classify_flap: Classify flapping flight

In KiranLDA/PAMLr: Suite Of Functions For Manipulating Pressure, Activity, Magnetism, Temperature And Light Data In R

Classify flapping flight

Description

This function uses activity data to classify migratory flapping flight.

Usage

classify_flap(dta, period = 12, to_plot = TRUE, method = "kmeans", tz = "UTC")

Arguments

dta	data stored as a list see str(data(PAM_data)) for example format
period	number of timepoints after which behaviour is considered migratory e.g. for hoopoes, 3x5min = 15 minutes of intense activity is considered flight
to_plot	can be true or false. If true then threshold is plotted according to plot_histogram()
method	for the time being only supports "kmeans", but will later also include maybe
tz	timezone, default is "UTC"

Value

timetable: a timetable for when the species was migrating or not,

classification: a classification timeseries where datetime corresponds to activity, and

no_movement: the value in classification which corresponds to no movement

low_movement: the value in classification which corresponds to low activity

high_movement: the value in classification which corresponds to high activity

migration: the value in classification which corresponds to migratory flapping flight

threshold: the threshold between low and high activity

References

Bäckman, J., Andersson, A., Alerstam, T., Pedersen, L., Sjöberg, S., Thorup, K. and Tøttrup, A.P., 2017. Activity and migratory flights of individual free‐flying songbirds throughout the annual cycle: method and first case study. Journal of avian biology, 48(2), pp.309-319.

Liechti, F., Bauer, S., Dhanjal-Adams, K.L., Emmenegger, T., Zehtindjiev, P. and Hahn, S., 2018. Miniaturized multi-sensor loggers provide new insight into year-round flight behaviour of small trans-Sahara avian migrants. Movement ecology, 6(1), p.19.

Bruderer, B., Peter, D., Boldt, A. and Liechti, F., 2010. Wing‐beat characteristics of birds recorded with tracking radar and cine camera. Ibis, 152(2), pp.272-291.

Examples

#specify the data location
data(hoopoe)
# make sure the cropping period is in the correct date format
start = as.POSIXct("2016-07-01","%Y-%m-%d", tz="UTC")
end = as.POSIXct("2017-06-01","%Y-%m-%d", tz="UTC")

# Crop the data
PAM_data= create_crop(hoopoe,start,end)
str(PAM_data)

behaviour = classify_flap(dta = PAM_data$acceleration, period = 12)

col=c("brown","cyan4","black","gold")
plot(PAM_data$acceleration$date[6000:8000],PAM_data$acceleration$act[6000:8000],
     col=col[behaviour$classification][6000:8000],
     type="o", pch=20,
     xlab="Date",
     ylab="Activity")

behaviour$timetable

KiranLDA/PAMLr documentation built on March 6, 2023, 1:40 p.m.