Controllability montpellier

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Controllability montpellier

  1. 1. When to adapt or when to transform? Using network controllability to assess how manageable are regime shifts ! ! Juan-Carlos Rocha
  2. 2. Regime Shifts Transformations Figures from Arctic Resilience Assessment Interim Report 2012
  3. 3. • Systems can be represented as a network of interacting elements • Identifying controlling nodes is possible using only structural information of the network: • # of driving nodes correlates with degree distribution • driver nodes tend to avoid high- degree nodes • Heterogeneous networks (most real) are difficult to control. Homogeneous dense networks are more controllable = fewer driving nodes Liu et al, 2012
  4. 4. Are regime shifts controllable? To what extent can we manage them? • Critics to Liu et al.: • Topology is not enough • Internal dynamics • “We argue that more important than issues of structural controllability are the questions of whether a system is almost uncrontrollable, whether it is almost unobservable…” Cowan et al, 2012
  5. 5. • Focus on edge dynamics: heterogeneous and sparse networks have more controllable edge dynamics than homogeneous dense networks. • Contradictory results? Are regime shifts controllable? To what extent can we manage them?
  6. 6. Driver … is any natural or human- induced factor that directly or indirectly causes a change in an [eco]system. A direct driver unequivocally influences ecosystem processes. An indirect driver operates more diffusely by altering one or more direct drivers.
  7. 7. Bivalves collapse Bivalves abundance Dissolved oxigen Biodiversity Habitat structural complexity Local water movements + + + + + Fishing Plankton and filamentous algae - Water turbidity - - B B R R Nutrients input Agriculture Urbanization SewageFertilizer Use Deforestation + + + + + Demand for food & fibre + mid-predator fish - - + + B Filtration + - Erosion + + Nutrients in water -+ + + + Logging + + Flooding + Disease - + sedimentation + - Shellfish harvest - + + B B Urban Storm Water Runoff + + Precipitation Variability + + Aquaculture + + Hurricane - +
  8. 8. My own critiques • Unmatched nodes change if the periphery of the causal networks change - The limits of the system blur • Unmatched nodes change when joining causal networks to understand cascading effects. • I believe there is opportunities to combine network science and resilience science to answer the question: When do we build resilience and where do we need transformational change? Causal Loop Diagrams for 19 regime shifts around the world
  9. 9. Subscribe to our newsletter www.stockholmresilience.su.se/subscribe Thank you! Does it make sense?? Ideas, tomatoes or opportunities for collaboration: e-mail: juan.rocha@su.se twitter: @juanrocha slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog

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