A Method for Investigating Population Declines of Migratory Birds Using Stable Isotopes: Origins of Harvested Lesser Scaup in North America
Bibliographic record
Abstract
BACKGROUND: Elucidating geographic locations from where migratory birds are recruited into adult breeding populations is a fundamental but largely elusive goal in conservation biology. This is especially true for species that breed in remote northern areas where field-based demographic assessments are logistically challenging. METHODOLOGY/FINDINGS: Here we used hydrogen isotopes (deltaD) to determine natal origins of migrating hatch-year lesser scaup (Aythya affinis) harvested by hunters in the United States from all North American flyways during the hunting seasons of 1999-2000 (n = 412) and 2000-2001 (n = 455). We combined geospatial, observational, and analytical data sources, including known scaup breeding range, deltaD values of feathers from juveniles at natal sites, models of deltaD for growing-season precipitation, and scaup band-recovery data to generate probabilistic natal origin landscapes for individual scaup. We then used Monte Carlo integration to model assignment uncertainty from among individual deltaD variance estimates from birds of known molt origin and also from band-return data summarized at the flyway level. We compared the distribution of scaup natal origin with the distribution of breeding population counts obtained from systematic long-term surveys. CONCLUSIONS/SIGNIFICANCE: Our analysis revealed that the proportion of young scaup produced in the northern (above 60 degrees N) versus the southern boreal and Prairie-Parkland region was inversely related to the proportions of breeding adults using these regions, suggesting that despite having a higher relative abundance of breeding adults, the northern boreal region was less productive for scaup recruitment into the harvest than more southern biomes. Our approach for evaluating population declines of migratory birds (particularly game birds) synthesizes all available distributional data and exploits the advantages of intrinsic isotopic markers that link individuals to geography.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".