In eldarrak/FLightR: Reconstruct Animal Paths from Solar Geolocation Loggers Data

Tree swallow BAS tag geolocator analysis

This file is UPDATED supplementary material for the FLightR paper: A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity, Movement ecology (2015). http://dx.doi.org/10.1186/s40462-015-0062-5. I have updated it for FLightR >= 0.4.0 if you want to see detailed explanation of what is going on check it in the godwit example

Install package and read data

require(devtools)
install_github("eldarrak/FLightR@0.4.0") # note the version
require(FLightR)

download.file(
 "https://raw.githubusercontent.com/eldarrak/FLightr/master/examples/tree_swallow_BAS_tag_example/749.csv",
 "749.csv")

Proc.data<-get.tags.data("749.csv")

Proc.data<-get.tags.data('749.csv', log.light.borders=log.light.borders, log.irrad.borders=log.irrad.borders, end.date='2012-05-13')

plot.slopes.by.location(Proc.data=Proc.data, location=c(-80.46, 42.62))

abline(v=as.POSIXct("2012-04-11")) # fine
abline(v=as.POSIXct("2011-07-13")) # fine

I will use both calibration periods - one in the beginning and another in the end. Now we create a data.frame where each line is one of the calibration periods. and the columns are start, end, x, y.

```r

Calibration.periods<-data.frame( calibration.start=as.POSIXct(c(NA, "2012-04-11")), calibration.stop=as.POSIXct(c("2011-07-13", NA)), lon=-80.46, lat=42.62) #use c() also for the geographic coordinates, if you have more than one calibration location (e. g., lon=c(5.43, 6.00), lat=c(52.93,52.94)) print(Calibration.periods)

Calibration<-make.calibration(Proc.data, Calibration.periods, model.ageing=TRUE)

Grid<-make.grid(left=-92, bottom=15, right=-70, top=45, distance.from.land.allowed.to.use=c(-Inf, Inf), distance.from.land.allowed.to.stay=c(-Inf, Inf))

all.in<-make.prerun.object(Proc.data, Grid, start=c(-80.46, 42.625), Calibration=Calibration)

nParticles=1e6 a= Sys.time() Result<-run.particle.filter(all.in, save.Res=F, threads=-1, nParticles=nParticles, known.last=TRUE, precision.sd=25, save.memory=T, k=NA, parallel=T, plot=T, prefix="pf", extend.prefix=T, cluster.type="SOCK", a=45, b=1500, L=90, adaptive.resampling=0.99, check.outliers=TRUE) b= Sys.time() b-a save(Result, file="TRES.749.0.4.0.new.boundaries.RData")

map.FLightR.ggmap(Result)

plot.lon.lat(Result) ````

This is it... There are of cours many more things one could do with the data but we are not going to do. Let me know what else you would like to see..

eldarrak/FLightR documentation built on Nov. 10, 2023, 7:53 a.m.