Mathematical Perspectives on Rewilding
Bibliographic record
Abstract
Achieving sustainable human-wildlife coexistence in well-functioning ecosystems is a vitally important and major challenge under global change. In response, rewilding is an emerging paradigm in ecosystem service provision through the re-establishment of natural ecological processes in self-sustaining ecosystems. Effective prediction of ecological changes in rewilding projects requires tools integrating quantitative methods with social-economic dimensions and thinking. We consider the current state of such quantitative treatments, highlighting opportunities for harnessing mathematics and statistics. We present an emerging quantitative framework, encompassing four key areas of the rewilding process: design and planning, ecological modelling, metrics for assessment, and coupled social-ecological systems, informed by recent progress in mathematical, statistical, and ecological modelling. The adaptive cycle concept is used to integrate these four key areas. Dynamical systems modelling informed by empirical knowledge allows us to address trans-disciplinary feedbacks, nonlinearities, and anticipate the potential for emerging properties and critical transitions/regime shifts during rewilding, predicting the range and likelihood of alternative scenarios. Our framework provides a possible foundation and new opportunities for a more robust quantitative and predictive methodology for rewilding. We argue that a project is more likely to achieve its goals, and in a more cost-effective way, if mathematical scientists are included from the beginning.
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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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".