Modelling population dynamics of seabirds: importance of the effects of climate fluctuations on breeding proportions
Bibliographic record
Abstract
Environmental factors and their interactions are likely to have shaped specific breeding and survival strategies in top predators. Understanding how climatic factors affect populations requires detailed investigation of the demographic parameters and population modelling. Here, we focus on the modelling of a southern fulmar population over a 39 year period in Terre Adélie, Antarctica, using Leslie matrix models to understand from a prospective and retrospective point of view, how vital rates and their variations, affect the cyclic population dynamics. The elasticity of population growth rate to adult survival was very high (0.95), as predicted by a slow–fast continuum in avian life histories. However, adult survival varied little between years (mean±SD: 0.92±0.07), and could not explain the strong fluctuations observed in the number of breeders and chicks. The high temporal fluctuations of the proportion of breeders (0.57±0.22) and breeding success (0.70±0.14) had the strongest impact on population dynamics, despite their weak elasticities (0.05). Before the 1980s, population fluctuations were mainly explained by a direct impact of sea‐ice extent (SIE) anomalies during summer (by a threshold effect) on the proportion of breeders. After 1980s, 3 years periodic population fluctuations were best predicted by 3 years cyclic variations in the proportion of breeders. SIE showed a marked change of periodicity during the 1980s, and SIE during winter fluctuated with a 3 years periodicity during 1980–1995. The marked change in population dynamics, through a change of the variations of the proportion of breeders, may be explained in the light of a regime shift that probably occurred around the 1980s, and which affected the sea ice environment, the availability of prey, and thus the demographic parameters and population dynamics of southern fulmars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".