Introduction
The move package provides a series of functions to import, visualize
and analyse animal movement data. With the move package movement data
associated with individual animals or a group of animals can be imported
as a single object. The minimum requirements are timestamps,
coordinates, and a unique ID per animal. Further attributes associated
with the individuals (e.g. sex, age, species, etc), the locations
(e.g. temperature, behavior, battery voltage, etc.), coordinates
(projection) and the study (e.g. citation, license terms) can be also
included.
Movebank (www.movebank.org) is a free online database of animal
tracking data, where data owners can manage their data and have the
option to share it with colleagues or the public. Movebank users retain
ownership of their data and choose whether and with whom to share it. If
the public or a registered user has permission to see a study, the study
can be downloaded as a .csv file and imported directly into R. Those
with Movebank accounts can also log in, browse and download data they
have access to from Movebank directly within the Move package (see the
”browsing Movebank” vignette for more information). Besides importing
data from Movebank, it is also possible to use personal data sets, as
long as they contain the minimum required information: timestamps,
coordinates, and animal IDs.
A series of functions allow you to plot, summarize, and analyse the
movement data. Individual Move objects can be stacked to a
MoveStackobject, which includes a series of animals and the
additional data that are associated with them. This allows you to work
with multiple animals at the same time. Tracks can be also bursted by a
specific characteristic, e.g.specific behaviors (migratory,
non-migratory, resting behavior), which facilitates analyzing segments
of the trajectory belonging to a specific behavior or variable. This
package also provides functions to calculate the dynamic Brownian Bridge
Movement Model (dBBMM) and the utilization distribution(UD) of a
trajectory.
This vignette gives an overview of the main functions of the
move library. For more functions and more details about
functions we recommend viewing the corresponding help files.
Data import
The import of tracking data into R can happen in three ways:
- Importing data directly from Movebank using the web interface of the
move package (with
getMovebankData(),
getMovebankLocationData(),
getMovebankNonLocationData()).
- Importing a .csv file downloaded from Movebank (or .zip files from
EnvDATA) using the
move() function.
- Importing non-Movebank data using the
move()
function.
In all cases multiple individuals can be imported simultaneously.
Import data directly
from Movebank
See the “browsing Movebank” vignette for detailed explanation.
Import data
downloaded from Movebank
Files from Movebank can be
imported with move(x="path"), where “path” is a character
string that locates the file on the hard drive. The files can also be
compressed csv or .zip files downloaded with the EnvDATA tool. It may look
like:
library(move)
myMoveObject <- move(x="C:/User/Documents/GPS_data.csv")
myMoveEnv <- move(x="C:/User/Documents/GPS_data_Annotated.zip")
Import non-Movebank
data
Any data set containing movement data (position and time information)
can be imported as a move object. It is mandatory to indicate the
columns that contain the coordinates and the timestamp column in the
correct format. The projection of the coordinates should be indicated,
the data frame that includes all of the imported data can be added (if
it contains additional information associated to the locations) and if
the data correspond to a single individual, its name/ID can be indicated
as a character, but if the data correspond to several individuals the
column with the animal names/IDs has to be indicated.
Any data set containing movement data (position and time information)
can be imported as a move object. You must define the columns that
contain the coordinates, the projection of the coordinates, and the
format of the timestamp column. If the data correspond to a single
individual, its name/ID can be indicated as a character, but if the data
correspond to several individuals, the column with the animal names/IDs
must be indicated. If the data frame contains additional columns, these
additional attributes can be included.
In the following example a move object is created from a custom file
read in as a data frame:
data <- read.csv(system.file("extdata","leroy.csv.gz",package="move"))
leroy <- move(x=data$location.long, y=data$location.lat,
time=as.POSIXct(data$timestamp, format="%Y-%m-%d %H:%M:%OS", tz="UTC"),
proj=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"),
data=data, animal=data$individual.local.identifier, sensor=data$sensor)
By entering the object an overview is provided. This overview
indicates the object class (Move), number of coordinates,
the extent of the coordinates, the coordinates reference system
(projection), the number of columns of the imported data.frame, and the
names of the columns of the data.frame, as well as the first and last
timestamp and the duration of the observation.
