,
Hugo Flávio [aut] | Title: | Refined Shortest Paths |
|---|---|
| Description: | The RSP toolkit is a method for analysing the fine scale movements of aquatic animals tracked with passive acoustic telemetry in estuarine environments, that accounts for the surrounding land masses. The animal movements between detections are recreated to have occurred exclusively in water and the utilisation distribution areas are limited by the land contours, providing realistic estimations of space use. |
| Authors: | Yuri Niella [aut, cre] ,
Hugo Flávio [aut] |
| Maintainer: | Yuri Niella <[email protected]> |
| License: | GPL-3 + file LICENSE |
| Version: | 1.0.5 |
| Built: | 2024-11-09 05:09:53 UTC |
| Source: | https://github.com/yuriniella/RSP |
Add group centroid location to an existing plot
addCentroids( input, type, tag = NULL, track = NULL, timeslot = NULL, shape = 21, size = 1.5, colour = "white", fill = "cyan" )addCentroids( input, type, tag = NULL, track = NULL, timeslot = NULL, shape = 21, size = 1.5, colour = "white", fill = "cyan" )
input |
The output of |
type |
One of "group" or "track". |
tag |
Animal of interest, when type = "track". |
track |
Track of interest, when type = "track". |
timeslot |
The timeslot of interest to plot the centroid location |
shape |
The shape of the points |
size |
The size of the points |
colour |
The colour of the points |
fill |
The fill of the points |
A ggplot with centroid locations
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) with timeslots: dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56, timeframe = 2) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Obtaing centroid coordinate locations of dBBMM: df.centroid <- getCentroids(input = dbbmm.data, type = "group", areas = areas.group, level = 0.95, group = "G1", UTM = 56) # Plot group centroid location: plotAreas(areas.group, base.raster = water, group = "G1", timeslot = 7) + addCentroids(input = df.centroid, type = "group", timeslot = 7)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) with timeslots: dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56, timeframe = 2) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Obtaing centroid coordinate locations of dBBMM: df.centroid <- getCentroids(input = dbbmm.data, type = "group", areas = areas.group, level = 0.95, group = "G1", UTM = 56) # Plot group centroid location: plotAreas(areas.group, base.raster = water, group = "G1", timeslot = 7) + addCentroids(input = df.centroid, type = "group", timeslot = 7)
Add recapture locations to an existing plot
addRecaptures( Signal, shape = 21, size = 1.5, colour = "white", fill = "dodgerblue" )addRecaptures( Signal, shape = 21, size = 1.5, colour = "white", fill = "dodgerblue" )
Signal |
The signal of the transmitter of interest |
shape |
The shape of the points |
size |
The size of the points |
colour |
The colour of the points |
fill |
The fill of the points |
A ggplot with the recapture locations
Add receiver stations to an existing plot
addStations(input, shape = 21, size = 1.5, colour = "white", fill = "black")addStations(input, shape = 21, size = 1.5, colour = "white", fill = "black")
input |
|
shape |
The shape of the points |
size |
The size of the points |
colour |
The colour of the points |
fill |
The fill of the points |
A ggplot with stations
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Plot example dBBMM with acoustic stations plotContours(dbbmm.data, tag = "A69-9001-1111", track = 1) + addStations(rsp.data)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Plot example dBBMM with acoustic stations plotContours(dbbmm.data, tag = "A69-9001-1111", track = 1) + addStations(rsp.data)
This function can be used to generate an animated plot of the RSP tracks.
