[R] mcp.area vs. kernel.area Output... ?

[T.D.Rudolph]

I am trying to estimate home range size using 2 different methods in the
adehabitat package, but I am slightly confounded by the results.

## Attached is an R object file containing animal relocations with a field
for "id", and "x" & "y" coordinates ## (in metres)
load("temp")
require(adehabitat)

## This produces the 95% Minimum Convex Polygon area for animal 2002007
mcp.area(xy=temp[,2:3], id=temp$id, percent=95, unin="m", unout="km2",
plotit=FALSE)[1] 

## This produces an estimation of area under the 95% Fixed Kernel Density
kernel.area(temp[,2:3], temp$id, h="href", levels=95, unin="m",
unout="km2")[1]

Now my question: Why are the two responses not more similar?  

Since the kernel estimation is more conservative, why is the area obtained
by this method more than two times bigger than what is obtained using the
Minimum Convex Polygon method?

Tyler

http://old.nabble.com/file/p26531917/temp temp 
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