The purpose of this report is to describe the SDM template developed by the USGS, the OPRHP, and the NYSDEC, which represents the first effort to create a strategy for applying decision analysis tools to the complex natural resource challenge of CyanoHAB mitigation and management. In this report we describe (1) the elements of the SDM process in general and (2) how the SDM process and the accompanying report template (appendix 1) can be used for managing CyanoHABs in New York State parks. Two case studies are presented in appendixes 2 and 3 as examples of how the decision-making process and report template may be applied to a specific location. The template is intended to serve as an example that other natural resource managers faced with CyanoHABs challenges can use to inform their decision-making processes.

This document includes a variety of lake management options that were discussed as part of the structured decision-making process. Not all the strategies identified are feasible, endorsed by the NYSDEC, or have a regulatory pathway. Further analysis, as well as consultation with NYSDEC permit administrators, is warranted before management actions are implemented.

Are decisions really that difficult? We make hundreds of decisions throughout the day without stopping to analyze them; how do we do that? Daniel Kahneman, who was awarded the 2002 Nobel Prize in Economic Sciences, realized that we can make so many decisions quickly by employing cognitive short cuts such as heuristics (Kahneman, 2011). These are rules of thumb, some ingrained, some learned, that usually help us make good decisions. Heuristics can also be traps, however, if we employ them in novel decision-making settings. For example, the “sunk cost effect” is a cognitive bias that arises out of several heuristics. Sunk costs refer to something (for example, time or money) that has already been spent and cannot be recovered (Hammond and others, 1999). Even if those costs are not relevant to the current decision, we are tempted to nevertheless take them into account. An example in natural resources is continuing to spend money on a monitoring project because “we’ve already invested millions to collect this data for 30 years,” even after a review finds that the data have never substantively affected any management decisions. Another cognitive shortcut that can lead to systematic error in our thinking is “anchoring” (Hammond and others, 1999). This refers to giving disproportionate weight to the first information we encounter. For example, after we see an initial cost estimate, it is difficult to avoid subconsciously comparing subsequent estimates to the initial number. Thus, one of the reasons that decisions are difficult is that our evolved cognitive processes sometimes do not serve us well in new situations.

Natural resource management decisions are often difficult for two reasons. First, the systems being managed are complex, highly variable, and poorly understood. These systems are not just ecological; they are coupled socioecological systems, in which the interactions between humans, habitats, plants, and animals play a role. Thus, to make decisions, we must consider the uncertain human and ecological responses to management actions.

Second, managing natural resources is often difficult because multiple stakeholders are usually interested in the problem and the stakeholders may care about different outcomes (Brignon and others, 2019). Some outcomes being sought by the decision maker may be legally mandated (for example, meeting legal water standards), whereas some are an expression of preference (for example, maximizing recreational opportunities). Achieving multiple outcomes often requires decision makers to balance multiple conflicting demands.

Faced with ingrained cognitive biases, complex systems, uncertainty, and multiple objectives, what is a decision maker to do? Decision makers have benefitted from processes that take them from problem identification to selection of a preferred action using a framework in which all the decision elements are clearly documented. Structured decision making is a systematic approach grounded in decision theory that breaks down complex decisions into their basic parts and reconstructs the problem in a framework that allows for collaborative examination and development of suitable actions (Gregory and others, 2012). There are many tools and techniques in SDM that can assist a decision maker through the process and address specific issues that make complex decision making difficult (Gregory and Keeney, 2002). For example, the deliberate process of SDM helps participants focus on understanding and capturing the interests and values of all the relevant stakeholders, reducing potential conflicts. SDM also has tools which allow participants to understand and make the complex tradeoffs among the economic, environmental, and political elements of a problem (Gregory and Keeney, 2002). Through its deliberate and deliberative process, SDM offers a transparent record of how a decision was reached that can be revisited as the problem or information changes.