animateTracks( input, base.raster, tags = NULL, drop.groups = NULL, by.group = FALSE, start.time, stop.time, land.col = "#BABCBF80", add.legend = TRUE, add.stations = FALSE, save.gif = FALSE, gif.name = "Animation.gif", height = 720, width = 720, xlim = NULL, ylim = NULL, nframes = 100, fps = 10 )animateTracks( input, base.raster, tags = NULL, drop.groups = NULL, by.group = FALSE, start.time, stop.time, land.col = "#BABCBF80", add.legend = TRUE, add.stations = FALSE, save.gif = FALSE, gif.name = "Animation.gif", height = 720, width = 720, xlim = NULL, ylim = NULL, nframes = 100, fps = 10 )
input |
The output of runRSP. |
base.raster |
The raster used to generate the transition layer used in runRSP. |
tags |
Character vector specifying which tags to include in the animation. |
drop.groups |
Character vector specifying any group(s) to the be removed from the animation. |
by.group |
Logical, if TRUE one facet will be plotted for each tracked group. Defauly is FALSE. |
start.time |
Character vector of the start point (format = "Y-m-d H:M:S") for the animation. |
stop.time |
Character vector of the stop point (format = "Y-m-d H:M:S") for the animation. |
land.col |
Colour of the land masses. Defaults to semi-transparent grey. |
add.legend |
Logical, if TRUE (default) a colour legend representing the monitored tags will be included. |
add.stations |
Logical, if TRUE the stations will be added to the animaltion. Default is FALSE. Only works if by.group = FALSE. |
save.gif |
Logical defining if the animation should be saved. |
gif.name |
If save.gif = TRUE, character vector for the GIF name. |
height |
If save.gif = TRUE, number of pixels for the GIF height. |
width |
If save.gif = TRUE, number of pixels for the GIF width. |
xlim |
Numeric vector defining the horizontal limits of the map. |
ylim |
Numeric vector defining the vertical limits of the map. |
nframes |
The number of frames to render (default 100). |
fps |
The framerate of the animation in frames/sec (default 10). |
An animation of the RSP tracks.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Animate and RSP track: animateTracks(input = rsp.data, base.raster = water, tags = "A69-9001-1111", add.stations = TRUE)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Animate and RSP track: animateTracks(input = rsp.data, base.raster = water, tags = "A69-9001-1111", add.stations = TRUE)
Estimates the RSP individually for all tracks of a particular animal.
calcRSP( df.track, tz, distance, min.time, time.step, transition, er.ad, path.list, verbose )calcRSP( df.track, tz, distance, min.time, time.step, transition, er.ad, path.list, verbose )
df.track |
Detection data for that individual as imported using RSPete. |
tz |
Time zone of the study area. |
distance |
Maximum distance between RSP locations. |
min.time |
Minimum time required between receiver detections (in minutes) for RSP to be calculated. Default to 10 minutes. |
time.step |
Time lapse in minutes to be considered for consecutive detections at the same station. |
transition |
TransitionLayer object as returned by LTDpath. |
er.ad |
Incremental error per additional RSP point. |
path.list |
A list of previously calculated paths. |
verbose |
Logical: If TRUE, detailed messages and progression are displayed. Otherwise, a single progress bar is shown. |
A dataframe with the RSP estimations for all identified tracks for that animal.
Calculates dynamic Brownian Bridge Movement Model (dBBMM) for each track and transmitter. Tracks shorter than 30 minutes are automatically identified and not included in the analysis.
