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  • Some of you may be wondering why we really need a more structured approach to decision making for natural resources.Well, the reason for this is that it is often difficult to make a decision that will lead to some management goal, especially when there’s a need to integrate across institutions, agencies, and scales – both spatial and temporal.This is not all that surprising when recognizing the many psychological traps such as overly discounting future returns and moving to action without knowing the aims of our actions. So, what we need to improve our decision making is a transparent, structured approach. In other words, to take the stuff that’s in our head and put it on paper so that we can logically work through the steps toward our decisions. This transparency is especially important when decisions are in the public eye, which has become very evident in the wake of the recent GC oil spill.
  • Some of you are probably familiar with SDM. This is a general approach that is based on the field of decision analysis, which has its roots in…. I want to acknowledge some of my mentors… who have developed a series of courses and workshops at NCTC, where I’ve had the opportunity to help facilitate SDM workshops and to teach SDM and AM to natural resource professionals. Many of you have probably heard of adaptive management, which is a special case of SDM where we have multiple linked decisions along with monitoring to reduce structural uncertainty. Today, I’ll introduce SDM in its most general sense, and it is meant to be inclusive of other formal, logical approaches that aim toward achieving management objectives._______________The term decision analysis was coined in 1964 by Ronald A. Howard[1], who since then, as a professor at Stanford University, has been instrumental in developing much of the practice and professional application of DA.
  • Applies to wide range of problemsCan integrate disciplines & scalesIncreases stakeholder involvement & buy-inSupports learning & improvement"a formalization of common sense for decision problems which are too complex for informal use of common sense“ -- Keeney 1982__________Before describing the framework of SDM, I wanted to give a brief overview of the range of problems we may encounter in NRM and that the approach to a problem depends on whether the objectives and underlying science can be bounded and agreed upon. Highly contentious management problems, such as those that involve control of mammalian predators, may have obscured objectives and disputes about the underlying science that would require conflict resolution or joint fact finding. Less contentious problems, however, would be more suited for SDM. Also, note that adaptive management is actually a special case of SDM, where we have multiple, linked decisions along with monitoring to reduce structural uncertainty. For my talk today, I’ll introduce SDM in its broadest sense and provide applications from my own experience.
  • *** Make fun of the acronym, but say that this is a useful heuristic –
  • Adaptive management is where we really begin to incorporate the scientific method in NRM, and this is a focus of my research program that explicitly incorporates learning-based quantitative models for improving management decisions.
  • ***2-day workshop, importance of providing structure to be filled in later through iterative loops through PrOACT. Problem framing: important to ID an actual decision where manager needs assistanceThis concept of developing a coarse framework before filling in the detailed models is often referred to as rapid prototyping. So, we often need multiple iterations through the PrOACT loop until we have a structure that provides a confident management recommendation.
  • …is now being applied in a range of natural resource problems, including reserve design, sustainable harvest planning, resource allocation. A great strength of this approach is that it integrates ecological and human dimensions of natural resource management problems.
  • There is a wide range of tools available to examine tradeoffs in a decision problem. I have experience applying a number of these approaches, but for this problem I chose Simple Multiattribute Rating Technique, which is commonly applied for solving deterministic problems with multiple objectives.Credit: MCR
  • Adaptive management is where we really begin to incorporate the scientific method in NRM, and this is a focus of my research program that explicitly incorporates learning-based quantitative models for improving management decisions.
  • *** Emphasize that I have participated in all of these and am involved with pushing this forward*** Mention that some of the background material for talk is drawn from the SDM course
  • *** replace scores with weights before averaging across stakeholdersThe first step in the SMART approach is to assign weights to the fundamental objectives. As only a subset of the stakeholders were available during the workshop, we used role playing to assign some of the weights. Let’s look at a couple examples. Private landowners have a slight preference for recovery over cost effectiveness followed by social acceptance, whereas DOD has a relatively strong weight placed on recovery relative to cost & social acceptance – this reflects their legal mandate to protect endangered species through habitat mgmt.
