Aggregated Mental Models Predict Observed Outcomes Following Eurasian Beaver (castor Fiber) Reintroduction

Outcome prediction is important for conservation; however, analysis may be hampered by specialist resource deficiencies. Mental modelling techniques offer a potential solution, drawing on accessible sources of knowledge held informally by local stakeholders. Mental models show linked social and ecological variables from the perspectives of community members, whose insights may otherwise be neglected. Currently, an important weakness in conservation mental modelling is inadequate attention paid to real-time model predictive validity. To address this knowledge gap, baseline mental model predictions concerning Beaver (Castor fiber) reintroduction in Southwest England were followed up at three years. Participants were invited to submit outcome observations for concept variables identified in their original models, blind to inferences based on model dynamic analysis, so that the two sets of data could be compared. Individual concept values and models were found to show weak and highly inconsistent predictive validity, however, multi-stakeholder aggregated mental models showed consistently strong predictive performance. This finding was enhanced by setting tighter thresholds for inclusion of individual model items in aggregation procedures. Threshold effects can be interpreted as a reflection of greater agreement: tighter thresholds retain more highly shared model components. It is proposed that enhanced real-time predictive validity for aggregated models is explained by a ‘wisdom of the crowd’ statistical effect, analogous to well-recognised crowd judgement effects observed in relation to much simpler questions. The findings show the scope for stakeholder mental modelling methods as an investigative tool, to supplement more conventional ecosystem assessments in predicting data-poor conservation outcomes.