Phenotypic plasticity or evolutionary change? An examination of the phenological response of an arctic seabird to climate change
Bibliographic record
Abstract
Abstract Phenological adjustments are an important aspect of a population's response to climate change. Changes in phenology can occur through either individual plasticity or evolutionary change within populations. Few studies have investigated both these processes in Arctic environments. Using 42 years of individual and pedigree data, we evaluated the contribution of plasticity and evolution to variation in breeding phenology at a colony of a high Arctic sea‐ice obligate seabird, Mandt's black guillemot (Cepphus grylle mandtii). Mean clutch initiation (first egg in a clutch) advanced 7.8 days, and both environmental (snowmelt) and demographic (years of breeding experience) factors varied among years. Earlier phenology was associated with earlier snowmelt and experienced mothers. Females advanced phenology at different rates as they aged but at similar rates in response to variation in snowmelt. Heritability of clutch initiation was negligible, and there was no evidence of evolution contributing to phenological changes. Earlier laying was associated with increased annual number of fledglings and annual adult survival at the individual level suggesting that the phenological changes are adaptive and are driven by phenotypic plasticity, but not genetic responses. We propose that species with a constrained breeding season (like many Arctic species) may have a limited ability beyond existing plasticity to respond to changing environmental conditions. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.001 | 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".