Warning Signals in Fish Collapse

Description: an example trying to detect early warning signals in data from fisheries collapses

Set up markdown format and image uploads.

render_wordpress()
opts_knit$set(upload = TRUE)
opts_knit$set(imgur.key = getOption("imgur"))

Load required libraries

require(warningsignals)
require(ggplot2)
require(reshape2)

Load the data

scotia <- read.csv("../../data/rawdata/sau_scotia.csv")

Visualize data

dat_scotia <- melt(scotia, 
    id = "Year")
p_scotia <- ggplot(dat_scotia, 
    aes(Year, value, fill = variable)) + 
    geom_area()
print(p_scotia)

Compute some indicators

Define some indicators

window_var <- function(X, 
    windowsize = (length(X)/2)) {
    out <- sapply(0:(length(X) - 
        windowsize), function(i) {
        var(X[(i + 1):(i + 
            windowsize)])
    })
    c(rep(NA, length(X) - 
        length(out)), out)
}

window_autocorr <- function(X, 
    windowsize = (length(X)/2)) {
    out <- sapply(0:(length(X) - 
        windowsize), function(i) acf(X[(i + 
        1):(i + windowsize)], 
        lag.max = 1, plot = F)$acf[2])
    c(rep(NA, length(X) - 
        length(out)), out)
}

Reformat the data, uses data.table to perform computations over species

require(data.table)
fish <- data.table(subset(dat_scotia, 
    Year < 1992))
tmp <- data.frame(species = fish$variable, 
    Year = fish$Year, Stock = fish$value, 
    variance = fish[, window_var(value), 
        by = "variable"]$V1, 
    acor = fish[, window_autocorr(value), 
        by = "variable"]$V1)

dat <- melt(tmp, 
    id = c("Year", "species"))

Cod are approaching a crash, but lobster are going strong, but both seem to show the same pattern.

ggplot(subset(dat, 
    species %in% c("Atlantic.cod"))) + 
    geom_point(aes(Year, 
        value)) + facet_grid(variable ~ 
    species, scales = "free_y")

ggplot(subset(dat, 
    species %in% c("American.lobster"))) + 
    geom_point(aes(Year, 
        value)) + facet_grid(variable ~ 
    species, scales = "free_y")

Note the indicator patterns vary widly and rather arbitrarily among species

dt <- data.table(dat_scotia)
indicator <- data.frame(dt[, 
    window_var(value), 
    by = "variable"], Year = dat_scotia$Year)
ggplot(indicator) + 
    geom_line(aes(Year, 
        V1)) + facet_wrap(~variable, 
    scales = "free_y")