I wonder if a potential solution could be to give the geomodel a negative bias when a observation misidentified and then corrected.
For example:
A Pacific species gets misidentified as an Atlantic one, leading to the geomodel thinking the Atlantic one might be found in the Pacific.
When the wrong ID is corrected, the geomodel wouldn’t just forget about that area but also leave behind a negativ imprint so that when someone add another false identification, the area doesn’t immediately gets added back to the model.
In general I think it would be good to have the model be rather conservative and not extend the range because of one observation (honestly not sure if it does)
But on the other hand something it might be interesting taking into account observations that get confirmed not just once but multiple times. This could be useful for species extending their range (due to shifting climate or human transportation).
So if a species gets observed in an area previously not covered by the model but gets multiple confirming ID, it could still be added to the model.