## class : Move
## features : 919
## extent : -73.93067, -73.84366, 42.70898, 42.7687 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 17
## names : timestamp, eobs.battery.voltage, eobs.horizontal.accuracy.estimate, eobs.key.bin.checksum, eobs.speed.accuracy.estimate, eobs.start.timestamp, eobs.status, eobs.temperature, eobs.type.of.fix, eobs.used.time.to.get.fix, ground.speed, heading, height.above.ellipsoid, utm.easting, utm.northing, ...
## min values : 1234354605, 3596, 3.07, 3258904, 0.27, 2009-02-11 12:14:59.000, A, 13, 3, 4, 0.01, 0, -169.6, 587507.837877134, 4729143.16566605, ...
## max values : 1236158219.998, 3666, 97.02, 4291715164, 33.04, 2009-03-04 09:15:01.000, A, 35, 3, 119, 31.71, 359.79, 349, 594679.382402579, 4735720.47868847, ...
## timestamps : 2009-02-11 12:16:45 ... 2009-03-04 09:16:59.998 Time difference of 21 days (start ... end, duration)
## sensors : gps
## indiv. data : eobs.fix.battery.voltage, manually.marked.outlier, visible, sensor.type, individual.taxon.canonical.name, tag.local.identifier, individual.local.identifier, study.name, study.timezone
## indiv. value: 0 NA true gps Martes pennanti 74 Leroy Urban fisher GPS tracking Eastern Standard Time
## unused rec. : 1071
## study name : Urban fisher GPS tracking
## date created: 2023-01-20 16:56:43
Import movement
objects of other packages
The following objects containing movement data can also be read in by
the move() function:
- object of class
ltraj from the library
adehabitatLT
- object of class
telemetry or a list of telemetry
objects from the library ctmm
- object of class
track from the library
bcpa
- object of class
track_xyt from the library
amt
- object of class
binClstPath or binClstStck
from the library EMbC
- object of class
data.frame containing data downloaded
from Movebank webpage or with getMovebankLocationData
Handling multiple
animals
If the data are from Movebank, the function will automatically
recognize that there are multiple individuals contained in the file. If
the custom dataset includes more than one animal, you must indicate the
column with the animal IDs in the argument animal. When
multiple individuals are imported, the move() function
automatically creates a stack of Move objects called
MoveStack. A MoveStack can also be created
from a list of multiple Move or MoveStack
objects with the function moveStack(). In this case all
objects have to be in the same projection, and their timestamps have to
be in the same time zone. Most functions of the Move package are capable
of working with MoveStacks.
In the following example a MoveStack is created from two
Move objects:
ricky<-move(system.file("extdata","ricky.csv.gz", package="move"))
data(leroy)
## if argument forceTz is not stated, the timestamp is converted to the computer timezone
leroyP<-spTransform(leroy, proj4string(ricky))
myStack <- moveStack(list(leroyP, ricky),forceTz="UTC")
In the overview the information of all individuals is combined.
## class : MoveStack
## features : 9877
## extent : -73.94032, -73.84366, 42.70898, 42.851 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 20
## names : timestamp, eobs.battery.voltage, eobs.horizontal.accuracy.estimate, eobs.key.bin.checksum, eobs.speed.accuracy.estimate, eobs.start.timestamp, eobs.status, eobs.temperature, eobs.type.of.fix, eobs.used.time.to.get.fix, ground.speed, heading, height.above.ellipsoid, utm.easting, utm.northing, ...
## min values : 1234354605, 3449, 2.05, 524362, 0.22, 2009-02-11 12:14:59.000, A, -12, 3, 3, 0, 0, -315.6, 586587.171471437, 4729143.16566605, ...
## max values : 1270056686, 3740, 97.02, 4294919150, 48.67, 2010-03-31 17:30:02.000, A, 35, 3, 120, 43.12, 359.79, 436.6, 594679.382402579, 4744838.73355732, ...
