Management and control of invasive brown hares (<em>Lepus europaeus</em>): contrasting attitudes of selected environmental stakeholders and the wider rural community
Bibliographic record
Abstract
Management of wildlife is often a contentious issue in which stakeholders are increasingly influential.The European hare (Lepus europaeus) is a non-native, invasive species, now established in Northern Ireland.It impacts the endemic Irish hare (L.timidus hibernicus), a priority species of conservation concern, via competition and hybridisation to the extent that control of European hares is a priority.We conducted a questionnaire survey among members of Countryside Alliance Ireland [CAI] -an organisation that promotes rural interests, including field sportsand non-members, to ascertain the contrasting attitudes to the lethal control of European hares in Northern Ireland; a total of 342 (20%) questionnaires were returned.We hypothesised that: (i) CAI members would exhibit greater support for intervention than nonmembers; and (ii) respondents in the core invasive range will differ in their outlook when compared to respondents from other zones.CAI members were more likely to be aware of the presence of the non-native species and to support lethal management.Both groups considered the threat posed to biodiversity by the European hare to be important.We conclude that members of rural interest groups may be important advocates of intervention, whilst non-members of field sports organisations may be more reluctant to support any proposed management plan involving lethal control.Active engagement to develop a mutual understanding, prior to developing management options, is crucial in ensuring long-term success.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".