Spatial Management to Reduce Entanglement Risk to North Atlantic Right Whales in Fishing Gear: A Case Study of U.S. Northeast Lobster Fishery 2002–2009
Bibliographic record
Abstract
Despite the use of gear requirements and access restrictions to manage lobster fishery interactions with north Atlantic right whales since 1997, the population is likely below 370 animals. The Dynamic Area Management (DAM) program (2002–2009) used “real-time” right whale sightings data to provide temporary protection using closures or whale-modified-gear to reduce entanglement. Our ex-post evaluation uses a flexible framework to identify strengths and weaknesses of the program. Biological and economic implications of the program are evaluated using a relative risk of entanglement index (RREI) calculated with spatially and temporally explicit data on density of right whales and fishing effort. An illustrative closure optimization model demonstrates the trade-offs between the non-monetary benefits of risk reduction and the opportunity cost of closures under alternative decision rules (benefit-ranking and cost-effectiveness). Annual aerial sampling to detect DAM areas was low (<3%), yet in some months’ the 17% of area covered by all northeast right whale management areas encompassed up to 70% of the region’s population. Despite their small spatial footprint, dynamic and static measures may have reduced total risk by 6.5% on average, and DAM zones may have created an indirect economic incentive for some fishers to adopt the whale-modified-gear. Similar RREI index values in some months with inverse levels of fishing effort and whale presence highlight the need to consider fishing and whales jointly to reduce risk. These temporal-spatial patterns are critical in policy instrument design. Further, optimization results illustrate how different decision rules can attain equivalent non-monetary benefits of risk reduction at different opportunity costs to industry; the implications of whale-modified-gear and compliance factors are explored. We recommend that DAMs be considered as part of a suite of policy instruments, and highlight how recent technological advances may support lower cost data collection and faster implementation given limited public sector budgets. This case study highlights the need for evaluation of past policy instruments with a lens beyond biological outcomes, and sets the stage for further empirical analysis to better understand harvester responses to management measures designed to protect right whales and the resulting private and public sector trade-offs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".