  • Transparency may be actually higher for qualitative vs. quantitative decision-making. Some quantitative approaches are black-boxy and non-transparent. Maybe a 5th line regarding uncertainty – linked to learning.If there is lots of uncertainty, easier to represent the system qualitatively.Two extreme approaches: purely quantitative would be doing the entire decision analysis in your head – whether explicitly going through the decision analysis or not. Purely quantitative would be writing out every step of the decision analysis so that anyone could retrace your steps. An intermediate option would be writing out a subset of the steps, but doing the rest in your head – a common example would be that you write out the management problem, objectives, and alternatives, then do the rest in your head without any explicit models or means to choose an alternative management strategy.

Webinar 1 sdm_overview_final_2013_9_9 Webinar 1 sdm_overview_final_2013_9_9 Presentation Transcript

  • Brady J. Mattsson brady.mattsson@gmail.com Structured Decision Making to Improve Coastal Conservation for an Uncertain Future
  • Outline  Existing challenges & approaches  Introduce another way forward  Illustrative example  Problem definition in focus
  • Complexities of Coastal Conservation  Uncertainties: climate change, management effectiveness, budgets  Governance: multiple stakeholders & decision makers  Values: multiple, often competing objectives
  • Traditional approaches acceptable?  Independent silos of science & management  Examples:  Global climate models  Species translocation  Non/semi-transparent decisions
  • Why Integrate Science & Management?  Decisions are hard: scale & complexity  Multidimensional: biology, politics, econ.  Jumping to action before defining goals  Many psychological pitfalls when deciding  Often need transparent, structured approach
  • Outline  Existing challenges & approaches  Introduce another way forward  Illustrative example  Problem definition in focus
  • Decision Analysis  Roots in math, computer sci, and economics  Natural resource conservation applications  Formal, logical method for solving problems  Focus on achieving objectives  Quantitative toolkit to I.D. smart choices
  • OBJECTIVES Transparent Obscured SCIENCE Well Understood Uncertain Disputed SDM = Decision Analysis Conflict Resolution Joint Fact FindingAdaptive Management Approaches to Problem Solving Lee, K.N., 1994. Island Pr.
  • What is Structured Decision Making?  Utilizes decision analysis & toolkit  Useful for collaborative problem solving  Iterative framework, values focused  Identify & resolve impediments to decision
  • Hammond et al. 2002. Smart choices: a practical guide to making better life decisions. [BOOK] Problem Objectives Alternatives Consequences Trade-offs ` Decide & take action Runge et al. 2011. An overview of structured decision making, revised edition. [ONLINE VIDEOS] Conroy & Peterson 2013. Decision Making in Natural Resource Management: A Structured, Adaptive Approach. [BOOK]
  • Problem Objectives Alternatives Consequences Trade-offs Decide & take action ` Monitoring Learning Adaptive Management Adaptive phase B.K. Williams, J.M. Nichols, M.C. Runge
  • Rapid Prototyping Real World Abstraction of the problem (Decision framework) Problem framing Information Model Analysis Reality Check Revise & Repeat Figure by J.F. Cochrane & A.M. Starfield
  • When Structured Decision Making?  Objectives may be initially unclear  Underlying science may be quite uncertain  Wide range of conservation problems  Forest stand rotation scheduling  Recovery plan for endangered species  Conservation strategy for an ecoregion  Continental harvest plans for waterfowl
  • Outline  Existing challenges & approaches  Introduce another way forward  Illustrative example  Problem definition in focus
  • Tidal marsh conservation in the face of climate change: San Francisco Bay case study Mattsson, B., J. Takekawa, K. Thorne , D. Crouse J. Cummings, G. Block, V. Bloom, M. Gerhart, S. Goldbeck, J. O’Halloran, B. Huning, N. Peterson, C. Sloop, M. Stewart, K. Taylor, and L. Valoppi
  • Decision Question To conserve SFB tidal marshes in light of uncertainty about future climate change, what are the smartest courses of action?
