@cthawley I agree that the model makes assumptions about species absences that might not be correct, there may be biases that aren't being fully accounted for, and uncertainty/error in those predictions is much large in places that are not well sampled (both errors of commission and errors of omission) - and explicitly modeling probability of being observed separate from probability of occurrence is probably a good future direction to better deal with these biases.

@pfau_tarleton you can read more about the methodology here and here. If you pull out the deep learning location encoding which essentially allows each species to draw on information from all the other species the model collapses to Logistic Regression niche model (LR in Table 1 in the first paper). But a huge part of the strength of this approach as opposed to a single species niche model is that the species learns from all other 80k species being modeled (much like the Computer Vision Model) so the model gets a good sense for co-occurrence, biogeography and the kind of things species distributions tend to do without having to rely so much on environmental covariates alone as a crutch as traditional niche models do. This is why the predictions are pretty good using just elevation as a covariate and not including other typical covariates like precipitation etc. We tested adding those covariates and didn't get significant improvement but made the model more complicated.

I agree this is just a baby step though, lots of avenues for improvement, and different approaches might be needed to push these into other applications and scales. We're focused on improving computer vision suggestions at the moment even though I'm also excited by some of these future directions.

Publicado el 23 de septiembre de 2023 20:33 por optilete optilete


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