Seeking sustainability within complex regional NRM systems


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

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Seeking sustainability within complex regional NRM systems

  1. 1. Seeking sustainability within complex regional NRM systems Graham Harris
  2. 2. Seeking sustainability within complex regional NRM systems Graham Harris
  3. 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. 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. 5. 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
  6. 6. 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
  7. 7. 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?
  8. 8. Here be monsters! We are not rational beings!
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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!
  13. 13. 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”......
  14. 14. 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
  15. 15. 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”
  16. 16. Inability to detect effects of management interventions: also there are multiple stressors and surprises!! Billions invested: no apparent result?
  17. 17. 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)
  18. 18. Clearly a tipping point has been reached! Death of Red Gum and Black Box forests
  19. 19. 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
  20. 20. 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)
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. The Environment Institute