## timestamps : 2009-02-11 12:16:45 ... 2010-03-31 17:31:26 Time difference of 413 days (start ... end, duration)
## sensors : gps
## indiv. data : eobs.fix.battery.voltage, manually.marked.outlier, visible, sensor.type, individual.taxon.canonical.name, tag.local.identifier, individual.local.identifier, study.name, study.timezone, behavioural.classification
## min ID Data : NA, NA, true, gps, Martes pennanti, 74, Leroy, Urban fisher GPS tracking, Eastern Standard Time, NA
## max ID Data : NA, NA, true, gps, Martes pennanti, 1016, Ricky.T, Urban fisher GPS tracking, Eastern Standard Time, NA
## individuals : Leroy, Ricky.T
## unused rec. : 2959
## date created: 2024-11-20 16:55:07
It is also possible to break down a MoveStack into a
list of Move objects using the split()
function. This is often useful to do calculations with functions that
require lists as an input as e.g. lapply(). This list of
Move objects can be transformed easily back into a
MoveStack with the function moveStack().
unstacked <- split(myStack)
myStack <- moveStack(unstacked, forceTz="UTC")
Handling duplicated
timestamps
Given that an animal cannot be at two places at the same time,
Move* objects account for this and cannot be created if
there are duplicated timestamps present in the data. There are several
options to remove these duplicated timestamps:
If the study is from Movebank and it contains duplicated timestamps
you can set the argument removeDuplicatedTimestamps=TRUE
when reading in the .csv file with the move() function.
This will retain the first of multiple records with the same animal ID
and timestamp, and remove any subsequent duplicates. In some cases, one
duplicate record might contain more complete information or a better
location estimate than the other. In case you want to control which of
the duplicate timestamps are kept and which are deleted, we recommend to
download the data as a .csv file from Movebank or to use the function
getMovebankLocationData(), find the duplicates using
e.g. getDuplicatedTimestamps(), decide which of the
duplicated timestamp to retain, and than create a move/moveStack object
with the function {move(). Another option is to edit the
records in movebank and mark the appropriate records as outliers.
One option is to remove the duplicated timestamps with the function
duplicated(), nevertheless in this case, depending on the
settings, the first or the last location will be retained. In some
cases, one duplicate record might contain more complete information or a
better location estimate than the other. In case you want to control
which of the duplicate timestamps are kept and which are deleted, you
can find the duplicates using
e.g. getDuplicatedTimestamps(), decide which of the
duplicated timestamp to retain, and than create a move/moveStack object
with the function move().
Time zone and
projection
GPS data are often collected in UTC, but for some analysis, it is
useful to transform the timestamps into the local time zone. This can be
done with the function with_tz() of the library
lubridate:
library(lubridate)
leroy@timestamps[1]
## [1] "2009-02-11 12:16:45 UTC"
leroyLocal <- leroy
timestamps(leroyLocal) <- with_tz(timestamps(leroyLocal), tz="America/New_York")
leroyLocal@timestamps[1]
## [1] "2009-02-11 07:16:45 EST"
When the Move of MoveStack object was created a projection was
declared, therefore these objects can be reprojected into any projection
with the function spTransform():
## [1] "+proj=longlat +datum=WGS84 +no_defs"
leroyProj <- spTransform(leroy, CRSobj="+proj=utm +zone=18 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
projection(leroyProj)
## [1] "+proj=utm +zone=18 +datum=NAD83 +units=m +no_defs"
Plotting
The functions plot(), points(),
lines() can be used with any Move* object and
all graphical parameters can be passed along.
plot(leroy, xlab="Longitude", ylab="Latitude", type="l", pch=16, lwd=0.5)
points(leroy, pch=20, cex=0.5)
plot(myStack, xlab="Longitude", ylab="Latitude",type="b", pch=16, cex=0.5, col=c("blue","magenta")[myStack@trackId])
Also ggplot() can be used, but in this case the
Move* object has to be transformed into a
data.frame.
library(ggplot2)
myStackDF <- as.data.frame(myStack)
ggplot(data = myStackDF, aes(x = location.long, y = location.lat, color = trackId)) +
geom_path() + geom_point(size = 0.5) + theme_bw() + coord_cartesian()
The tracks can also be plotted on a background map like open street
map:
library(ggmap)
require(mapproj)
leroyDF <- as.data.frame(leroy)
register_stadiamaps(your_stadia_key)
m <- get_map(bbox(extent(leroy)*1.1),maptype="stamen_terrain", source="stadia", zoom=12)
ggmap(m)+geom_path(data=leroyDF, aes(x=location.long, y=location.lat))
- using
leaflet: for an example see the vignette
“leafletPlot”.