dynBBMM( input, base.raster, tags = NULL, start.time, stop.time, timeframe = NULL, UTM, debug = FALSE, verbose = TRUE, window.size = 7, margin = 3 )dynBBMM( input, base.raster, tags = NULL, start.time, stop.time, timeframe = NULL, UTM, debug = FALSE, verbose = TRUE, window.size = 7, margin = 3 )
input |
The output of runRSP. |
base.raster |
The water raster of the study area. For example the output of |
tags |
Vector of transmitters to be analysed. By default all transmitters from runRSP will be analysed. |
start.time |
Sets the start point for analysis (format = "Y-m-d H:M:S"). |
stop.time |
Sets the stop point for analysis (format = "Y-m-d H:M:S"). |
timeframe |
Temporal window size for fine-scale dBBMM in hours. If left NULL, a single dBBMM is calculated for the whole period. |
UTM |
The UTM zone of the study area. Only relevant if a latlon-to-metric conversion is required. |
debug |
Logical: If TRUE, the function progress is saved to an RData file. |
verbose |
Logical: If TRUE, detailed check messages are displayed. Otherwise, only a summary is displayed. |
window.size |
The size of the moving window along the track. Larger windows provide more stable/accurate estimates of the brownian motion variance but are less well able to capture more frequent changes in behavior. This number has to be odd. |
margin |
The margin used for the behavioral change point analysis. This number has to be odd. |
List of calculated dBBMMs and metadata on each track used for the modelling.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56)
Calculate water areas per group or track
getAreas(input, type = c("group", "track"), breaks = c(0.5, 0.95))getAreas(input, type = c("group", "track"), breaks = c(0.5, 0.95))
input |
The output of |
type |
one of "group" or "track". If set to "track", UD rasters for each track are also supplied. |
breaks |
The contours for calculating usage areas in squared metres. By default the 95% and 50% contours are used. |
A list of areas per track, per group
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(input = dbbmm.data, type = "group", breaks = c(0.5, 0.95))# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(input = dbbmm.data, type = "group", breaks = c(0.5, 0.95))
When a long study period is analysed the dBBMM can crash R since they are computationally heavy. You can use this function to calculate the dBBMMs and overlapping areas (between pairs of groups) according to a fixed step (i.e. timeframe in number of days), and export the output to disk as it goes. If your computer runs out of memory and kills R, you can then resume the calculations by setting a new output name (name.new) and specifying the previous one already stored in disk (name.file), and defining the new start date to resume the calculations (start.time). It currently only works with the 50
getAreaStep( input, base.raster, UTM, timeframe = 1, start.time = NULL, save = TRUE, name.new = NULL, name.file = NULL, groups = NULL )getAreaStep( input, base.raster, UTM, timeframe = 1, start.time = NULL, save = TRUE, name.new = NULL, name.file = NULL, groups = NULL )
input |
The output of |
base.raster |
The water raster of the study area. For example the output of |
UTM |
The UTM zone of the study area. Only relevant if a latlon-to-metric conversion is required. |
timeframe |
The intended temporal interval of interest (in number of days) to perform the calculations. Default is 1 day. |
start.time |
Character vector identifying the initial date (format = "Y-m-d") to start the calculations. |
save |
Logical (default is TRUE). Do you want to save the calculated areas to disk? |
name.new |
File name (character) to save calculations output to disk. |
name.file |
File name (character) of previous calculations output to be imported (if any). |
groups |
Vector of group names (character) of interest to perform calculations. |
A dataframe of dBBMM areas per group and the corresponding overlaps
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate dBBMM and overlaps in steps: df.areas <- getAreaStep(input = rsp.data, base.raster = water, UTM = 56, timeframe = 1, name.new = "save.csv", groups = c("G1", "G2"))# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate dBBMM and overlaps in steps: df.areas <- getAreaStep(input = rsp.data, base.raster = water, UTM = 56, timeframe = 1, name.new = "save.csv", groups = c("G1", "G2"))
When a timeslot dBBMM analysis is conducted, this function can be used to obtain centroid latitude and longitude locations between all utilization distribution contours at group or track level.
getCentroids(input, areas, type, level, group, UTM)getCentroids(input, areas, type, level, group, UTM)
input |
The output of |
areas |
The output of |
type |
Character vector specifying the type of getAreas analysis performed: "group" or "track". |
level |
Numeric vector defining the contour level of dBBMM of interest to extract the centroid positions. |
group |
Character vector defining the group of interest for the analysis, when getAreas is of type "group". |
UTM |
Numeric vector representing the UTM zone of the study area. |
A dataframe containing the centroid positions per each timeslot
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) with timeslots: dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56, timeframe = 2) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Obtaing centroid coordinate locations of dBBMM: df.centroid <- getCentroids(input = dbbmm.data, areas = areas.group, type = "group", level = 0.95, group = "G1", UTM = 56)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) with timeslots: dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56, timeframe = 2) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Obtaing centroid coordinate locations of dBBMM: df.centroid <- getCentroids(input = dbbmm.data, areas = areas.group, type = "group", level = 0.95, group = "G1", UTM = 56)
Obtain the total distances travelled (in metres) for the tracked animals, using only the receiver locations and also adding the RSP positions.
