introduction/basic terms • systems ecology case studies: motto … · 2015-04-14 · theory is...
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Course Overview
14.Apr.2015Andreas Fischlin, ETH Zurich
• Fundamentals of Systems Theory: Modeling, System Representation, Stability analysis, Simulation
• Ecosystem models from recent research: agroecosystems, climate and forests etc.
• Systems Ecology Case Studies: Motto - 1,2,3 from the simple to the complex
Insertion: Simulation tool «Easy ModelWorks»
• Introduction/Basic Terms
1 2 3
• Fundamentals of Systems Theory: Modeling, System Representation, Stability analysis, Simulation
14.Apr.2015Andreas Fischlin, ETH Zurich
Overview 14. April 2015
Fundamentals of System Theory!
!Modeling!
System Representations
14.Apr.2015
Fundamentals of System Theory
• Modelling!• Systems Representations!• Simulation!• Stability and Stability Analysis
Andreas Fischlin, ETH Zurich
14.Apr.2015Andreas Fischlin, ETH Zurich
The System In the Ecosystem?
14.Apr.2015Andreas Fischlin, ETH Zurich
What Type of System?
14.Apr.2015Andreas Fischlin, ETH Zurich
What Type of System?
14.Apr.2015Andreas Fischlin, ETH Zurich
What Type of System?
14.Apr.2015Andreas Fischlin, ETH Zurich
What Type of System?
14.Apr.2015Andreas Fischlin, ETH Zurich
What Type of System?
Massachusetts Institute of Technology, 2007. The Large Scale Network Analysis Project. web.mit.edu/networks/visualizations.html
14.Apr.2015Andreas Fischlin, ETH Zurich
Lessons to be learned:
• Systems have common properties regardless of their nature !– Thus their accessibility => Systems Theory!
• Systems can be described in a common language (System Representations)!
• All Systems can be modeled!• Systems need to be modeled to become
understood!
14.Apr.2015Andreas Fischlin, ETH Zurich
An Ecosystem is:• a “conventional” ecosystem
such as a forest, lake, meadow etc.!
• any ecological system(autecology .. biosphere)!
• for a systems ecologist: also a systems theoretical concept
14.Apr.2015
1 Modelling!
!
!
• Top down vs. bottom up!
• Mathematical Derivation of Model Equations vs. «System Dynamics» Approach
Andreas Fischlin, ETH Zurich
14.Apr.2015
„Systems“ Thinking
Andreas Fischlin, ETH Zurich
14.Apr.2015Andreas Fischlin, ETH Zurich
14.Apr.2015
„Systemic“ Thought
Andreas Fischlin, ETH Zurich
14.Apr.2015
The more complex the more stable!
Andreas Fischlin, ETH Zurich
Really?
14.Apr.2015
Lord Robert M. May !
made ecologists aware that complexity, e.g. biodiversity, does not imply stability
Andreas Fischlin, ETH Zurich
May, R.M., 1972. Will a large complex system be stable? Nature, 238: 413-414
14.Apr.2015Andreas Fischlin, ETH Zurich
14.Apr.2015
Top-Down vs. Bottom-Up
Andreas Fischlin, ETH Zurich
14.Apr.2015
Top-Down vs. Bottom-Up
Andreas Fischlin, ETH Zurich
MakroskopischeSicht
MikroskopischeSicht
see also Overhead Slide
Macroscopic view
Microscopic view
14.Apr.2015
Bottom-Up• Given starting points!
• Oriented towards discipline traditions
Andreas Fischlin, ETH Zurich
• Starts holistically!
• Stepwise refinement only as needed (economical)!
• Reaches typically the set goals (synthesis)
Top-Down
• Not economical!
• Likely to introduce incompatibilities!
• Synthesis fails often:!– Lack of resources, time!– Underestimation of
needed efforts
• Prejudices are obstacles!
• Too high aggregation
14.Apr.2015
Holisms vs. Reductionism
The most important aspect of ecosystem theory is the recognition of holism - the consideration of whole-system phenomena and an explanation of how these phenomena arise from interactions among lower-level phenomena.!
