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diveMove
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diveMove
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#using diveMove to identify diving behaviour
#including number of dives, depth duration
#used for each seal to identify foragers (dived deeper than 50m) and non foragers (did not)
#import dive data for each seal
#example seal
####1813####
rm(list = ls())
#set WD for dive data
setwd("D:/WEDE ANALYSIS/diveMove/depth data")
#Back data ORI1300-3MPD3GT - depth every second
x <- read.table("D:/WEDE ANALYSIS/diveMove/depth data/WS1813_DTS_Back.txt", sep = ",", dec = ".", skip = 8)
names(x) <- c("Depth", "Temp", "Speed", "X")
x_1 <- subset(x, select = c("Depth", "Temp"))
BackStLocal<-as.POSIXct("21/11/2018 09:39:37", format="%d/%m/%Y %H:%M:%S", tz= "Antarctica/McMurdo") #now we need to link time to the depth
BackEndLocal<-BackStLocal+(nrow(x_1)-1) #calculate end time based on the number of rows
DBackSeq<-as.data.frame(seq(BackStLocal,BackEndLocal, by=1))
dive_1813_back <- cbind(x_1,DBackSeq)
names(dive_1813_back)<-c("Depth","Temp","Time")
#Jaw data - depth every second
setwd("D:/WEDE ANALYSIS/ToRose/ToRose")
Jaw<-read.table("WS1813_DT_Jaw.txt",sep = ",",dec = ".",skip=7) #this skips over the 7 lines of header to bring in the data
JawStLocal<-as.POSIXct("20/11/2018 22:10:33", format="%d/%m/%Y %H:%M:%S", tz= "Antarctica/McMurdo")
#calcualte end time based on the number of rows
JawEndLocal<-JawStLocal+(nrow(Jaw)-1)
DJawSeq<-as.data.frame(seq(JawStLocal,JawEndLocal, by=1))
DTJaw_1813 <-cbind(Jaw,DJawSeq)
names(DTJaw_1813)<-c("Depth","Temp", "X", "Time")
DTJaw_1813$X <- NULL
####adding ZOC'd depths####
#take only the tag used for analysis - jaw or back, dont need both
ZOC_jaw1813 <- read.table("D:/WEDE ANALYSIS/ToRose/ToRose/ZOCd/Djaw1813.txt",
sep = ",",dec = ".", skip = 1)
#neg1 <- as.data.frame(ZOC_jaw1813[,1]*-1)
names(ZOC_jaw1813) <- c("Depth_ZOC")
####cbind those to dback or djaw####
Jaw_1813 <- cbind(DTJaw_1813, ZOC_jaw1813)
summary(Jaw_1813) #374.9
write.csv(Jaw_1813, "D:/WEDE ANALYSIS/diveMove/depth data/formatted/ZOCd/1813_jaw.csv", row.names = TRUE)
library(tagtools)
plott(X=list(depth = Jaw_1813$Depth_ZOC),fsx= 100)
#dive analysis for diveMove
#reading in data from csv
TDR_1813 <- read.csv("D:/WEDE ANALYSIS/diveMove/depth data/formatted/ZOCd/1813_jaw.csv")
#checking variable format
class(TDR_1813$Depth)
class(TDR_1813$Temp)
class(TDR_1813$Time) #character - wrong, need to fix
TDR_1813$Time_v2 <-as.POSIXct(TDR_1813$Time, tz = "Antarctica/McMurdo",
format = c("%Y-%m-%d %H:%M:%S"))
class(TDR_1813$Time_v2) #correct
head(TDR_1813)
TDR_1813$X <- NULL
TDR_1813$Time <- NULL
library(diveMove)
#Now create the TDR class for diveMove, dtime sets the sampling interval at 1 seconds
#aka one pressure reading per 1 seconds.
TDR_1813 <- createTDR(TDR_1813$Time_v2,
TDR_1813$Depth_ZOC,
speed = FALSE,
dtime = 1,
file = "TDR_1813")
#need to ZOC data if there is drift in the pressure sensor data
#(zoc.method) zoc your data and choose the method,
#(dive.thr) set your dive threshold (meters) the point a dive should be considered a dive ( 10 meters),
#(wet.thr) set the duration (in seconds) at which the subject must be below the dive.thr ( 30 seconds).
dcalib_TDR <- calibrateDepth(TDR_1813,
dive.thr = 10, #metres, based on R.Beltrane supp data 2021
dry.thr = 60, #seconds, less than this is trivial wet
wet.cond = TRUE, #logical, to assess wet vs dry
wet.thr = 30, #seconds, based on R.Beltrane supp data 2021
dive.model = c("unimodal"),
zoc.method = "offset",
offset = 0, #data already ZOCd using external program (igorpro8 ethographer)
k = c(3, 5760),
probs = c(0.5, 0.02),
descent.crit.q=0.01,
ascent.crit.q=0,
knot.factor=20)
#Record is truncated at beginning and end
#1 phase detected
#60 dives detected
dives_TDR <- diveStats(dcalib_TDR)
att_TDR <- timeBudget(dcalib_TDR, FALSE)
stamps_TDR <- stampDive(dcalib_TDR)
tdrX <- diveStats(dcalib_TDR)
stamps <- stampDive(dcalib_TDR, ignoreZ=TRUE)
tdrX.tab <- data.frame(stamps, tdrX)
summary(tdrX.tab)
write.csv(tdrX.tab,"E:/WEDE ANALYSIS/diveMove/depth data/TDRs/1813_TDRsum.csv")
sumtab_TDR <- data.frame(stamps_TDR, dives_TDR)
summary(sumtab_TDR) #summary data for all dives deeper than 10m
#finding deep vs shallow dives
shallow <- droplevels(subset(sumtab_TDR, sumtab_TDR$maxdep <= 50))
#all dives with max depth shallower and equal to 50m
deep <- droplevels(subset(sumtab_TDR, sumtab_TDR$maxdep > 50))
#dives >50m: 22
#dives <50m: 38
#dive duration for seal 1813
#divetim max: 1758 mins
#min: 2 min
#mean: 308.9
sd(tdrX.tab$divetim)
#SD: 543.7096
#max maxdep: 373.9
#mean maxdep: 80.54
sd(tdrX.tab$maxdep)
#sd: 126.94
#bottdep.mean min: 10.10 - shallowest dive
#bottdep.mean mean: 78.41 - mean depth
#bottdep.mean max: 369.40 - max dive
##Plotting dcalib_TDR object. How diveMove characterizes data
plotTDR(dcalib_TDR, surface=TRUE)
#Plot specific dives range based on the dive number.
