Habitat loss on the breeding grounds is a major contributor to population declines in a long-distance migratory songbird
Bibliographic record
Abstract
Many migratory species are declining and for most, the proximate causes of their declines remain unknown. For many long-distance Neotropical migratory songbirds, it is assumed that habitat loss on breeding or non-breeding grounds is a primary driver of population declines. We integrated data collected from tracking technology, community science and remote sensing data to quantify migratory connectivity (MC), population trends and habitat loss. We quantified the correlation between forest change throughout the annual cycle and population declines of a long-distance migratory songbird, the Connecticut warbler (Oporornis agilis, observed decline: −8.99% yr−1). MC, the geographic link between populations during two or more phases of the annual cycle, was stronger between breeding and autumn migration routes (MC = 0.24 ± 0.23) than between breeding and non-breeding locations (MC = −0.2 ± 0.14). Different Connecticut warbler populations tended to have population-specific fall migration routes but overlapped almost completely within the northern Gran Chaco ecoregion in South America. Cumulative forest loss within 50 km of breeding locations and the resulting decline in the largest forested patch index was correlated more strongly with population declines than forest loss on migratory stopover regions or on wintering locations in South America, suggesting that habitat loss during the breeding season is a driver of observed population declines for the Connecticut warbler. Land-use practices that retain large, forested patches within landscapes will likely benefit breeding populations of this declining songbird, but further research is needed to help inform land-use practices across the full annual cycle to minimize the impacts to migratory songbirds and abate ongoing population declines.
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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 | 0.000 |
| 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".