Shugart, 1984. A theory of forest dynamics - The ecological implications of forest succession models.
Andreas Fischlin, ETH Zurich
Both can be justified, but only side by side!
14.Apr.2015
Mathematical Derivation of Model Equations vs. «System
Dynamics» Approach
Andreas Fischlin, ETH Zurich
14.Apr.2015Andreas Fischlin, ETH Zurich
“System Dynamics” ApproachSome steps are more prominent than others!1" Problem question"
2" Sift facts and data"
3" Verbal model"
4,5" Mathematical model - Calibration"
6,7" Simulation model"
8" Simulation experiments"
9" Interpretation of simulation results"
10" Model and parameter identification"
11" Model validation"
12" Model application"
Recipe Systems Analysis
14.Apr.2015Andreas Fischlin, ETH Zurich
Some steps are more prominent than others!1" Problem question"
2" Sift facts and data"
3" Verbal model"
4,5" Mathematical model - Calibration"
6,7" Simulation model"
8" Simulation experiments"
9" Interpretation of simulation results"
10" Model and parameter identification"
11" Model validation"
12" Model application"
Classical Approach, e.g. Physics
Recipe Systems Analysis
14.Apr.2015Andreas Fischlin, ETH Zurich
14.Apr.2015
Spectrum of Systems
Andreas Fischlin, ETH Zurich
Overhead Slide
14.Apr.2015
Spatial and temporal scales correlated
Andreas Fischlin, ETH Zurich
Holling, 1992
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The Modeling Dilemma
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Generality
Precision Realism
14.Apr.2015Andreas Fischlin, ETH Zurich
14.Apr.2015Andreas Fischlin, ETH Zurich
2 System Representations
• Qualitative Representations, e.g. Relational Graph "
• State Variable Representations
14.Apr.2015Andreas Fischlin, ETH Zurich
Qualitative Representations, e.g. Relational Graph
14.Apr.2015Andreas Fischlin, ETH Zurich
A Relational Graph Is
Following tuple of sets:
S = (X , R) = (XI, XD, XO, RI, RID, RD, RDO, RO, RIO)
14.Apr.2015Andreas Fischlin, ETH Zurich
Where do you get the better overview? Judge yourself!P Population NR Natural Resources CI Capital Investment CIAF Capital Investement in Agriculture Fraction POL Pollution
14.Apr.2015Andreas Fischlin, ETH Zurich
Where do you get the better overview? Judge yourself!
14.Apr.2015Andreas Fischlin, ETH Zurich
Overhead Slides
Ex. ForClim and EPOVIR
Relational Graph Useful Ex Post
14.Apr.2015Andreas Fischlin, ETH Zurich
State Variable Representations
14.Apr.2015Andreas Fischlin, ETH Zurich
Important Model Properties• Time (continuous vs. discrete)!
• Deterministic vs. stochastic!
• Linear vs. non-linear!
• Ordinary vs. partial differential equations (concentrated vs. distributed parameters)!
• Canonical vs. non-canonical forms!
• Autonomous vs. non-autonomous system!
• Reachability, observability and controllability!
• Complexity
14.Apr.2015Andreas Fischlin, ETH Zurich
Ordinary Difference and Differential Equation Systems
System Re-presentations
Continuous timet ∈ ℜ
Discrete timek = 0,1,2,...
Input vector
State vector
System order
Output vector
Derivative vector
14.Apr.2015Andreas Fischlin, ETH Zurich
Model of Time
Overhead Slide
Model of Time
14.Apr.2015Andreas Fischlin, ETH Zurich
Canonical Form of Linear Systems
Ex.: Exponential Growth:
Ex.: Proportional controller (negative feedback):
Continuous timet ∈ ℜ
Discrete timek = 0,1,2,...
14.Apr.2015Andreas Fischlin, ETH Zurich
Canonical Form of Non-linear Systems
Overhead Slides
Canonical Form Non-linear Systems
14.Apr.2015Andreas Fischlin, ETH Zurich
Overview 14. April 2015
Fundamentals of System Theory!
!Modeling!
System Representations