Share

3.2 Movement trajectories

We will start with simply creating trajectories between successive locations. As stated above, there are 2 types of trajectories but their are also 2 forms of Type II trajectories if we have time recorded. Depending on the duration between locations we can have uniform time lag between successive relocations termed regular trajectories and non-uniform time lag that results in irregular trajectories. We will begin this section with simply creating irregular trajectories from relocation data because, even though we set up a time schedule to collection locations at uniform times, climate, habitat, and satellites do not always permit such schedules of data collection.

  1. Exercise 3.2 - Download and extract zip folder into your preferred location
  2. Set working directory to the extracted folder in R under File - Change dir...
  3. First we need to load the packages needed for the exercise

    library(adehabitatLT)
    library(chron)
    library(spatstat)#for "duplicate" function
  4. Now open the script "MovementsScript.R" and run code directly from the script

    #We are again going to be using more of the mule deer dataset than from the earlier exercises
    muleys <-read.csv("DCmuleysedited.csv", header=T)
    str(muleys)
  5. Check for duplicate locations in dataset. The reason for this is very important and will be apparent shortly.

    summary(duplicated(muleys))

    #Sort data to address error in code if needed
    #muleys <- muleys[order(muleys$id),]
  6. For trajectories of type II (time recorded), the conversion of the date to the format POSIX needs to be done to get proper digits of date into R.

    da <- as.POSIXct(strptime(muleys$GPSFixTime,format="%Y.%m.%d %H:%M:%S"))
    head(da)
    muleys$da <- da #attach "da" to muleys dataset
    str(muleys)

    #Create time lag between successive locations to censor data if needed.
    timediff <- diff(muleys$da)
    muleys <-muleys[-1,]
    muleys$timediff <-as.numeric(abs(timediff))
    str(muleys)#check to see timediff column was added to muleys

    #Look at number of locations by animal ID
    summary(muleys$id)

    #Remove outlier locations or known outliers collected too far apart in time
    newmuleys <-subset(muleys, muleys$X > 599000 & muleys$X < 705000 
    & muleys$Y > 4167000 & muleys$timediff < 14401)
    muleys <- newmuleys
    str(muleys)
  7. Let’s create a Spatial Points Data Frame in UTM zone 12 adding ID, time diff, burst to xy coordinates

    data.xy = muleys[c("X","Y")]
    #Creates class Spatial Points for all locations
    xysp <- SpatialPoints(data.xy)
    #proj4string(xysp) <- CRS("+proj=utm +zone=12 +ellps=WGS84")

    #Creates a Spatial Data Frame from
    sppt<-data.frame(xysp)
    #Creates a spatial data frame of ID
    idsp<-data.frame(muleys[2])
    #Creates a spatial data frame of dt
    dtsp<-data.frame(muleys[24])
    #Creates a spatial data frame of Burst
    busp<-data.frame(muleys[23])
    #Merges ID and Date into the same spatial data frame
    merge<-data.frame(idsp,dtsp,busp)
    #Adds ID and Date data frame with locations data frame
    coordinates(merge)<-sppt

    plot(merge)#visualize data
    str(merge)
  8. Now create an object of class "ltraj" by animal using the ID field and display by each individual (i.e., ltraj[1]).

    ltraj <- as.ltraj(coordinates(merge),merge$da,id=merge$id)
    plot(ltraj)
    plot(ltraj[1])
    head(ltraj[1])#Describes the trajectory for the first deer

    #> head(ltraj[1])#Describes the trajectory
    *********** List of class ltraj ***********
    #Type of the traject: Type II (time recorded)
    #Irregular traject. Variable time lag between two locs
    #
    #Characteristics of the bursts:
    # id burst nb.reloc NAs date.begin date.end
    #1 D12 D12 100 0 2011-10-12 06:00:52 2011-10-24 21:00:48
    #
    #infolocs provided. The following variables are available:
    #[1] "pkey"

    plot(ltraj[2])
    plot(ltraj[3])
    plot(ltraj[4])
    plot(ltraj[5]
    plot(ltraj[6])
    plot(ltraj[7])
  9. Let’s create a histogram of time lag (i.e., interval) and distance between successive locations for each deer. This is a nice way to inspect the time lag between locations as you don’t want to include a location if too much time has passed since the previous and it also shows why a trajectory is irregular.

    hist(ltraj[1], "dt", freq = TRUE)
    windows()#opens a new window to show both figures
    hist(ltraj[1], "dist", freq = TRUE)
    windows()

    hist(ltraj[2], "dt", freq = TRUE)
    windows()
    hist(ltraj[2], "dist", freq = TRUE)
    windows()

    hist(ltraj[3], "dt", freq = TRUE)
    windows()
    hist(ltraj[3], "dist", freq = TRUE)
    windows()

    hist(ltraj[4], "dt", freq = TRUE)
    windows()
    hist(ltraj[4], "dist", freq = TRUE)
    windows()

    hist(ltraj[5], "dt", freq = TRUE)
    windows()
    hist(ltraj[5], "dist", freq = TRUE)
    windows()

    hist(ltraj[6], "dt", freq = TRUE)
    windows()
    hist(ltraj[6], "dist", freq = TRUE)
    windows()

    hist(ltraj[7], "dt", freq = TRUE)
    windows()
    hist(ltraj[7], "dist", freq = TRUE)