Contrasting bottom‐up effects of warming ocean on two king penguin populations
Bibliographic record
Abstract
Breeding success is often correlated with climate, but the underlying bottom-up mechanisms remain elusive-particularly in marine environments. Consequently, conservation plans of many species often consider climate change as a unilateral threat, ignoring that even nearby populations can show contradicting trends with climate. Better understanding the relationship between climate and environment at different scales can help us interpret local differences in population trends, ultimately providing better tools to evaluate the global response of a species to threats such as global warming. We studied a growing king penguin population nesting at Kerguelen island (Southern Indian Ocean), hosting one of the largest colonies in the world. We used a unique dataset of foraging, breeding success, and climate data spanning over 25 years to examine the links between climate, marine environment, and breeding success at this colony. The results were then compared to the neighboring population of Crozet, which experienced the steepest decline for this species over the past few decades. At Crozet, penguins experienced lower breeding success in warmer years due to productive currents shifting away from the colony, affecting foraging behavior during chick rearing. At Kerguelen, while chick mass and survival experienced extreme variation from year to year, the annual variation was not associated with the position of the currents, which varied very little compared to the situation in Crozet. Rather than being affected by prey distribution shifts, we found evidence that chick provisioning in Kerguelen might be influenced by prey abundance, which seem to rather increase in warmer conditions. Furthermore, warmer air temperature in winter increased chick survival rate, likely due to reduced thermoregulation cost. Investigating the mechanisms between climate and fitness allowed us to predict two different fates for these populations regarding ongoing global warming.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".