Not that I disagree it could be a sequoia, bristlecone or something else.
I already knew Pando wouldn’t be it when I looked it up, but your bounding box radius was too small. They say aspen colonies, such as Pando can grow up to 8km and not necessarily circular. You had it at 1km diameter.
When looking around the map, most areas of dense aspen observations are in cities and most likely those are not large colonies but individual ones planted? I don’t know.
Another example of “lost bird that gets a lot of photos”, there appears to be a brown booby (Sula leucogaster) that got blown off course and now appears to be stuck in the Nimisila Reservoir near Canton, Ohio. There are 37 records of S. leucogaster from this reservoir (and one from Canton proper that is likely the same individual), and comments from the iNatters suggest it’s the same bird that has been stuck there for over a year. Additionally, the coloration of the bird in these observations is identical, and there is only ever one individual present in each photo.
There are a number of observations of S. leucogaster from this reservoir in eBird as well, which are also probably the same bird.
This actually raises questions about how reliable the data from iNat is. If I were conducting a study using the data from iNat and didn’t know the context of this booby, I might incorrectly assume that there is some isolated breeding colony of S. leucogaster in Ohio given there are 38 different observations of this species from the area, when in actuality it’s the same bird sighted 38 different times over a long time span.
iNat is said to not be used as popalution data resource, so it shows how people are interacting with species, separately from each other. If you really need to see if it’s the same specimen or not you can check photos, and dates of them are not that far apart for a real colony.
Perhaps it’s not what researchers are “supposed” to do, but one thing I’ve learned in science is that researchers will often use data in ways they aren’t supposed to in order to make claims. Sometimes these are novel and exciting (and valid) new ways to look at data, other times they’re blatant misrepresentations of the data collected used to support spurious conclusions.
The other, other thing I’ve noticed is that researchers rarely fact-check data before they do an analysis. In my area of biology we have several big databases and researchers in this field have been caught for years doing analyses without first double-checking the data, resulting in things like taxa being recorded where they never occurred, or species lists being inflated by using record with junior synonyms and invalid names.