A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs (2019) / Chapter Skim
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3 Best Practices for Developing Structured Decision Support Systems for Coral Interventions
3 Best Practices for Developing Structured Decision Support Systems for Coral Interventions
Pages 51-82
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From page 51... ...
Decision support tools and best practices exist to guide the evaluation of tradeoffs in achieving various management objectives that can be expected given the state of knowledge. An adaptive decision approach provides a structured framework for evaluating potential management actions using an iterative process that allows for continuous learning about the linked human–natural system and improvement of management decisions.
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From page 52... ...
Though typically implementation of these tools has been limited to management of local stressors, they have utility for evaluating coral interventions as part of an overall management strategy. DECISION SUPPORT CONTEXT OF CORAL INTERVENTIONS Coral reef management is dynamic and complex, and the expectation is that a number of different actions will be required across temporal and spatial scales as coral reefs respond to multiple human pressures and deteriorating environmental conditions.
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The arrows within the figure represent the feedback between management responses and the pressures, stressors, and environmental condition. The iterative nature of management responses is depicted in detail in Figure 3.2.
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Effective decision making under uncertainty benefits from an adaptive and structured decision-making strategy as shown in Figure 3.2 by allowing for an iterative process of planning, acting, evaluating, and responding over dynamic spatial and temporal scales. Adaptive management consists of planning, evaluating options, establishing monitoring goals, and iteratively adjusting management plans depending on the continuing evaluation of changes in coral reef function, structure, and health (Holling, 1973; Walters, 1986; Walters and Holling, 1990)
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From page 55... ...
The first step in any structured decision-making process is problem formulation to establish the scale of the decision context and the specific objectives, regulatory landscape, and array of stakeholders to be involved in the process (e.g., Gregory et al., 2012; Runge, 2011)
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(2015) conducted a needs assessment survey with coral reef managers to identify data needs around the relationship between climate change and coral reef health, and how those needs might best support the development of prototype decision support tools.
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. Although the Coral Reef Protection Plan emphasizes conventional contaminants and stressors, it provides a good example of structuring management objectives, subobjectives, and attributes to carry through the entire adaptive management process.
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58 A DECISION FRAMEWORK FOR CORAL INTERVENTIONS TABLE 3.1 Coral Reef Protection Objectives Identified by EPA Based on Stakeholder Meetings for the Coral Reef Protection Plan Fundamental Category Objective Subobjective 1 Subobjective 2 Environmental Protect, restore, Individual coral Endangered or and enhance colonies threatened colonies ecological integrity Non-endangered nor of coral reef threatened colonies systems Coral reef communities Economic Protect, restore, Property protection and enhance from storm waves economic benefits Economic benefits Tourism/visitation from coral reef from reef-related systems Fisheries activities Increase employment in reef-related industries Social Protect, restore Traditional uses of Availability of coral and enhance social reef resources fish species and benefits from coral resources for traditional reef systems uses (e.g., festivals, local markets) Traditional fishing and harvesting of reef resources Recreational benefits Human health Protect, restore, Protection of Mortality from storm and enhance human lives from waves human health storm waves Morbidity from storm benefits from coral waves reef systems Sustenance from fisheries species Pharmaceutical discoveries Governance/ Foster long-term political public support commitments and trust SOURCE: Recreated from Carriger et al., 2018.
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From page 59... ...
Biophysical outputs can include such attributes as coral growth and cover over time, coral diversity, herbivore biomass, coral disease, macroalgae cover, and other metrics identified by researchers, stakeholders, and decision makers as critical indicators of coral reef health and resilience (e.g., Anthony et al., 2015; Maynard et al., 2017; McClanahan et al., 2012)
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Table 3.2 provides an overview of the most common biophysical models used to assess coral reef condition. A critical first step in the development of a quantitative model or set of quantitative models is constructing a conceptual model of coral reef interactions.
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From page 61... ...
of different genotypes under changing environmental conditions Physiological Follows physiological dynamics of energetic Cunning et al., 2017; models (Figure exchange among a coral host, symbiotic Muller et al., 2009 3.3h) algae, and the microbiome (e.g., dynamic energy budget models)
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can account for the risks and benefits associated with interventions that affect evolutionary dynamics. Such effects include the benefits of increasing stress-tolerant genotypes in assisted gene flow and managed selection, the benefits of genetic diversity and the combinations of different genotypes in managed breeding, or the risks of missed evolutionary opportunities in stress- eduction r
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In addition to more accurately assessing the potential benefits of added recruits, structured population models can help evaluate stage- or size-dependent decisions (e.g., the
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. Accounting for herbivore dynamics might be particularly relevant if management considerations include fisheries management in conjunction with coral interventions.
