The anipaths package is for displaying uncertainty of
the animals’ paths. It is important to see this uncertainty to have a
good understanding of how precisely we know where an animal is
travelling. Here I will be using several examples to display
visualizations of uncertainty using a spline-based general additive
model (GAM) for path interpolation. The data sets are built into the
anipaths package. I will create animations of multiple
trajectories, blur ellipses, and tails of the animals’ movements.
The tail animation does not depict uncertainty but it does project
the animals’ previous locations. There will be a line following the
animals current location passing through its previous locations. In
order to choose the tail animation I would need to set the
uncertainty.type argument to a value of 1.
The package anipaths contains a collection of telemetry
observations for turkey vultures will use to demonstrate the
functionality of the package originally analyzed in:
Dodge S, Bohrer G, Bildstein K, Davidson SC, Weinzierl R, Mechard MJ, Barber D, Kays R, Brandes D, Han J (2014) Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Philosophical Transactions of the Royal Society B 20130195.
library(anipaths)
vultures$POSIX <- as.POSIXct(vultures$timestamp, tz = "UTC")
vultures_paths <- vultures[format(vultures$POSIX, "%Y")
%in% c(2009, 2010, 2011), ] ## limit attention to 2009, 2010, 2011
delta.t <- "week"
#tails
animate_paths(paths = vultures_paths,
delta.t = delta.t,
coord = c("location.long", "location.lat"),
Time.name = "POSIX",
ID.name = "individual.local.identifier",
uncertainty.type = 1)## NULLUnderstanding uncertainty associated with interpolated trajectories is critical to gaining scientific insight into animal behavior. Depicting trajectory in animations can be challenging, but one potentially informative approach is to plot multiple hypothetical trajectories that are all consistent with the observed data. The more uncertainty, the more widespread and varied the trajectories will be both in space, and in their shape/smoothness. Put another way, instead of having one trajectory as in the previous example, it is often better to display multiple trajectories to represent uncertainty.
Next, I will create multiple trajectories animation with a background
image. This displays additional paths that the animal could plausibly
have taken. To create a background create a list object with the
coordinates and map type or state background to be
TRUE (requires Google Maps API key). To choose the multiple
trajectories animation choose the uncertainty.type argument
to be a value greater than one which chooses how many paths you want to
display. You can choose tail.colors to be
unique so that the tail colors of each animal will be
different.
background <- geodata::world(path = ".")
sf::st_crs(background)$proj4string ## matches default projection in animate_paths()
delta.t <- "week"
animate_paths(paths = vultures_paths,
delta.t = delta.t,
coord = c("location.long", "location.lat"),
Time.name = "POSIX",
ID.name = "individual.local.identifier",
uncertainty.type = 5,
tail.colors = "unique",
tail.wd = 0.7,
background = background)
system("rm -r gadm") ## remove geodata map from machineI will be using a different data set of blue and fin whales to demonstrate multiple trajectories but with more uncertainty. This data set uses satellite telemetry to study blue and fin whales in Southern California from 2014-2015:
Irvine LM, Winsor MH, Follett TM, Mate BR, Palacios DM (2020) An at-sea assessment of Argos location accuracy for three species of large whales, and the effect of deep-diving behavior on location error. Animal Biotelemetry 8:20.
whales$POSIX <- as.POSIXct(whales$timestamp)
whales_paths <- whales[format(whales$POSIX, "%Y") %in% c(2014),]
delta.t <- 3600*12
animate_paths(paths = whales_paths,
delta.t = delta.t,
coord = c("location.long", "location.lat"),
Time.name = "POSIX",
ID.name = "individual.local.identifier",
uncertainty.type = 5,
tail.colors = "unique",
tail.wd = 0.5)Third, I will use the same vultures data set but this time I will
display blurred ellipses around a point to show the uncertainty of a
specific animal. Unfortunately, the background image does not yet work
with blurred ellipses. To choose a blurred ellipses animation, set the
uncertainty.type argument to a value of
blur.
#blur ellipses
delta.t <- "week"
animate_paths(paths = vultures_paths,
delta.t = delta.t,
coord = c("location.long", "location.lat"),
Time.name = "POSIX",
ID.name = "individual.local.identifier",
uncertainty.type = "blur",
tail.colors = "red") ## Would you like anipaths to write over existing files? Y/NThe whales data set contains more trajectory uncertainty than the vultures relative the observed spatial scale. The blurred ellipses will be larger in this animation making it easier to see than the previous example.
library(ggmap)
whales$POSIX <- as.POSIXct(whales$timestamp, tz = "GMT")
whales_paths <- whales[format(whales$POSIX, "%Y") %in% 2015,]
delta.t <- 3600*12
animate_paths(paths = whales_paths,
delta.t = delta.t,
coord = c("location.long", "location.lat"),
Time.name = "POSIX",
ID.name = "individual.local.identifier",
uncertainty.type = "blur",
tail.colors = "green")## Would you like anipaths to write over existing files? Y/N