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 or spatial and temporal scales * Hard to predict (e.g. the weather) * Must be studied as a whole, as well as piece by piece * Relevant for all kinds of organisms – from microbes to human beings * Relevant for environments that range from frozen polar regions and volcanic vents to temperate forests and agricultural lands as well as neighborhoods 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. Complexity – Canonical Example The Bak Sandpile Add sand to a pile, one grain at a time Record the size of the avalanches Result: Many small, few large avalanches. Construct a frequency distribution of avalanche sizes The distribution fits a power law.

6. # Patches Power Law Phenomena Eelgrass Habitat of Juvenile Cod. Analysis by Miriam O Patch Size ß = Korchak Dimension A CASI image of eelgrass was analyzed at a resolution of 16m 2 Patch size was defined by contiguous pixels at this resolution. Result:Power law relation of patch frequency to patch area. But is this due to complex dynamics ?

7. ß # Patches 16m 2 64m 2 144m 2 256m 2 400m 2 Complexity of Eelgrass Habitat of Juvenile Cod. Analysis by Miriam O Korchak dimension ß found to be a power law function of resolution

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

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

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 or spatial and temporal scales * Hard to predict (e.g. the weather) * Must be studied as a whole, as well as piece by piece * Relevant for all kinds of organisms – from microbes to human beings What are the Implications?

12. Implications of Power Laws 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? * Hard to predict (e.g. the weather) * Interactions that span multiple levels or spatial and temporal scales

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

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

15. 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. Interaction of Environmental Complexity with Human Organizational Complexity

16. Natural Science Social Science History Matters! Health: Environment, Individuals, Communities 195519651975 15 20 25 30 35 40 45 Catch Year 195519651975 Million DKK 0 100 200 300 400 500 Investment Year Million DKK 0 200 400 600 800 1000 0 20 40 60 80 100 1985 1975 Interaction of Biocomplexity (e.g., Catch) with Organizational Complexity (e.g., Investment)

17. Implications of Complexity * Must be studied as a whole, as well as piece by piece * Relevant for all kinds of organisms – from microbes to human beings Health: Environment, Individuals, Communities Investment Catch 195519651975 15 20 25 30 35 40 45 Year 195519651975 Million DKK 0 100 200 300 400 500 Year Million DKK 0 200 400 600 800 1000 0 20 40 60 80 100 1985 1975

18. Summary Complexity 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.