getDistances(input, t.layer)getDistances(input, t.layer)
input |
RSP dataset as returned by RSP. |
t.layer |
A transition layer. Can be calculated using the function |
A dataframe containing the total distances travelled during each RSP track.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances travelled distance.data <- getDistances(rsp.data, t.layer = tl)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances travelled distance.data <- getDistances(rsp.data, t.layer = tl)
Calculate in-water distances from RSP locations to a point of reference
getDistPoint(input, point, t.layer, transmitter = NULL)getDistPoint(input, point, t.layer, transmitter = NULL)
input |
The output of |
point |
Point of reference (lon, lat) to where distances will be calculated. |
t.layer |
A transition layer. Can be calculated using the function |
transmitter |
The animal(s) of interest for calculating distances. If not specified, by default, distances will be calculated for all animals in the dataset. |
The RSP detections object with a distance (in metres) column appended.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances to a point of reference df.dist <- getDistPoint(input = rsp.data, point = c(151.0291, -33.81771), t.layer = tl)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances to a point of reference df.dist <- getDistPoint(input = rsp.data, point = c(151.0291, -33.81771), t.layer = tl)
Calculate overlaps between different groups
getOverlaps(input)getOverlaps(input)
input |
The output of |
A list of Overlaps (per timeslot if relevant), as well as the respective overlap rasters.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Get overlaps between groups overlap.data <- getOverlaps(areas.group)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Get overlaps between groups overlap.data <- getOverlaps(areas.group)
Automatically estimates the RSP for all tracked individuals within a particular study area.
includeRSP( detections, transition, tz, distance, time.step, er.ad, min.time, max.time, verbose, debug = FALSE, recaptures )includeRSP( detections, transition, tz, distance, time.step, er.ad, min.time, max.time, verbose, debug = FALSE, recaptures )
detections |
Detection data for that individual as imported using RSPete. |
transition |
TransitionLayer object as returned by LTDpath. |
tz |
Timezone of the study area. |
distance |
Distance (in metres) by which RSP point should be spaced (between detections at different stations). Defaults to 250 metres. |
time.step |
Time lapse (in minutes) between RSP points added between detections at the same station. Defaults to 10 minutes. Must not be larger than |
er.ad |
Increment rate of the position errors for the estimated locations (in metres). If left unset, defaults to 5% of the |
min.time |
Minimum time required between receiver detections (in minutes) for RSP to be calculated. Default to 10 minutes. |
max.time |
Maximum time allowed between receiver detections (in hours) for RSP to be calculated. Defaults to 24 hours. |
verbose |
Logical: If TRUE, detailed messages and progression are displayed. Otherwise, a single progress bar is shown. |
debug |
Logical: If TRUE, the function progress is saved to an RData file. |
recaptures |
If the recapture locations will be included in the analysis. |
A list with the RSP estimations of individual tracks per transmitter.
Identifies fine-scale data among total detection dataset to be used for RSP estimation. Tracks are then named based on the interval between consecutive detection dates.
nameTracks(detections, max.time = 24, recaptures, tz)nameTracks(detections, max.time = 24, recaptures, tz)
detections |
Detections data frame |
max.time |
Temporal lag in hours to be considered for the fine-scale tracking. Default is to consider 1-day intervals. |
recaptures |
If the recapture locations will be included in the analysis. |
tz |
Time zone of the study area. |
A dataframe with identified and named individual tracks for RSP estimation.
