Limitation of population recovery: a stochastic approach to the case of the emperor penguin
Bibliographic record
Abstract
Major population crashes due to natural or human‐induced environmental changes may be followed by recoveries. There is a growing interest in the factors governing recovery, in hopes that they might guide population conservation and management, as well as population recovery following a re‐introduction program. The emperor penguin Aptenodytes forsteri population in Terre Adélie, Antarctica, declined by 50% during a regime shift in the mid‐1970s, when abrupt changes in climate and ocean environment regimes affected the entire Southern Ocean ecosystem. Since then the population has remained stable and has not recovered. To determine the factors limiting recovery, we examined the consequences of changes in survival and breeding success after the regime shift. Adult survival recovered to its pre‐regime shift level, but the mean breeding success declined and the variance in breeding success increased after the regime shift. Using stochastic matrix population models, we found that if the distribution of breeding success observed prior to the regime shift had been retained, the emperor penguin population would have recovered, with a median time to recovery of 36 years. The observed distribution of breeding success after the regime shift makes recovery very unlikely. This indicates that the pattern of breeding success is sufficient to have prevented emperor penguin population recovery. The population trajectory predicted on the basis of breeding success agrees with the observed trajectory. This suggests that the net effect of any facors other than breeding success must be small. We found that the probability of recovery and the time to recovery depend on both the mean and variance of breeding success. Increased variance in breeding success increases the probability of recovery when mean success is low, but has the opposite effect when the mean is high. This study shows the important role of breeding success in determining population recovery for a long‐lived species and demonstrates that demographic mechanisms causing population crash can be different from those preventing population recovery.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".