Hi Chris, I have a question about the error modelling step in the CTMM workflow. My species of interest are caribou and wolves that move vast distances, and my sampling frequency ranges from 1hr to 3 locations a day. I am using Telonics GPS/Irridium collars (Gen 4), however, I do not receive an HDOP value with each location. I do have access to calibration datasets, but without the HDOP value, It my understanding that I can't calibrate the error (is that correct). It's not clear why we don't receive HDOP values, we do receive the GPS horizontal error metric.
As my animals are relatively big movers (compared to an estimated GPS error of around 10m), I thought it was reasonable to follow the vignettes instruction to fit with uncalibrated data under a prior. I used the 10 recommended for GPS data (is this a unitless number, or does it mean 10m). I used the 2 functions below to then convert the data to a trajectory, and fit models (the whole workflow works with lists as i have numerous animals).
#convert to Data.trj lists
prepare_ctmm_data<-function(x){
#convert to movestack
DATA<-move(x=x$longitude,y=x$latitude,
time=as.POSIXct(x$observationDate, format="%Y-%m-%d %H:%M:%OS", tz="UTC"),
proj=CRS("+proj=longlat"), data=x,animal=x$filter)
#convert to ctmm telemetry object
DATA.trj<-as.telemetry(DATA,timeformat = "auto",timezone="UTC", projection=TPEQD)
#assign UERE error priori
for (e in 1:length(DATA.trj)){
print(paste("applying error prior to Data.trj object",e))
uere(DATA.trj[[e]])<-10
UERE<-uere(DATA.trj[[e]])
UERE$DOF[]<-2
DATA.trj[[e]]@UERE<-UERE
}
return(DATA.trj)
}
fitting_function<-function(i){
B.GUESS<-ctmm.guess(DATA.trj[[i]],CTMM=ctmm(error=T,range=FALSE),interactive=F)
ctmm.select(DATA.trj[[i]],B.GUESS,verbose=F,trace=2)}
My questions:
-is this a reasonable approach?
- As you know (being the author) the appendix of "A comprehensive framework for handling location error in animal tracking data" provides the following for the Telonics gen 4 sensors. Based on this, should I update my code to uere(DATA.trj[[e]])<-6.9?
Thanks! Robin