Hopefully this script can help get you going. I've used UTM projection in this example but it is possible to use lat/longs. I've always found it easiest to convert lat/long to UTM

if your data is in the following format for example,

LATITUDE = "46°44.729'N"

LONGITUDE = "091°48.038'W"

#you can try to convert to decimal degrees by doing this:

minLong=as.numeric(substr(LONGITUDE,5,10))

minLat=as.numeric(substr(LATITUDE,4,9))

LONG=as.numeric(substr(LONGITUDE,1,3))+(minLong/60)

LAT=as.numeric(substr(LATITUDE,1,2))+(minLat/60)

LAT;LONG

# But here's a working example that might help you get going

require(adehabitatHR)

# build a sample data.frame

n=1000

data = as.data.frame(list(Long = c(runif(n,min = 442300, max = 443400),

runif(n,min = 441300, max = 442400)),

Lat =c(runif(n,min = 5510300, max = 5510600),

runif(n,min = 5510000, max = 5510600)),

ID = c(rep("Fish 1",n),rep("Fish 2",n))))

# I think a requirement of the adehabitatHR data structure is

# that the location inputs need to be called x and y

# assuming your data is in UTM to start, you can create a new df

# called "data.UTM" with

data.UTM=as.data.frame(list(x=data$Long,y=data$Lat))

# Creates coordinates into SpatialPoints class

coordinates(data)=data.UTM

rm(data.UTM)

# In the current version of my dataframe, the ID's are located

# in the 3rd column, hence the data[,3] in the kernelUD function

# below. If your IDs are in the 5th column, replace the 3 with a 5

kud=kernelUD(data[,3])

# Create some plots below

## This is a function I made that control the transparency

# of colours. It's really useful for plotting home ranges

# over a lake so that you can see the extent of the home range

# and the size of a lake.

transcols=function(x,alpha){

rgbcols=col2rgb(x)

rgbcol=NULL

trans=c("00","1A","33","4D","66","80","99","B3","CC","E6","FF")

alpha2=alpha*10

newtrans=trans[alpha2]

for(i in 1:length(x)){

rgbcol[i]=rgb(rgbcols[1,i],rgbcols[2,i],rgbcols[3,i],

maxColorValue=255)

}

newcol=paste(rgbcol,newtrans,sep="")

return(newcol)

}

#Calculate the 95% homerange (output is in hectares)

HR = getverticeshr(kud,percent=95,unin="m",unout="ha")

# Check out the sizes of the home range

HR

plot(HR,col=transcols(c("blue","red"),alpha = 0.5))

# Calculate the core area (output is in hectares)

CA = getverticeshr(kud,percent=50,unin="m",unout="ha")

# Check out the sizes of the core area

CA

plot(CA,col=transcols(c("blue","red"),alpha = 0.5),add=T)

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Hi Leon,

The input data in your example is given in  UTM.

I always prepare the input data for each individual as follows:

ID colum (with animal ID) e.g. 1

Y column (UTM coordinate)

X column (UTM coordinate)

date (e.g. day/month/year)

Just send me a message if you want me to send you the R script for KDE and MCP. 

Cheers,

Flora

Hopefully this script can help get you going. I've used UTM projection in this example but it is possible to use lat/longs. I've always found it easiest to convert lat/long to UTM

if your data is in the following format for example,

LATITUDE = "46°44.729'N"

LONGITUDE = "091°48.038'W"

#you can try to convert to decimal degrees by doing this:

minLong=as.numeric(substr(LONGITUDE,5,10))

minLat=as.numeric(substr(LATITUDE,4,9))

LONG=as.numeric(substr(LONGITUDE,1,3))+(minLong/60)

LAT=as.numeric(substr(LATITUDE,1,2))+(minLat/60)

LAT;LONG

# But here's a working example that might help you get going

require(adehabitatHR)

# build a sample data.frame

n=1000

data = as.data.frame(list(Long = c(runif(n,min = 442300, max = 443400),

runif(n,min = 441300, max = 442400)),

Lat =c(runif(n,min = 5510300, max = 5510600),

runif(n,min = 5510000, max = 5510600)),

ID = c(rep("Fish 1",n),rep("Fish 2",n))))

# I think a requirement of the adehabitatHR data structure is

# that the location inputs need to be called x and y

# assuming your data is in UTM to start, you can create a new df

# called "data.UTM" with

data.UTM=as.data.frame(list(x=data$Long,y=data$Lat))

# Creates coordinates into SpatialPoints class

coordinates(data)=data.UTM

rm(data.UTM)

# In the current version of my dataframe, the ID's are located

# in the 3rd column, hence the data[,3] in the kernelUD function

# below. If your IDs are in the 5th column, replace the 3 with a 5

kud=kernelUD(data[,3])

# Create some plots below

## This is a function I made that control the transparency

# of colours. It's really useful for plotting home ranges

# over a lake so that you can see the extent of the home range

# and the size of a lake.

transcols=function(x,alpha){

rgbcols=col2rgb(x)

rgbcol=NULL

trans=c("00","1A","33","4D","66","80","99","B3","CC","E6","FF")

alpha2=alpha*10

newtrans=trans[alpha2]

for(i in 1:length(x)){

rgbcol[i]=rgb(rgbcols[1,i],rgbcols[2,i],rgbcols[3,i],

maxColorValue=255)

}

newcol=paste(rgbcol,newtrans,sep="")

return(newcol)

}

#Calculate the 95% homerange (output is in hectares)

HR = getverticeshr(kud,percent=95,unin="m",unout="ha")

# Check out the sizes of the home range

HR

plot(HR,col=transcols(c("blue","red"),alpha = 0.5))

# Calculate the core area (output is in hectares)

CA = getverticeshr(kud,percent=50,unin="m",unout="ha")

# Check out the sizes of the core area

CA

plot(CA,col=transcols(c("blue","red"),alpha = 0.5),add=T)

Did you find any solution? Would you like to share any script?? I'm trying to work with this package on my data and its getting really difficult! :(

There are many great tutorials on using adehabitatHR, and one of the best is written by Walter David and Justin Fischer. It can be found on the Penn State website;

https://ecosystems.psu.edu/research/labs/walter-lab/manual/home-range-estimation

Good luck!

Dear Andrea del Pilar Tarquino-Carbonell I would not recomend to use the adhabitat package in its old Version, but use the new package like Owen Bidder mentioned. https://cran.r-project.org/web/packages/adehabitatHR/vignettes/adehabitatHR.pdf

Hope this will help, the most important thing is to organise the data in the right structure

Got it! Thanks a lot :)