  • Who are the Decision-makers? Policy – USFWS, USACE, Bay Conservation and Development Commission, SFB Regional Water Quality Control Board, EPA Planning – SFB Joint Venture, State Coastal Conservancy Land Management – FWS Refuges, DFG Wildlife Areas, NPS, East Bay Regional Parks No single, individual decision-maker for SFB tidal marsh restoration, management, and protection. SFB Joint Venture -- platform for coordination
  • What Is the Primary Objective?  Perpetuate tidal marsh ecosystem functions, services, and human benefits by maximizing resilience of the system.  Ecosystem functions – interactions of biota with the environment (nesting habitat, food webs)  Ecosystem services – indirect benefits to society from healthy ecosystems (water quality, carbon sequestration)  Human benefits – direct benefits to interest groups (fishing, recreation)  Resilience – capacity of ecosystem to respond to disturbance
  • A. Marsh migration (3a,3b,11,12,19) – upslope movement B. Climate restoration (4,6,7,8,13,21) – engineer and manage marshes considering SLR and extreme events C. Wildlife enhancement (16,17,18) – add habitat features, captive rearing, translocation D. Outreach (20) – education, involvement Group Alternatives (n=22) into Categories
  • Alternative Allocations & SLR (2010-2050) 0 20 40 60 80 100 2010 2020 2030 2040 2050 %Allocation Year Status Quo Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) Sea-Level
  • 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Status Quo Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Marsh Migration Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Climate Restoration Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Climate Restoration Sans Wildlife Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) Alternative Allocations & SLR (2010-2050)
  • Simplified Influence Diagram Human benefits Marsh ecosystem integrity Clapper Rail recovery Climate Marsh condition (e.g., acreage, elevation) Tidal marsh management actions Perpetuate tidal marsh ecosystem functions, services, and human benefits by maximizing resilience of the system.
  • Decision Analysis Toolkit Deterministic Stochastic Single Objective Numerical solutions Linear programming Integer programming Dynamic Programming Decision trees Simulation Bayesian Decision Network Stochastic Dynamic Prog. Multiple Objectives Even swaps SMART AHP Goal programming Decision trees (with MAU) Bayesian Decision Network SMART (with probabilities) AHP (with likelihoods) Figure by M.C. Runge
  • ‘Empty’ Bayes Decision Network Objectives: rail recovery, marsh integrity, human benefits Constraint: Budget Strategies: 5 alternative allocations External influences: storm events
  • Optimal Solution Under Uncertainty 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Status Quo Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22) 0 20 40 60 80 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 %Allocation Year Climate Restoration Marsh Migration Climate Restoration Wildlife Static Restoration (Action 22)
  • Next Steps  Engage broader set of stakeholders & scientists  Revisit model structure & inputs  Consider additional focal species & tidal zones  Address finer, actionable spatial resolution  Expand response horizon to year 2100  Dynamic optimization or heuristics to find smartest spatiotemporal actions (e.g. Wilson et al 2011)  Crucial uncertainties reducible via adaptive management
  • Problem Objectives Alternatives Consequences Trade-offs Decide & take action ` Monitoring Learning Adaptive Management Adaptive phase B.K. Williams, J.M. Nichols, M.C. Runge
  • Outline  Existing challenges & approaches  Introduce another way forward  Illustrative example  Problem definition in focus
  • Defining Problems is Not Natural  When faced with a challenging situation, tendency is: “what should I do?”
  • Problem Definition is Essential …castles made of sand slip into the sea, eventually – J. Hendrix
  • Decision: Defined 1. An outcome of a cognitive process leading to selection of a course of action* among several alternatives 2. An irrevocable expenditure* of resources *Note:  No action is a decision  Priority list is NOT
  • Turning a Problem into a Decision Problem: “Bird species native to Hawaiian islands cannot disperse and risk extinction from sea level rise, invasive species, and hurricanes” Decision Statement: “How should we implement a management program to maximize the likelihood that native bird species persist on Hawaiian Islands?”
  • Identifying a Suitable Decision  Has the decision already been made?  Decision support needed?  Who are the decision makers and stakeholders?  Authority & resources to implement?  Open to SDM?
