Harvester perceptions of climate vulnerability: Contributions to building climate resilient fisheries
Bibliographic record
Abstract
The uncertainty of future ocean conditions caused by climate change challenges the conventional fisheries management model that assumes resource extraction occurs in a steady-state environment. As managers respond to climate impacts and focus on long-term preparedness, an overarching goal is to minimize the vulnerability of fishing businesses and communities. However, during the adaptation process, challenges can arise when perceptions of climate change vulnerability differ among scientists, managers, and harvesters. A harvester’s perception of their risk to climate change influences their willingness to plan for and respond to change, yet these views are often overlooked in adaptation planning. To better understand this dynamic, we conducted a regional survey to evaluate the perceptions harvesters hold regarding the impacts of climate change on commercially fished species in the Northeastern United States and the resulting risks from those changes. The waters in this region of the Northwest Atlantic shelf are warming faster than the global average resulting in shifting distributions of species, altered seasonal migrations, and changes in productivity. Respondents’ perceptions aligned with an analysis conducted by scientists on the directionality of climate impacts for 12 out of 27 (44%) of the most commercially important species in the region. Additionally, an understanding of the variability in perceptions of climate change vulnerability emerged: 72% of respondents believe climate change is occurring, 53% believe climate change will harm them personally, and 28% have already seen a negative impact on their ability to catch fish. Respondents who believe that climate change is occurring had higher perceptions of vulnerability on average than those who do not believe it is occurring. Despite a sense of vulnerability to climate change, respondents did not rank it among the top three concerns (fisheries regulations, market access, and access to working waterfronts) for their fishing businesses. Investigating harvester’s perceptions is an opportunity to share their experiences and understand the diversity of perspectives regarding the impacts of climate change. Increasing the inclusion of social science indicators and diverse perspectives will increase climate resilience of fisheries management.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".