Weather and regional effects on winter counts of Rusty Blackbirds ( Euphagus carolinus )
Bibliographic record
Abstract
A long-term and severe population decline of Rusty Blackbirds (Euphagus carolinus) has motivated biologists to search for possible causes of the decline. Several hypotheses have been forwarded, one of which is that habitat destruction on the overwintering grounds is responsible. Climate change is another possible explanation. We evaluated the population trend of Rusty Blackbirds in Arkansas by modeling their abundance recorded during Christmas Bird Counts conducted between 1965 and 2020. We used generalized additive modeling to evaluate population trends and explored the influence of weather, effort, habitat, and region on those trends. We found that counts of Rusty Blackbirds have increased by about 40 birds in Arkansas between 1965 and 2020; most of the increase occurred after 1995. We also found that proportion of forest land in each count circle’s county was inversely related to counts of Rusty Blackbirds but that temperature was a more important variable. During warmer years, fewer Rusty Blackbirds were counted. Rusty Blackbird geographic distribution also changed by decade; that change accounted for about 15% of the deviance in counts of Rusty Blackbirds. Finally, we observed a relationship between temperature and distribution; Rusty Blackbirds tended to overwinter in the northern portions of the state during warm years and more southerly portions of the state during cold years. Our analytical approach will be useful to anyone evaluating geographic shifts in populations that might be associated with climate change.
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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".