Demographic data and population indices for American kestrels in the North America, 1986–2019
Bibliographic record
Abstract
American kestrels (Falco sparverius, hereafter kestrel) have been declining across most North America at a steady rate of approximately −1.4% per year since the 1960s. Though kestrel populations have been intensively studied throughout their North American range, no long-term trends in demographic parameters (vital rates) have been detected nor have potential causes of decline been explicitly identified. Here we synthesized multiple long-term datasets (1986–2019) at the continental scale within an integrated population model to estimate long-term trends in survival, abundance, and fecundity vital rates. We used data from individual nests from multiple nest box studies conducted in Alabama, Alaska, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, Nebraska, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin and Wyoming to parameterize a normal model to estimate productivity vital rates. Specifically, we used the brood size for each nest. Additionally, we also used data from the North American Breeding Bird Survey (BBS; see details on survey design, field and analytical methods in Sauer et al. 2011) for use in a state space model. The BBS is a large-scale coordinated survey that occurs each spring throughout the United States and Canada. Lastly, we used kestrel band recovery data from the U.S. Geological Survey for use in a Seber Dead Recovery Model; both datasets are publicly available and encompassed North America.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".