Is restocking a useful tool for increasing rabbit densities?
Bibliographic record
Abstract
The European rabbit (Oryctolagus cuniculus) is endemic to Mediterranean ecosystems in the Iberian Peninsula, where it is a key species. In recent years its populations have declined due to several factors including habitat transformation and viral diseases. At the same time, corrective measures including population restocking in areas with low population densities using rabbits from other geographical areas have been performed. In this study we evaluate the impact restocking has had on the population dynamics of native rabbits in the Doñana National Park. As part of the natural processes monitoring program carried out in the Doñana Biological Station (ESPN-EBD-CSIC), rabbit censuses were conducted in spring and late summer in 2005–2015 at dusk and at night along six fixed transects in habitats harbouring rabbit populations. In order to take into account restocked rabbit numbers, annual reports from the Doñana Natural Area and data provided by the Lynx team of the LIFE project were incorporated into the study. In all, 52 336 rabbits from different parts of western Andalusia were released in the Doñana Biosphere Reserve in 2005–2015. Yet, rabbit populations underwent significant declines, above all in 2013 and 2015, with decreases in some areas of up to 80%. These results show intensive rabbit restocking did not increase the native rabbit populations numbers. The impact of the release of rabbits on the population dynamics of the species in Doñana is discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".