[R-sig-eco] adeHabitatLT trajectory partitioning

[Dylan Irion]

Hi List,

I am having a little trouble understanding the biological relevance of 
the parameters used to define the models in the vignette examples for 
the function modpartltraj.

In the gus example 10 models are generated with a mean dist from 0 to 
130km. The max step length for gus is ~117km /day so I assumed the 130km 
is a rounded up approximation of this, to cover the whole spectrum of 
observed step lengths. The parameter that is confusing me is the 5km 
standard deviation of the models. The vignette says this was chosen 
after a visual exploration of the distribution of distances for gus, but 
I don't see it. Does this value perhaps come from the standard error of 
the distances, ~3.6km?

Directly from the vignette:
> Now, let us suppose that the distances between successive relocations 
> have been generated by a normal distribution, with different means 
> corresponding to different behaviours. Let us built 10 models 
> corresponding to 10 values of the mean distance ranging from 0 to 130 
> km/day: > (tested.means <- round(seq(0, 130000, length = 10), 0)) [1] 
> 0 14444 28889 43333 57778 72222 86667 101111 115556 130000 Based on 
> the visual exploration of the distribution of distance, we set the 
> standard deviation of the distribution to 5 km. We can now define 10 
> models characterized by 10 different values of means and with a 
> standard deviation of 5 km: > (limod <- as.list(paste("dnorm(dist, 
> mean =", + tested.means, + ",sd = 5000)")))


Is there anyone familiar with these functions that may be able to shed 
some light for me?

Cheers,
Dylan Irion

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