Competition between marine mammals and fisheries in contemporary harvested marine ecosystems
Bibliographic record
Abstract
Competitive interactions between marine mammals and fisheries represent some of the most complex challenges in marine resource management worldwide. The development of commercial fisheries and recovering marine mammal populations have contributed to a decrease in fish availability. Whilst ecosystem-based fisheries management (EBFM) can counteract this decrease, achieving the EBFM objectives faces certain major obstacles including insufficient or unreliable data, inapplicable assessment models, as well as inadequate management decisions that do not account for fisheries-induced morphological alterations (FIMA) and marine mammal management. Despite a body of evidence addressing various aspects of marine mammal-fisheries competition, little is known about the effects of marine mammal-fisheries biological interactions affecting the fish viability and food web stability. We review the research on marine mammal-fisheries competitive biological interactions (hereafter biological competition) by focussing on (1) the prerequisites for marine mammal-fisheries biological competition and the relevant methodologies to explore them and (2) recent studies revealing the implications of FIMA and trophic interactions for the biological competition. We also discuss the implications of FIMA, eco-evolutionary feedback and prey-predator dynamics for EBFM implementation in contemporary harvested ecosystems. Our main findings reveal a lack of data about marine mammals’ prey choice and selectivity, the need for better representation of marine mammals in modelling approaches and lastly, the necessity for additional research linking FIMA, trophic interactions and the EBFM objectives. To conclude, interdisciplinary approaches may serve to link all of the efforts needed to effectively and holistically support the implementation of EBFM.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".