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Teaching numeric disciplines to higher education students in many life sciences disciplines is highly challenging. In this study, we test whether an approach linking field observations with predictive models can be useful in allowing students to understand basic numeracy and probability, as well as developing skills in modelling, understanding species interactions and even community/ecosystem-service interactions.  We presented a field-based lecture in a morning session (on rocky shore ecology), followed by an afternoon session parameterising a belief network using a simple, user-friendly interface. The study was conducted with students during their second week of a foundation degree, hence having little prior knowledge of these systems or models. All students could create realistic predictive models of competition, predation and grazing, although most initially failed to account for trophic cascade effects in parameterising their models of the rocky shore they had previously seen. The belief network was then modified to account for a marine ecosystem management approach, where fishing effort and economic benefit of fishing were linked to population abundance of different species, and management goals were included. Students had little difficultly in applying conceptual links between species and ecosystem services in the same manner as between species. Students evaluated their understanding of a range of variables from rocky shore knowledge to marine management as increasing over the session, but the role of the predictive modelling task was indicated as a major source of learning, even for topics we thought may be better learned in the field. The study adds evidence to the theories that students benefit from exposure to numeric topics, even very early in their degree programmes, but students grasp concepts better when applied to real world situations which they have experience of, or perceive as important.

Teaching basic numeracy, predictive models and socioeconomics to marine ecologists through Bayesian belief networks