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Social networks and information silos influence conservation knowledge in tourist populations
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Abstract
Marine tourism is a rapidly expanding industry with significant impacts on marine ecosystems and species. The behavior of individuals engaging with these ecosystems for economic or recreational purposes plays a strong role in the success of conservation efforts. To mitigate some of the negative effects human actions can have on ecosystems, effective dissemination of conservation knowledge and policies is required. This study used an agent-based model to explore how social interactions and information silos within and between local and tourist populations influenced social learning about conservation knowledge. Specifically, we examined how demographic group size and interaction frequency among locals and tourists in marine tourism settings influence conservation knowledge dissemination. Our results revealed that both the frequency of interactions and the relative sizes of demographic groups significantly affected knowledge accumulation among tourists. In general, higher interaction probabilities led to more social learning, particularly among well connected and large groups. However, reduced interaction probabilities between certain demographic groups acted as information silos, which resulted in lower knowledge levels in these groups, although this could be mitigated by increased educational efforts. These findings suggest that while increasing interaction opportunities is important, the context and structure of these interactions are equally important. Tailoring educational interventions to the specific dynamics of group-identity based social interactions in marine tourism can enhance the effectiveness of conservation efforts and optimize outcomes for wildlife protection.
DOI
https://doi.org/10.32942/X2MH3H
Subjects
Life Sciences
Keywords
agent-based model, conservation knowledge, information silos, social learning, tourism
Dates
Published: 2026-04-04 15:04
Last Updated: 2026-04-04 15:04
License
Additional Metadata
Data and Code Availability Statement:
Modeling code and input data available via GitHub at https://github.com/jwillou/socialdata.
Language:
English
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