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
5.
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
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