Assessing ecological changes in and around marine reserves using community perceptions and biological surveys
Bibliographic record
Abstract
Abstract 1. Well‐enforced partial or total no‐fishing zones (collectively known as marine protected areas, or MPAs) can help restore degraded coral reefs and enhance fish populations. 2. A comparison was made of community perceptions of ecological changes in an MPA with concurrent scientific data on these changes in the same MPA. Such analyses are particularly important in community‐based MPAs where local support is a key determinant of ecological success. 3. The no‐take MPA in question was initially launched in partnership with the community in 1995 and formalized in 1998. The perceptions data come from interviews with community members in 1999 and 2004, the biological data come from underwater visual censuses of the MPA from 1998 to 2004. 4. Community members perceived more fish within the MPA and slight increases in catch outside the MPA. In contrast, fish censuses showed a high degree of stochastic variation and only minor increases in fish abundance, size and diversity in and around the MPA between 1998 and 2004. 5. Possible explanations for these discrepancies include different temporal, spatial or species frames of reference and/or limitations to the biological survey technique. Other options include wishful thinking, external influences, a desire to please, or confounding with other benefits. 6. This study demonstrates some of the strengths and weaknesses of community perceptions and biological data. In order to improve our understanding about the changes that occur over time in an MPA and engender community support for the long‐term viability of MPAs, it is important to develop diverse and efficient monitoring schemes. Copyright © 2010 John Wiley & Sons, Ltd.
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 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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".