What is a trajectory and why is it useful? A trajectory is the path connecting the relocations of a tracked animal. Aspects of the trajectory, such as the distance between each relocation and the direction (angle) of each segment, are very useful for modeling animal movement and testing predictions of hypotheses.
In the last post, I formatted telemetry data for analyses. In this post, I will show one way of creating and analyzing animal paths from telemetry data, using some simulated data on turtles.
The data are in a csv file (tracking_sample.csv).
# Read the csv file
turtles <- read.csv("tracking_sample.csv",
stringsAsFactors = FALSE)
# The file should be in your working directory.
# Examine the structure of the data. Note presence of some NA's
str(turtles)## 'data.frame': 170 obs. of 6 variables:
## $ id : chr "T001" "T001" "T001" "T001" ...
## $ date: chr "2013-07-07" "2013-07-12" "2013-07-21" "2013-07-28" ...
## $ time: chr "9:24:00 AM" "8:57:00 AM" "9:53:00 AM" "11:30:00 AM" ...
## $ x : int 347725 347670 347682 347662 347877 348037 348035 347828 347593 347635 ...
## $ y : int 4944678 4944599 4944619 4944609 4944554 4944498 4944496 4944548 4944543 4944489 ...
## $ zone: int 18 18 18 18 18 18 18 18 18 18 ...We can check everything is working by visualizing our data.
To create trajectories, we don’t need to create a SpatialPointsDataFrame and can work directly with the dataframe.
Let’s examine how we can quantify the path, or trajectory of the tracked animals. The function as.ltraj in the adehabitatLT package makes this easy.
To start, we need to give as.ltraj three arguments:
- xy: the coordinates
- date: a POSIXct object with date and (if present) time
- id: a variable to group points based on individual
Note: If you want distances calculated in metres, you should have your coordinates in UTM. For converting between coordinate types, you can use the spTransform function in the sp package.
# Load library
library(adehabitatLT)
# You will need to install the package the first time with:
# install.packages("adehabitatLT")# We need to make sure that date is correctly formatted, and that there is an ID column
turtles.ltraj <- as.ltraj(xy = turtles[,c("x", "y")],
date = as.POSIXct(paste(turtles$date, turtles$time, sep = " ")),
id = turtles$id)In other data sets, it is common to receive an error saying “non unique dates for a given burst”
Two common issues in real telemetry data cause this error:
- Some combinations of date & time are repeated for an animal (because of a mistake in data entry)
- Time is incorrectly formatted and
as.POSIXctis dropping the time section.
If you’re receiving that error, look into those issues first.
##
## *********** List of class ltraj ***********
##
## Type of the traject: Type II (time recorded)
## * Time zone unspecified: dates printed in user time zone *
## Irregular traject. Variable time lag between two locs
##
## Characteristics of the bursts:
## id burst nb.reloc NAs date.begin date.end
## 1 T001 T001 29 0 2013-07-07 09:24:00 2015-05-09 07:57:00
## 2 T002 T002 33 0 2013-07-06 11:13:00 2014-10-26 11:27:00
## 3 T003 T003 40 0 2013-07-07 10:33:00 2015-10-08 07:21:00
## 4 T004 T004 28 1 2013-07-13 09:00:00 2014-10-26 08:57:00
## 5 T005 T005 40 1 2013-07-05 10:59:00 2015-09-07 08:54:00
##
##
## infolocs provided. The following variables are available:
## [1] "pkey"# Each element of the list is a dataframe for one individual
head(turtles.ltraj[[1]]) # The first six locations of the first animal## x y date dx dy dist dt R2n
## 1 347725 4944678 2013-07-07 09:24:00 -55 -79 96.260064 430380 0
## 2 347670 4944599 2013-07-12 08:57:00 12 20 23.323808 780960 9266
## 3 347682 4944619 2013-07-21 09:53:00 -20 -10 22.360680 610620 5330
## 4 347662 4944609 2013-07-28 11:30:00 215 -55 221.923410 510840 8730
## 5 347877 4944554 2013-08-03 09:24:00 160 -56 169.516961 515400 38480
## 6 348037 4944498 2013-08-09 08:34:00 -2 -2 2.828427 785160 129744
## abs.angle rel.angle
## 1 -2.1789691 NA
## 2 1.0303768 -3.07383938
## 3 -2.6779450 2.57486344
## 4 -0.2504431 2.42750195
## 5 -0.3366748 -0.08623173
## 6 -2.3561945 -2.01951967The trajectory for each animal measures:
- distance,
- dt (the difference in seconds between relocations),
- R2n (the squared distance between the first relocation of the trajectory and the current relocation), and
- two angle measurements (absolute and relative).
For a complete description of these parameters, check-out the package tutorial (type vignette("adehabitatLT") into R).
Now that we have our trajectory object, let’s put it in a more useful format so that we can plot and analyze further. We can combine all the trajectories into one dataframe using a short for loop. A loop carries out some code for each element in a sequence. In our case we want to add each element (individual) of the trajectory list together into one dataframe. We also want to add a column that identifies the individual for each movement.
# Create a dataframe to hold all of the contents of bltu.paths with a column for id.
# Put first element into the dataframe
total.path.df <- data.frame(turtles.ltraj[[1]], id = attr(turtles.ltraj[[1]], "id"))
# Use a 'for' loop to fill the larger dataframe with the rest of the trajectories.
for(i in 2:length(turtles.ltraj)) {
total.path.df <- rbind(total.path.df,
data.frame(turtles.ltraj[[i]], id = attr(turtles.ltraj[[i]], "id")))
}
# Calculate distance travelled per day and add it to the dataframe
total.path.df$distperday <- total.path.df$dist / (total.path.df$dt/60/60/24)Now we have a dataframe with:
- a row for every relocation,
- a column for the movement per day in metres, and
- a column that identifies the individual.
We can summarize this data using the aggregate function and then make a graph using ggplot to examine some differences.
# Aggregate to show mean distance per day for each turtle
path.summary <- aggregate(distperday~id, data = total.path.df, FUN = mean)
path.summary$sd <- aggregate(distperday~id, data = total.path.df, FUN = sd)$distperday
# Look at summmary dataframe
path.summary## id distperday sd
## 1 T001 11.412104 10.442225
## 2 T002 17.833178 24.710424
## 3 T003 8.490008 10.868595
## 4 T004 9.568305 7.933765
## 5 T005 12.985299 15.394588# Make a graph to visualize data using ggplot
library(ggplot2)
# Create limits used for error bars in graph
limits <- aes(ymax = path.summary$distperday + path.summary$sd,
ymin = path.summary$distperday - path.summary$sd)
# Make plot. Choose the dataframe (data) and aesthetics (aes; for the x and y)
path.plot <- ggplot(data = path.summary, aes(x = id, y = distperday, colour = id)) +
geom_point(size = 3) + # add points
geom_errorbar(limits, width = 0.2) + # adds error bars
labs(x = "Animal number",
y = "Mean distance travelled per day (m)" ) + # Axis labels
theme_classic() + # Make plot black and white with no background grid
theme(legend.position = "none")
path.plot # call plot
Now we have all the information about paths in an easy to use dataframe that we can join to other information (eg. a dataframe with body size, sex, habitat type) to perform analyses in R.
For a more in-depth look at analyzing animal tracks using adehabitatLT, check out this tutorial, type vignette("adehabitatLT") into R.
In the next post, I will construct home ranges in R with minimum convex polygons and kernels.