Habitat ecology of Nearctic–Neotropical migratory landbirds on the nonbreeding grounds
Bibliographic record
Abstract
Abstract Of the approximately 716 bird species that breed in North America, 386 (54%) are considered Nearctic–Neotropical migrants by the U.S. Fish and Wildlife Service. In the past 50 yr, scores of these migratory species, including some once considered common, have declined dramatically. Nearctic–Neotropical migrants normally spend 6–8 months in tropical habitats, making the identification, availability, and management of Neotropical habitats critical issues for their conservation. Yet, for most species, complete and nuanced information about their use of tropical habitats and the relative effects of breeding vs. wintering conditions on survival, productivity, and population trends is not available, though many studies point to Neotropical overwintering habitats as being a strong driver of population change. Particularly important for long-distance Nearctic–Neotropical migrants is an understanding of how “carry-over effects” arise and influence population trends when conditions on wintering grounds and tropical stopover areas affect subsequent reproductive performance on breeding grounds. For example, why some species show strong carry-over effects from tropical habitats while others do not is not fully understood. In recent years, many studies have offered insights into these issues by taking advantage of new scientific methods and technological innovations. In this review, we explore threats facing North American breeding birds that migrate to the Neotropics, summarize knowledge of habitat selection and use on the wintering grounds, describe how conditions at one point in the annual cycle may manifest in subsequent seasons or life history stages, and discuss conservation concerns such as climate change and the potential for phenological mismatch.
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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 | 0.000 |
| 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 | 0.000 | 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".