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Activity Number:
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512
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #308554 |
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Title:
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Nonlinear Forecasts of the Gulf of Maine Ecosystem
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Author(s):
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Barbara Bailey*+ and Andrew Pershing
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Companies:
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San Diego State University and University of Maine
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Address:
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Dept of Mathematics and Statistics, San Diego, CA, 92182-7720,
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Keywords:
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neural network models ; prediction intervals ; bootstrap
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Abstract:
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Time series of physical and biological properties of the ocean are a valuable resource for developing models for ecological forecasting. Both the physics of the oceans and organisms living in it can exhibit nonlinear dynamics. We describe the development of a model that forecasts the abundance of Calanus finmarchicus from hydrographic data from the Gulf of Maine. We present the results of a neural network model including model diagnostics and forecasts. Measures of uncertainty and prediction intervals are obtained by the nonparametric bootstrap.
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