Chris, I’m thinking to be most effective for use in informing Computer Vision rankings, it should be proximity to a combination of your #1 and #4 – where it is regular and expected including invaded areas.
The question then becomes, how do we define that range for the vast number of taxa in iNaturalist that have enough high-grade observations for Computer Vision to include (or that soon will)? I think the only practical solution to that is going to have to be driven by the geographic data already in iNaturalist for those same high-grade observations – even though that leads to some circularity. That doesn’t bother me too much because it is only a tool to suggest IDs, and should still deal with things way beyond previously reported ranges pretty well.
In cases where atlases or ranges already exist for taxa in iNaturalist, those should certainly be considered as part of the “geographic data already in iNaturalist” and probably be given higher weight it determining “range proximity” to help with the circularity issue above – although in the case of atlases in particular, they can potentially be either incomplete or overly broad in defining a taxon range (or both!).
My final thought has to do with all the other potential taxa that don’t yet have enough high-grade observations for Computer Vision to grab. I would like to see any implementation of geography-informed Computer Vision include, via a link next to each listed suggestion, any other taxa in the same genus (family?) with too few observations to be included in Computer Vision, but with range proximities at least as close as the suggestion being shown. This would (1) remind the user that Computer Vision is not an infallible or complete list possibilities, (2) give the user additional tools to actually find the right ID.
Sorry if this last is veering too far into the weeds of implementation, but I think how we want to use taxon ranges needs to guide how we want to define taxon ranges in iNaturalist.