Transcript Resilience in landscape exploitation systems

Dynamic resilience in landscape exploitation systems
Cameron Fletcher, David Hilbert, Andrew Higgins, Peter Roebeling, John Ludwig
CSIRO Sustainable Ecosystems
CSIRO Complex Systems Science
Emerging Science Area
www.csiro.au
Synopsis
Important points
 We have created a dynamic, generic model of landscape exploitation systems
 We analyse the topology of state space to summarize properties across all systems
 We aim to analyse these systems at multiple scales, across multiple objectives
Definition
 Landscape exploitation systems are systems in which human beings harvest a
renewable natural resource using human-made capital to create an “economic”
good. They are therefore very general, including hunting-gathering, swidden
agriculture, grazing and intensive agriculture systems.
Outline
Synopsis
►
Motivation
Predator-prey analogy
Exploitation systems
Model structure
State space topology
First results
Multi-objective optimization
A spatial mosaic
Motivation
Outline
Synopsis
Motivation
►
Predator-prey analogy
Exploitation systems
Model structure
State space topology
First results
Multi-objective optimization
A spatial mosaic
Population
The predator-prey analogy
Time
Human-made capital
Human-made capital
Natural capital
A model exploitation system
Time
Time
Natural capital
Outline
Synopsis
Motivation
Predator-prey analogy
►
Exploitation systems
Model structure
State space topology
First results
Multi-objective optimization
A spatial mosaic
A range of generic systems
Hunting-gathering
• Natural capital:
• Edible rainforest plants
• Bush meat
• Human-made capital
• Human beings
• Bows and arrows etc.
Swidden agriculture
• Natural capital:
• Rainforest nutrients
• Human-made capital
• Human beings
• Rudimentary tools
Grazing system
• Natural capital:
• Native grasses
• Human-made capital
• Cattle, sheep
• Some industrial tools
Intensive agriculture
• Natural capital:
• Some key soil nutrients
• Human-made capital
• Cultivated crop plants
• Tractors
• Industrial tools
Outline
Synopsis
Motivation
Predator-prey analogy
Exploitation systems
►
Model structure
State space topology
First results
Multi-objective optimization
A spatial mosaic
The local model
ExploiterConsuming natural capital
Manager
Reinvestment
Creating production
Earning a profit
Natural capital growth
=
Intrinsic growth
-
Consumption
Human-made capital growth
=
Reinvestment
f(Consumption)
-
Depreciation
The local model
Intrinsic growth
Consumption
N
 N

N  rN 1    C max H
C max H  N
 K
H  saCmax H
Savings rate
N
 bH
Cmax H  N
Production
Depreciation
Making a choice
Reinvestment
=
Savings Fraction x
Profit
Outline
Synopsis
Motivation
Predator-prey analogy
Exploitation systems
Model structure
►
State space topology
First results
Multi-objective optimization
A spatial mosaic
Human-made capital
Space-space topology
Natural capital
Human-made capital
Space-space topology
Natural capital
Human-made capital
Space-space
State
space topology
topology
Natural capital
Outline
Synopsis
Motivation
Predator-prey analogy
Exploitation systems
Model structure
State space topology
►
First results
Multi-objective optimization
A spatial mosaic
First results – simple strategies
???
0.7
Economics
Natural capital
Control parameter
Economics
Control parameter
Dynamics
Natural capital
Human-made capital
Dynamics
Human-made capital
Economics
Dynamics
Economics
Human-made capital
Dynamics
Human-made capital
Results
Natural capital
Natural capital
Control parameter
Control parameter
Normalized performance
Normalized performance
Results
Control parameter
Normalized performance
Normalized performance
Control parameter
Control parameter
Control parameter
Economic (solid), basin size (dashed) and return time (dotted) performance
Outline
Synopsis
Motivation
Predator-prey analogy
Exploitation systems
Model structure
State space topology
First results
►
Multi-objective optimization
A spatial mosaic
Multi-objective optimization
Multi-objective optimization
 We can investigate dynamic qualities like resilience
 We can investigate traditional measures like profits
objective
Economic
performance
Normalized
 Is there a formal way to combine out investigations of both?
Pareto front
Control
Dynamic
parameter
objective
Dynamic performance
Dynamic performance
Results – Multi-objective optimization
Economic performance
Dynamic performance
Dynamic performance
Economic performance
Economic performance
Economic performance
Trade-offs between dynamic and economic performance
Outline
Synopsis
Motivation
Predator-prey analogy
Exploitation systems
Model structure
State space topology
First results
Multi-objective optimization
►
A spatial mosaic
Spatial systems
Spatial systems
 Region built up of independent farms, with independent exploiters, each
with their own management strategies and goals
 Management strategies and goals are functions of economic and social
forces across the region, and they change with time
 How does the behaviour of the total system emerge from the many
diverse local behaviours?
Spatial systems and multi-scale optimization
Multi-scale optimization
 Across a spatial system, each exploiter will manage towards different
optima
 In addition, the global system will exert some pressure to find a “social
optimum”
Local scale private
(economic?) objective
 Can we capture this multi-scale optimization using the tools we have
developed?
Pareto front
Large-scale social
(dynamic?) objective
CSIRO Sustainable Ecosystems
CSIRO Sustainable Ecosystems
Name
Cameron Fletcher
Name
David Hilbert
Title
Ecological Modeller
Title
Ecological Modeller
Phone
+61 7 4091 8820
Phone
+61 7 4091 8835
Email
[email protected]
Email
[email protected]
Web
www.csiro.au
Web
www.csiro.au
Thank You
Contact CSIRO
Phone
1300 363 400
+61 3 9545 2176
Email
[email protected]
Web
www.csiro.au
www.csiro.au