The Newtonian world….

2. The Newtonian world…. • Mechanistic and deterministic, predictable

3. The Newtonian World

4. However, Newtonian concepts are not capable of explaining ecological change, which is also strongly coupled to human systems

5. We live in a world in which Newtonian mechanics has limits The shear number of interacting species limits prediction of change using Newtonian concepts alone Contingencies, historical events, and ongoing evolution limit prediction into the future. Interactions can develop which we often cannot anticipate, and these in turn guide the expression of other interactions and outcomes.

6. Complex adaptive systems theory • A more recent conceptualization of how to look at nature and our interaction with it • Combines equilibrium and non-equilibrium perspectives – systems are positioned at the border between disorder and order

7. Complex adaptive systems

8. Characteristics of complex adaptive systems • Path dependence • Emergence • Self-organization • Cross-scalar structure

9. CASs have path dependency • Events that have unfolded in the past constrain the immediate future Querty

10. Path dependency Windows, 1.0

11. Path dependency Apple’s birthplace

12. CASs exhibit emergence

13. Everyday examples of emergence • Ant colonies • Slime molds • Water • Hurricane • Stock market • City • Consciousness • Life

14. CASs are dynamic and adaptive – they self organize • Process where some form of order arises from local interactions between parts of an initially disordered system. • The process is spontaneous, and is not controlled by an external agent.

15. CASs are cross-scalar • Global properties emerge from interactions among many individual components, which then may feed back to influence the subsequent development of these interactions.

16. Studying CASs • Their characteristics can be simulated in • Cellular automata • Agent-based modeling

17. Cellular automata

18. In the Game of Life, simple rules are assigned to cells, and then the cellular automata is allowed to play out over time. Each individual cell is sensitive to the neighborhood around it and responds to it according to the rules that define it. Often, how the game plays out is sensitive to initial conditions. The original configuration of black and white grid cells will determine how the system evolves. Game of Life because the basis for studying artificial life.

19. Agent-based modeling • Agents are programmed to interact with other agents • Agents have local instead of global knowledge • Agents can adapt and respond to local conditions

20. In an agent-based model, individual agents have local rules and a much more mobile capacity than with cellular automata. They can ‘see’ their local environment and respond accordingly. With many agents, their individual behaviors can scale up to create emergent patterns. In this agent-based model, the agents are kids in different neighborhoods. They interact with each other and their neighbors in how they buy and talk about candy sold by three companies. Agent-based models are ways to simulate the dynamics of the world that are not readily done with Newtonian equations.

21. Many video games, like Minecraft, are designed around an agent-based framework.

22. Gaia theory (Lovelock and Margulis, 1974) • The Earth is a complex adaptive system • Biotic and abiotic components of the Earth have co-evolved through feedbacks • Cumulative global effects can arise through essentially local phenomena. • These feedback loops can support life • Adaptation is open-ended and path dependent • Daisyworld illustrates the complex adaptive systems dynamics of Gaia theory

23. Daisy world Daisyworld

24. NetLogo Daisyworld • Download and install NetLogo • Run Daisyworld https://ccl.northwestern.edu/netlogo/

25. How complexity is used in ecology • To represent the interaction with order and disorder in ecological system

26. How complexity is used in ecology • Provides a more nuanced way to understand stability or resilience – the way a system changes

27. How complexity is used in ecology • To describe successional trajectories • Convergence (Clementsian) • Whatever the starting points or variations in initial conditions, system self-organizes toward same particular end state • Divergence (Gleasonian) • System becomes more diverse over time. Initial differences and disturbances tend to persist and grow.

28. The working vocabulary of complex adaptive systems • Vocabulary varies depending upon intellectual tradition, but in general these phenomena are recognized • Resilience • Engineering resilience • Ecological resilience • Stability domains (also known as multiple stable states) • Critical transitions or tipping points

29. Resilience theory • Articulated by C.S. Holling in 1970’s • Another way of framing the idea of stability in complex systems • Forms the basis of ideas about adaptive management

30. Stability in resilience theory is different from our usual definition of it • Stability in its usual sense often implies a tendency to return to a “normal” mode of equilibrium functioning – as in when we get sick and recover. • However, ecological and social systems are not organized around a single equilibrium like an individual human is • They can tip from one to one or more various states or domains

31. Two types of resilience • Engineering resilience and ecological resilience • They each describe properties relating to how ecosystems can respond and adapt to disturbance

32. Engineering resilience • Equilibrium concept • Denotes the return of ‘normal’ functioning • System rebounds to its pre-disturbance state • Measure of time to recovery

33. Ecological resilience • Amount of disturbance or change that can be accommodated before a system crosses a threshold and reorganizes into another state, or stability domain • Captures the adaptive nature of ecosystems

35. Stability domains

36. Longleaf pine stability domains Fire-reinforcing longleaf pine stability domain Fire-resisting longleaf pine stability domain

37. Rocky nearshore marine stability domains

39. Coral reef stability domains

41. Diadema antillarum urchin die off (1983-1984) • Pathogen swept through Caribbean from Atlantic side of Panama Canal. • Infected reef devoid of Diadema in 2 weeks. • Spread through Caribbean and tropical west Africa. • 95% of Diadema perished • Macroalgae, typically held in check by urchins, enveloped reefs • Many of the herbivorous fish had already been depleted by fishing

47. Critical transitions • A system may change, or tip, from one stability domain to another when a threshold is crossed • Tipping implies a sudden and sometimes irreversible change • There is still gradual change in ecological systems, but coexists with these sudden transitions