Adaptive management for the sustainable exploitation of lagoon resources in remote islands: lessons from a massive El Niño-induced giant clam bleaching event in the Tuamotu atolls (French Polynesia)
Bibliographic record
Abstract
SUMMARY Small-scale mariculture of high-value species for trade in remote islands can offer valuable alternative livelihoods to local communities. The endangered giant clam speciesTridacna maximais naturally abundant in some atolls in French Polynesia (FP) and has been the focus of commercial mariculture activities since 2012. Shortly after spat collectors became operational in two atoll lagoons, FP rose to become one of the main exporters of giant clams for the aquarium trade. However, this activity has been threatened recently by a mass clam-bleaching event triggered by the 2015–2016 El Niño. This study reviews the roles that international (Convention on International Trade in Endangered Species of Wild Fauna and Flora) and national regulatory frameworks play in the development of this activity in a small island context, and how they can indirectly promote better science and monitoring in order to inform adaptive management strategies. The links between the nine main groups of stakeholders show the necessary adaptation measures required to mitigate climate-driven mortalities. While this case study remains specific to giant clam farming in FP, general lessons are provided that could help in mitigating economic impacts from climate-related events on other islands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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 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".