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Adaptive management Dr. e. r. irwin FISH 7380. Managing “Adaptively” Adaptation defined: The adjustment of strategy based on improved understanding or.

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Presentation on theme: "Adaptive management Dr. e. r. irwin FISH 7380. Managing “Adaptively” Adaptation defined: The adjustment of strategy based on improved understanding or."— Presentation transcript:

1 Adaptive management Dr. e. r. irwin FISH 7380

2 Managing “Adaptively” Adaptation defined: The adjustment of strategy based on improved understanding or observed change The term “adaptive” predates natural resources by at least a generation First used to describe management of engineering systems Based on the fact that you don’t always fully understand the system you’re managing

3 What ARM is Claimed to Be Resource tracking Goal-directed management Strategic planning Sequential decision making Assessment of management impacts Applied Science What I’ve been doing all along

4 What ARM is Not Not just the doing of science, even if management- oriented Not just the tracking of resources, or activities, or even impacts Not strategic planning per se Not the identification of goals and objectives Not a post-hoc assessment of management Most likely not what you’ve been doing all along

5 What ARM is “Managing natural resources in the face of uncertainty, with a focus on its reduction” Dual management focus Achieving the goals of resource management Increasing the level of understanding about resource dynamics pursuant to these goals Emphasis on uncertainty, and the value of reducing uncertainty through learning

6 Framework for Resource Management resource status Resource status action return time

7 resource status resource status action return time So what makes good decisions so difficult? environmental variation imprecise control uncertain resource status uncertain resource processes ambiguous objectives Framework for Resource Management

8 Conditions for an Adaptive Approach Sequential decision-making Agreed-upon management objectives Acceptable range of available actions Limited understanding of the biological processes driving resource dynamics Opportunity to improve management through a better understanding about these processes Opportunity to gain that understanding through smart decision-making

9 So What’s New? Explicit accounting for uncertainty Typically through the use of models incorporating different hypotheses about system dynamics Focus on improving management through improved biological understanding Use of data accumulated over time Involves acquisition of useful data as a goal of management Involves design (or redesign) of monitoring programs specifically to reduce uncertainty

10 Adaptive Decision-making decision t … monitoring assessment decision t+1 … Biological status and improvement in understanding are used in the next round of decision-making in the next time period Actual vs. predicted responses are used to improve understanding Management objectives guide decision making at each point in time System responses to decisions are predicted with resource models Monitoring used to track actual system responses

11 What Makes it Adaptive? You account for where you are and what you know at each point in time You learn by doing, and learn as you go You anticipate how well your decisions will contribute to both management and understanding Management is used to support assessment, just as assessment is used to support management Basically, the process Recognizes competing hypotheses about resource dynamics Recognizes uncertainties about which is most appropriate Accounts for uncertainties in decision-making, so as to reduce them in the future

12 Alternatives to ARM Ad hoc management Seat-of-the pants management Based on anecdotal information, absence of stated objectives Inadequate biological basis for action Wait-and-see Risk-aversive strategy that seeks to minimize management impacts as information accumulates Steady-state management Attempts to sustain resource system in some targeted steady state Conventional objective-based management Optimal management decisions based on an assumed resource model

13 Example: Adaptive Harvest Management Used for setting annual waterfowl harvest regulations over the last decade Regulations are used to influence harvest rates, which in turn influence population dynamics Harvest regulations are set each year based on Breeding population status Pond conditions on the breeding grounds Uncertainty about regulations impacts

14 What is good for the duck is good for the darter: adaptive flow management. E. R. Irwin & M. C. Freeman USGS

15 Adaptive Flow Management (AFM)  Iterative approach to management that acknowledges uncertainty and the need to learn.  Process where all stakeholders decide initial flow treatment and assessment ensues.  Return to table to evaluate success of flow management.  Re-prescribe flow treatment if needed; continue assessment.

16 Objectives  Assess the potential to use adaptive flow management to define suitable criteria for productive fisheries and community diversity, while accommodating economic and societal needs.  Summarize empirical relations among biological and hydrological parameters from research in regulated Southeastern rivers.

