A State-Transition Dbn For Management Of Willows In An American Heritage River Catchment

Expansion of willows in the naturally mixed landscape of vegetation types in the Upper St. Johns River Basin in Florida, USA, im- pacts upon biodiversity, aesthetic and recre- Ational values. Managers need an inte- grated knowledge base to support decisions on where, when and how to control willows. Modelling the spread of willows over space and time requires spatially explicit data on willow occupancy, an understanding of dis- persal mechanisms and how the various life- history stages of willows respond to envi- ronmental factors and management actions. We describe an architecture for a manage- ment tool that integrates environmental spa- Tial data from GIS, dispersal dynamics from a process model and Bayesian Networks (BNs) for modelling the inuence of environmen- Tal and management actions on the key life- history stages of willows. In this paper we fo- cus on modelling temporal changes in willow stages using a form of Dynamic Bayesian Net- work (DBN). Starting from a state-transition (ST) model of the willow\u27s lifecyle, from ger- mination to seed-producing adult, we de- scribe the expert elicitation process used to develop a ST-DBN structure, that follows the template described by Nicholson and Flores (2011). We present a scenario-based evalua- Tion of the prototype ST-DBN model