Monitoring data for a new large offshore marine protected area reveals infeasible management objectives
Bibliographic record
Abstract
Abstract Predicting and measuring changes resulting from marine protected areas (MPAs) has posed a challenge for practitioners, partly because ecosystems are complex and can change in unanticipated ways, but also due to MPA characteristics such as design factors, conservation objectives (COs), and monitoring programs, that can leave little chance of meeting stated goals. We consider these design factors for the Laurentian Channel MPA, a large offshore Canadian protected area established to protect against fishing impacts. Specifically, in this study we evaluated (1) whether it is realistic to expect improvements in the MPA for four previously established taxa‐specific COs, and (2) whether existing scientific surveys are capable of detecting changes in these CO taxa even if they occurred. Three CO species were sampled in scientific multispecies research vessel trawl surveys (Black Dogfish, Smooth Skate, and Northern Wolffish) and a fourth CO, sea pen taxa, were enumerated using seafloor imagery. Simulations indicate that trawl surveys have very little chance of detecting change in the abundance of the three fish species examined, while seafloor imagery data had higher statistical power for sea pen taxa. Moreover, we show that expecting change related to the removal of fishing is unrealistic due to the fact that the MPA was established in an area of minimal fishing pressure. While positive change is unlikely to be induced by the MPA, or be detected if they occurred, this MPA could provide conservation benefits if COs and monitoring approaches were realigned to match the unique features of this area that represents largely unimpacted sensitive benthic habitats.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".