Author
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FIELDING, DENNIS |
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Submitted to: Ecological Modelling
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 10/31/2003 Publication Date: 7/1/2004 Citation: Fielding, D.J. 2004. Intraspecific competition and spatial heterogeneity alter life history traits in an individual-based model of grasshoppers. Ecological Modelling 175:169-187 Interpretive Summary: Because of their prominent status as economic pests of agriculture and rangeland, a great deal of research has been conducted on grasshopper biology and ecology over the last 100 years. No attempt has been made to synthesize this knowledge and use it to develop a comprehensive model of grasshopper population dynamics. This paper represents the first step in such a model. To aid in our understanding of the evolution of grasshopper life histories, the model incorporates methods of evolutionary computation to find the optimal combination of traits that allow grasshoppers to thrive in the unpredictable environment of temperate grasslands. Results from simulations are in agreement with predictions from mathematic analysis of the test problems. This computer model shows promise as a means of integrating the voluminous information regarding grasshopper ecology and biology, and as a means of grasshopper population forecasting under a variety of environmental conditions. Technical Abstract: To aid in our understanding of the evolution of grasshopper life histories and their influence on population dynamics, an individual-based simulation model was developed that incorporates methods of evolutionary computation. Life history attributes, such as size of eggs, and timing of diapause, were 'genes' of the grasshoppers in the simulations. Offspring inherited these traits from their parents. The more successful combination of traits left more offspring and come to dominate the population after several generations. The solutions arrived at by the simulated populations under a variety of conditions (such as variable growth rates and season lengths) were compared to predictions from mathematic analysis of the test problems. The solutions by the two methods were in close agreement, lending confidence in results that may be obtained in more complex simulations. This computer model shows promise as a means of integrating the voluminous information regarding grasshopper ecology and biology, and as a means of grasshopper population forecasting under a variety of environmental conditions. |
