Is ‘re-mobilisation’ nature restoration or nature destruction? A commentary
Bibliographic record
Abstract
Abstract Coastal dunes are experiencing increases in vegetation cover and reduced mobility levels in many sites around the world. Ecology-led approaches to coastal dune management perceive this change as ‘undesirable’ because the increase in plant cover leads to a reduction in partially vegetated to bare sand habitats and the species depending on them. This has generated a shift in the management paradigm where the objective is to revert this trend by intervening in the landscape, with actions ranging from re-introducing grazing and mowing, to mechanical removal of dune form and vegetation (dune ‘rejuvenation’). In some cases, such as many coastal dunes in Britain, this has also led to low controls on visitor pressure and allowing/promoting human trampling as a ‘natural’ way to free up areas of bare sand. This commentary critically analyses the main principles (and terminology) underlying this relatively recent shift in management paradigm, and questions assumptions such as ‘bare sand is good’ and/or ‘mobility is natural’ in the context of dune evolutionary cycles and responses to abiotic and biotic drivers. We review the limitations and dangers of this approach and argue that it is not sustainable given the current climatic and environmental conditions, and that it can increase the risk of coastal erosion and force dune systems to deviate from adapting and changing to direct/indirect drivers. Finally, we present the benefits of a management approach that focuses on minimizing human impacts so that natural processes continue to occur.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".