Biogeography of boreal passerine range dynamics in western North America: past, present, and future
Bibliographic record
Abstract
Many of the Neotropical migrant bird species that breed throughout the Canadian boreal region are not found in the Alaskan boreal region, separated by the northwestern cordilleran mountains, despite the presence of climatically suitable habitat. We asked whether biological or climatic factors constrain certain species from crossing this geographic barrier. Analyzing a comprehensive dataset for 80 boreal passerine species, we used phylogenetic logistic regression to evaluate the relative importance of physical, migratory and competition metrics versus current and paleoclimatic suitability factors. Controlling for current climatic suitability within boreal Alaska, we found that species with the greatest climatic suitability across the northwestern cordillera, presently and also during the mid‐Holocene period, were most likely to be regular breeders in the Alaskan boreal region. Migratory strategy also played a role, but could not be disentangled from its strong phylogenetic basis. Our analysis suggests that the perceived barrier of the northwestern cordillera may be easily weakened as climate change improves conditions there for many forest species. The weakening of this barrier may lead to relatively rapid range expansions and the reshuffling of species communities. Species’ realized distributional shifts will be a function of the interplay between a changing climate and static topographic features.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".