Eager about beavers? Understanding opposition to species reintroduction, and its implications for conservation
Bibliographic record
Abstract
Abstract The range of keystone species is increasing in some parts of the world, particularly Europe, through a combination of natural recolonization, government‐sanctioned and covert reintroductions. Reintroductions are an important conservation tool, particularly in the increasingly popular approach of rewilding. There is relatively little understanding of the politics, broadly conceived, of species reintroduction, particularly around how people who live alongside these newly introduced species might react, and what underpins this reaction, and how the method of reintroduction affects reactions. Here, we explore these issues through a case of beavers in central Scotland, which were covertly reintroduced. We explore opposition to reintroduction as manifest in beaver killing and dam destruction by land managers, quantifying these using the sensitive ‘bean count’ method. We also explore what underpins land managers' reactions, particularly their views and values around land and land management. We found considerable resistance. We found that beaver killing and dam destruction were widespread, both before and after beavers became a protected species. Nevertheless, beaver populations and ranges in Scotland continue to grow. We found attitudes were grounded in a strong set of relational values around land custodianship. We find a range of views towards beavers, including widespread opposition, particularly regarding the covert nature of beaver introduction, the challenge beavers and beaver protection provide to ideas of proper land management and custodianship, and a lack of trust in formal methods of beaver governance. We argue that species reintroductions policies and research should give careful consideration to potential opposition, its material impacts on reintroduction projects and how it is grounded in wider environmental values and politics. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".