Marine protected areas for dive tourism
Bibliographic record
Abstract
Marine and coastal tourism deliver economic benefits to coastal communities that far surpass those generated by fisheries, yet its potential contribution to global marine conservation remains underexamined. Marine Protected Areas (MPAs) help restore biodiversity and enhance nearby fisheries, but their direct tourism benefits are not well understood. Here, we estimate the global demand for recreational scuba dive tourism, map the distribution and protection status of all marine dive sites globally, and develop a bioeconomic model to estimate the revenue gains from upgrading unprotected dive sites to fully protected MPAs. We estimate that 33.1 million scuba dives occur annually in marine environments worldwide, with 70% taking place within MPAs. However, only 15% of these MPA-affiliated dive sites are highly or fully protected. We show that designating all unprotected recreational dive sites, representing less than 1% of the global ocean, as highly or fully protected MPAs would improve fish biomass and biodiversity while generating an additional US$2 billion per year in direct tourism revenue (not including economic multipliers). Importantly, 62% of marine diving currently occurs in developing countries, underscoring the potential for dive tourism to support both marine conservation and local livelihoods in regions where such benefits are most needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".