Five ways seascape ecology can help to achieve marine restoration goals
Bibliographic record
Abstract
Context: Marine restoration is increasingly recognized as a key activity to regenerate ecosystem integrity, safeguard biodiversity, and enable ocean sustainability. Global policies such as the Kunming-Montreal Global Biodiversity Framework include area-based targets to improve ecosystem integrity and connectivity. Achieving these targets requires scaling up restoration in ecologically and socially meaningful ways. Objectives: The objective was to establish a consistent language and framework for seascape restoration practitioners that complements existing marine restoration guidelines and can help to achieve cross-scale restoration targets. Methods: We proposed that the integration of the 5Cs of seascape ecology-Context, Configuration, Connectivity, Consideration of scale, and Culture- can offer a valuable framework for advancing marine restoration practice and policy. We synthesized existing ecological and social science evidence to demonstrate how the 5Cs framework can be applied to seascape restoration efforts. Results: We established a consistent language and framework for marine restoration practitioners and recommended four key operational pathways: (1) focusing on the recovery of interconnected habitats across the land-sea interface; (2) integrating the 5Cs from site selection through to monitoring; (3) representing social, historical, cultural, and ecological variables when assessing site suitability; and (4) fostering transdisciplinary collaborations to support integrative, multifaceted projects. Conclusions: Integrating landscape ecology concepts and methods into coastal restoration will enable the effective scaling up of regenerative actions. Applying the 5Cs can help achieve global restoration targets through more strategic, inclusive, and effective marine restoration across coastal seascapes.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".