17 Stakeholders decide flow regime based on management goals. Societal Economic Assessment = management and research to define ecological relations as system is managed AFM Transfer knowledge Resource

18 Approach  Compiled data from multiple projects to determine components of flow regime essential for biological processes.  Quantify changes in flow regime.  Constructed hypotheses testable in an Adaptive Flow Management framework.

19 What is required for AFM?  Stakeholders that realize “adaptive” allows for adjustment of management regime as new information becomes available  Testable hypotheses with measurable objectives to refine management  Ability to embrace paradigm shifts, radical thinking  Baseline and reference data (?)

20 Examples of AFM scenarios  Striped bass in the Roanoke River, VA.  Long-term flow and juvenile recruitment data were evaluated to establish alternative flows from dam.  Robust Redhorse sucker in Oconee River, GA.  Spring flows provided to allow for spawning windows.  Flow-advisory team established to monitor success of management and potential modifications.

21 Adaptive management roadmap Identify stakeholders with respect to flows below Harris Dam Meet with potential stakeholders and explain the adaptive management process Form a workgroup of individuals representing all stakeholders

22 Stakeholders Middle Tallapoosa Property Owners Lake Harris HOBOs Alabama Rivers Alliance Bass Federation Alabama Power Company USFWS NPS USFS AL DCNR USGS

23 Next step---Workgroup Identify clear, focused management objectives that represent all legitimate uses of the river. For example: Maintain biotic integrity within a certain range in specified segments in the river; Increase angler catch rates of sport fishes to a certain level in specified segments in the river; Maintain the economic value of the project at a specified percentage of current value;

24 Setting biological management goals (versus flow management goals)

25 Establish Management Goals (versus setting fixed-flow criteria)  Multiple-use riverine systems; all stakeholders goals must be considered.  Not only a habitat-based approach for establishing flow criteria for fishes.  Fish-habitat relations not linear; species specific.  We don’t know “how much”, “how variable”or “how long.”  Allows for flexibility in relation to natural flows.

26 Manipulation/Predicted Response  Implementation of a continuous flow.  Provision of stable flows and mitigate temperature.  Provide predictable boatable flow windows.  Increase density and diversity of fishes and invertebrates.  Increased recruitment, growth, and abundance of fishes.  Increased recreational use.

27 Workgroup Identify the array of flow management options. For example: Provide a baseflow during non-generation periods. Provide a certain number of contiguous days during which flow fluctuations are limited, during specified seasons. "Ramp" flows up and down at the beginning and end of peaking releases.

28 Workgroup Identify limits of acceptable management outcomes for APC and for the regulatory agencies. What must management achieve to be acceptable from all perspectives represented in the workgroup? Construct a set of meaningful hypotheses about relations between management objectives and flow parameters

29 Workgroup Incorporate alternative hypotheses into a set of models (decision analysis) that that predict outcomes with respect to management objectives given different flow management strategies and observed levels of variation in inflow (using historical gage data)

30 Base flow (during non-generation intervals) Presen t Faunal response: e.g. Fish Abundance, IBI Threshold ab c d

31 Workgroup Estimate the relative likelihood that each model (i.e., using alternative hypotheses) appropriately describes outcomes as a result of a change in flow management strategies

32 Decision Support Models Powerful tools for assessment, learning and defining options for management. Demonstrate how these models will help us decide what to do at R.L. Harris. Discuss the methods by which we will build the models.


34 Bridging the GAP Conservation assessment Resource management Development of Quantitative Planning Tools for the Flint River Basin

35 Habitats Expected effects Populations The Traditional “Black Box” Approach Resource Development Conservation Restoration Resource Management Decision-Making

36 Quantitative Decision Modeling Management Actions External Physical Influences Aquatic Community External Biological Influences Stakeholder benefits Explicitly incorporates uncertainty

37 Types of Uncertainty System uncertainty due to environmental and demographic variation Statistical uncertainty due to the use of sample data to estimate parameters Process uncertainty due to incomplete understanding of system dynamics Factor AFactor B Population response Factor B Population response Factor A Population response or