Plot areas for a specific group and, if relevant, track and timeslot. If the base raster is in a geographic coordinate system, plotAreas will attempt to convert the dbbmm results to that same geographic system, so everything falls in place.
plotAreas( areas, base.raster, group, timeslot, title = NULL, col, land.col = "#BABCBF80" )plotAreas( areas, base.raster, group, timeslot, title = NULL, col, land.col = "#BABCBF80" )
areas |
The areas object used to calculate the space use areas at group level. |
base.raster |
The raster used in the dbbmm calculations. |
group |
Character vector indicating the group to be displayed. |
timeslot |
The timeslot to be displayed. Only relevant for timeslot dbbmms. |
title |
Plot title. |
col |
Character vector of colours to be used in the plot (same length as the number of contour levels). |
land.col |
Colour of the land masses. Defaults to semi-transparent grey. |
A plot of the overlapping areas between two groups.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Plot areas at group level plotAreas(areas.group, group = "G1", base.raster = water)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Plot areas at group level plotAreas(areas.group, group = "G1", base.raster = water)
Plot dynamic Brownian Bridge Movement Model (dBBMM) contours
plotContours( input, tag, track = NULL, timeslot, scale.type = "categorical", breaks = c(0.95, 0.75, 0.5, 0.25), col, title, land.col = "#BABCBF80" )plotContours( input, tag, track = NULL, timeslot, scale.type = "categorical", breaks = c(0.95, 0.75, 0.5, 0.25), col, title, land.col = "#BABCBF80" )
input |
The dbbmm object as returned by |
tag |
Choose a single tag to plot |
track |
If a single tag was chosen, you can use 'track' to define a specific track to be plotted. |
timeslot |
The timeslot to be plotted. Only relevant for timeslot dbbmms. |
scale.type |
Character vector selecting the type of scale to plot space use areas. By default a "categorical" scale is set, but alternatively can be set to "continuous" to return the space use areas with a continuous scale. |
breaks |
When scale.type = "categorical", this is a numeric vector selecting the use areas to plot. By default, the 99%, 95%, 75%, 50% and 25% areas will be returned. |
col |
The colours to be used when scale.type = "categorical". Must match the number of breaks. |
title |
The title of the plot. |
land.col |
Colour of the land mass. |
dynamic Brownian Bridge Movement Model plot.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Plot example dBBMM plotContours(dbbmm.data, tag = "A69-9001-1111", track = 1)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Plot example dBBMM plotContours(dbbmm.data, tag = "A69-9001-1111", track = 1)
Generates a density plot for inspecting the distribution of elapsed times (in hours) between all consecutive acustic detections. By default the plot is created including all monitored groups and transmitters. Alternatively, can be set to be performed at group level using the type argument.
plotDensities(input, group)plotDensities(input, group)
input |
RSP dataset as returned by RSP. |
group |
Character vector defining the group to which calculate density distributions. By default, density is calculated for all animals and groups tracked. |
Density plots of hours elapsed between consecutive acoustic detections.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Plot distribution of acoustic detections plotDensities(rsp.data, group = "G1")# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Plot distribution of acoustic detections plotDensities(rsp.data, group = "G1")
Compare the outputs of total distances travelled (in kilometres) for the tracked animals, using only the receiver locations and adding the RSP positions. Data on the total distances travelled are stored in the 'distances' objtect.
plotDistances(input, group, compare = TRUE)plotDistances(input, group, compare = TRUE)
input |
output of |
group |
Define a specific group to be plotted, rather than the overall results. |
compare |
By default, a comparative plot is returned showing distances travelled with Receiver and RSP location types. If FALSE, only the RSP total distances travelled will be returned. |
A barplot of total distances travelled as a function of location type (Loc.type) and the distances travelled during each RSP track.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances travelled distance.data <- getDistances(rsp.data, t.layer = tl) # Plot distances travelled plotDistances(distance.data, group = "G1")# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Calculate distances travelled distance.data <- getDistances(rsp.data, t.layer = tl) # Plot distances travelled plotDistances(distance.data, group = "G1")
Plot specific dBBMM overlapping areas for a specific combination of groups and, if relevant, a specific timeslot. If the base raster is in a geographic coordinate system, plotOverlaps will attempt to convert the dbbmm results to that same geographic system, so everything falls in place.
