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2012 Jan 25
0
adehabitatLT -- movement based kernel density
...can be estimated from the mean and the variance of the transit time between receivers. ?If I obtain an estimate of the diffusion coefficient from multiple observations how can I use it to predict the movement based kernel distribution between two receivers ? So rather than estimating the diffusion coeffiient from the data as demonstrated in the documentation for adehabitatLT I would like to use BRB.D() to visualize the kernel distribution between two locations based on known movement parameters, or at least explore the idea. However, I am not sure how to proceed. Below is an example of the type of data...