Extracting temporal and
spatial information of tracks
There are a series of functions that extract temporal (e.g. time lag
between locations) and spatial (e.g. distance between locations)
information from Move* objects.
- Extract time lag between locations. Units should be always stated to
be able to interpret the results properly.
## from a move object (a vector is returned)
timeLag(leroy, units="mins")[1:5]
## [1] 14.88333 14.18330 14.45007 15.04998 14.90000
## from a moveStack object (a list of vectors is returned)
str(timeLag(myStack, units="mins"))
## List of 2
## $ Leroy : num [1:918] 14.9 14.2 14.5 15 14.9 ...
## $ Ricky.T: num [1:8957] 27.55 2.55 1.883 2.217 0.583 ...
- Extract distance between locations. Returned values will be in map
units, if the projection is in geographic coordinates (lon/lat) the
units will be in meters:
## from a move object (a vector is returned)
distance(leroy)[1:5]
## [1] 6.382461 5.606722 12.671263 9.651801 11.733921
## from a moveStack object (a list of vectors is returned)
str(distance(myStack))
## List of 2
## $ Leroy : num [1:918] 6.38 5.61 12.67 9.65 11.73 ...
## $ Ricky.T: num [1:8957] 153.35 12.62 7.81 10.23 13.18 ...
- Extract speed between locations. Returned values will be in map
units/second, if the projection is in geographic coordinates (lon/lat)
the units will be in m/s.
## from a move object (a vector is returned)
speed(leroy)[1:5]
## [1] 0.007147213 0.006588408 0.014615000 0.010688607 0.013125191
## from a moveStack object (a list of vectors is returned)
str(speed(myStack))
## List of 2
## $ Leroy : num [1:918] 0.00715 0.00659 0.01461 0.01069 0.01313 ...
## $ Ricky.T: num [1:8957] 0.0928 0.0825 0.0691 0.0769 0.3766 ...
- Extract heading (also known as azimuth or direction of
travel/movement) of trajectory. North is represented by 0:
## from a move object (a vector is returned)
angle(leroy)[1:5]
## [1] 101.44448 157.41345 26.47859 -133.22121 -108.37639
## from a moveStack object (a list of vectors is returned)
str(angle(myStack))
## List of 2
## $ Leroy : num [1:918] 101.4 157.4 26.5 -133.2 -108.4 ...
## $ Ricky.T: num [1:8957] 115.7 70.2 -18.2 -78.9 76.5 ...
## from a move object (a vector is returned)
turnAngleGc(leroy)[1:5]
## [1] 55.96892 -130.93488 -159.69985 24.84488 -179.16947
## from a moveStack object (a list of vectors is returned)
str(turnAngleGc(myStack))
## List of 2
## $ Leroy : num [1:917] 56 -130.9 -159.7 24.8 -179.2 ...
## $ Ricky.T: num [1:8956] -45.5 -88.4 -60.7 155.4 -108.9 ...
Bursting a track
It can be interesting to compare different parts of the track with
each other. For example, how do data points differ between winter and
summer, or between behaviors like migrating and non-migrating, or
between day and night. To indicate which point of the data set belongs
to which category, a track is ’bursted’ with the function
burst(). A track is bursted by supplying a vector with the
specific behavior/variable associated with each location. The length of
this vector has to be one shorter than the number of coordinates. The
returned object belongs to the class MoveBurst.
In the following example, the trajectory of ‘leroy’ is bursted
according to day and night.
library(solartime)
elev<-computeSunPosition(timestamps(leroy), coordinates(leroy)[,2],coordinates(leroy)[,1])[,'elevation']
DayNight <- c("Night",'Day')[1+(elev>0)]
## assigning to each segment if it is during daytime or night
leroy.burst <- burst(x=leroy, f=DayNight[-n.locs(leroy)])
The MoveBurst object has a very similar structure to a
Move object. The MoveBurst object has an
additional slot @burstId which contains the information of
the assignment of each location to each behavior/category (see
str(leroy.burst)). In the overview of the
MoveBurst there is an additional information feature under
“bursts” with the information how many locations fall within each
category. MoveBurst objects cannot be stacked into a
MoveStack.
