An assessment of future rewilding potential in the United Kingdom
Bibliographic record
Abstract
Abstract Restoring ecosystems is an imperative for addressing biodiversity loss and climate change, and achieving the targets of the Kunming–Montreal Global Biodiversity Framework. One form of restoration, rewilding, may have particular promise but may also be precluded by requirements for other forms of land use now or in the future. This opportunity space is critical but challenging to assess. We explored the potential area available for rewilding in Great Britain until the year 2080 with a multisectoral land‐use model with several distinct climatic and socioeconomic scenarios. By 2080, areas from 5000 to 7000 km 2 were either unmanaged or managed in ways that could be consistent with rewilding across scenarios without conflicting with the provision of ecosystem services. Beyond these areas, another 24,000–42,000 km 2 of extensive upland management could provide additional areas for rewilding if current patterns of implementation hold in the future. None of these areas, however, coincided reliably with ecosystems of priority for conservation: peatlands, ancient woodlands, or wetlands. Repeatedly, these ecosystems were found to be vulnerable to conversion. Our results are not based on an assumption of support for or benefits from rewilding and do not account for disadvantages, such as potential losses of cultural landscapes or traditional forms of management, that were beyond the modeled ecosystem services. Nevertheless, potential areas for rewilding emerge in a variety of ways, from intensification elsewhere having a substantial but inadvertent land‐sparing effect, popular demand for environmental restoration, or a desire for exclusive recreation among the wealthy elite. Our findings therefore imply substantial opportunities for rewilding in the United Kingdom but also a need for interventions to shape the nature and extent of that rewilding to maintain priority conservation areas and societal objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".