Uncertainty and Adaptive Management

Transcription

1 Uncertainty and Adaptive Management

2 Looking ahead OODA loops and adaptive management. Predictions and uncertainty as an essential part of the adaptive management process.

3 Adaptive Management- Observe, Orient, Decide and Act Loop, Late 1960s Orient Unfolding Events Observe Decide Act If your OODA loop time is slower than the unfolding events, you will loose. "OODA.Boyd" by Patrick Edwin Moran - Own work. Licensed under CC BY 3.0 via Wikimedia Commons -

4 Adaptive management- two versions Jones et al 2005 From Rist et al Based on Holling 1978, Walters Not inherently probabilistic. Who is making the decisions?

5 Waynt et al (1995) proposed including risk assessment into an adaptive management cycle Long- time ago but understood that the systems were non- equilibrium and dynamic. Context Analysis Socioeconomic Context Resource Management Intervention Waynt et al 1995 Ecological Context Objectives (Policy) Management Options Risk Assessment Exposure or Stress Intervention and Monitoring Risk Characterization Ecological Effects

6 Waynt et al proposed including risk assessment into an adaptive management cycle Context Analysis Risk Assessment Ecological risk assessment is part of the adaptive management process. Socioeconomic Context Resource Management Intervention Waynt et al 1995 Ecological Context Objectives (Policy) Management Options Exposure or Stress Intervention and Monitoring Risk Characterization Ecological Effects

7 Before and After Control Impact process Context Analysis Risk Assessment There is a before and after control impact segment, classic BACI. Socioeconomic Context Resource Ecological Context Objectives (Policy) Exposure or Stress Risk Characterization Ecological Effects Management Intervention Waynt et al 1995 Management Options Intervention and Monitoring

8 The process has been sketched out and updated van der Brink et al 2016, Landis et al 2017 Derivation of the endpoints Ecological risk assessment: Social goals (economic, cultural, well-being) that correspond to the multiple resources at a site. Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. Constraints Management and remediation options: Decision making

9 Two interconnected parts Social goals (economic, cultural, well-being) that correspond to the multiple resources at a site. Derivation of the endpoints Inputs describing the potential outcomes from the remediation with measurements (monitoring) Ecological risk assessment: Estimates of risk to multiple endpoints across the management region. Constraints Management and remediation options: Regulations, funding, legislature, Leadership Council, business plans, conflicting goals, Tribes. Decision making

10 Goals and Constraints are essential to set the stage for the evaluation and decision making. Social goals (economic, cultural, well-being) that correspond to the multiple resources at a site. Derivation of the endpoints Inputs describing the potential outcomes from the remediation with measurements (monitoring) Ecological risk assessment: Estimates of risk to multiple endpoints across the management region. Constraints Management and remediation options: Decision making

11 The Loop- Risk and Adaptive management Ecological risk assessment: Inputs describing the potential outcomes from the remediation Management and remediation options: Decision making Estimates of risk to multiple endpoints across the management region. Ecological risk assessment ties the loop together. It also should identify the critical uncertainties

12 Implementation Inputs describing the potential outcomes from the remediation Ecological risk assessment: Management and remediation options: Decision making Estimates of risk to multiple endpoints across the management region. Here is the Risk Assessment Probability Distributions for multiple endpoints Spatial Distribution of the risk

13 Implementation Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. Management and remediation options: Decision making Important variables to measure determined by the risk assessment and the uncertainty analysis

14 Implementation Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. Management and remediation options: Decision making What will Work? Evaluation of the change in risk due to management options, conflicts with various management goals (endpoints).

15 Implementation Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. So be clear about who is making the decision. What management actions to take? Reconciling the conflicts the actions will cause. Management and remediation options: Decision making Identify the uncertainty in the management actions

16 Implementation Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. Management and remediation options: Run the plan Decision making

17 Implementation Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. Now do we need to revisit our priors and update our analysis?? Management and remediation options: Decision making

18 Implementation Start the process again Ecological risk assessment: Inputs describing the potential outcomes from the remediation Estimates of risk to multiple endpoints across the management region. The OODA loop is complete Management and remediation options: Decision making

19 Can use data to learn how the interactions in the Bayesian networks work. South Queensland Estuaries- Scarlett Graham thesis

20 South East Queensland Region Water quality is valuable to the region. Climate: Mild winters; hot rainy summers Pacific Ocean Intensive land use near coast. Environmental DNA (edna) samples of benthic eukaryotes were collected from estuaries.

21 Research Objectives Predict water quality and benthic communities in a single integrated, quantitative assessment/model. Put benthic edna information into context with water quality management objectives. Learn about knowledge gaps for future research.

22 Research Objectives Predict water quality and benthic communities in a single integrated, quantitative assessment/model. Put benthic edna information into context with water quality management objectives. Learn about knowledge gaps for future research.

23 Water Quality Endpoint Regional water quality objectives are available for each estuary. Focus on Dissolved Oxygen (DO) and Chlorophyll- a (Chl- a) objectives. Calculate risk to achieving regional objectives: High Relative Risk = Achieves < 50% of the time Moderate Relative Risk = Achieves 50 % and < 75% Low Risk Relative = Achieves 75%

24 Parameterizing the model Relationships between variables were determined using machine- learning algorithms within Netica. Case- files of thousands of records for each estuary were compiled (total 8,000). Each relationship in the model is based on estuary- specific observations. Method consistent with other BNs predicting benthos. Norsys (2014), Lucena- Moya et al. (2015)

25 Information Utilized Data Sources 1. Water quality (monthly monitoring ) 2. Land use (2000, 2006, 2013) 3. Rainfall ( ) Other Information 5. Estuary science from published literature 6. Input from Queensland scientists and environmental managers.