  • What is the Decision?  Type: Allocation of resources? Choosing among options? Series of linked choices?  Frequency, timing, spatial scope?  Constraints? Legal, regulatory, other? Dissolvable?  Key sources of uncertainty?
  • Collaborative Decision Making: Roles  Coordinators: champion SDM process/implementation  Coaches: neutral, SDM experts  Stakeholders: can influence or are influenced by decision  Technical advisors: modelers, analysts, biologists, etc.  (Facilitators: neutral, maintain team cohesiveness)
  • Prototyping Concept
  • Building Capacity for Decision Analysis  National Conservation Training Center (USA)  http://nctc.fws.gov/courses/SDM/home.html  Adaptive Management Conference Series (USA)  NERP Environmental Decisions hub (Australia)  www.nerpdecisions.edu.au/  Others?
  • Objectives Trade-offs Donothing Allactions Allhighpriority Allexceptlandaq. Allmed/high #5butnolandaq. #3butnolandaq. 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 1 2 3 4 5 6 7 Score Management strategy alternative Suitable Potential *
  • Objectives Hierarchy Marsh ecosystem processes Marsh migration Buffer extreme events Marsh ecosystem function Marsh ecosystem services Human benefits Revenue for marsh conservation Marsh elevation Sediment dynamics Marsh 1’ productivity Marsh food web Flood mitigation Carbon sequestration Water qualityErosion mitigation Population integrity Recreation Property values Aesthetics Human survival Shore stabilization Biodiversity Perpetuate ecosystem functions and services Fundamental Objectives Legend
  • Qualitative vs. Quantitative Analysis Qualitative only Plus Quantitative Cost/time investment Lower Higher Transparency Risk of litigation Lower Higher Higher Lower Capacity to learn Lower Higher $ Translocation & habitat LADU persist Kaho’olawe approval Sea-level rise Catastrophes $ Translocation & habitat LADU persist Kaho’olawe approval Sea-level rise Catastrophes Max potential…
  • SFB Potential Inundation Current conditions: 310 km2 vulnerable to 100-yr floods, most behind levees 50-cm SLR: 372 km2 or 20% increase by 2053 or 40 yrs 150-cm SLR: 495 km2 or 60% increase by 2105 or 100 yrs with much uncertainty about the rate (Knowles 2010, SFEWS)
  • Giselle Block, FWS R8 I&M Valary Bloom, FWS R8 Recovery Branch Debby Crouse, Apprentice Coach, FWS Endangered Spp. Jonathan Cummings, Apprentice Coach, Univ. Vermont Matt Gerhart, State Coastal Conservancy Steve Goldbeck, Bay Conservation & Development Commission Jaime O’Halloran, U. S. Army Corp of Engineers Beth Huning, SFB Joint Venture Brady Mattsson, Coach, USGS Western Ecological Research Center Nadine Peterson, State Coastal Conservancy Christina Sloop, SFB Joint Venture Mendel Stewart, FWS – SFB National Wildlife Refuges John Takekawa, Coordinator, USGS Western Ecological Research Center Karen Taylor, Co-coordinator, CA Dept. of Fish & Game Karen Thorne, Coordinator, USGS Western Ecological Research Center Laura Valoppi, South Bay Salt Ponds Restoration Project Workshop Participants
  • Eliciting Predicted Outcomes
  • Eliciting Utilities
  • 1. Do nothing – walk away 2. Status quo -- keep existing strategy with its nominal climate adaptation 3. Acquire upslope habitat for marsh migration from undeveloped lands 4. Engineering solutions for future restorations 5. Retrofit ongoing or post-construction restoration sites 6. Design marshes with flexibility to facilitate future SLR adaptation 7. Manage with conservation reserves or easements versus fee title 8. Remove development to facilitate marsh expansion 9. Captive breeding program for T&E 10. Create artificial habitat elements and structures for T&E 11. Develop community outreach, education, and involvement Outside-the-Box Actions • Build a water control structure at the Golden Gate to stop the tide • Mendel’s bucket brigade – each person scoops 2.5 million buckets and dumps them inland to lower sea level – problem solved! Alternative Actions (n=22)