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
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.
Definition of Complexity
Murray Gell-Mann:
Complexity refers to phenomena
that show scaling (power laws),
due to non-linear interactions.
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.
Power Law Phenomena
Eelgrass Habitat of Juvenile Cod.
Analysis by Miriam O
# Patches  k  (PatchSize)

ß = Korchak Dimension
A CASI image of
eelgrass was analyzed
at a resolution of 16m2
Patch size was defined
by contiguous pixels
at this resolution.
Patch Size
Result: Power law relation of patch frequency to patch area.
But is this due to complex dynamics ?
Complexity of Eelgrass Habitat of Juvenile Cod.
Analysis by Miriam O
Korchak dimension ß found to be
a power law function of resolution
0
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
-0.2
-0.4
ß
-0.6
-0.8
-1
-1.2
-1.4
-1.6
16m2
64m2
144m2
256m2
400m2
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.
Episodically Antagonistic Rates –
More Examples
Hurricanes
Fronts
Jets
ENSO
Eddies
Fish Population Dynamics
Langmuir cells 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.
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?
Episodically antagonistic rates
Non-linear interactions
-Unknown
-Possibly
-Fish – Yes
-Physics – No
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?
Implications of Power Laws
* Hard to predict (e.g. the weather)
* Interactions that span multiple levels or spatial and
temporal scales
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
How many
regime shifts
are in this
time series?
Are regime shifts low
frequency events due
to complex dynamics?
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
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.
Interaction of Biocomplexity (e.g., Catch) with
Organizational Complexity (e.g., Investment)
100
45
40
35
Catch
80
60
30
40
25
20
20
0
15
1955
1965
1000
400
800
Investment
Million DKK
Million DKK
500
300
200
100
0
1975
1985
1975
Social
Science
600
400
200
0
1955
1965
Year
Health:
Environment,
Individuals,
Communities
1975
Natural
Science
Year
History
Matters!
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
45
100
40
80
35
60
30
Catch
40
25
20
20
0
15
1955
1965
1985
1975
1000
400
800
Million DKK
Million DKK
500
300
200
100
0
1975
600
Investment
400
200
0
1955
1965
Year
1975
Year
Health:
Environment,
Individuals,
Communities
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.