plotOverlaps( overlaps, areas, base.raster, groups, timeslot, level, title = NULL, col, land.col = "#BABCBF80" )plotOverlaps( overlaps, areas, base.raster, groups, timeslot, level, title = NULL, col, land.col = "#BABCBF80" )
overlaps |
An overlap object as returned by |
areas |
The areas object used to calculate the overlaps. |
base.raster |
The raster used in the dbbmm calculations. |
groups |
Character vector indicating the two groups to be displayed. |
timeslot |
The timeslot to be displayed. Only relevant for timeslot dbbmms. |
level |
Value of the use area to plot. Must match one the levels calculated in the overlaps. |
title |
Plot title. By default, the names of the groups being compared are displayed. |
col |
Character vector of three colours to be used in the plot (one for each group and one for the overlap). |
land.col |
Colour of the land masses. Defaults to semi-transparent grey. |
If one of your groups has more than one usage area, or an overlaps contour has more than one area (both potentially caused by having multiple tags/tracks in a single group), ggplot2 will issue the following warning when plotting the map: Warning message: Raster pixels are placed at uneven horizontal intervals and will be shifted. Consider using geom_tile() instead. This is simply because empty cells are cleared out to improve plotting efficiency, which means there will be an empty space between the multiple areas to be drawn. Please be aware that this has no effect on the plot itself.
A plot of the overlapping areas between two groups.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Get overlaps between groups overlap.data <- getOverlaps(areas.group) # Plot overlaps plotOverlaps(overlaps = overlap.data, areas = areas.group, base.raster = water, groups = c("G1", "G2"), level = 0.95)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Run dynamic Brownian Bridge Movement Model (dBBMM) dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56) # Get dBBMM areas at group level areas.group <- getAreas(dbbmm.data, type = "group", breaks = c(0.5, 0.95)) # Get overlaps between groups overlap.data <- getOverlaps(areas.group) # Plot overlaps plotOverlaps(overlaps = overlap.data, areas = areas.group, base.raster = water, groups = c("G1", "G2"), level = 0.95)
If you are reading this it's because RSP failed to detect all of your receivers within the base raster provided, or any of your receiver location was found to be in land. This function allows you to visually identify the station(s) with problem. Please either extend your raster to include all stations or fix receiver locations to be in-water.
plotRaster( input, base.raster, coord.x, coord.y, size = 1, land.col = "#BABCBF80" )plotRaster( input, base.raster, coord.x, coord.y, size = 1, land.col = "#BABCBF80" )
input |
Either a data frame containing the coordinates of the stations or the output of one of
|
base.raster |
Raster object. Imported for example using |
coord.x, coord.y
|
The names of the columns containing the x and y positions of the stations in the spatial object. |
size |
The size of the station dots |
land.col |
Colour of the land masses. Defaults to semi-transparent grey. |
A plot of your base raster extent and the receiver locations.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Plot raster and acoustic stations plotRaster(input.example, base.raster = water, coord.x = "Longitude", coord.y = "Latitude", size = 1)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Plot raster and acoustic stations plotRaster(input.example, base.raster = water, coord.x = "Longitude", coord.y = "Latitude", size = 1)
This function can be used to plot a map of a particular RSP track of interest.
