Ecosystem‐Based Fisheries Management for Social–Ecological Systems: Renewing the Focus in the United States with <i>Next Generation</i> Fishery Ecosystem Plans
Bibliographic record
Abstract
Abstract Resource managers and policy makers have long recognized the importance of considering fisheries in the context of ecosystems; yet, movement towards widespread Ecosystem‐based Fisheries Management (EBFM) has been slow. A conceptual reframing of fisheries management is occurring globally, which envisions fisheries as systems with interacting biophysical and human subsystems. This broader view, along with a process for decision making, can facilitate implementation of EBFM. A pathway to achieve these broadened objectives of EBFM in the United States is a Fishery Ecosystem Plan (FEP). The first generation of FEPs was conceived in the late 1990s as voluntary guidance documents that Regional Fishery Management Councils could adopt to develop and guide their ecosystem‐based fisheries management decisions, but few of these FEPs took concrete steps to implement EBFM. Here, we emphasize the need for a new generation of FEPs that provide practical mechanisms for putting EBFM into practice in the United States. We argue that next‐generation FEPs can balance environmental, economic, and social objectives—the triple bottom line—to improve long‐term planning for fishery systems.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".