The framework and process for using SDM are conceptualized by the PrOACT cycle, which has five steps: problem, objectives, alternatives, consequences, and tradeoffs (fig. 2; Hammond and others, 1999; Gregory and others, 2012). Decision makers, their staff, experts, and stakeholders can collaboratively work through the steps in PrOACT to articulate a clear description of the problem, specify the objectives they want to achieve, identify viable alternatives, analyze the consequences of the alternatives, and deliberate the tradeoffs among alternatives.

Problem Framing.—The first step in a structured decision process is to define the decision problem, often through a problem statement that describes the decision maker, the context in which a decision is to be made, and the nature of the decision (Gregory and Keeney, 2002; Robinson and others, 2016). The problem statement provides participants with an agreed-upon vision of what the group is working towards and creates the path for deliberations. Elements of a problem statement include identification of the decision maker, the problem scope (spatial, political, temporal), legal authorities or regulatory guidance for the decision, who and what the problem affects, and why making a decision is important.

Objectives.—The second step in a structured decision process is to describe the fundamental objectives; that is, the outcomes the decision maker seeks as a consequence of the decision. SDM is values focused; that is, it recognizes that at the heart of every decision is the attempt to achieve outcomes of value. SDM focuses on articulating the objectives and then using them to develop and then to evaluate potential management alternatives (McGowan and others, 2015). Different stakeholders may want different, even conflicting, objectives as outcomes from the decision, and this can be accommodated in an SDM analysis. For public agencies, the objectives should (1) be consistent with the organization’s mission and authority; (2) consider the values of the public, local stakeholders, and other interested entities; and (3) incorporate the shared goals of multiple agencies. It is often valuable to separate fundamental objectives (those pursued for their own sake) from means objectives (those pursued only insofar as they help achieve fundamental objectives), as the focus of the decision analysis should be on the fundamental objectives.

Alternatives.—The third step in an SDM process is to generate an array of alternatives; that is, mutually exclusive choices of actions that are within the power of the decision maker that address the decision problem. For complex and especially novel decision problems, a suitable set of management actions may not already exist and may have to be created. Alternatives can be single actions or actions grouped by theme (in other words, portfolios; Blomquist and others, 2010). Generation of alternatives is a creative process that is separate from evaluation of alternatives; it can be helpful to encourage bold brainstorming at an early stage to explore possible solutions, and to consider the objectives explicitly when designing alternatives. Having a set of alternatives that differ sharply from each other allows the decision maker to hone in on the aspects of the solution that are most important.

Consequences.—The fourth step in an SDM process is to estimate how well each alternative action under consideration is likely to achieve each fundamental objective. Performance on each fundamental objective reveals the strengths and weaknesses of each alternative (Gregory and others, 2012). A key technical and scientific task in an SDM analysis is to evaluate each alternative against the set of objectives, using the best available science to forecast what would happen if each alternative were implemented. To handle all the information in an organized and transparent manner, alternatives and objectives are often arranged in a matrix known as a consequence table.

Tradeoffs.—The fifth step in a structured decision process is to examine the consequence analysis and choose the alternative that best navigates the embedded tradeoffs (Runge and others, 2020a). Even after a full consequence analysis of the alternatives is completed, it can be difficult to know what to do. Often, no single alternative performs better than all other alternatives on every fundamental objective. Thus, to choose a path forward, the decision maker must figure out how to balance achievement across objectives. In other cases, a decision maker must compare a safe alternative with a lower performance against a riskier alternative with a higher performance—the decision maker needs to decide how to weigh performance against risk.

Fundamental objectives are the outcomes sought as a result of a decision. They represent the values of the decision makers and stakeholders and, in the case of decisions by public agencies, are often societal values expressed through statutes and regulations. Fundamental objectives may be multifaceted and competing. Importantly, fundamental objectives are sought for their own sake, not primarily as a means to achieving other objectives.

