Distributional range shift of a marine fish relates to a geographical gradient of emotions among recreational fishers
Bibliographic record
Abstract
As the effects of climate change increase, distributional range shifts of species are also expected to be magnified, necessitating a better understanding of their social-ecological implications for the adaptive management of fisheries and biodiversity conservation. In this paper, we focused on the human dimensions of recreational fisheries in the context of an ongoing distributional range shift of a target species. Specifically, we mined data on YouTube from recreational anglers and spearfishers targeting the white grouper (Epinephelus aeneus), a species expanding northwards in the northwestern Mediterranean Sea (Italy, France, and Spain). We retrieved 453 videos from Italy and Spain. We analyzed the social engagement of the videos (i.e., number of views, likes, and comments) and applied sentiment analysis to all the comments posted on these videos. Results showed that social engagement is overall higher for spearfishers than anglers. We documented an overall positive polarity and positive emotions in the comments of the posted videos, but specific negative polarity and negative emotions were more common in angling videos than in spearfishing ones. Most importantly, we detected a significant positive correlation between the emotions of joy and surprise and the latitude at which white grouper was caught. This result suggests that recreational fishers may respond to the arrival of the white grouper by showing more joy and surprise at higher latitudes where the species is rare than at lower latitudes where the species is common. Our study illustrates how digital data from social media can be used to monitor social-ecological interactions, such as tracking species distributional range shifts and the human responses to them, with potential management implications. Specifically, these results may be informative to adapt necessary tailored-management actions by improving engagement with fishers and enhancing more effective communication strategies, finally evoking environmental stewardship.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".