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Ecological Indicators: Software Development
Sergei RodionovJoint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington
Outline
Part I. Red noise problem. Upgrade to the regime shift detection method (STARS).
Part II. Knowledge management system for the Bering Sea.
0 10 20 30 40
-2
0
2
-4
-2
0
2
4
a)
b)
R egim e 1
Regim e 2
AR (1) = 0.8
True and spurious regime shifts
Entry Form for STARS
www.BeringClimate.noaa.gov
Estimates of AR1 for PDO
0 10 20 30 40 50 60 70 80S u b sam p le s ize .
0
0.2
0.4
0.6
0.8
AR
1 co
effi
cien
t
M PK
IP4
O LS
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
-1
0
1
-2
-1
0
1
2
-2
-1
0
1
2
a) PDO
b) A fter P rewhitening
c) R esiduals
1948 19761999
1948
PDO Index Before and After Prewhitening
Part II. Dealing with Information Overload
Dimensionality reduction (e.g., principal component analysis, singular value decomposition, multidimensional scaling)
Knowledge management system
Components of the KMS
Data Explorer Rule Explorer Inference Engine Graphical Interface Search Facility Reporting Facility
Data Explorer
List of rules (partial) with the IF variables that affect walleye pollock recruitment
Part of the influence diagram for walleye pollock recruitment
Rule Explorer
Rule Edit Form
Example of a rule
IF ENSO event = El Niño,AND Aleutian low circulation type = W1,THEN SAT at St. Paul = above normal; CF = 10.
Running the project to find the value of the target variable
Questions about the variable in the terminal nodes
Search Form
Confidence Factors
CF = (P(C | e) − P(¬C | e)) * 100%,
CF = Degree of Belief – Degree of Disbelief
CFcomb = CFold + CFnew − (CFold * CFnew)/100.
Custom Property Window
Report documenting the inference process
“A spoon is valuable at the lunch time.”
Russian proverb