Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns

Aim As global change accelerates, accurate predicti­­­ons of species
distributions and biodiversity patterns are critical to limit biodiversity
loss. Numerous studies have found that coarse-grain species distribution
models (SDMs) perform poorly relative to fine-grain models because they
mismatch environmental information with observations. However, it remains
unclear how grain-size biases vary in intensity across space and time,
possibly generating inaccurate predictions for specific regions, seasons
or species. For example, coarse-grain biases may intensify in patchy,
discontinuous landscapes. Such biases may accumulate to produce highly
misleading estimates of continental and seasonal biodiversity patterns.
Location: United States and Canada Time Period: 2004-2021 Major taxa
studied: Birds (Aves) Methods We fit presence-absence SDMs characterizing
the summer and winter distributions of 572 bird species native to the US
and Canada across five spatial grains from 1 to 50 km, using observations
from the eBird citizen science initiative. We combined these predictions
to generate seasonal biodiversity estimates across the US and Canada,
which we validated using observations from 322 independent sites. Results
We find that in both seasons, 1km models more accurately predicted species
presence, absence, and richness at local sites. Coarse-grain models (even
at 3 km) consistently under-predicted range area, potentially missing
important habitat. This bias intensified during summer (83-86% of species)
when many birds have smaller ‘operational scales’ via localized home
ranges while breeding. Biases were greatest in desert regions with
patchier habitat and for range-restricted and habitat specialist species.
Predictions based on coarse-grain models overpredicted avian diversity in
the west and underpredicted it in the great plains, prairie pothole region
and boreal zones. Main conclusions We demonstrate that coarse-grain models
can bias seasonal and continental estimates of biodiversity patterns
across space and time and that grain-related biases intensify during
summer and in patchier landscapes, especially for range-restricted and
habitat specialist species at risk of population declines.