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Complexity in Fisheries Ecosystems

Complexity in Fisheries Ecosystems. David Schneider Ocean Sciences Centre, Memorial University St. John’s, Canada. ENVS 6202 – 26 Sept 2007. Complexity in Fisheries Ecosystems. Definition(s) of Complexity Examples Several criteria Implications of Complexity. Definition of Complexity.

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Complexity in Fisheries Ecosystems

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  1. Complexity in Fisheries Ecosystems David Schneider Ocean Sciences Centre, Memorial University St. John’s, Canada ENVS 6202 – 26 Sept 2007

  2. Complexity in Fisheries Ecosystems • Definition(s) of Complexity • Examples • Several criteria • Implications of Complexity

  3. Definition of Complexity Ecological Society of America Fact Sheet Common characteristics of complexity include: * Nonlinear or chaotic behavior * Interactions that span multiple levels orspatialand temporal scales * Hard to predict (e.g. the weather) * Must be studied as a whole, as well as pieceby piece * Relevant for all kinds of organisms – frommicrobes to human beings * Relevant for environments that range from frozenpolar regions and volcanic vents to temperateforests and agricultural lands as well asneighborhoods and industries or urban centers.

  4. Definition of Complexity Murray Gell-Mann: Complexity refers to phenomena that show scaling (power laws), due to non-linear interactions.

  5. Construct a frequency distribution of avalanche sizes The distribution fits a power law. Complexity – Canonical ExampleThe Bak Sandpile Add sand to a pile, one grain at a time Record the size of the avalanches Result: Many small, few large avalanches.

  6. Power Law Phenomena Eelgrass Habitat of Juvenile Cod. Analysis by Miriam O A CASI image of eelgrass was analyzed at a resolution of 16m2 Patch size was defined by contiguous pixels at this resolution. ß = Korchak Dimension # Patches Patch Size Result: Power law relation of patch frequency to patch area. But is this due to complex dynamics ?

  7. Complexity of Eelgrass Habitat of Juvenile Cod. Analysis by Miriam O Korchak dimension ß found to be a power law function of resolution ß 64m2 144m2 256m2 400m2 16m2 # Patches

  8. More Examples of Power Laws Avalanches Earthquake magnitude Fire frequency Fire size River discharges Watershed evolution Tree fall area in the tropics Stock market fluctuations Q: What do these phenomena have in common? A: Antagonistic rates, one acting episodically with respect to the other.

  9. Episodically Antagonistic Rates – More Examples Hurricanes ENSO Fronts Jets Eddies Langmuir cells Fish Population Dynamics Stable------Cyclic-------Chaotic Fisheries Economics Stable? Cyclic? or Build/Collapse? Q: What do these phenomena have in common? A: Antagonistic rates, one acting episodically with respect to the other.

  10. Definition of Complexity Criteria: Power laws Episodically antagonistic rates Non-linear interactions Fish and the Environment in the Pacific Hsieh et al 2005 Power laws? -Unknown Episodically antagonistic rates -Possibly Non-linear interactions -Fish – Yes -Physics – No

  11. Common Characteristics of Complexity * Interactions that span multiple levels orspatialand temporal scales * Hard to predict (e.g. the weather) * Must be studied as a whole, as well as pieceby piece * Relevant for all kinds of organisms – frommicrobes to human beings What are the Implications?

  12. Fisheries scientists are used to the idea of limits on prediction set by high variance. But what if uncertainty has a heavy left tail ? What if there is usually a larger rare event, lying outside of past experience? Implications of Power Laws * Hard to predict (e.g. the weather) * Interactions that span multiple levels orspatialand temporal scales

  13. Are regime shifts low frequency events due to complex dynamics? Implications of Power Laws How many regime shifts are in this time series?

  14. Discussion of Implications Wilson 1994 Fogarty 1995 Wilson 2002 Implications of Power Laws * Hard to predict (e.g. the weather) * Interactions that span multiple levels orspatialand temporal scales

  15. Interaction of Environmental Complexity with Human Organizational Complexity Goals Coasts under Stress To identify the important ways in which changes in society and the environment interact. To identify how these changes have affected, or will affect, the health of people, their communities, and the environment in the long run.

  16. 100 Catch 45 40 80 35 60 30 40 25 20 20 0 1975 15 1985 1955 1965 1975 500 1000 Investment 400 800 300 600 Million DKK Million DKK 200 400 100 200 0 0 1955 1965 1975 Year Year Health: Environment, Individuals, Communities Interaction of Biocomplexity (e.g., Catch) with Organizational Complexity (e.g., Investment) Natural Science Social Science History Matters!

  17. 100 45 40 80 35 60 30 40 25 20 20 0 1975 15 1985 1955 1965 1975 500 1000 400 800 300 600 Million DKK 200 Million DKK 400 100 200 0 0 1955 1965 1975 Year Year Implications of Complexity * Must be studied as a whole, as well as pieceby piece * Relevant for all kinds of organisms – frommicrobes to human beings Catch Investment Health: Environment, Individuals, Communities

  18. SummaryComplexity in Fisheries Ecosystems A new way of thinking about fisheries and fisheries ecosystems. Applies to organisms, schools, populations, habitats, ecosystems. Several criteria, from loose to strict. Cannot rely on: Euclidean geometry, Newtonian mechanics, Equilibrium dynamics.

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