Seasonality drives global‐scale diversity patterns in waterfowl (<scp>A</scp>nseriformes) via temporal niche exploitation
Bibliographic record
Abstract
Abstract Aim Birds (class A ves) overall follow the expected latitudinal gradient in species richness. However, there is a poorly understood secondary peak in bird richness at about 45° N latitude, at least in N orth A merica. Waterfowl ( A nseriformes) species richness conforms to this secondary peak, yet this group is commonly excluded from bird species richness studies and consequently the drivers of waterfowl richness are poorly understood. Here, we explore the drivers of waterfowl richness on a global scale, emphasizing the secondary peak. Location Global. Methods We mapped total waterfowl species richness for the breeding and non‐breeding seasons. Considering a wide range of potential environmental drivers (climate, productivity and habitat), we initially ran univariate ordinary least squares models for the candidate set of predictors. We then used regression trees ( RT ) to multivariately assess their importance for the richness pattern while allowing for nonlinear and non‐stationary processes. Results We found seasonal variability in plant productivity (measured by the normalized difference vegetation index, NDVI ) to be the most important predictor of breeding season richness in both univariate regressions and multivariate RT models. For the non‐breeding season, winter actual evapotranspiration ( AET ) was the best predictor. In the regions of highest richness, including the secondary mid‐latitude peak, an overwhelming portion of the species were migratory species. Main conclusions Predictors commonly used to explain the large‐scale richness patterns of birds, such as annual AET or annual NDVI explained little of the variation in richness in A nseriformes. Instead, measures reflecting intra‐annual variability and seasonal productivity in the relevant season were the best predictors. This finding, combined with the high proportion of migrants in the richness peaks, strongly suggests that the patterns of richness in A nseriformes reflect the group's exploitation of seasonal environmental variability via short‐ and long‐distance migration, i.e. temporal niche exploitation at an annual and global scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".