knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
An R package for estimating geographic space available to animals based on telemetry data.
library(remotes) install_github("AustralianAntarcticDivision/availability")
library(availability)
Using the vector-AR method (see [1]):
## load observed track, 2-column matrix of longitude and latitude ## the track points should be equally sampled in time realtrack <- ... ## your data here arf <- surrogateARModel(realtrack) ## fit AR model to track st <- surrogateAR(arf, realtrack) ## simulate new track
Or using the crawl-based track simulator:
library(crawl) ## fit a crawl model to your raw track data fit <- crwMLE(...) ## regularly-spaced times for which you want positions time_step <- 3 ## e.g. using a time step of 3 hours predTime <- seq(my_starting_date, my_ending_date, by = time_step*3600) ## extract predicted positions at those times predObj <- crwPredict(fit, predTime = predTime, speedEst = TRUE, flat = TRUE) ## keep only regularly-interpolated locations pr <- data.frame(date = predTime, predObj[predObj$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
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