Multilevel stakeholder networks for Australian marine biosecurity: well-structured for top-down information provision, requires better two-way communication
Ryan R. J. McAllister, CSIRO
Heleen Kruger, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), Department of Agriculture, Water and the Environment, Australian Government
Nyree Stenekes, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), Department of Agriculture, Water and the Environment, Australian Government
Robert Garrard, CSIRO
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The structure of stakeholder networks impacts the ability for environmental governance to fulfil core functions: share information; agree on problem framing and actions; and resolve conflict. Managing pest and disease incursions presents particular challenges. Rapid coordination of action is needed in times of crisis, but any hope of success during crisis requires a foundation of ongoing communication and surveillance. Recent Australian strategic planning for marine biosecurity identified the critical role of an independent national marine pest network in providing ongoing communication. We surveyed stakeholders in the existing marine pest network to map how they share information. Constructing a multilevel, directed network, with 304 organizations and 12 policy forums, we applied statistical network theory to identify which subnetwork configuration patterns were present more or less than by chance. We mapped configurations against how they shape the network’s propensity for information sharing. What we found was a marine pest network with a predisposition for bridging; evidence of hubs for both provision and receiving of information; and organizations reporting greater levels of information provision to others compared to receiving information. Our assessment is that the network is well structured for top-down information provision, but that a more sustainable network will require attention to building two-way communication particularly with community groups.
configurations; exponential random graph model; social network analysis
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