A Decision Framework for the Adaptive Management of an Exploited Species with Implications for Marine Reserves
Bibliographic record
Abstract
Marine reserves have both conservation and fishery benefits. Nevertheless, there are no general criteria about when and where to establish new reserves, how to evaluate their efficacy, and how to conduct adaptive management to achieve conservation goals. We applied a decision-theory framework to optimally allocate conservation resources between improving data on population status and establishing a reserve for species conservation. Our goal was to maximize reserve benefits given the constraints of a population growth rate that would permit sustainability of resources. We illustrate our decision framework with a retrospective analysis of a 7-year time series on abundance of the leopard grouper (Mycteroperca rosacea) in the Sea of Cortés, Mexico. We used the lower bound of the distribution of the population growth rate (lambda) as a decision rule for determining how many years of monitoring are needed to detect reserve effects. We determined the minimum time frame needed to estimate lambda based on a stated level of risk tolerance for four sites. As expected, the coefficient of variation for the lambda declined with the number of years of data. This increased precision with additional years of data resulted from the high degree of annual variability in the system. Where populations were slow to respond to reserves, more data were needed to detect a positive lambda value. For the leopard grouper case study, confidence in the estimate of lambda increased with the number of years of data. Our decision framework may be used to identify the minimum number of years of data needed before a management decision about reserve establishment could be made that is reasonably likely to meet its management objectives.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".