An R package for estimating geographic space available to animals based on telemetry data.
library(remotes)
install_github("AustralianAntarcticDivision/availability")library(availability)Using the vector-AR method (see [1]):
## load observed track, 2-column matrix of longitude and latitude
## the track points should be equally sampled in time
realtrack <- ... ## your data here
arf <- surrogateARModel(realtrack) ## fit AR model to track
st <- surrogateAR(arf, realtrack) ## simulate new trackOr using the crawl-based track simulator:
library(crawl)
## fit a crawl model to your raw track data
fit <- crwMLE(...)
## regularly-spaced times for which you want positions
time_step <- 3 ## e.g. using a time step of 3 hours
predTime <- seq(my_starting_date, my_ending_date, by = time_step*3600)
## extract predicted positions at those times
predObj <- crwPredict(fit, predTime = predTime, speedEst = TRUE, flat = TRUE)
## keep only regularly-interpolated locations
pr <- data.frame(date = predTime, predObj[predObj$locType == "p", ])
## construct the corresponding transition and covariance matrices of the
## state space model
model <- surrogateCrawlModel(fit, time_step)
## and finally simulate the track
stcrw <- surrogateCrawl(model, as.matrix(pr[, c("mu.x", "mu.y", "nu.x", "nu.y")]), pr$date)Please note: this package was developed with version 1 of the crawl
package. It should also work with crawl v2, but note that v2 only
works with projected coordinates (not longitude and latitude).
More detailed usage examples are in the package vignette.
[1] Raymond B et al. (2015) Important marine habitat off East Antarctica revealed by two decades of multi-species predator tracking. Ecography. doi:10.1111/ecog.01021
[2] Reisinger RR et al (2018) Habitat modelling of tracking data from multiple marine top predators reveals important habitat in the Southern Indian Ocean. Diversity and Distributions. doi:10.1111/ddi.12702