seeking sustainability within complex regional nrm systems
DESCRIPTION
Presented by Graham Harris as part of the 2009 Place and Purpose Symposium run by the Landscape Science ClusterTRANSCRIPT
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Seeking sustainability within complex regional NRM systems
Graham Harris
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Seeking sustainability within complex regional NRM systems
Graham Harris
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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
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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
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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
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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
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Biosphere Anthroposphere
ComplexMiddleground
Biophysicalconstraints
ThermodynamicsEvolution
RealistScientificApproach
Analytical tools Participatory tools
SociologyEconomics
ValuesBeliefsRelativismPostmodernism
Worldviews and semiotics
UncertaintyIncompleteknowledge
NarrativeEngagement
DecisionsRisk
EmergenceThresholdsRegime shifts
Expertise?
Key slide 1
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Here be monsters!
We are not rational beings!
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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
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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
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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
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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!
More is different – things don’t scale well
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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”......
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then now
things
stuff
STOCKS
FLOWS
PAST PRESENTComplex systems
EcosystemsHuman systems
We cannot ignore the flows between human and natural systems
contingency
interactions
description
Small scale processSpatially discretePatterned Temporally evolving
2
Not Gaia; MedeaNo homeostasis
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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”
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Inability to detect effects of management interventions:also there are multiple stressors
and surprises!!
Billions invested: no apparent result?
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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)
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Death of Red Gum and Black Box forests
Clearly a tipping point has been reached!
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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
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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)
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µ scale
Macro-scale
Big, slow driversBiophysical constraintsClimate changeExtreme events
Small scale “hot spots”Spatially discreteBehaviour, PhysiologyEvolution
Meso-scale world
The non-equilibrium hierarchical patch dynamics view
models
ResilienceMultiple statesHysteresis
Diverse emergentcomponentsInteractionsStocks and flows
Localdrivers
management
3
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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
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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
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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
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The Environment Institute