Where the Bee Sucks -- A Dynamic Bayesian Network Approach to Decision Support for Pollinator Abundance Strategies

TL;DR

This paper introduces a dynamic Bayesian network-based decision support system to help policymakers evaluate policies for supporting pollinator populations by integrating expert knowledge and evidence in complex ecosystems.

Contribution

It presents a novel application of integrating decision support systems with dynamic Bayesian networks for policy evaluation in pollination ecosystems.

Findings

Effective policy evaluation through expert integration

Quantitative assessment of policy impacts on pollinator health

Framework adaptable to other ecological decision-making contexts

Abstract

For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies. Policymakers 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. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees and other pollinators is well-documented, but incomplete. In order to estimate the potential effectiveness of various candidate policies to support pollination services, there is an urgent need to quantify the effect of various combinations of variables on the pollination ecosystem service, utilising available information, models and expert judgement. In this paper, we present a new application of the integrating decision support system methodology for combining inputs from multiple panels of experts to evaluate policies to support an abundant pollinator population.