Harbouring pests: rabbit warrens in agricultural landscapes
Bibliographic record
Abstract
Context Warrens are central to rabbit biology and available warren space can set a limit to the number of rabbits living in an area. Therefore, quantifying and analysing the distribution of rabbit warrens is a key step towards the management of the species in agricultural lands where it causes significant damage to crops. Aims The present study investigates the distribution and spatial pattern of wild rabbit (Oryctolagus cuniculus) warrens in an intensively managed agricultural landscape within the rabbit’s native range in semiarid southern Spain, where rabbits constitute an emerging pest problem. Methods All natural rabbit warrens within two sites were mapped, and information on their size, use and protection was recorded. The effects of environmental variables (e.g. habitat features and distances to key resources) in determining warren occurrence were evaluated using binomial generalised linear models (GLM). Key results The main variable explaining warren occurrence was the distance to the nearest neighbouring warren. Habitat variables and the distances to key resources played only a secondary role, and were mainly related to frequent ploughing linked to agricultural practices that prevent warren construction. Conclusions Habitat instability resulting from agricultural practices (i.e. frequent ploughing and intensive human disturbance) promotes warren construction on stable grounds only and partly explains the clumped spatial pattern found. However, warren occurrence in intensively managed agricultural areas seems to be more constrained by the proximity of neighbouring warrens that would facilitate rabbit recolonisation from patches nearby. Despite becoming increasingly scarce as a result of agricultural intensification, these unploughed remnants may act as safe islands for digging warrens. Implications The management of unploughed patches and the connectivity among them in semiarid agroecosystems of southern Spain is therefore of utmost importance to the management of rabbits as an agricultural pest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".