Nonbreeding distributions of four declining Nearctic-Neotropical migrants are predicted to contract under future climate and socioeconomic scenarios
Bibliographic record
Abstract
Climate and land use/land cover change are expected to influence the stationary nonbreeding distributions of 4 Nearctic–Neotropical migrant bird species experiencing population declines: Cardellina canadensis (Canada Warbler), Setophaga cerulea (Cerulean Warbler), Vermivora chrysoptera (Golden-winged Warbler), and Hylocichla mustelina (Wood Thrush). Understanding how and where these species’ distributions shift in response to environmental drivers is critical to inform conservation planning in the Neotropics. For each species, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data were used in an ensemble modeling approach with covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) to predict distributions in response to varying climatic and land use/land cover conditions. Our findings suggest that distribution contraction, upslope elevational shifts in suitable conditions, and limited shifts in latitude and longitude will occur in 3 of 4 species. Cardellina canadensis and S. cerulea are expected to experience a moderate distribution contraction (7% to 29% and 19% to 43%, respectively), primarily in response to expected temperature changes. The V. chrysoptera distribution was modeled by sex, and females and males were projected to experience a major distribution contraction (56% to 79% loss in suitable conditions for females, 46% to 65% for males), accompanied by shifts in peak densities to higher elevations with minimal changes in the upper elevation limit. Expected changes in precipitation had the greatest effect on V. chrysoptera. Hylocichla mustelina experienced the smallest distribution change, consistent with the species’ flexibility in habitat selection and broader elevational range. We recommend defining priority areas for conservation as those where suitable conditions are expected to remain or arise in the next 25 years. For V. chrysoptera in particular, it is urgent to ensure that mid-elevation forests in Costa Rica and Honduras are adequately managed and protected.
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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) | 0.001 | 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".