Challenges of Gauging the Impact of Area-Based Fishery Closures and OECMs: A Case Study Using Long-Standing Canadian Groundfish Closures
Bibliographic record
Abstract
The United Nations Convention on Biological Diversity was established in 1993. Canada is a signatory nation that has adopted, and exceeded, the UN Aichi biodiversity target to protect 10% of coastal and marine areas through marine protected areas or “other effective area-based conservation measures” (OECMs) by 2020. However, the science of OECMs as contributors to biodiversity conservation is relatively young and their definition and efficacy testing continue to evolve. Here, we examine whether areas closed to fishing on the Scotian Shelf in Atlantic Canada, where the groundfish community had collapsed in the early 1990s, have the potential to serve as OECMs for groundfish recovery. Using long-term research survey data, we show that three long-term area-based fishing fleet closures did not enhance per capita population growth rates of the majority of 24 common groundfish species. At a regional scale, 10 out of 24 species are currently at less than 50% of their pre-collapse (1979–1992) biomass, reflecting a sustained diminished productivity, even though fishing mortality has been drastically reduced through a moratorium in 1993. Additional measures are needed to protect severely depleted groundfish, especially when the causes of continued diminished productivity are still largely unresolved. The importance of OECMs as a risk-averse approach toward sustainability is globally accepted and they can be considered a tool toward the overarching UN Sustainable Development Goals (SDG-14). Our study provides further impetus toward articulating the criteria of OECMs and improving their design, monitoring, and testing, while placing OECMs within the broader context of sustainable ecosystem-based management.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".