This notebook demonstrates how easy it is to retrieve data relevant to analysing environmental drivers of small pelagics using ERDDAP servers and the R package rerddap. In the examples, the code needed to actually get the data, which is short and simple, are separated from code to map or graph the data once in the R workspace, so that the more complicated plotting code doesn’t obscure how simple it is to extract the desired data.

ERDDAP servers provide access to literally petabytes of data, including satellite data, fisheries survey data, glider data, animal tracking data and more.

require("akima")
Loading required package: akima
require("dplyr")
Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
require("ggplot2")
Loading required package: ggplot2
require("mapdata")
Loading required package: mapdata
Loading required package: maps
require("maps")
require("plot3D")
Loading required package: plot3D
require("rerddap")
Loading required package: rerddap

MUR SST

MUR (Multi-scale Ultra-high Resolution) is an analyzed SST product at 0.01-degree resolution going back to 2002, providing one of the longest satellite based time series at such high resolution (see https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1). We extract the latest data available for a region off the west coast.

require("rerddap")
sstInfo <- info('jplMURSST41')
# get latest daily sst
murSST <- griddap(sstInfo, latitude = c(22., 51.), longitude = c(-140., -105), time = c('last','last'), fields = 'analysed_sst')

and plot the results:

require("ggplot2")
require("mapdata")
mycolor <- colors$temperature
w <- map_data("worldHires", ylim = c(22., 51.), xlim = c(-140, -105))
ggplot(data = murSST$data, aes(x = lon, y = lat, fill = analysed_sst)) + 
    geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
    geom_raster(interpolate = FALSE) +
    scale_fill_gradientn(colours = mycolor, na.value = NA) +
    theme_bw() + ylab("latitude") + xlab("longitude") +
    coord_fixed(1.3, xlim = c(-140, -105),  ylim = c(22., 51.)) + ggtitle("Latest MUR SST")