I suspect this question will open up a can of worms, but here goes:

I seem to have a dilemma with modelling my species occurrence data: the records I have are only where a species was caught (and how much was caught), not where they fished for a species and didn’t find any (i.e., what I have is presence-only data). At least with the tree model, I can’t generate a prediction output unless I exclude every benthic category from the predictor layer that didn’t have a corresponding catch record (otherwise the tool complains that there are levels present in the data that were not used to fit the model). It would seem the only way around this would be to manually exclude every benthic class from the raster that isn’t overlapped by the catch records, which seems…wrong.

Are any of the model techniques in MGET appropriate for modelling presence-only data?

I understand that there’s a huge body of literature (and caveats) out there about modelling presence-only data, but I’m hoping to confine this question just to the specific modelling tools found within MGET.

Stephen Ban, Ph.D., RPBio.

National Ocean Spatial Analysis Manager

Canadian Parks and Wilderness Society

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