Pathways between Climate, Fish, Fisheries, and Management: A Conceptual Integrated Ecosystem Management Approach
Bibliographic record
Abstract
The abundance and distribution of marine fishes is influenced by environmental conditions, predator–prey relationships, multispecies interactions, and direct human impacts, such as fishing. The adaptive response of the system depends on its structure and the pathways that link environmental factors to the taxon in question. The “Star Diagram” is a socio-ecological model of marine ecosystems that depicts the general pathways between climate, fish, and fisheries, and their intersection with climate policy and resource management. We illustrate its use by identifying the key factors, pathways and drivers that influence walleye pollock, crab, and sockeye salmon, under a warming scenario on the eastern Bering Sea shelf. This approach predicts that all three species will see reduced populations under a long-term warming scenario. Going forward, the challenge to managers is to balance the magnitude of the effect of harvest and the adaptability of their management system, with the scale and degree of resilience and the behavioral, physiological, or evolutionary adaptation of the ecosystem and its constituents. The Star Diagram provides a novel conceptual construct that managers can use to visualize and integrate the various aspects of the system into a holistic, socio-ecological management framework.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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 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".