In galuardi/SatTagSim: Simulation from Electronic Tags

library(SatTagSim)
library(knitr)

username =  as.character(Sys.info()[7])
setwd(paste0('C:/Users/', username,'/Google Drive/PHD/COLLABORATIONS/SCRS_SIMS/'))

# datadir = 'D:/SKYDRIVE_BACKUP/PHD_SIMS/SCRS_SIMS'

mycol = colorRampPalette(c("lightcyan", "royalblue", "blue", "lemonchiffon", "orange", "red"), space = "Lab")
load('sim_variance_WABFT.Rdata')
load('../box4.Rda')

Example using Western tagged bluefin >185cm

Confidence intervals were constructed for simulations by reducing the number of tracks simulated in a single run and increasing the number of runs to a statistically significant amount.

For each month, 84 fish were released at locaitons proportional to where they were observed that month in the tagging data (n = 1008/run). This was replicated 1000 times for a total of 1,008,000 simulated fish.

For each run, a movement probability matirx was constructed, for a total of 1000 matrices. For each cell in each matrix position, the mean, median, standard deviation and 95% confidence interval were derived. Confidence intervals were defined as the 2.5% - 97.5% range of the 1000 runs.

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ex7 = expand.grid(1:7, 1:7)
## Quantile range
##
par(mfrow=c(2,2))
par(mar = c(2,4,2,4))
sapply(1:4, function(x) {
  image.plot(1:7, 1:7, t(boxmedian[[x]]), col = mycol(49), xlab = '', ylab = '', zlim = c(0, 1))
  # text(ex7[,1], ex7[,2], round(as.vector(t(boxmean[[x]]/rowSums(boxmean[[x]]))), 2), font = 2)
  text(ex7[,1], ex7[,2], paste0(round(t(boxmean[[x]]),2),"\n" ,"(",round(t(boxq.low[[x]]), 2), "-", round(t(boxq.high[[x]]), 2), ")"), font = 2, cex = .65)
  grid(7,7,col = 'grey50')
  title(seasons[x])
}
)

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ex4 = expand.grid(1:4, 1:4)

par(mfrow=c(2,2))
sapply(1:4, function(x) {
  image.plot(1:4, 1:4, t(boxmean4[[x]]), col = mycol(100), xlab = '', ylab = '', zlim = c(0, 1)) # highlights the mean value
  # image.plot(1:7, 1:7, round(t(boxq.high[[x]]),2)-round(t(boxq.low[[x]]),2), col = mycol(49), xlab = '', ylab = '', zlim = c(0, 1)) # highlights the 95% CI range
  # text(ex7[,1], ex7[,2], round(as.vector(t(boxmean[[x]]/rowSums(boxmean[[x]]))), 2), font = 2)
  text(ex4[,1], ex4[,2], paste0(round(t(boxmean4[[x]]),2),"\n" ,"(",round(t(boxq.low4[[x]]), 2), " - ", round(t(boxq.high4[[x]]), 2), ")"), font = 2)
  grid(4,4,col = 'grey50')
  title(seasons[x])
}
)

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plot(box7, col = rev(grey.colors(14))[1:7], border = 'orange', add=F, lwd = 3)
fields::world(add=T, col = 'cornsilk', fill=T)
text(coordinates(box7)[,1], coordinates(box7)[,2], 1:7, col = 'red', cex = 1.5)
box()
degAxis(1)
degAxis(2)
title('7-box operating model')

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plot(box4, col = grey.colors(4), border = 'orange', add=F, lwd = 3)
fields::world(add=T, col = 'cornsilk', fill=T)
text(coordinates(box4)[,1], coordinates(box4)[,2], 1:4, col = 2, cex = 3)
box()
degAxis(1)
degAxis(2)
title('4-box operating model')