An Object-oriented Spatial and Temporal Bayesian Network for Managing Willows in an American Heritage River Catchment

Willow encroachment into the naturally mixed landscape of vegetation types in the Upper St. Johns River Basin in Florida, USA, impacts upon biodiversity, aesthetic and recreational values. To control the extent of willows and their rate of expansion into other extant wetlands, spatial context is critical to decision making. Modelling the spread of willows requires spatially explicit data on occupancy, an understanding of seed production, dispersal and how the key life-history stages respond to environmental factors and management actions. Nicholson et al. (2012) outlined the architecture of a management tool to integrate GIS spatial data, an external seed dispersal model and a state-transition dynamic Bayesian network (ST-DBN) for modelling the influence of environmental and management factors on temporal changes in willow stages. That paper concentrated on the knowledge engineering and expert elicitation process for the construction and scenario-based evaluation of the prototype ST-DBN. This paper extends that work by using object-oriented techniques to generalise the knowledge organisational structure of the willow ST-DBN and to construct an object-oriented spatial Bayesian network (OOSBN) for modelling the neighbourhood spatial interactions that underlie seed dispersal processes. We present an updated architecture for the management tool together with algorithms for implementing the dispersal OOSBN and for combining all components into an integrated tool.