The resulting MoveBurst can be plotted highlighting the
different categories in different colors. Note that R by default only
uses 8 different colors, if there are more than 8 categories colors will
be recycled, in this case we recommended specifying the colors.
plot(leroy.burst, type="l", col=c("red", "black"), asp=1)
legend("bottomleft",legend=c("day","night"), col=c("red", "black"), lty=1)
The plotBursts() function plots a circle at the midpoint
of each burst segment (consecutive coordinates that belong to a single
burst). The size of the cycles can have different meanings, depending on
what is calculated in the sizeFUN argument (for details see
?plotBursts). The default size refers to the relative time
of the burst segment compared to the whole track.
### in the default the size of the cicles is relative to the total time spent within each burst segment
plotBursts(leroy.burst,breaks=5, col=c("red", "black"), pch=19, add=F,main="Size of points: total time spent in burst segment", asp=1)
legend("bottomleft",legend=c("day","night"), col=c("red", "black"), pch=19)
## here, e.g., the size of the circles is relative to the total distance moved within each burst segment
plotBursts(object=leroy.burst, breaks=5, sizeFUN=function(x){sum(distance(x))},col=c("red", "black"), pch=19, add=F,main="Size of points: total distance moved in burst segment", asp=1)
legend("bottomleft",legend=c("day","night"), col=c("red", "black"), pch=19)
A MoveBurst object can be split into a list of
Move objects, one per each burst segment:
leroyB.split <- split(leroy.burst)
par(mfrow=c(1,4))
plot(leroyB.split[[1]], type="b", pch=20, main=paste0(names(leroyB.split[1])," 1"))
plot(leroyB.split[[2]], type="b", pch=20, main=paste0(names(leroyB.split[2])," 1"))
plot(leroyB.split[[3]], type="b", pch=20, main=paste0(names(leroyB.split[3])," 2"))
plot(leroyB.split[[4]], type="b", pch=20, main=paste0(names(leroyB.split[4])," 2"))
Corridor behavior
The corridor() function identifies sections of the track
that suggest corridor use behavior (i.e. parallel and fast movements).
For details see LaPoint et al. (2013) Animal Behavior, Cost-based
Corridor Models, and Real Corridors. Landscape Ecology. https://doi.org/10.1007/s10980-013-9910-0.
The speed threshold and how parallel the segments should be can be
adjusted in the function. The proportion of speeds that are high enough
to be a valid corridor point is be defined in the argument “speedProp”.
The default value selects speeds that are greater than 75% of all
speeds. The proportion of the circular variances low enough to be a
valid corridor point is defined in the argument “circProp”. Low values
of the circular variance indicate that the segments are (near) parallel.
The default value selects variances that are lower than 25 % of all
variances.
LeroyCorr <- corridor(leroy)
plot(LeroyCorr, type="l", xlab="Longitude", ylab="Latitude", col=c("black", "grey"), lwd=c(1,2))
legend("bottomleft", c("Corridor", "Non-corridor"), col=c("black", "grey"), lty=c(1,1), bty="n")
The resulting object is of class MoveBurst which in the
@data slot contains new attributes associated to the
locations, where “segMid_x” and “segMid_y” are the coordinates of the
midpoint of each segment, and “speed”, “azimuth”, “pseudoAzimuth” and
“circVar” are the variables used to identify the segments that suggest
corridor behavior.
Dynamic Brownian Bridge
Movement Model (dBBMM)
The dBBMM is a method to calculate the occurrence distribution of a
given track, i.e. it estimates where the animal was located during the
observed (tracking) period. It calculates the probability landscape for
the transition between any two known consecutive locations given the
amount of time it had available (assuming a conditional random
(Brownian) motion between locations). It is “dynamic” because it allows
the variance to change along the trajectory, using the behavioral change
point analysis in a sliding window. For details see Kranstauber et. all
(2012) A dynamic Brownian bridge movement model to estimate utilization
distributions for heterogeneous animal movement. Journal of Animal
Ecology. https://doi.org/10.1111/j.1365-2656.2012.01955.x
The Move* object used to calculate the dBBMM needs to be
projected and, the location error, margin, window size and raster
options need to be specified. Note that in most cases different values
of window size and the margin do not have a great effect on the results.
