| This dissertation investigates the problem of uncertainty in complex ecological models. The term “complex” is used to convey both the common and scientific meanings. Increasingly, ecological models have become complex because they are more complicated; ecological models are generally multi-variate and multi-leveled in structure. Many ecological models are complex because they simulate the dynamics of complex systems. As a result, and as science moves from the modern/normal to postmodern/post-normal paradigm view of the world, the definition of uncertainty and the problem of uncertainty estimation in models tread the lines between the technical and the philosophical. With this in mind, I have chosen to examine uncertainty from several perspectives and under the premise that the needs and goals of uncertainty estimation, like ecological models themselves, are evolving. Each chapter represents a specific treatment of uncertainty and introduces new methodologies to evaluate the nature, source, and significance of model uncertainty. In the second chapter, ‘Determining the significance of threshold values uncertainty in rule-based classification models’, I present a sensitivity analysis methodology to determine the significance of uncertainty in spatially-explicit rule-based classification models. In the third chapter, ‘Process level sensitivity analysis for complex ecological models ’, I present a sensitivity analysis methodology at the process level, to determine the sensitivity of a model to variations in the processes it describes. In the fourth chapter, ‘A Component Based Approach for the Development of Ecological Simulations’, I investigate how the process of developing an ecological simulation can be advanced by using component-based simulation frameworks. I conclude with reflection on the future of modeling and studies of uncertainty. |
