Continental species distribution and biodiversity predictions depend on modeling grain
Bibliographic record
Abstract
Abstract As global change accelerates, accurate predictions of species distributions and biodiversity patterns are critical to prevent population declines and biodiversity loss. However, at continental and global scales, these predictions are often derived from species distribution models (SDMs) fit at coarse spatial grains uninformed by ecological processes. Coarse-grain models may systematically bias predictions of distributions and biodiversity if they are consistently over- or under-estimating area with suitable habitat, and this bias may intensify in regions with heterogenous landscapes or with poor data coverage. To test this, we fit presence-absence SDMs characterizing both the summer and winter distributions of 572 North American bird species – nearly the entire avian diversity of the US and Canada – across five spatial grains from 1 to 50 km, using observations from the eBird citizen science initiative. We find that across both seasons, models fit at 1 km performed better under cross-validation than those at coarser scales and more accurately predicted species’ presences and absences at local sites. Coarser-grain models, including models fit at 3 km, consistently under-predicted range area relative to 1 km models, suggesting that coarse-grain estimates of distributions could be missing important habitat. This bias intensified during summer (83% of species) when many birds have smaller ‘operational scales’ via localized home ranges and greater habitat specificity while breeding. Biases were greatest in heterogenous desert and scrubland regions and lowest in more homogenous boreal forest and taiga-dominated regions. When aggregating distributions to produce continental biodiversity predictions, coarse-grain models overpredicted diversity in the west and underpredicted it in the great plains, prairie pothole region and boreal/taiga zones. The modern availability of high-performance computing and high-resolution observational and environmental data provides opportunities to improve continental predictions of species distributions and biodiversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".