Dr Lael Parrott at the Landscape Science Cluster Seminar, May 2009

Concepts & methods from complex systems studies that can help inform NRM
Dr. Lael Parrott Director, Complex Systems Laboratory Géographie, Université de Montréal, Canada
[email_address]
http:// www.geog.umontreal.ca/syscomplex
Presented to the Landscape Science Cluster, University of Adelaide, 28 May 2009.

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

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

What is a complex system?
Complexity: Implications & challenges for natural resource management
Modelling ecological complexity
Examples : Modelling ecological resilience; Understanding community assembly
Conclusion
Outline

Complex Systems

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 .)

What is a complex system?

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

Complex Systems Key characteristics
Hierarchy and scaling
Emergence
Self-organisation
Uncertainty
Memory
Adaptation

Accepting complexity means accepting that ecosystems and landscapes are…
Dynamic and subject to rapid change;
Infused with memory of past events;
Self-organised in space and time.
(Parrott & Li, 2005, Ecological Society of America Evening Session on Ecological Complexity ) Complexity Implications for natural resource management

Integrating concepts from complex systems research into management paradigms
(e.g., acceptance of multiple stable states; harnessing self-organisation; dealing with uncertainty)
Determining how to treat multi-scale interactions
Development of new methods to monitor and describe complex structures and dynamics
Complexity Challenges for natural resource management

Modelling Ecological Complexity

Use a « bottom-up » approach: Model local interactions and let the higher level dynamics emerge
Individual- and agent-based models
Importance of space
Modelling ecological complexity Considerations

Example: Modelling ecological resilience

Model: WIST (Weather-driven, Individual-based, Spatially-explicit, Terrestrial ecosystem model)
Fully configurable, multi-species, multi-trophic individual-based model
Includes environment (soil, atmospheric conditions), topography & weather
Landscape is modelled as a grid
MPI version available for parallel processing infrastructures
(Parrott & Kok 2001, 2002 in Ecological Modelling ) Modelling ecological resilience

In WIST:
Animal individuals: respire; search for food; eat; move about; reproduce…
Plant individuals: grow; photosynthesize; disperse seeds…
Modelling ecological resilience

WIST is a virtual ecosystem in which...
Processes and interactions are deterministic and rule-based
Complex, spatiotemporal dynamics emerge at the landscape level
Modelling ecological resilience

Initial conditions:
500m x 500m surface
50 species of annual & perennial grasses and herbs
3 herbivore species
Simulations for 100 years at 10 minute time steps
Introduce « clear-cuts » randomly over the landscape
(Parrott, 2004, Ecological Complexity) Case 1: Study of the resilience of a grassland ecosystem subject to frequent disturbance Modelling ecological resilience

Ecosystem biomass: spatiotemporal dynamics

Case 2: Relationship between grazing intensity and spatiotemporal complexity in a model ecosystem
Initial conditions:
30 species of annual & perennial grasses and herbs
Introduce herbivores (“rabbits”)
Vary grazing intensity (# of herbivores and duration)
50-year simulations
(Parrott, 2004, ESA Annual Meeting) Modelling ecological resilience

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

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

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

Example: Understanding community assembly through modelling

Understanding community assembly Species Interaction

Understanding community assembly
Spatial version of the individual-based « Tangled Nature » model of multi-species community dynamics Doctoral project of Élise Filotas; Collaborators: Martin Grant, Physics, McGill University & Per Rikvold, Physics, Florida State University.
Separate communities linked via dispersal (Filotas et al., Ecological Complexity , 2008)

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)

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

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 )

Understanding community assembly Dispersal rate
In this model...
community and landscape level properties emerge from local level inter-species interactions.
We demonstrate...
the important role of biotic interactions in structuring communities and the emergence of mutualist-dominated interaction webs in low-dispersal landscapes;
the role of facilitation as a building block for more biologically diverse communities.

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

Using multi-criteria optimisation methods & genetic algorithms, we can find assembly sequences that maximize diversity & productivity while respecting constraints on food web properties such as connectance. (Côté & Parrott, 2005; Côté, Parrott & Sabourin, 2007)
Important implications for ecosystem design & restoration projects.

Conclusions

Embracing complexity requires new tools for natural resource management;
models, monitoring programmes
New modelling methods suited for studying complex ecological interactions across multiple scales are being developed;
can simulate emergent properties in space and time
Managing future landscapes and ecosystems as complex, coupled social-ecological systems will require multidisciplinary teams, combining approaches from the natural, applied and human sciences.
Conclusions

Modelling of human and ecological components of landscapes for decision support in the Boreal Forest & the Saint Lawrence River estuary. - Come to the Place & Purpose symposium to learn more!
Future Landscapes: Managing within complexity by L. Parrott and W. Meyer, manuscript in preparation.
Work in progress...

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.

Concepts & methods from complex systems studies that can help inform NRM
Dr. Lael Parrott Director, Complex Systems Laboratory Géographie, Université de Montréal, Canada
[email_address]
http:// www.geog.umontreal.ca/syscomplex
Presented to the Landscape Science Cluster, University of Adelaide, 28 May 2009.

Dr Lael Parrott at the Landscape Science Cluster Seminar, May 2009

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