For details on the different ways to specify the raster options see the
‘Details’ section of ?brownian.bridge.dyn. In the following
examples, the calculation is done on a raster with a pixel size of
100m:
leroyRed <- leroy[1:200] # reducing dataset for example
leroy.prj <- spTransform(leroyRed, center=TRUE) # center=T: the center of the coordinate system is the center of the track. Units are in meters
dBB.leroy <- brownian.bridge.dyn(leroy.prj, ext=.85, raster=100, location.error=20)
The resulting object will be of class DBBMM,
DBBMMStack, or DBBMMBurstStack depending on
the input class. All of them contain a raster or a rasterStack were all
pixels of each raster sum up to 1 (besides the
DBBMMBurstStack, for details see ‘Calculating a dBBMM from
a MoveBurst’ section)
Plotting the result provides limited visual information, as the
values correspond to the probability the animal was present in a given
pixel during the observed period. These results can be used to estimate
probability of the animal being present in a certain area during the
tracking period (see ‘Utilization distribution’ section), or to
calculate the probability of the animal being present in a certain
environment during the tracking period by overlaying the resulting
raster over a categorical raster (e.g. of land cover) and summing up the
dBBMM pixel values that fall within each category.
plot(dBB.leroy, main="dBBMM")
Utilization
distribution (UD)
From the Dynamic Brownian Bridge object, the UD can be calculated,
which represents the minimum area in which an animal has some specified
probability of being located.
UDleroy <- getVolumeUD(dBB.leroy)
par(mfrow=c(1,2))
plot(UDleroy, main="UD")
## also a contour can be added
plot(UDleroy, main="UD and contour lines")
contour(UDleroy, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))
The obtained UD can be subset to a specific probability:
par(mfrow=c(1,3))
## mantaining the lower probabilities
ud95 <- UDleroy
ud95[ud95>.95] <- NA
plot(ud95, main="UD95")
## or extracting the area with a given probability, where cells that belong to the given probability will get the value 1 while the others get 0
ud95 <- UDleroy<=.95
plot(ud95, main="UD95")
ud50 <- UDleroy<=.5
plot(ud50, main="UD50")
Calculating a dBBMM
from a MoveBurst
The dBBMM can also be calculated for a MoveBurst, for
all burst types, or only for a subset of them. The result will provide
one raster per burst segment in an object of class
DBBMMBurstStack. By default it is calculated for all burst
types:
leroyBRed <- leroy.burst[1:155] # reducing dataset for example
leroyB.prj <- spTransform(leroyBRed, center=TRUE) # center=T: the center of the coordinate system is the center of the track. Units are in meters
dBB.leroyB <- brownian.bridge.dyn(leroyB.prj, ext=1.25, raster=100, location.error=20)
plot(dBB.leroyB)
The UD can also be calculated for each raster layer of the resulting
DBBMMBurstStack. In this case to calculate the UD, each
raster layer needs to be standardized so the sum of all pixels is 1.
This can be done with the function UDStack()
leroyBud <- UDStack(dBB.leroyB)
UDleroyB <- getVolumeUD(leroyBud)
plot(UDleroyB)
To calculate the UD for each burst type, the layers of each burst
type have to be summed up and converted to the .UD
class.
par(mfrow=c(1,2))
## select all layers corresponding to "Day", sum them up, and convert them to class .UD
daylayers <- grep("Day", names(dBB.leroyB))
rasterDay <- sum(dBB.leroyB[[daylayers]])
rasterDayUD <- as(rasterDay,".UD")
UDleroy.day <- getVolumeUD(rasterDayUD)
plot(UDleroy.day, main="UD of 'Day'")
## same for layer corresponding to "Night"
nightlayers <- grep("Night", names(dBB.leroyB))
rasterNight <- sum(dBB.leroyB[[nightlayers]])
rasterNightUD <- as(rasterNight,".UD")
UDleroy.night <- getVolumeUD(rasterNightUD)
plot(UDleroy.night, main="UD of 'Night'")
To calculate the dBBMM for a specific or a subset of burst types,
these have to be stated in the argument “burstType”. In this case the
dBBMM is calculated only taking into account the burst segments
“Night”:
dBB.leroyB.night <- brownian.bridge.dyn(leroyB.prj, ext=.75, raster=100, location.error=20, burstType="Night")
plot(dBB.leroyB.night,nr=1)
From the resulting object, the UD per layer, or the total UD can be
calculated as above.