38 Quantifying Uncertainty Empirical Models Expert Judgement Combination

39 Reducing Uncertainty: Bayesian Learning Prior EstimatePosterior Estimate New Information

40 Learning How a System Works (Adaptation) Current state Management action Actual future state Model A (hypothesis) Predicted State A Model B (hypothesis) Predicted future State B Info t Info t+1 Bayes’ Rule

41 Max pulse length (d) Etowah River Lower Tallapoosa River >10,800 cfs >10,050 cfs 5.7 fish/PAE 17 spp. < 1 ind. = 10 spp. 24 spp. est. 78 known spp. 58% recent 15.7-20.7 fish/PAE 33, 41 spp. < 1 ind. = 25, 35 spp. 43, 47 spp. est. 76 known spp. 86% recent

42 Redbreast Sunfish Spawning Success  156 nests monitored daily (23 May-24 June 1999).  Mean daily nest failure was 14% for all life stages.  Nest failure = 32% after 2-unit generation event.  71% of nests with swim-up fry failed (1-unit).  Only a total of 3 SUF observed after 2-units.

43 Daily flow pattern shows loss of stable-flow periods in hydropeaking regime

44 Thermal regimes are altered by Harris Dam operations

45 Spawning Windows Longest period without hydropeaking July-August (hours) Number of YOY/100 PAEs

46 Years between stable low-flow periods of >10 days in hydropeaking reaches Data for July-September

47 Flow regime below Harris Dam on the Tallapoosa River Hourly flows, April - August 1995

48 Availability of shallow habitats is high in a hydropeaking reach of the Tallapoosa River… PHABSIM data; Freeman, Bowen, Bovee and Irwin, 2001, Ecol. Appl. 11:179-190 Habitat availability, April-June, based on hourly flows

49 Maximum period of habitat stability, April- June, based on hourly flows But hydropeaking greatly reduces temporal habitat stability Freeman, Bowen, Bovee and Irwin, 2001, Ecol. Appl. 11:179-190






55 What is next? Refine models using empirical evidence or expert opinion. Add to the model. All other fundamental objectives. To do this we will need to input appropriate data. We need to change something at the dam. Remember, this is a learn as you go process.


57 Workgroup Identify a starting point for changing the flow regime below Harris Dam; the starting point should have a high likelihood (according to the models) of achieving management objectives. Use models to identify an appropriate time-frame for assessing whether or not management objectives are met

58 Workgroup and technical advisors Design a monitoring program designed to assess attainment of management goals under a given flow management strategy Collect data under new management regime for appropriate time-period

59 Workgroup and technical advisors After the agreed-upon period for monitoring, use monitoring results to assess attainment of management goals. Based on the monitoring information, revise likelihood estimates for alternative models. Reassess the probabilities of attaining management objectives under alternative management strategies

60 Workgroup and technical advisors If management objectives are not being met under the current flow regime, choose a new strategy more likely to be successful based on the revised models. Return to step (k)

61 Workgroup Stakeholders agree to implement the change in flow regime, to monitor results for the appropriate period, how and when attainment of objectives will be assessed, and to then further modify the flow regime depending on outcomes relative to management objectives

62 Where are we now? Utility has provided some data that will be incorporated into the model. We need more disclosure. Utility has been “secretly” testing options at the dam. The other stakeholders are restless. The scientists are frustrated (but still hopeful?) A facilitator (or group dynamics psychologist) is needed for the next stakeholder meeting. Values need to be added.

63 Framework: 1. Define ecosystem flow requirements develop initial numerical estimates of key aspects of river flow necessary to sustain native species and natural ecosystem functions; 2. Determine the influence of human activities accounting for human uses of water, both current and future, through development of a computerized hydrologic simulation model that facilitates examination of human- induced alterations to river flow regimes; 3. Identify areas of incompatibility assessing incompatibilities between human and ecosystem needs with particular attention to their spatial and temporal character; 4. Search for collaborative solution collaboratively searching for solutions to resolve incompatibilities; 5. Conduct water management experiments design and implement water management experiments to resolve critical uncertainties that frustrate efforts to integrate human and ecosystem needs; and 6. Design and implement an adaptive management plan using the knowledge gained in steps 1- 5, create an adaptive management program to facilitate ecologically sustainable water management for the long term. The Ecologically Sustainable Water Management (ESWM) Framework

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