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Ecological Indicators: Software Development

Develops specialized software integrating regime shift detection methods and knowledge management systems for ecological monitoring in the Bering Sea region, addressing information overload and facilitating data exploration and rule-based inference.

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Ecological Indicators: Software Development

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  1. Ecological Indicators: Software Development Sergei Rodionov Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

  2. Outline • Part I. Red noise problem. Upgrade to the regime shift detection method (STARS). • Part II. Knowledge management system for the Bering Sea.

  3. True and spurious regime shifts

  4. Entry Form for STARS www.BeringClimate.noaa.gov

  5. Estimates of AR1 for PDO

  6. PDO Index Before and After Prewhitening

  7. Part II. Dealing with Information Overload • Dimensionality reduction (e.g., principal component analysis, singular value decomposition, multidimensional scaling) • Knowledge management system

  8. Components of the KMS • Data Explorer • Rule Explorer • Inference Engine • Graphical Interface • Search Facility • Reporting Facility

  9. Data Explorer

  10. List of rules (partial) with the IF variables that affect walleye pollock recruitment

  11. Part of the influence diagram for walleye pollock recruitment

  12. Rule Explorer

  13. Rule Edit Form

  14. Example of a rule IF ENSO event = El Niño, AND Aleutian low circulation type = W1, THEN SAT at St. Paul = above normal; CF = 10.

  15. Running the project to find the value of the target variable

  16. Questions about the variable in the terminal nodes

  17. Search Form

  18. Confidence Factors CF = (P(C | e) − P(¬C | e)) * 100%, CF = Degree of Belief – Degree of Disbelief CFcomb = CFold + CFnew − (CFold * CFnew)/100.

  19. Custom Property Window

  20. Report documenting the inference process

  21. “A spoon is valuable at the lunch time.” Russian proverb

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