26 Results BN Model Summer Fall Winter Spring Season Water Temp (C) 13 to to to to ± 4.7 Dissolved Oxygen (%) 16 to to to to to ± 19 Lower Logan Middle Logan Region Logan WWTP Improvements Yes No Intensive Landuse 0 to to to to to ± 13 Total Monthly Rainfall (mm) 0 to to to to ± 92 Surface Water Salinity (ppt) 0 to to 5 5 to to to ± to 8 8 to to to 1000 Total Nitrogen (mg/l) 0 to to to 1 1 to 3.5 Total Phosphorous (mg/l) 0 to to to to 1.6 Turbidity (NTU) ± ± ± 0.41 Chlorolphyll-a (ug/l) 0 to 2 2 to 4 4 to to to 63 0 to 5 5 to to to 31 0 to to 5 5 to to 15 0 to to 5 5 to to 18 0 to to 5 5 to to ± 11 Diatom ± 6.9 Dinoflagellates ± 3.3 Green Algae ± 3.9 Fungi ± 4.3 Model with inputs selected for the Middle Logan risk region. Meiofauna 0 to 5 5 to to to ± 6.4 Protozoan 0 to 5 5 to to to ± 7.6

27 Results BN Model Current Conditions Lower Logan Middle Logan Region Summer Fall Winter Spring Season Logan WWTP Improvements Yes No Intensive Landuse 0 to to to to to ± 13 Total Monthly Rainfall (mm) 0 to to to to ± 92 Surface Water Salinity (ppt) 0 to to 5 5 to to to ± to to to to 31 0 to 8 8 to to to 1000 Water Temp (C) ± 4.7 Total Nitrogen (mg/l) 0 to to to 1 1 to 3.5 Total Phosphorous (mg/l) 0 to to to to 1.6 Turbidity (NTU) ± ± ± 0.41 Dissolved Oxygen (%) 16 to to to to to ± 19 Chlorolphyll-a (ug/l) 0 to 2 2 to 4 4 to to to 63 0 to 5 5 to to to 31 0 to to 5 5 to to 15 0 to to 5 5 to to 18 0 to to 5 5 to to 18 0 to 5 5 to to to 30 0 to 5 5 to to to ± 11 Diatom ± 6.9 Dinoflagellates ± 3.3 Green Algae ± 3.9 Fungi ± 4.3 Meiofauna ± 6.4 Protozoan ± 7.6 % Probability of achieving water quality objective state Middle Logan 16% likelihood of meeting DO High Relative Risk 52% likelihood of meeting Chl- a Moderate Relative Risk

28 Interactive Capability Summer Predictions Winter Predictions Summer Fall Winter Spring Season to to to to 31 Water Temp (C) ± 2.3 Dissolved Oxygen (%) 16 to to to to to ± 19 Summer Fall Winter Spring Season to to to to 31 Water Temp (C) ± 1.8 Dissolved Oxygen (%) 16 to to to to to ± 18 Logan WWTP Improvements Yes No Intensive Landuse 0 to to to to to ± 13 Total Monthly Rainfall (mm) 0 to to to to ± 97 Surface Water Salinity (ppt) 0 to to 5 5 to to to ± to 8 8 to to to 1000 Total Nitrogen (mg/l) 0 to to to 1 1 to 3.5 Total Phosphorous (mg/l) 0 to to to to 1.6 Turbidity (NTU) ± ± ± 0.39 Chlorolphyll-a (ug/l) 0 to 2 2 to 4 4 to to to ± 11 Logan WWTP Improvements Yes No Intensive Landuse 0 to to to to to ± 13 Total Monthly Rainfall (mm) 0 to to to to ± 73 Surface Water Salinity (ppt) 0 to to 5 5 to to to ± to 8 8 to to to 1000 Total Nitrogen (mg/l) 0 to to to 1 1 to 3.5 Total Phosphorous (mg/l) 0 to to to to 1.6 Turbidity (NTU) ± ± ± 0.42 Chlorolphyll-a (ug/l) 0 to 2 2 to 4 4 to to to ± 10 DO: 7.3% probability of meeting objective Chl- a: 46% probability of meeting objective DO: 31% probability of meeting objective Chl- a: 60% probability of meeting objective

29 Model Sensitivity Stressor Variables: Endpoints are most sensitive to Salinity. Season Rainfall Land use Salinity Water Temp Turbidity Total Nitrogen Total Phosphorous Dissolved Oxygen Chlorolphyll-a Diatom Dinoflagellate Green Algae Fungi Meiofauna Protozoan Intermediate WQ: Endpoints are more sensitive to Nitrogen than Phosphorous. Season Rainfall Land use Salinity Water Temp Turbidity Total Nitrogen Total Phosphorous Dissolved Oxygen Chlorolphyll-a Diatom Dinoflagellate Green Algae Fungi Meiofauna Protozoan

30 The Description of Place Framework for making decisions Adaptive Management Use of Bayesian networks and probabilistic tools Incorporation of Data and other evidence Run different scenarios Now to use the tools.

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