This document summarizes how concepts from complex systems studies can inform natural resource management. It discusses how ecosystems and landscapes are complex systems with emergent properties arising from local interactions. Agent-based models are useful for modeling ecological complexity across scales. Examples shown include a model of grassland resilience under disturbance and the relationship between grazing and spatial complexity. Understanding community assembly is explored through a spatial model linking local communities. The document concludes that embracing complexity requires new tools like multi-scale models and monitoring to manage social-ecological systems.

2. How do we deal with complexity in the design, management & restoration of ecosystems and landscapes? Ecosystems and landscapes are complex systems

3. APPLICATIONS ecosystem & landscape design, management & restoration environmental indicators THEORY ecological complexity METHODS ecological informatics (models, analysis, data management & visualisation) Complex Systems Laboratory Research Program

5. Complex Systems

6. What is a complex system? “ The whole is more than the sum of the parts.” A system composed of multiple interacting elements having a comportment that is difficult to analyse or describe using only one scale or resolution. (Parrott, 2002. Trans. of the ASAE .)

7. What is a complex system?

8. What is a complex system? Locally interacting entities Emergent structures & processes Feedback What is a complex system?

12. Modelling Ecological Complexity

14. Example: Modelling ecological resilience

19. Ecosystem biomass: spatiotemporal dynamics

21. Modelling ecological resilience Measuring spatiotemporal complexity Dataset: biomass recorded monthly for 10mx10m grid cells

22. Modelling ecological resilience (Parrott, 2005, Ecological Complexity; Parrott et al., 2008, Ecological Informatics . ) Measuring spatiotemporal complexity

23. Results over many simulations: Grazing intensity (# of herbivores) Grazing duration (years) Spatiotemporal complexity of vegetation biomass (Parrott, 2004, ESA Annual Meeting) Modelling ecological resilience

24. Example: Understanding community assembly through modelling

25. Understanding community assembly Species Interaction

27. Understanding community assembly dispersal Regional species pool (2 20 species; random interaction web) random species introductions Landscape of locally connected communities (typical grid size: 128 x 128 cells) Species in potentia Local community ( n interacting species)

28. Understanding community assembly Spatial patterns of community similarity (Filotas et al., in revision )

29. Understanding community assembly low dispersal regional species pool high dispersal Dispersal rate Dispersal rate Fraction of interacting pairs Structure of the interaction webs (Filotas et al., in revision )

31. Understanding community assembly Using modelling to find optimal community assembly sequences (Côté & Parrott, 2005; Côté, Parrott & Sabourin, 2007)

32. Understanding community assembly

34. Conclusions

37. Acknowledgements Natural Sciences and Engineering Research Council of Canada Les Fonds Québécois sur la Recherche en Nature et Technologies Canadian Foundation for Innovation Québec Supercomputing Network ALL of the students in the Complex Systems Laboratory, past & present.