/website_visuals/oahfocus_copy.csv")

#create color palette based on subsets by asset type
asset_pal <- colorFactor(terrain.colors(7), oahfocus$AssetType)

leaflet(oahfocus) %>%
  addProviderTiles('Esri.OceanBasemap') %>% 
  setView(-122, 40, zoom = 5) %>% 
  addCircleMarkers(
        radius = 6, 
        weight = 7,
        color = ~asset_pal(AssetType), 
        popup = paste("Project ID: ", oahfocus$ProjectID, "<br>","Organization: ", oahfocus$Orgnztion, "<br>"), stroke=FALSE, fillOpacity = 0.5) %>% 
  addLegend(pal = asset_pal, values = oahfocus$AssetType, title = "Asset Type", opacity = 1)

Gap Analysis

A main recommendation of the West Coast OAH Panel is that a gap analysis be conducted on the combined regional monitoring network. This is crucial for identifying areas of future research and priorities for expanding the network. This gap analysis shows gaps in carbonate complete monitoring. Carbonate complete monitoring is important because its robustness allows for clearer indication of the potential for biological impact. Our gap analysis was also completed to search for gaps in high frequency monitoring and gaps across the entire monitoring network. Thus, based on varying priorities in monitoring, more specific gaps can be identified for specific research needs.

Gaps in carbonate complete monitoring

library(raster)
library(sp)
library(tidyverse)
library(leaflet)

carb_gaps <- raster("/Users/rttaylorburnscom/github/resilienseas.github.io/ccgap.tif")
carbgappal <- colorRampPalette(c("slateblue4", "slateblue", "plum", "orangered2"))
leaflet() %>% 
      addTiles() %>%
      addProviderTiles('Esri.OceanBasemap') %>% 
      addRasterImage(carb_gaps, colors = carbgappal(4)) %>% 
      addLegend("topright", colors=carbgappal(4), values= values(gaprasters), labels = c("Sufficient Data", "Low Priority Gaps", "High Priority Gaps", "Severe Gaps"), title = "Severity of Gap")

“Gaps” were identified by combining distance calculations between each given monitoring site, and oceanographic variability between monitoring sites. This assumes it is ideal to increase monitoring where current monitoring is sparse or conditions may be more variable. “Severe gaps” represent the top 0.1% of model results, representing greatest relative distance and greatest relative variability.

Hotspots and Thresholds Analysis

Using data from a 2013 NOAA Ocean Acidification Cruise, we predicted the location of ocean acidification hotspots along the West Coast. Hotspots are areas in which ocean acidification levels have the potential to have biological effects. For examples, an aragonite saturation state of 1.7 has been correlated with shellfish die offs.

Low aragonite saturation state = high ocean acidification levels, while high saturation state = low ocean acidificaiton levels

library(raster)
library(tidyverse)
library(leaflet)

aragonite_map <- raster("/Users/rttaylorburnscom/github/resilienseas.github.io/website_visuals/WCOA13_aragonite_raster_clipped.tif")

pal <- colorNumeric("RdYlBu", values(aragonite_map),na.color = "transparent", reverse = FALSE)

leaflet() %>% 
  addTiles() %>% 
  addProviderTiles('Esri.OceanBasemap') %>% 
  setView(-120, 43.9, zoom = 6) %>% 
  addRasterImage(aragonite_map, colors = pal) %>% 
  addLegend(
    pal = pal, values = values(aragonite_map),
    title = "Aragonite Saturation State")