The mission of the OPRHP, “to provide safe and enjoyable recreational and interpretive opportunities for all New York State residents and visitors and to be responsible stewards of our valuable natural, historic and cultural resources,” suggests some important objectives that may be fundamental considerations in the prevention, management, and mitigation of CyanoHABs. A draft set of candidate fundamental objectives, in the context of management of CyanoHABs in OPRHP parks, is presented in the sidebar. This set of objectives may not be comprehensive but is instead meant to be illustrative of the considerations the OPRHP and its partners may be weighing when designing management and mitigation strategies.

This draft set of fundamental objectives could be used in a site-specific context in multiple ways. One approach, if there is time to assemble a group of staff, experts, and stakeholders, is to brainstorm fundamental objectives from scratch, using that as an opportunity to listen and reflect on what is important to everyone. In this approach, the template can be used to organize the objectives after the fact or to check for objectives that might have been missed. A second approach is to use the template objectives hierarchy (see sidebar) to guide discussions about the objectives. Each category can be discussed in turn, with site-specific considerations being used to screen, modify, and articulate the fundamental objectives that matter. See the report section “Undertaking a Structured Decision-Making Process for Cyanobacterial Harmful Algal Blooms in a New York State Park” for additional guidance on facilitating an SDM process.

The reduction of ambient waterbody nutrient concentrations is considered the best management strategy to minimize or eliminate the frequency of CyanoHABs (Burford and others, 2019; U.S. Environmental Protection Agency, 2020). Nutrient-reduction strategies, however, are long-term solutions and do not address the immediate need for mitigation measures during a CyanoHAB event. Short-term mitigation and management strategies are therefore often used to reduce the intensity or occurrence of CyanoHABs (Burford and others, 2019; New York State Department of Environmental Conservation, 2020a; U.S. Environmental Protection Agency, 2020). Furthermore, the environmental context matters—not all control measures will be effective for all waterbodies. Management of CyanoHABs, then, often involves a collection of individual actions: some actions designed to address the long-term solution, some actions designed to address the short-term solution, some actions to mitigate harm to other resources while the CyanoHAB treatments take effect, and some actions to communicate to the public what is happening. Thus, the alternatives available to a decision maker are alternative collections of actions. How are these alternative strategies assembled, and from what component actions?

The central technical task in an SDM process is to evaluate the potential alternatives against the fundamental objectives (Gregory and others, 2012), using the best available information. This typically requires predicting how well each alternative will achieve each objective. While data may exist for estimating some outcomes if both the management alternative and the objective are familiar, data may not exist for other outcomes (especially involving novel treatments, novel conditions, or outcomes that have not been studied previously). Two approaches are common: predictive models and expert judgment (Conroy and others, 2008). A model may be mathematical, conceptual (for example, an influence diagram), or a map (Moore and others, 2011). Formal expert elicitation methods can be used to query topical experts for their best professional judgement while minimizing the introduction of bias (Hanea and others, 2017).

The decision analysis steps described in the “Structured Decision Making” section—problem framing, articulating objectives, designing alternative strategies, and consequence evaluation—can help decision makers see the nature of the decisions they face and understand the consequences of different alternatives. But selecting the alternative to implement may remain challenging. Often, no one alternative is clearly superior to all others. Instead, the decision maker must navigate tradeoffs. The field of decision analysis offers a diverse set of tools to support decision makers at this stage of the process, and although the quantitative techniques can be valuable, the underlying principles are often enough to provide insight to the decision maker.

Most predictions about ecological systems are made with uncertainty because the system itself is only partially understood, because the proposed actions are novel, or both. Predictions about CyanoHABs are no exception: CyanoHABs arise from, at times, complex conditions within the ecosystem of a lake; the efficacy of many of the treatments is not well understood; and we often do not know how the efficacy is affected by various lake conditions. One way to capture uncertainty is to consider the alternative hypotheses about the underlying causes of CyanoHABs in a given lake; this exercise may lead to several different predictions about the outcomes for a given action or set of actions.

