Data and code from: Opposite, but insufficient, phenological responses to climate in two circumpolar seabirds: relative roles of phenotypic plasticity and selection
Bibliographic record
Abstract
The magnitude of climate change has been greatest in the Arctic, accelerating climate-induced shifts in phenology, but wildlife responses vary. Variation may be due to the relative importance of phenotypic plasticity or phenotypic selection. Here, we examine and contrast the environmental drivers of plasticity in breeding phenology of two circumpolar seabirds at their receding summer range limit using unique datasets of marked individuals covering 25 and 30 years. Based on prior knowledge of the local ecosystems, we predicted that climate would generate opposing patterns of plasticity in the two populations. Laying phenology of kittiwakes in the Gulf of Alaska was associated with a large-scale climate oscillation (Pacific Decadal Oscillation) while the Arctic-breeding murres adjusted laying to sea-ice conditions. Kittiwakes laid earlier after experiencing colder climate about two years prior and laying dates did not advance over the study, but murres laid earlier when warmer climate led to earlier spring sea-ice break-up, and murre laying dates advanced by one week since 1990. Selection favoured earlier laying in both species. Both populations adjusted breeding phenology to environmental variation, but we anticipate opposing effects on phenology with continued climate change. Ice-constrained species can likely adapt to some extent because plasticity can provide the necessary shift to this physical barrier, although individuals were only able to adjust by ~one week while ice conditions advanced by over a month. In more temperate regions, where phenology is driven by bottom-up effects, plasticity and selection counteract one another leading to limited adaptability. We provide insights into the likely adjustments by Arctic marine animals to an increasingly warmer and ice-less summer.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.079 |
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".