I am currently investigating how to best discriminate between animal movement tracks based on space-use patterns and characteristics of the moves themselves. Ideally I would like to use several complementary (i.e. non-correlated) statistics to be able to come up with statements like: "these two tracks resembled each other, as they covered areas of similar size, but one of the tracks was characterised by a larger number of highly directed and highly area-restricted moves than the other". The first statement could be measured using home range statistics, but I have not found a good way to measure distribution of moves. And perhaps there are other independent characteristics of animal moves that I haven't thought about. Suggestions are warmly welcomed!
Have you tried the Lotka-Volterra equation? Using the predator-prey pattern, i think will help in your investigation.
Pattern formation of a predator–prey model:
Article Pattern formation of a predator–prey model
Complex dynamics of Holling type II Lotka–Volterra predator–prey system with impulsive perturbations on the predator:
Article Complex dynamics of Holling II Lotka-Volterra predator-prey ...
Thanks a lot Roger de Oliveira , that sounds like a good suggestion.
I presume that you have lists of locations (e.g., Latitude & Longitude from GPS collars) for different individuals. There are several R packages that deal with splitting animal movements to segments and calculating various parameters that characterize the moves and the habitat use. See if any of these does what you are interested in: https://arxiv.org/pdf/1805.03227.pdf
https://cran.r-project.org/web/packages/moveHMM/vignettes/moveHMM-guide.pdf
https://cran.r-project.org/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf
Dear Jacob, make sure to have look at the recent papers by Justin Calabrese and coworkers. They use time-continuous, not time discrete ones, to fit tracking data, which allows them to better take autocorrelation into account. They also developed a powerful R script to actually use this new approach. I am not an expert on all this, but it seems to me that this is the best you can find in movement ecology. Cheers, Volker
I strongly agree with Volker’s suggestion to first consider Justin Calabrese’s and Chris Fleming’s recent work on how best to characterise space-use patterns from the auto-correlated location data that comprise tracking datasets. These guys provide some methodical advances that are well-based on theory. For comparing trajectories borrow some great ideas from movement geographers, especially Patrick Laube at the University of Zurich. He and his colleagues have done a lot of excellent work that hasn’t yet completely ’migrated’ to study of movement ecology.
Have you tried the DISTANCE OR CALHOME SOFTWARES.
Try to get one of them and use it in your data processing and mapping.
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