[AniMov] kernelUD error

Hazel, Julia julia.hazel at jcu.edu.au
Sun Mar 6 08:54:56 CET 2011

Dear Clément and Paolo,

Thank you very much for quick and helpful replies, giving me plenty to think about and work on.

At this stage UDs serve as a helpful tool for exploring my data. In this I can simply assign NA for rare instances of 'animal that did not move' during an arbitrary short period.

In reality the animal was not stationary but during some periods it moved less than my tracking system could record. From a biological perspective it would be nice to assign a 'pseudo-kernel' or maybe just guesstimate a small radius... cannot foresee a rigorous way to estimate this. Also recognise that my 'locations' derived from a passive acoustic array will not support strict interpretation of UD probabilities... but getting beyond the scope of this email!

Many thanks again,

From: Clément Calenge [clement.calenge at oncfs.gouv.fr]
Sent: Saturday, 5 March 2011 20:38
To: Animal Movement
Cc: Hazel, Julia
Subject: Re: [AniMov] kernelUD error

Dear Julia,

> Many thanks to those who create and maintain AniMov software!
> After a steep learing curve with adehabitat I'm finding it very useful.
> However with kernels for short periods, I sometimes get an error.
> It seems this is caused by a set of locations that don't change.
> ### adehabitatHR_0.1  R 2.12.1 Windows XP
> #demo: error with animal at same location during period of interest
> X<- rep(699889, 10)
> Y<- rep(3161559, 10)
> Name<- rep("Brock", 10)
> test<- data.frame (Name, X, Y)
> coordinates(test)<- c("X", "Y")
> ud<- kernelUD(test)
> #Error in points2grid(points, tolerance, round, fuzz.tol) :
> #  cannot determine cell size from constant coordinate 1

Yes, this error is expected: the UD is a continuous probability density
function estimated for each pixel of a grid. The function uses the X and
Y range of the relocations to determine the extent of the grid. When
this range is equal to zero, the function cannot identify this range.
 From a *purely technical* point of view, this can be fixed by first
building a grid with the desired extent, and then use it as the
parameter grid in the function kernelUD. However, you will have another
problem when the function will have to calculate the smoothing
parameter: whatever the method you choose, it depends on the variability
of the X and Y coordinates. So that the only *technical* way to have a
result from kernelUD in such a case is also to specify the smoothing
parameter "manually". For example:

## Prepares the data
X<- rep(699889, 10)
Y<- rep(3161559, 10)
Name<- rep("Brock", 10)
test<- data.frame (Name, X, Y)
coordinates(test)<- c("X", "Y")

## Prepares the grid
fakeda <- data.frame(X = c(699889-200, 699889+200), Y=c(3161559-200,
coordinates(fakeda) <- c("X","Y")
grid <- ascgen(fakeda, nrcol=100)

## estimate the UD with h=100
kud <- kernelUD(test, h=100, grid=grid)

Now, you have to think about the scientific issue raised by this
problem: for example, the 95% home range is defined as the minimum area
where the probability to find the animal is equal to 95%. In your case,
the home range is not an area, but a point. What is the meaning of the
home range in this context? I do not think that any statistical method
will succeed to estimate the home-range of an animal that does not move.


> #demo: no error after small change in one location
> XX<- c(rep(699889, 9), 699890)
> YY<- c(rep(3161559, 9), 3161560)
> Name<- rep("Brock", 10)
> test2<- data.frame (Name, XX, YY)
> coordinates(test2)<- c("XX", "YY")
> ud2<- kernelUD(test2)
> Apologies in advance if this problem arises from newbie misunderstanding. However unchanging locations (over a limited time) are a valid occurrence in my data so I will be very grateful for the solution.
> Thanks and best wishes,
> Julia
> Julia Hazel Ph.D.
> Adjunct Research Fellow
> School of Earth and Environmental Sciences
> James Cook University, Townsville
> QLD 4811
> Phone: 0407 431 382 (international +61 407 431 382)
> Email: Julia.Hazel at jcu.edu.au
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