plotTDR(dcalib_TDR, diveNo = 1:20, surface = FALSE)
#extractDive(dcalib_TDR, diveNo=1:1717)
plotTDR(dcalib_TDR, diveNo=1:10, what="phases")
#Prints out the phase information
getDPhaseLab(dcalib_TDR, 1:20)
getTDR(dcalib_TDR)
getGAct(dcalib_TDR, "phase.id")
getDAct(dcalib_TDR)
plotDiveModel(dcalib_TDR, diveNo = 7)
####BOUTS ID ####
## Too long for checks
## Using the Example from '?diveStats':
utils::example("diveStats", package="diveMove",
ask=FALSE, echo=FALSE, run.donttest=TRUE)
## Postdive durations
postdives <- tdrX.tab$postdive.dur[tdrX.tab$phase.no == 2]
postdives.diff <- abs(diff(postdives))
## Remove isolated dives
postdives.diff <- postdives.diff[postdives.diff < 2000]
## Construct histogram
lnfreq <- boutfreqs(postdives.diff, bw=0.1, plot=FALSE)
startval <- boutinit(lnfreq, 50)
## Drop names by wrapping around as.vector()
startval.l <- list(a1=as.vector(startval[[1]]["a"]),
lambda1=as.vector(startval[[1]]["lambda"]),
a2=as.vector(startval[[2]]["a"]),
lambda2=as.vector(startval[[2]]["lambda"]))
## Fit the 2 process model
bout.fit <- bouts2.nls(lnfreq, start=startval.l, maxiter=500)
summary(bout.fit)
plotBouts(bout.fit)
## Estimated BEC
bec2(bout.fit)
#ok now using my data
View(tdrX.tab) #1813 dive data
## Postdive durations
postdives <- tdrX.tab$postdive.dur[tdrX.tab$phase.no == 1] #changed to 1 as my tdr thing has no phase 2
postdives.diff <- abs(diff(postdives))
## Remove isolated dives
postdives.diff <- postdives.diff[postdives.diff < 2000]
## Construct histogram
lnfreq <- boutfreqs(postdives.diff, bw=0.1, plot=FALSE)
startval <- boutinit(lnfreq, 50)
## Drop names by wrapping around as.vector()
startval.l <- list(a1=as.vector(startval[[1]]["a"]),
lambda1=as.vector(startval[[1]]["lambda"]),
a2=as.vector(startval[[2]]["a"]),
lambda2=as.vector(startval[[2]]["lambda"]))
## Fit the 2 process model
bout.fit <- bouts2.nls(lnfreq, start=startval.l, maxiter=500)
summary(bout.fit)
plotBouts(bout.fit)
## Estimated BEC
bec2(bout.fit)
#22.28
?bec2(bout.fit)
?`bout-methods`
#another way
## Too long for checks
## Using the Example from '?diveStats':
utils::example("diveStats", package="diveMove",
ask=FALSE, echo=FALSE, run.donttest=TRUE)
postdives <- tdrX.tab$postdive.dur[tdrX.tab$phase.no == 1]
postdives.diff <- abs(diff(postdives))
## Remove isolated dives
postdives.diff <- postdives.diff[postdives.diff < 2000]
lnfreq <- boutfreqs(postdives.diff, bw=0.1, plot=FALSE)
startval <- boutinit(lnfreq, 50)
p <- startval[[1]]["a"] / (startval[[1]]["a"] + startval[[2]]["a"])
## Fit the reparameterized (transformed parameters) model
## Drop names by wrapping around as.vector()
init.parms <- list(p=as.vector(logit(p)),
lambda1=as.vector(log(startval[[1]]["lambda"])),
lambda2=as.vector(log(startval[[2]]["lambda"])))
bout.fit1 <- bouts.mle(bouts2.LL, start=init.parms, x=postdives.diff,
method="L-BFGS-B", lower=c(-2, -5, -10))
coefs <- as.vector(coef(bout.fit1))
## Un-transform and fit the original parameterization
init.parms <- list(p=unLogit(coefs[1]), lambda1=exp(coefs[2]),
lambda2=exp(coefs[3]))
bout.fit2 <- bouts.mle(bouts2.ll, x=postdives.diff, start=init.parms,
method="L-BFGS-B", lower=rep(1e-08, 3),
control=list(parscale=c(1, 0.1, 0.01)))
plotBouts(bout.fit2, postdives.diff)
## Plot cumulative frequency distribution
plotBouts2.cdf(bout.fit2, postdives.diff)
## Estimated BEC
bec <- bec2(bout.fit2)
## Label bouts
labelBouts(postdives, rep(bec, length(postdives)),
bec.method="seq.diff")