Local-scale impacts of extreme events drive demographic asynchrony in neighbouring top predator populations
Bibliographic record
Abstract
Extreme weather events are among the most critical aspects of climate change, but our understanding of their impacts on biological populations remains limited. Here, we exploit the rare opportunity provided by the availability of concurrent longitudinal demographic data on two neighbouring marine top predator populations (the black-browed albatross, Thalassarche melanophris , breeding in two nearby colonies) hit by an exceptionally violent storm during one study year. The aim of this study is to quantify the demographic impacts of extreme events on albatrosses and test the hypothesis that extreme events would synchronously decrease survival rates of neighbouring populations. Using demographic modelling we found that, contrary to our expectation, the storm affected the survival of albatrosses from only one of the two colonies, more than doubling the annual mortality rate compared to the study average. Furthermore, the effects of storms on adult survival would lead to substantial population declines (up to 2% per year) under simulated scenarios of increased storm frequencies. We, therefore, conclude that extreme events can result in very different local-scale impacts on sympatric populations. Crucially, by driving demographic asynchrony, extreme events can hamper our understanding of the demographic responses of wild populations to mean, long-term shifts in climate.
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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 | 0.001 |
| 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".