Contribution of released captive-bred Mallards to the dynamics of the natural population
Bibliographic record
Abstract
The consequences of releasing captive-bred game animals into the wild have received little attention, despite their potential demographic impact, as well as costs and/or benefits for recipient populations. If restocking aims at increasing harvest opportunities, increased hunting pressure is expected, which would then be supported by either wild or released individuals. On the other hand, the wild recipient population may benefit from the release of captive-bred conspecifics if this reduces hunting pressure on the former through dilution of risk or selective harvesting of captive-bred individuals. Here, we modelled a Mallard (Anas platyrhynchos) population consisting of wild individuals supplemented by captive-bred conspecifics, a very common practice in Europe over the last 40 years. The objective was to test the effect of an increase of harvest rate on released and wild individuals, respectively. Our results show that, due to the low reproductive value of the released Mal-lards, the population was hardly affected by a change in harvest of these low performance individuals. Conversely, a 15 percent increase in harvest rate of the wild individuals would lead to a quick decline of the population. We discuss these results in the context of the Camargue population, located in the South of France, which has experienced an increase in Mallard harvest without apparent reduction of population size. We suggest that this has only been possible due to the release of captive-bred Mallards.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".