Issues when
calculating the dBBMM
Below some common issues that may arise while calculating the dBBMM
and possible solutions to them.
Calculation takes
very long
There can be several reasons why the calculation is taking very
long:
- long trajectories of thousands or tens of thousands locations will
take some time
- the pixel size for which the calculation is done will also be
reflected on the calculation time. The smaller the pixel size, the
longer the calculation time. Always make sure that the pixel size of the
raster is larger than the location error.
- the time lag between locations is very small,
- because the data were collected at a very high resolution or
- because sometimes, there is a location that is just seconds or
milliseconds apart from the next, even though the GPS schedule was set
to minutes or hours.
By default the dBBMM will take the shortest time lag (in minutes) of
the trajectory divided by 15 as the time interval taken for every
integration step. If the shortest time lag is e.g. 15min, than the
trajectory will be divided into steps of 1min. But if for some reason
there is one time lag of 1sec (0.0167mins), the trajectory will be
divided into steps of 0.001 mins, increasing the calculation time
immensely.
Using the argument time.step the integration step time
can be set manually. As a general rule of thumb one can take the
intended GPS schedule in minutes divided by 15.
## check the timeLag of data. If there are timelags shorter than the intended scedule, use the argument "timestep"
rickyRed <- ricky[1:100]
summary(timeLag(rickyRed,"mins"))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 1.875 2.117 9.422 6.883 60.700
ricky.prj <- spTransform(rickyRed, center=TRUE)
ts <- median(timeLag(rickyRed,"mins"))
BB.ricky <- brownian.bridge.dyn(ricky.prj, ext=.45, dimSize=100, location.error=20,time.step=ts/15)
For trajectories collected at a very high resolution, e.g. 1Hz, or
trajectories containing high resolution bursts, the
time.step argument can be set to 0.017 min (1sec), as there
is no need to estimate where the animal was at a shorter time interval,
because it is known where it was every 1sec.
In any case giving higher values to the time.step
argument will speed up the calculation time.
Error: extent is
not large enough or resulting UD is one “big blob”
This problem is mostly due to data that contains large chunks of
missing data. During these large time gaps, the uncertainty of where the
animal may have been is very large, or in other words the animal could
have been anywhere.
Here is an example:
## creating a gappy data set
leroyWithGap <- leroy[-c(50:500,550:850)]
leroyWithGap_p <- spTransform(leroyWithGap, center=TRUE)
## calculate the dBBMM with the default extent gives an error that it is too small
dbb <- brownian.bridge.dyn(leroyWithGap_p, raster=100, location.error=20)
## Error in .local(object, raster, location.error = location.error, ext = ext, : Lower x grid not large enough, consider extending the raster in that direction or enlarging the ext argument
## making the extent bigger seems to solve the problem
dbb <- brownian.bridge.dyn(leroyWithGap_p, raster=100, location.error=20, ext=4)
## but than the UD is not very informative
ud <- getVolumeUD(dbb)
par(mfrow=c(1,2))
plot(ud, main="UD")
contour(ud, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))
plot(ud, main="UD with locations")
points(leroyWithGap_p, col="red", cex=.5, pch=20)
contour(ud, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))
And the solution of such a case:
The solution is to remove the variance of the segments corresponding
to the large time gaps. For this first the variance is calculated with
the brownian.motion.variance.dyn() function, then the
segments corresponding to the large time gaps are set to FALSE, and
finally the dBBMM is calculated.
