Presented by Graham Harris as part of the 2009 Place and Purpose Symposium run by the Landscape Science Cluster

1. Seeking sustainability within complex
regional NRM systems
Graham Harris

2. Seeking sustainability within
complex regional NRM systems
Graham Harris

3. Rapid change on Earth
• The world is changing as issues become
more pressing – need for systems thinking
– Interactions between energy, carbon,
climate, water, water, soils, biodiversity,
food security, population, animal disease
• John Beddington, UK: “The perfect storm”
– Tipping point in 25-50 years?
– Poor assessments of risk: Dan Gardner
• Urgent need for new regional approaches

4. Multiple capitals
• World is overlapping set of stocks and flows
with non-linear, adaptive interactions
– Biodiversity: genes, populations, species
– Biogeochemistry: water, energy, nutrients
– Capitals: natural, physical, human, financial
• Complexity, emergence, thresholds, tipping
points, surprises (inc. financial crashes)
• So the natural world is not just complicated it
is formally complex: uncertain, unpredictable

6. What is sustainability?
• “development that meets the needs of the
present without compromising the ability of
future generations to meet their needs”
– Strong sustainability – more than just
economic welfare and “choice” - there are
absolutes, so “the capacity to endure”
• Act here and now so that the environment
and quality of life later and elsewhere will
not be eroded

7. The flip side of sustainability
• The (inverse) flip side is risk...
– Seeking sustainability means minimising risk
amidst complexity and uncertainty
– Risk is about reality, beliefs and culture
• So we require analytical tools to understand
the behaviour of interacting systems and...
• Participatory tools to deal with beliefs and
values, debate options, communicate risk
and act

8. Worldviews and semiotics Key slide 1
Biophysical
constraints
Emergence
Thresholds
Thermodynamics Regime shifts Sociology
Evolution Economics
Complex
Biosphere Middle Anthroposphere
ground
Realist Values
Uncertainty Narrative
Scientific Beliefs
Incomplete Engagement
Approach Relativism
knowledge Decisions
Risk Postmodernism
Analytical tools Participatory tools
Expertise?

9. Here be monsters!
We are not rational beings!

10. Cause and effect
• Need to understand relationships between
parts and wholes, wholes and parts
– Local <-> regional <-> global
– Scaling, fractals, emergence
• BMPs to catchment outcomes – EU WFD
– Risk, load apportionment: DEFRA, EA
– Local actions to regional outcomes
• Cause and effect across scales is a problem
– Global CO2 reductions: national jurisdictions

11. The science “framing issue”
• Usual scientific debate framed around
balance and equilibrium – has very old roots
– Theory, data collection and analysis issues
• Philosophical basis is idealised (Wimsatt)
– Not appropriate for complex systems
• Analysis tools – monitoring and assessment
generally about stocks not flows
• NRM institutions, bureaucracy, policy only
focussing on the participation tools

12. The Complexity “turn” (sociologists!)
• Adaptive interactions between capitals
– agents, institutions, systems evolve
• Resilience and tipping points
– Precariousness and thresholds
• Uncertainty: knowledge and models partial
– Emergence, surprises will occur
• Multiple stressors – “causal thickets”
– Predict-act frameworks unreliable
• Many players, institutions, governance

13. More is different – things don’t scale well
Make no mistake: “complexity” is a
major shift in world view which
requires changes in culture and
practice
Business as usual is not an option!

14. The uniqueness of place
• The concept of place arises from complexity
– Nested spatial and temporal heterogeneity,
contingent history, stocks and flows
• Requires complexity of governance: decision
theory, robustness and resilience
– No universal Best Management Practices
• Perhaps there never will be a simple theory
of place – so just how much is predictable?
– We are “waiting for Carnot”......

15. We cannot ignore the flows between human and natural systems 2
STOCKS description
Not Gaia; Medea things
No homeostasis
contingency
Complex systems
PAST then Ecosystems now PRESENT
Human systems
Small scale process
Spatially discrete interactions
stuff
Patterned
Temporally evolving FLOWS

16. Incentives and restoration
• Targets, reference sites, valuation
techniques and MBIs at risk from
contingency, uncertainty and emergence
• Complexity makes restoration difficult
– Change leads to new “non-homologous”
novel ecosystems (Hobbs et al.) Base lines??
• Focus on inputs rather than outcomes
reflects complexity of situation and
difficulties with “programs of measures”

17. Inability to detect effects of management interventions:
also there are multiple stressors
and surprises!!
Billions invested: no apparent result?

18. New models for self organising systems
• Urgent search for new models for complex
(fractal, SO) landscape systems
– Agent Based, CA, emulation (Young) or high
level analytical (Kirchner, Rodriguez-Iturbe)
• Search for techniques to predict thresholds
– critical slowing down (Scheffer, Carpenter)
• But will the warnings be timely or sufficient?
• GRID models of everything everywhere –
including uncertainty (Beven)

19. Clearly a tipping point has been reached!
Death of Red Gum and Black Box forests

20. The evolution of modelling
• From “mean field” simulations, to Neural
Networks, to Genetic Algorithms, to Agent
Based, to Adaptive Cellular Automata
– populations –> individuals -> information
• Discrete, spatial, adaptive, self-organised
properties (no “equilibrium” solutions)
• Landscapes as spatially heterogeneous,
information processing, self-organising,
uncertain, temporally evolving entities
– New approaches to industrial ecology

21. Hierarchical (nested) dynamics
• The small and fast are really important
– Emergence and non-linearity
• Both bottom up and top down causation
– Philosophers have real problems with this!
• Modelling from the middle-out: emulation
– Systems biology idea attributed to Sydney
Brenner but actually a very old concept
• Capturing the essence whilst recognising
uncertainty (Unknown Unknowns again)

22. The non-equilibrium hierarchical patch dynamics view
3
Big, slow drivers
Biophysical constraints
Climate change
Macro-scale Extreme events
models management
Meso-scale world
Resilience
Multiple states
Local Hysteresis
drivers µ scale
Small scale “hot spots”
Spatially discrete Diverse emergent
Behaviour, Physiology components
Evolution Interactions
Stocks and flows

23. New data – spatial and temporal
• New data from web enabled sensors and
systems: “everything, everywhere”
– High resolution DEMs, GIS, time series
– Stocks and flows, history, development
• Insights into small scale pattern and process
– The “high frequency” wave of the future
– “Beethoven symphonies” with orchestration
• Use of personal devices: GPS, mobile phones
with on-board cameras and other sensors

24. New theories of risk management
• Need new risk management tools: Scenarios
for future likely paths
– Decision frameworks with “minimum regret”
to manage unpredictable events
– Lempert et al – Robust Decision Making
• “predict-act” oversold: need adaptive mgmt
– Therefore more likely “observe-reflect-act”
– Data, models, uncertainty, robust options
• The past is no guide to the future

25. Approaching the undefinable
• If “sustainability” is a complex goal and the
uncertainty is great
– Then how to proceed?
• One option is to reduce unsustainable
practices and apply biophysical limits
– Moving in the right direction
• The other is Robust (‘minimum regrets’)
Decision Making – data and models
– Risk management under uncertainty

26. The Environment
Institute