The Basics of Complex Adaptive Systems Theory

1. The Basics of Complex Adaptive Systems Theory

2. New Reality Climate variability - change in average, variation, and/or extreme values

3. New Reality Seastedt et al. 2008, Frontiers in Ecology, 547-553 Novel ecosystems will be increasingly common

4. “Complexity” management approach Management objective Management objective C A Ecosystemcharacteristics Complexity management objective B Time Silvicultural interventions Modified from Puettmann et al.

5. Climate change challenge: increase ecosystem resilience and adaptability This can be achieved by viewing and managing ecosystems as complex adaptive systems

6. The most important ideas about ecosystems come from Complex Systems Science: Much of the order/pattern we see in the world comes, not from top down control, but from local-level (bottom-up) interactions among system components. (self-organization) Examples: grass roots social movements, viral YouTube, ant colonies, microbial networks

7. Ecosystems are “Complex Adaptive Systems” • A system with many parts • The parts interact (inter-dependent, feedbacks) • Emergence or synergy • The whole is greater than the sum of the parts (interactions give rise to emergent properties). • Bottom-up self-organization • Adaptive, Evolving • System memory • Fuzzy, open boundaries

8. Feedbacks are the key to self-organization of terrestrial ecosystems Ehrenfeld et al. (2005)

9. Ecosystems are Complex Adaptive Systems

10. https://www.youtube.com/watch?v=g5evD6AQeCQ

11. Characteristics of Complex Adaptive Systems • Unpredictable: because interactions non-linear • Contagion: easy spread due to interconnection • Modularity: some parts more intra-connected than inter-connected (e.g., above- and belowground foodwebs) • Redundancy • Resilient

12. Forest ecosystems: cross-scale interactions and emergence of self-organization Forest structure is emergent property

13. “mindful practice” “use it or lose it” Neural networks underpin the brain as a complex adaptive system • Feedback loops, cross scales • Non-linear, sometimes chaotic • Indeterminate, unpredictable • Self-organization • Emergent properties • System memory • Non-equilibrium, open to outside • Fuzzy boundaries • Adaptive “neurons that wire together, fire together”

14. https://www.youtube.com/watch?v=BfnY9gn6ktk

15. COMPLEXITY Perspective Equilibrium vs. Non-Equilibrium Perspective of System Dynamics Equilibrium Perspective The future is the basically the same as the past Most systems return to a stable state Time is reversible • The future is never the same as the past • Systems continue to evolve • Time is like an arrow -not reversible

16. Equilibrium vs. Non-Equilibrium Perspective of System Dynamics Equilibrium Perspective Complexity Perspective • Climate essentially stable at time scales relevant to ecosystem management • After disturbance, ecosystems follow a predictable trajectory back to a climax state • Zonal soils and zonal plant communities best reflect the regional climate • Climate variable over short and long time scales relevant to ecosystem management • Disturbance and recovery are ongoing processes; • Both ecosystems and soils change over time

17. Forest DynamicsEquilibrium view Complexity view Time succession disturbance • Essentially one pathway to stability • Disturbance seen as an aberration

18. http://www.ted.com/talks/gavin_schmidt_the_emergent_patterns_of_climate_changehttp://www.ted.com/talks/gavin_schmidt_the_emergent_patterns_of_climate_change