Dear list, I am intersted in estimating movement based kernel densities for fish that were relocated at fixed receivers positioned along the coast. These data tend to display both a drift movment between receivers and a random movement component that 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 I am working with. I would be grateful for some thoughts or suggstions. Regards, Juliane X Y ID VR2 Fish_ID Date VR2 Number Habitat 2 345481 3020908 21 BPN 1646 2006-08-18 08:51:27 31 pass 3 345481 3020908 22 BPN 1646 2006-08-18 08:52:05 31 pass 4 345481 3020908 24 BPN 1646 2006-08-18 08:53:20 31 pass 5 345481 3020908 43 BPN 1646 2006-08-18 09:09:05 31 pass 6 345580 3020891 206 BPS 1646 2006-08-18 09:53:20 30 pass 7 345580 3020891 215 BPS 1646 2006-08-18 09:59:07 30 pass
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