Data from: Priorities and motivations of marine coastal restoration research
Bibliographic record
Abstract
Active restoration is becoming an increasingly important conservation intervention to counteract the degradation of marine coastal ecosystems. Understanding what has motivated the scientific community to research the restoration of marine coastal ecosystems and how restoration research projects are funded is essential if we want to scale-up restoration interventions to meaningful extents.Here, we systematically review and synthesize data to understand the motivations for research on the restoration of coral reefs, seagrass, mangroves, saltmarsh, and oyster reefs. We base this analysis off a published database of marine restoration studies, originally designed to estimate the cost and feasibility of marine coastal restoration, derived from mostly scientific studies published in peer-reviewed and some grey literature. For the present study, the database was updated with fields aimed at assessing the motivations, outcomes, and funding sources for each project. We classify restoration motivations into five categories: biotic, experimental, idealistic, legislative, and pragmatic. Moreover, we evaluate the variables measured and outcomes reported by the researchers and evaluate whether projects adhered to the Society for Ecological Restoration’s (SER) standards for the practice of ecological restoration. The most common motivation of the scientific community to study restoration in marine coastal ecosystems was experimental i.e. to seek experimental data to answer ecological research questions or improve restoration approach, as expected since mostly peer-reviewed literature was evaluated here. There were differences in motivations among the five coastal ecosystems. For instance, biodiversity enhancement was the most common case for a biotic motivation in mangrove restoration projects. The most common metrics evaluated were growth/productivity, survivorship, habitat function, physical attributes and reproduction. For most ecosystems, ecological outcomes were frequently reported, with socio-economic implications of the restoration rarely mentioned, except for mangroves. Projects were largely funded by governmental grants with some investment from private donations, non-governmental organizations, and the involvement of volunteers. Our findings and database provide critical data to align future research of the scientific community with the real social, economic and policy needs required to scale-up marine coastal restoration projects.
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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 | 0.051 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".