Social dimensions of shark–human interactions in a large remote marine protected area
Bibliographic record
Abstract
Abstract The impact of shifting marine conservation policy, including marine protected area (MPA) designation, in shaping interactions between humans and imperilled species, such as sharks, remains understudied, despite its importance in determining the success of these interventions. We investigated perceptions of shark–human interactions (SHI) among the community in a remote, large‐scale MPA (Ascension Island) where two recent shark attacks and perceived general increases in interactions (mostly with Galapagos and silky sharks), including depredation in recreational fisheries, have occurred. From 2023 to 2024, informal semistructured interviews ( n = 34) were conducted with island residents and analysed using two theory‐driven thematic frameworks to understand the level and drivers of conflict. We showed considerable social impact of SHI, including reduced human well‐being and substantial lifestyle changes, with both depredation and human attacks driving dispute‐level conflict. Strong social and familial connections on island resulted in narratives around attacks persisting and trauma resulting from attacks drove heightened perceived risk. Underlying conflict was further exacerbated by the perceived recurrent and unpredictable nature of negative SHI compounded with the interactions being perceived as abnormal with limited information on socio‐ecological drivers. Some felt excessive chumming by historic recreational fisheries, mostly engaged in by non‐residents, had also involuntarily exposed them to heightened risk by increasing SHI. There was also no consensus of what shark species were behind the increased interactions. Management resolutions were perceived as minimal but were not widely viewed as negative. However, divergent views on the use of lethal control and the need for conservation measures, such as banning shark exploitation, were evident. A key theme emerged around the need for wider community participation in the research and management processes. Policy implications . Our results highlight the critical importance of demystifying marine species, particularly in terms of understanding socio‐ecological drivers of human–wildlife interactions, to combat escalation into human–wildlife conflict. This is particularly important to maintain support for large‐scale MPAs and species‐specific conservation. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".