Population response to environmental productivity throughout the annual cycle in a migratory songbird
Bibliographic record
Abstract
Abstract Environmental factors affect migratory animal populations in every phase of their annual cycle and have significant impacts on breeding success and survival. The Breeding Bird Survey provides a long‐term database for examining population trends in North American birds, allowing us to examine large‐scale environmental factors that influence population abundance. We examined plant productivity as measured by normalized difference vegetation index (NDVI) over a 24‐year period from 1983–2006 in bird conservation regions (BCRs) that overlapped Bullock's oriole ( Icterus bullockii ) breeding, moult, and wintering ranges to ask whether plant productivity in 1 year influences population abundance in the subsequent breeding season. Bullock's orioles have a moult‐migration strategy, with a stopover moult in the Mexican monsoon region, which necessitates examining each stationary phase of the bird's annual cycle to understand the impacts of environmental factors on population abundance. Our results show increased breeding abundance in three (Great Basin, Coastal California and Shortgrass Prairies) of the six BCRs in which the species breeds following years with high NDVI values. We did not detect a response of breeding abundance to high NDVI values in the previous year in either the moulting region or in their primary over‐wintering area in central Mexico. Our results demonstrate that large‐scale annual variation in primary productivity on the breeding grounds can have an impact on breeding abundance in the following season, but further studies on migratory connectivity and on ecological mechanisms during the non‐breeding seasons are needed to understand why we did not detect an influence of productivity during these periods.
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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".