More Features of GLATOS
Overview
Teaching: 15 min
Exercises: 0 minQuestions
What other features does GLATOS offer?
Objectives
GLATOS has some more advanced analystic tools beyond filtering and creating events.
GLATOS can be used to get the residence index of your animals at all the different stations. GLATOS offers 5 different methods for calculating Residence Index, here we will showcase 2 of those. residence_index requires an events objects to create a residence_index, we will use the one from the last lesson.
# Calc residence index using the Kessel method
rik_data <- glatos::residence_index(events,
calculation_method = 'kessel')
rik_data
# Calc residence index using the time interval method, interval set to 6 hours
rit_data <- glatos::residence_index(events,
calculation_method = 'time_interval',
time_interval_size = "6 hours")
rit_data
Both of these methods are similar and will almost always give different results, you can explore them all to see what method works best for your data.
GLATOS strives to be interoperable with other scientific R packages. Currently, we can crosswalk GLATOS data over to the package VTrack. Here’s an example:
?convert_glatos_to_att
# The receiver metadata for the walleye dataset
rec_file <- system.file("extdata",
"sample_receivers.csv", package = "glatos")
receivers <- read_glatos_receivers(rec_file)
ATTdata <- convert_glatos_to_att(detections_filtered, receivers)
# ATT is split into 3 objects, we can view them like this
ATTdata$Tag.Detections
ATTdata$Tag.Metadata
ATTdata$Station.Information
And then you can use your data with the VTrack package. You can call its abacusPlot function to generate an abacus plot:
# Now that we have an ATT dataframe, we can use it in VTrack functions:
# Abacus plot:
VTrack::abacusPlot(ATTdata)
To use the spacial features of VTrack, we have to give the ATT object a coordinate system to use.
# If you're going to do spatial things in ATT:
library(rgdal)
# Tell the ATT dataframe its coordinates are in decimal lat/lon
proj <- CRS("+init=epsg:4326")
attr(ATTdata, "CRS") <-proj
Here’s an example of the Centers of Activity function from VTrack.
?COA
coa <- VTrack::COA(ATTdata)
coa
Let’s take a look at a plot of the COAs from VTrack. We’ll use animal 153 for this.
# Plot a COA
coa153 <- coa %>% filter(Tag.ID == 153)
data(greatLakesPoly) # Get spacial object from glatos package
# plot the object and zoom in to lake Huron. Set colour of water to blue. Add labels to the axises
plot(greatLakesPoly, xlim=c(-85, -82), ylim=c(43, 46), col='blue', xlab="Longitude", ylab="Latitude")
# Create a palette
color <- c(colorRampPalette(c('pink', 'red'))(max(coa153$Number.of.Detections)))
#add the points
points(coa153$Longitude.coa, coa153$Latitude.coa, pch=19, col=color[coa153$Number.of.Detections],
cex=log(coa153$Number.of.Stations) + 0.5) # cex is for point size. natural log is for scaling purposes
# add axises and title
axis(1)
axis(2)
title("Centers of Activities for 153")
Here’s an example of a VTrack function for getting metrics of dispersal.
# Dispersal information
# ?dispersalSummary
dispSum<-dispersalSummary(ATTdata)
View(dispSum)
# Get only the detections when the animal just arrives at a station
dispSum %>% filter(Consecutive.Dispersal > 0) %>% View
VTrack has some more analysis functions like creating activity space models.
GLATOS also includes tools for planning receiver arrays, simulating fish moving in an array, and some nice visualizations (which we will cover in the next episode).
Key Points