Assessment of the response of pollinator abundance to environmental pressures using structured expert elicitation

Barons, Martine J., Hanea, Anca M., Wright, Sophia K., Baldock, Katherine C. R., Wilfert, Lena, Chandler, David, Datta, Samik, Fannon, Jessica, Hartfield, Chris, Lucas, Andrew, Ollerton, Jeff, Potts, Simon G. and Carreck, Norman L. (2018) Assessment of the response of pollinator abundance to environmental pressures using structured expert elicitation. Journal of Apicultural Research, 57 (5). pp. 593-604. ISSN 0021-8839

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Abstract

Policy-makers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. For policy-makers wishing to make evidence-based decisions which will best support pollinator abundance and pollination services, one of the problems faced is how to access the information and evidence they need, and how to combine it to formulate and evaluate candidate policies. This is even more complex when multiple factors provide influence in combination. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees, and other pollinators are well documented, but incomplete. In order to estimate the potential effectiveness of various candidate policy choices, there is an urgent need to quantify the effect of various combinations of factors on the pollination ecosystem service. Using high-quality experimental evidence is the most robust approach, but key aspects of the system may not be amenable to experimentation or may be prohibitive based on cost, time and effort. In such cases, it is possible to obtain the required evidence by using structured expert elicitation, a method for quantitatively characterizing the state of knowledge about an uncertain quantity. Here we report and discuss the outputs of the novel use of a structured expert elicitation, designed to quantify the probability of good pollinator abundance given a variety of weather, disease, and habitat scenarios.

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