Many ecological models quantify patterns that result from biological processes, extrapolate the patterns to make predictions into the future, and infer causality. These models describe the results of biological processes but are limited in understanding the interactions within the system. However, many ecological processes are complex systems comprised of many heterogeneous components that learn and adapt to a changing landscape that create patterns as emergent properties from individual decisions. This dissertation proposes a generic framework of how biological processes work. From this framework a modeler can capture biological processes by explicitly modeling time, space, and the essence of the decision making of the components of a biological process to understand causality.
Three models are presented exploring the continuum of capturing biological realism. The first explores the potential effects the doubling of CO 2 will have on four wildlife species. Through a logistic regression, the model quantifies current wildlife distribution patterns and projects these characteristics onto the changed landscape after the doubling of CO2 . Causality is inferred and little is understood of the biological interactions.
The second model places virtual moose onto a Geographic Information System (GIS) landscape and based on a series of physiological characteristics the moose select the optimum activity budget to maximize fitness within the landscape. The model simulates space and the decision making in a more biological realistic manner than the first model, but time is generalized.
The third model is agent-based in which virtual cougars are placed in a GIS landscape and make tradeoffs each time step as they move through the landscape. The tradeoffs include hunting, mating, and remaining safe. The model explicitly models time, space, and the essence of a cougar decision making thus modeling in a more biological realistic manner.
Due to the magnitude and speed at which change is now occurring, models based on observations that quantify and describe processes and patterns and attempt to replicate these processes may no longer suffice. In order to predict the success or survival of the ecological elements in these new situations, we must understand the mechanisms that drive the elements.
|School Location:||United States -- Connecticut|
|Source:||DAI-B 69/06, Dissertation Abstracts International|
|Subjects:||Ecology, Environmental science|
|Keywords:||Agent-based modeling, Biological processes, Ecological modeling, GIS|
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