## calculate the dynamic brownian motion variance of the gappy track
dbbv <- brownian.motion.variance.dyn(leroyWithGap_p, location.error=20, window.size=31, margin=11)
## the intended GPS fix rate of leroy was 15min, so we will ignore for example all segments that have a larger time lag than 5hours. The 'dBMvariance' object resulting from the function above, contains the slot '@interest' in which those segments marked as FALSE won't be included in the calculation of the dBBMM. Therefore we set all segments with time lag larger than 300mins to false
dbbv@interest[timeLag(leroyWithGap_p,"mins")>300] <- FALSE
## then we use the 'dBMvariance' object to calculate the dBBMM
dbb.corrected <- brownian.bridge.dyn(dbbv, raster=100, ext=.45,location.error=20)
## now the UD makes more sense
ud.corrected <- getVolumeUD(dbb.corrected)
par(mfrow=c(1,2))
plot(ud.corrected, main="UD")
contour(ud.corrected, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))
plot(ud.corrected, main="UD with locations")
points(leroyWithGap_p, col="red", cex=.5, pch=20)
contour(ud.corrected, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))
Interpolating a
trajectory
The function interpolateTime() allows to interpolate
trajectories. The interpolation can be done to obtain a regular track
with a specific number of locations, to obtain a track with location at
a specific regular time interval, or to obtain positions at given
timestamps. The new locations can be interpolated using an euclidean,
great circle or rhumb line function.
## providing the number of locations. In this case the interpolated trajectory will have 500 locations with a regular timelag
interp500p <- interpolateTime(leroyRed, time=500, spaceMethod='greatcircle')
plot(leroyRed, col="red",pch=20, main="By number of locations")
points(interp500p)
lines(leroyRed, col="red")
legend("bottomleft", c("True locations", "Interpolated locations"), col=c("red", "black"), pch=c(20,1))
summary(timeLag(interp500p, "mins"))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.54 11.54 11.54 11.54 11.54 11.54
## providing a time interval. In this case the interpolated trajectory will have a location every 5mins
interp5min <- interpolateTime(leroyRed, time=as.difftime(5, units="mins"), spaceMethod='greatcircle')
plot(leroyRed, col="red",pch=20, main="By time interval")
points(interp5min)
lines(leroyRed, col="red")
legend("bottomleft", c("True locations", "Interpolated locations"), col=c("red", "black"), pch=c(20,1))
## providing a vector of timestamps
ts <- as.POSIXct(c("2009-02-12 06:00:00", "2009-02-12 12:00:00", "2009-02-12 23:00:00",
"2009-02-14 06:00:00", "2009-02-14 12:00:00", "2009-02-14 23:00:00"),
format="%Y-%m-%d %H:%M:%S", tz="UTC")
interpCusom <- interpolateTime(leroyRed, time=ts, spaceMethod='greatcircle')
plot(leroyRed, col="red",pch=20, main="By custom timestamps")
points(interpCusom)
lines(leroyRed, col="red")
legend("bottomleft", c("True locations", "Interpolated locations"), col=c("red", "black"), pch=c(20,1))
Storing, loading and
exporting Move* objects
A Move* object can be transformed into:
leroyDF <- as.data.frame(leroy)
leroySPDF <- as(leroy,"SpatialPointsDataFrame")
leroy.ltraj <- as(leroy,"ltraj")
Storing and loading as .RData
## save the Move* object as a RData file
save(leroy, file="C:/User/Documents/leroy.RData")
## load an Move* object saved as a RData file
load("C:/User/Documents/leroy.RData")
Storing as text file
## save as a text file
leroyDF <- as.data.frame(leroy)
write.table(leroyDF, file="C:/User/Documents/leroyDF.csv", sep=",", row.names = FALSE)
Storing as ESRI shapefile file
## save as a shape file
library(rgdal)
writeOGR(leroy, "C:/User/Documents/leroySHP/", layer="leroy", driver="ESRI Shapefile")
Storing as kml or kmz
## kml or kmz of movestack ##
library(plotKML)
## open a file to write the content
kml_open('myStack.kml')
## write the movement data individual-wise
for(i in levels(trackId(myStack))){
kml_layer(as(myStack[[i]],'SpatialLines'))
}
## close the file
kml_close('myStack.kml')
## export KML using writeOGR ##
for(i in 1:nrow(myStack@idData)){
writeOGR(as(myStack[[i]], "SpatialPointsDataFrame"),
paste(row.names(myStack@idData)[i], ".kml", sep=""),
row.names(myStack@idData)[i], driver="KML")
writeOGR(as(myStack[[i]], "SpatialLinesDataFrame"),
paste(row.names(myStack@idData)[i], "-track.kml", sep=""),
row.names(myStack@idData)[i], driver="KML")
}