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Understanding Variability in Fish Populations: An Investigation of Time Series Prediction Methods For Improved Stock P

Main Objectives: (Year 1). What can be learned from 50 years of observational effort (CalCOFI icthyoplankton surveys) about how the ecosystem system works and how to build our models?Quantify ecosystem dynamics from historical time series data.Use historical CalCOFI ithyoplankton surveys to me

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Understanding Variability in Fish Populations: An Investigation of Time Series Prediction Methods For Improved Stock P

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    1. Understanding Variability in Fish Populations: An Investigation of Time Series Prediction Methods For Improved Stock Projections (Year 1) Christian Anderson, Chih Hao Hsieh, Roger Hewitt, George Sugihara

    2. Main Objectives: (Year 1) What can be learned from 50+ years of observational effort (CalCOFI icthyoplankton surveys) about how the ecosystem system works and how to build our models? Quantify ecosystem dynamics from historical time series data. Use historical CalCOFI ithyoplankton surveys to measure the effect of fishing on the variability of fish stocks. Develop methods to produce operational predictive models and methods to forecast future states in the fishery. the main scientific problem is forecasting future states in the fishery. the main scientific problem is forecasting future states in the fishery.

    3. Plan: Year 2 Determine if the higher variance of exploited species comes from nonlinear instability. Continue to develop improved forecast methods and extend operational forecast models for other fisheries (Alaska). Find low-dimensional model embeddings that identify predictive physical variables. Investigate how these methods can identify coupled subsystems for ecosystem-based management.

    4. Takens 1981 Let M be a compact manifold of dimension m, F a smooth (c2) vector field, and h a smooth function on M. It is a generic property that

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