Are restoration plans missing the target? Land tenure and cyclone risks reshuffles priorities for mangrove restoration
Bibliographic record
Abstract
Coastal wetlands, vital for fisheries habitats, have suffered extensive losses. Ecosystem restoration offers opportunities to improve fish catch and restore the valuable services these ecosystems provide. Successful restoration is dependent on choosing a site where restoration is feasible, which encompasses biophysical, social, governance, logistical, and resource factors. However, factors that influence feasibility such as land tenure (governance feasibility) and future climate risks (biophysical feasibility) are often overlooked in quantitative analyses of site selection. We ask how spatial priorities for restoration change when considering how feasibility is affected by land tenure, cyclone risk, and both factors together. Specifically, we analyzed a case‐study of mangrove restoration to improve fish catch in Queensland, where there is interest in restoring coastal habitats to support fish habitats. We found that the rank order of planning units by restoration feasibility was highly influenced by both land tenure and cyclones, with cyclones changing ranks with clustered regions along the coastline and land tenure variably changing ranks throughout. In planning units where fisheries benefit is expected to be high, but cyclone risk substantially reduces restoration feasibility, practitioners could consider strategically planting mangroves near established mangrove forest and selecting resilient species for restoration. Formalizing regulations for incentives to private land holders and amending legislation for easier permitting are additional suggestions for addressing land tenure challenges. Our study emphasizes the importance of systematic approaches to considering feasibility in spatial planning for restoration to minimize the risk of failure.
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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 | 0.001 | 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".