Mental model analysis and beaver conservation in England

Samenvatting

This thesis explores stakeholder perspectives on the first free-living reintroduction of former-native Eurasian beaver, Castor fiber into England, known as the River Otter Beaver Trial (ROBT), reported in 2020. The beaver is a keystone species, typically transforming river systems to the benefit of ecosystem health and biodiversity within recovered natural range, in addition to intrinsic species value. Consequently, beavers are a focus of intense conservation interest in Britain, as elsewhere in Europe. Beaver ecology interacts strongly with human landscape management priorities most notably in farming and fishing, almost certainly contributing along with over-hunting to their historic decline. Hence, stakeholder positioning has great relevance to conservation management. Structures of beaver-relevant stakeholder knowledge, termed mental models, can be elicited through interviewing to form subjective cognitive maps as raw material for analysis and research. Accounting for the uncertainties of informal knowledge, the format known as fuzzy cognitive mapping is helpful to compare key parameters including dynamic modelling of ‘outputs.’ In aggregate, combinations of mental models to represent shared understanding are known to align well with independent empirical assessments of how ecosystems work. To strengthen the role of mental modelling in this context, important gaps in theory were identified and addressed through baseline and follow-up investigations of an initial research sample of 48 river Otter beaver-system stakeholder mental models. Retrospective model analysis showed that conservation policy and actions reconcile key stakeholder mental model divergences, reducing the potential for harmful human-wildlife conflict. The influence of emotion on mental model properties is poorly recognised in the literature, despite a known role for emotion in regulating human wildlife interactions. In the present sample, emotion – mental model correlations were tested and found to be more marked in aggregation, pointing to a role for emotion in signalling ‘what matters’ for individuals and socially, and suggesting roles for mental models in decision-making and appraisal theories of emotion. Beaver-relevant appraisal mechanisms likely to influence stakeholder choices are outlined. Future mental model predictive validity was also most marked in aggregated models, despite hard to predict intercurrent social events outside of the beaver domain. Predictive effects were greater when thresholds for inclusion of shared model material in aggregate were tightened to suggest a more ‘average’ representative combined model. It is proposed that this is an example of the ‘wisdom of the crowd’ statistical phenomenon, underlining the importance of socially distributed knowledge and possibly shared emotional experience. In synthesis, these insights strengthen the case for aggregated, emotionally informed mental model methods in planning and monitoring large, socially complex conservation projects. They may also contribute legitimacy through showing public engagement and respectful, inclusive approaches to non-specialist local expertise. In conclusion, the overall thesis findings suggest that mental modelling has much to offer in understanding and meeting knowledge gaps in conservation assessments, and strengthening understanding of stakeholder priorities, interactions and outcomes at a time of increasing awareness of human-wildlife conflict and continued declines in British and global biodiversity and ecosystem health.