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Each modeling framework has unique data needs for parameterization that are dependent on the ecological, spatial, and temporal scales of the dynamics modeled. Across ecological scales, data needs will include selection strength and trait-based genetic variation for genetic models, nutrient uptake and assimilation rates in physiological models, demographic rates in structured population models, and species interaction rates in community models.
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From page 66... ...
Sensitivity and uncertainty analyses Sensitivity analysis is the process of identifying how model projections change as a function of model inputs. Sensitivity analysis can be local, changing one input at a time by a specific amount and recording the change in model projections; or global, using a probabilistic or other type of framework to evaluate model sensitivity to all inputs simultaneously and thereby incorporating dependencies, feedbacks, and correlations (Cariboni et al., 2007)
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, although note that Bayesian networks are also extremely useful for quantifiable uncertainty precisely because they are probabilistic. The proposed interventions are largely untested at scale, as discussed in Chapter 2.
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These combinations, along with uncertainty in knowledge about the reef system and future environment, will yield a range of predicted changes across alternatives with tradeoffs in their ability to meet management objectives and minimize risk. For example, some intervention strategies may support the growth of a small subset of coral species that provide fish habitat but not the solid reef structure that is needed to provide coastal protection from storm waves.
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plots human sensitivity to coral reef rarity (J) and the maximal fishing rate (σ)
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, criteria Decision trees Probabilistic representation of Flower et al., 2017; outcomes; can backcalculate the van Oppen et al., optimum strategy 2017 System dynamics Systems-based modeling approaches; Chang et al., 2008; models typically deterministic but time Rocha, 2010 varying; capture feedback loops Bayesian networks Models based on conditional Ban et al., 2014; (BNs) or Bayesian probabilities; acyclic (e.g., no Renken and Mumby, belief networks (BBNs)
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, linked by underlying biophysical models. o NOTES: In a MCDA analysis, "Sw" and "Vf" represent the underlying stakeholder weights across objectives, and value functions that quantitatively relate each criterion to the alternatives being evaluated, respectively.
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It is far more likely that there will be a series of interventions at different times and that these will be combined with management of local stressors. That said, in an iterative, adaptive management context, first evaluating each of these interventions by biophysical, economic, and social criteria using MCDA can lead to an understanding of relative differences across interventions that could be useful for subsequent analyses.
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. In this decision tree, a more structured decision methodology is nested under "Risk/ benefit analysis," including ecological modeling of ecosystem strategy impacts (biophysical modeling as described in this document)
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74 FIGURE 3.6 Qualitative decision tree for determining whether to implement coral interventions. SOURCE: van Oppen et al., 2017.
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Bayesian Networks Bayesian networks offer another alternative for structuring decision making and are the focus of a more complete example in Chapter 4. Key activities and hypothesized relationships are identified by nodes and connecting lines in a graphical format.
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76 FIGURE 3.7 System dynamics model of disease diffusion in a coral reef. Flows are represented by the blue arrows and stocks are the boxes.
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Bayesian network design may be based on a conceptual model for the specific application and can incorporate results from the underlying biophysical modeling to generate conditional probability tables to quantify the relationships across nodes. Environmental data or information on drivers and pressures can relate probabilistically to biological and ecological variables that in turn relate probabilistically to ecosystem values.
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The dark grey nodes represent the combined effect of upstream nodes, including fishing pressure, sedimentation, pollution, and nutrient loading. Other pressures include cyclones, surface temperature, and salinity, leading to bleaching and potential disease.
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Measurable evaluation metrics that relate to the biophysical models will form the basis of the monitoring program discussed in the next step. Various biological community, disturbance, ecological process, and site characteristic metrics have been proposed in the context of evaluating coral reef health and resilience (e.g., Ford et al., 2018; Lam et al., 2017; McClanahan et al., 2012; Obura and Grimsditch, 2009)
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Monitoring provides data on predicted biophysical metrics, both to compare to model predictions over time and thereby improve the underlying biophysical modeling, but also to demonstrate and evaluate how well objectives are being achieved. Effective monitoring programs are based on the management objectives identified in Step 1 (Legg and Nagy, 2006)
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Monitoring the causes of failure would also involve monitoring for potential risks, such as the introduction of pathogens or nonnative species. Steps 7, 8, and 9: Evaluate, Communicate, and Adapt As interventions are applied and monitoring data come in, the final three steps of the adaptive management circle involve evaluating progress toward decision objectives, communicating the results of the monitoring program, and potentially adapting or revising the management strategy.
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This effort provides a data- and values-informed basis for selecting and evaluating management options against a set of objectives. Recommendation: A structured, adaptive management framework that considers all drivers and pressures affecting coral reefs should be developed to evaluate tradeoffs across alternatives and identify when and where new coral intervention(s)
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