Managing Small-Scale Commercial Fisheries for Adaptive Capacity: Insights from Dynamic Social-Ecological Drivers of Change in Monterey Bay
Bibliographic record
Abstract
Globally, small-scale fisheries are influenced by dynamic climate, governance, and market drivers, which present social and ecological challenges and opportunities. It is difficult to manage fisheries adaptively for fluctuating drivers, except to allow participants to shift effort among multiple fisheries. Adapting to changing conditions allows small-scale fishery participants to survive economic and environmental disturbances and benefit from optimal conditions. This study explores the relative influence of large-scale drivers on shifts in effort and outcomes among three closely linked fisheries in Monterey Bay since the Magnuson-Stevens Fisheries Conservation and Management Act of 1976. In this region, Pacific sardine (Sardinops sagax), northern anchovy (Engraulis mordax), and market squid (Loligo opalescens) fisheries comprise a tightly linked system where shifting focus among fisheries is a key element to adaptive capacity and reduced social and ecological vulnerability. Using a cluster analysis of landings, we identify four modes from 1974 to 2012 that are dominated (i.e., a given species accounting for the plurality of landings) by squid, sardine, anchovy, or lack any dominance, and seven points of transition among these periods. This approach enables us to determine which drivers are associated with each mode and each transition. Overall, we show that market and climate drivers are predominantly attributed to dominance transitions. Model selection of external drivers indicates that governance phases, reflected as perceived abundance, dictate long-term outcomes. Our findings suggest that globally, small-scale fishery managers should consider enabling shifts in effort among fisheries and retaining existing flexibility, as adaptive capacity is a critical determinant for social and ecological resilience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".