How an undocumented sort order in a public biodiversity API put 97% of my background points in January or February—and what I changed so that kind of bias gets caught before a model ever sees it.

Exploratory analysis is supposed to be the calm part of a project.

For QueleaGuard, it was where I discovered that 122 of my 126 background records—96.8%—fell in January or February. Among the real red-billed quelea observations, only 20.3% did.

Nothing had crashed. Every validation check up to that point had passed.

If I had gone straight to training, the model might have produced impressive-looking results for entirely the wrong reason. That is exactly what made the problem dangerous.

Why the Background Points Exist at All

QueleaGuard investigates where red-billed quelea occur around Ahero Rice Irrigation Scheme in Kenya.

The occurrence data comes from GBIF, which aggregates biodiversity observations, including citizen-science sightings from platforms such as eBird.…