Intentional and unintentional changes to avian and mammalian diversities in the <scp>UK</scp>
Bibliographic record
Abstract
Rewilding is emerging as a promising restoration strategy to tackle the challenges posed by global change and maintain natural ecosystems and their biodiversity. However, rewilding has also been criticized for the absence of a consistent definition and insufficient knowledge about its possible outcomes. Here, we explored the effects of rewilding on filling functional gaps created by the extirpation of native species. We contrasted rewilding with three other mechanisms for change in community composition—species extirpation, species introduction, and unassisted colonization—in terms of their impacts on changes in avian and mammalian diversity in the UK. We found that (i) while rewilding increases functional diversity most on average, introduced/naturalized birds contribute more functional uniqueness to native functional space than other groups of birds; and (ii) changes in functional diversity associated with “rewilded” organisms were species‐dependent and idiosyncratic. Our results suggest that although rewilding can expand or infill native functional trait space to some extent, such effects require careful assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".