Ecological and biological modeling for natural resource management: applications to wetland classification and evaluation

The goal of wetland assessment is to identify and quantify the condition of wetlands, taking into account the presences of threats likely to impact the services and functions the wetlands provide. There are a wide variety of methods available for undertaking wetland assessment; most rely on data collection across a broad range of attributes at wetland sites to gauge wetland condition. This thesis examines the practice of wetland assessment in West Gippsland, south-eastern Australia and it investigates the contribution, and potencies, of component biological, chemical, hydrological and physical data inputs, individually and collectively, to the identification of high social, economic and environmental value wetlands in the region. A systematic analysis using statistics and data-mining techniques was undertaken of the inventory data for 163 representative wetlands to discover pertinent relationships between the values of different site characteristics and the classification of high-value wetlands. Binary logistic regression and neural networks were used to build models mimicking the wetland assessment process, and an assessment of their abilities to do so was conducted. The influences of two wetland classification schemes: Corrick and Norman (1980) scheme, and Ecological Vegetation Classes (EVCs), on the naming of high-value wetlands were also investigated