README

## Calculate the maximum linear speed between two consecutive locations V <- vmax(turtle.dup) ## Calculate the maximum one-way linear speed of a loop trip VLP <- vmaxlp(turtle.dup) ## Run ddfilter turtle.dd <- ddfilter(turtle.dup, vmax=V, vmaxlp=VLP)

# Entire area p1 <- to_map(turtle.dup, bgmap=Australia, point.size = 2, line.size = 0.5, axes.lab.size = 0, multiplot = FALSE, point.bg = "red", title.size=15, title="Entire area")[[1]] + geom_point(aes(x=lon, y=lat), data=turtle.dd, size=2, fill="yellow", shape=21)+ geom_point(aes(x=x, y=y), data=data.frame(x=c(154, 154), y=c(-22, -22.5)), size=3, fill=c("yellow", "red"), shape=21) + annotate("text", x=c(154.3, 154.3), y=c(-22, -22.5), label=c("Retained", "Removed"), colour="black", size=4, hjust = 0) # Zoomed in p2 <- to_map(turtle.dup, bgmap=SandyStrait, xlim=c(152.7, 153.2), ylim=(c(-25.75, -25.24)), axes.lab.size = 0, point.size = 2, point.bg = "red", line.size = 0.5, multiplot = FALSE, title.size=15, title="Zoomed in")[[1]] + geom_path(aes(x=lon, y=lat), data=turtle.dd, size=0.5, colour="black", linetype=1) + geom_point(aes(x=lon, y=lat), data=turtle.dd, size=2, colour="black", shape=21, fill="yellow") ## plot gridExtra::grid.arrange(p1, p2, layout_matrix=cbind(1,2))

library(adehabitatHR); library(raster)

## Tracking data
data(flatback)
flatback <- track_param(flatback, param = "time") # calculate time between successive locations
flatback_id <- unique(flatback$id)


## Data range with 5km buffer
buff <- 5e+3
xmin <- min(flatback$x) - buff; xmax <- max(flatback$x) + buff
ymin <- min(flatback$y) - buff; ymax <- max(flatback$y) + buff

## Make a grid layer
cell.size <- 1e+3 # (1km x 1km)
x <- seq(xmin, xmax, cell.size)
y <- seq(ymin, ymax, cell.size)
xy.df <- expand.grid(x = x, y = y)
xy.coords <- SpatialPixels(SpatialPoints(xy.df))
xy.sp <- SpatialPoints(xy.coords, proj4string = CRS("+init=epsg:3577"))
z <- rep(1, nrow(xy.df))
xyz <- cbind(xy.df, z)
grid_spdf <- SpatialPixelsDataFrame(xy.coords, xyz)

## UD per turtle
ud_raster <- list()
for(i in 1:length(flatback_id)){
  
  ## ID
  ID <- flatback_id[i]

  ## Tracking data
  turtle.data <- with(flatback, flatback[id %in% ID, ])

  ## Creates an object of class Itraj
  data.ltraj <- with(turtle.data, as.ltraj(turtle.data[,c("x", "y")], date=DateTime, id=ID, burst=ID))
  
  ## Parameters for BRB
  TM = quantile(turtle.data$pTime, probs = 0.95, na.rm = TRUE)*3600
  LM = 50
  dp = BRB.likD(data.ltraj, Tmax=TM, Lmin=LM)
  HM = 100
  
  ## Estimate the UD
  ud <- BRB(data.ltraj, D=dp, Tmax=TM, Lmin=LM, hmin=HM, grid=grid_spdf, type="UD")
  
  ## Convert the UD to raster
  ud_raster[[i]] <- raster(ud)
}

ggplot(data = a$results, aes(x = x))+ geom_errorbar(aes(ymin = y_lwr, ymax = y_upr), width = 0.2, colour = 'darkgrey', size = 1) + geom_point(aes(y = y), size = 2) + geom_hline(yintercept = a$h.asymptote*0.95, linetype = 2) + scale_x_continuous(breaks = seq(0, 15, 3), limits = c(1.9,15.1), name = "Animals tracked (n)") + scale_y_continuous(limits = c(0.5,1), name = "Overlap probability") + theme_light()

References

If you use the function ddfilter, please cite

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Mar Ecol Prog Ser 457:171-180 doi: 10.3354/meps09747

If you use the functions dupfilter or depthfilter, please cite

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Mar Biol 163:1-14 doi: 10.1007/s00227-015-2771-0

If you use the functions asymptote, boot_overlap, combn_overlap or boot_area, please cite

Shimada, T, Thums, M, Hamann, M, et al. (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods Ecol Evol 12(2):288-297 doi: 10.1111/2041-210X.13506

SDLfilter

Overview

Installation

Usage

1. Location filtering

1-1. Load tracking data

1-2. Remove temporal and spatial duplicates

1-3. Remove biologically unrealistic fixes

1-4. Plot data

2. Assessing sample sizes (probability-based approach)

2-1. Input UDs

2-2. Calculate overlap probability from 2000 random permutation (> sample size x 100)

2-3. Find the minimum sample size required to estimate the general distribution

2-4. Plot the estimated overlap probabilities relative to the sample sizes (black points), the 95% confidence intervals (grey lines), and 95% of the estimated asymptote (dashed line).

References

Current version