plotTracks( input, base.raster, type = c("both", "points", "lines"), group, tag, track, size = c(0.33, 0.3), alpha = c(0.5, 0.5), land.col = "#BABCBF80" )plotTracks( input, base.raster, type = c("both", "points", "lines"), group, tag, track, size = c(0.33, 0.3), alpha = c(0.5, 0.5), land.col = "#BABCBF80" )
input |
The output of runRSP. |
base.raster |
The raster used to generate the transition layer used in runRSP |
type |
One of "points", "line" or "both". Defaults to "both", i.e. both lines and points are plotted for the generated tracks. |
group |
Choose a single group of fish to plot |
tag |
Choose a single tag to plot |
track |
If a single tag was chosen, you can use 'track' to define a specific track to be plotted. |
size |
The size/width of the points and lines to be plotted. if type = "both", the line size will be the one specified and the point size will be 10% larger than the specified. |
alpha |
One or two transparency values (for points and lines, respectively). For no transparency, alpha = 1. |
land.col |
Colour of the land masses. Defaults to semi-transparent grey. |
A plot showing the RSP track locations.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Plot a specific RSP track plotTracks(rsp.data, base.raster = water, tag = "A69-9001-1111", track = "Track_1")# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude") # Plot a specific RSP track plotTracks(rsp.data, base.raster = water, tag = "A69-9001-1111", track = "Track_1")
Open and sort the detections dataset for applying RSP estimation, using the tagging data to assign species names and indexes for each tracked animal.
prepareDetections(detections, spatial, coord.x, coord.y)prepareDetections(detections, spatial, coord.x, coord.y)
detections |
A list of detections provided by an actel function. |
spatial |
A list of spatial objects in the study area |
coord.x, coord.y
|
The names of the columns containing the x and y positions of the stations in the spatial object. |
A standardised data frame to be used for RSP calculation.
Estimates the RSP for a series of animals tracked with acoustic transmitters. Intermediate
locations between consecutive acoustic detections (either on the same or different receivers) are estimated
according to the defined distance and time.step arguments. The error of estimated locations increase proportionally as
the animal moves away from the first detection, and decreases as it approaches the second detection (argument er.ad). If
the animal is not detected for a long time (default is a daily absence), the detections are broken into a
new track (argument max.time).
runRSP( input, t.layer, coord.x, coord.y, distance = 250, tags = NULL, recaptures = FALSE, time.step = 10, min.time = 10, max.time = 24, er.ad, verbose = FALSE, debug = FALSE )runRSP( input, t.layer, coord.x, coord.y, distance = 250, tags = NULL, recaptures = FALSE, time.step = 10, min.time = 10, max.time = 24, er.ad, verbose = FALSE, debug = FALSE )
input |
The output of one of |
t.layer |
A transition layer. Can be calculated using the function |
coord.x, coord.y
|
The names of the columns containing the x and y positions of the stations in the spatial object. |
distance |
Distance (in metres) by which RSP point should be spaced (between detections at different stations). Defaults to 250 metres. |
tags |
Vector of transmitters for which to calculate RSP. By default all transmitters will be analysed. |
recaptures |
Logical: if TRUE, a recapture.csv dataset containing the recapture locations of tracked animals will be included in the analysis. |
time.step |
Time lapse (in minutes) between RSP points added between detections at the same station. Defaults to 10 minutes. Must not be larger than |
min.time |
Minimum time required between receiver detections (in minutes) for RSP to be calculated. Default to 10 minutes. |
max.time |
Maximum time allowed between receiver detections (in hours) for RSP to be calculated. Defaults to 24 hours. |
er.ad |
Increment rate of the position errors for the estimated locations (in metres). If left unset, defaults to 5% of the |
verbose |
Logical: If TRUE, detailed messages and progression are displayed. Otherwise, a single progress bar is shown. |
debug |
Logical: If TRUE, the function progress is saved to an RData file. |
Returns a list of RSP tracks for each transmitter detected, as well as auxiliary information.
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude")# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Create a transition layer with 8 directions tl <- actel::transitionLayer(x = water, directions = 8) # Import example output from actel::explore() data(input.example) # Run RSP analysis rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude")
Suggest plot dimensions for a given raster
suggestSize(input, max)suggestSize(input, max)
input |
The raster being plotted |
max |
the desired size for the longest edge |
A width/height vector (rounded)
# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Find suggested size to save projected map suggestSize(water, max = 10)# Import river shapefile water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"), size = 0.0001, buffer = 0.05) # Find suggested size to save projected map suggestSize(water, max = 10)