Using an <scp>EBFM</scp> lens to guide the management of marine biological resources under changing conditions
Bibliographic record
Abstract
Abstract The management of natural resources is currently more challenging than ever before. Climate change and human population growth pose a threat to marine ecosystems as we know them. In order to preserve ecosystems, biodiversity and ecosystem services, management of biological resources must adopt a holistic strategy. Ecosystem‐Based Fisheries Management (EBFM) enables this by managing natural resources at the ecosystem level. However, EBFM objectives and implementation can be unclear at times, particularly when framed in the context of shifting conditions. In this research, the strategies available for managing marine biological resources within the EBFM framework and in a changing environment are reviewed. The purpose of this publication is to guide the decision on whether and how to change current management strategies in order to achieve policy goals. The manuscript starts with a revision of ecosystem indicators and ecosystem models used to detect and describe changes in ecosystem dynamics and stocks productivities under present and future conditions. Then, the different frameworks and methods available for integrating this information into the decision‐making process are summarised. Currently, some of the options available to include ecosystem realism into the fisheries advice include using ecosystem models in the Management Strategy Evaluation (MSE) process, adjusting single species reference points with ecosystem information and implementing risk‐equivalent empirical approaches. However, barriers that are impeding the adoption of these techniques exist. I concluded the study by identifying them and providing literature‐based solutions to overcome them from an interdisciplinary perspective.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".