We keep on getting told that habitat photos are bad for the CV. I really doubt that and would like to see some statistics to back that up. Two main reasons:
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They shouldn’t matter to the CV: One or two photos of some trees amidst a hundred or a thousand macro photos of a bug or bird should be intergrated out by any good statistical model. They’d be treated as outliers and will just add a tiny bit of background noise to the machine learning’s pattern recognition algorithms.
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They might even help: We forget that the CV is not trying to identify species the way that we would, by honing in on diagnostic characters of the critter. It’s assigning labels to photos with the right assemblage of pixels that typically get identified as a certain taxon. So if a certain habitat shot is typical for people to actually confidently identify a certain bird (i.e., which pictures become RG), then that habitat might actually help the algorithm get more confidence in the probability peaks it’s assigning to a certain taxon. For example, the oak leaf itself might become part of the signature characteristic of a particlar oak leaf miner insect in the iNat CV model. Or if a certain robber fly tends to eat a certain prey insect, the pixels of the prey might become part of the statistically typical iNat photo for that predator and therefore part of its key signature in the CV model. That’s ok. If the leaf or prey is not actually diagnostic, it’ll eventually get swamped out by other random sightings and we’re back to point #1.
So, I don’t see sufficient cause to actively discourage people from posting habitat photos, which are admittedly useful, even if it’s not preferred.