Consider a hypothetical lake that has experienced CyanoHABs in 5 of the last 10 years but did not have a known history of CyanoHABs before then. The manager of the lake has a variety of long-term objectives, including minimizing the frequency of CyanoHABs in the future. The manager is considering four alternative strategies: maintenance of the current management strategies, in-lake treatment of cyanobacteria, a comprehensive restoration of the watershed, and a substantial reengineering of the lake. During the consequence analysis, the manager asks scientists familiar with the lake to predict the frequency of CyanoHABs occurrence in the future under each of the alternative strategies. The scientists determine how the outcomes of each alternative strategy depend on the cause of the CyanoHABs (table 4). If cyanobacteria are in low concentrations throughout the lake but are accumulating in nearshore areas during windy days, and if water-quality data are not indicative of eutrophic conditions, then in-lake treatment designed to eradicate the cyanobacteria in affected areas is the best option. If the CyanoHABs are resulting from a decrease in water quality driven by known recent increases in nutrient runoff in the watershed, then watershed management would be best. If the frequency of CyanoHABs has increased because the climate has changed and the new temperature and precipitation patterns favor CyanoHABs, then reengineering of the lake to accommodate climate-driven changes would provide the largest reduction in the frequency of CyanoHABs. That is, predictions of the future frequency of CyanoHABs for alternative strategies differ on the basis of the causal hypotheses. This sort of uncertainty, sometimes called critical uncertainty, is relevant to the decision maker. The experts can provide another valuable piece of information: an estimate of the likelihood of each hypothesis, based on the best available evidence. Together, the predictions of the outcomes under each hypothesis and the likelihoods of the hypotheses can help the decision maker to determine a course of action.

A common impediment to identifying a preferred alternative is critical uncertainty. The hypothetical example in table 4 illustrates the concept. The decision maker does not know what action to take because of underlying scientific uncertainty. However, not all scientific uncertainty is relevant to a decision maker; in many cases, the choice of action is clear, even in the face of uncertainty about what the precise outcome will be. The technical methods associated with a problem such as that illustrated in table 4 involve calculating the expected value of information (Runge and others, 2011), which measures how much the expected outcome could be improved by resolving uncertainty before committing to a course of action relative to making a decision in the face of uncertainty.

In the hypothetical lake in table 4, the decision maker has a choice: take action with the available information (in the face of uncertainty) or first undertake research to resolve uncertainty before committing to action. If the decision maker chooses to take action in the face of uncertainty, the outcome associated with each alternative can be averaged over the hypotheses. So, for example, the current management strategies alternative has a predicted bloom probability of 0.5, 0.5, or 0.7 under hypotheses 1, 2, and 3, respectively; the weighted average of these values is 0.55. The action with the lowest expected probability of CyanoHABs in the face of uncertainty is watershed management (blue cell). This alternative does well under hypotheses 2 and 3 and is the same as current management strategies under hypothesis 1. The expected frequency of CyanoHABs is 0.20. If, instead, the decision maker invests in the research needed to identify the cause of the blooms and could take action after knowing which hypothesis was the case, the expected frequency of blooms in the future would be 0.05 (the lowest probability under each hypothesis is the same, 0.05). Thus, the value of information—the value of the research to the decision maker—is that it reduces the expected frequency of blooms from 20 percent to 5 percent. The challenge the decision maker faces is whether that benefit is worth the cost of the research. As with decisions involving multiple objectives, there are formal methods for analyzing critical uncertainty, but sometimes the concepts themselves provide the insight needed.

Even when a well-structured decision process is used to articulate objectives, identify alternatives, evaluate alternatives, and choose a preferred action, implementation can still present challenges. This “decision-implementation gap” is common in many conservation settings (Wright and others, 2020, p. 1). Forethought at the planning stage about some of the typical challenges can help increase the likelihood of the preferred action being implemented.

The first challenge is having adequate funding in place to undertake the preferred alternative. Treatment of CyanoHABs can be expensive and might fall outside of the normal operating budget of a park. Sources of funding might include special budget requests to the OPRHP, special budget requests to the State legislature, grant applications to Federal agencies, or cooperative projects with nongovernmental organizations. