Community science reveals links between migration arrival timing advance, migration distance, and wing shape
Bibliographic record
Abstract
Substantial global data show that many taxa are shifting their phenologies in response to climate change. For birds, migration arrival dates in breeding regions have been shifting earlier, and there is evidence that both evolutionary adaptation and behavioural flexibility influence these shifts. As more efficient flyers may be able to demonstrate more flexibility to respond to changing conditions during migratory flight, we hypothesize that differences among passerine species in flight efficiency, as reflected by morphology, may be associated with the magnitude of shifts in arrival date in response to climate warming. We applied a logistic model to eighteen years of eBird data to estimate mean arrival date for 44 common passerines migrating to northeast North America. We then used linear mixed-effects models to estimate changes in mean arrival date and compared these changes to morphological proxies for flight efficiency and migratory distance using phylogenetic generalized least squares models. On average, passerine species shifted their arrival dates 0.120 days earlier each year, with 27 of the 44 species shifting to significantly earlier arrival times, and two shifting to significantly later ones. Of the 15 species with non-significant shifts, 13 trended toward earlier arrivals. Longer migration distances and higher wing aspect ratios were associated with greater shifts towards earlier arrivals. Migration distance and aspect ratio were also significantly correlated to each other. This suggests that changes in arrival date are affected by factors pertaining to migratory flight over long distances namely, flight efficiency and migration distance. These traits may be able predict the magnitude of arrival date shift, and by extension identify species that are most at risk to climate change due to inflexible arrival timing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".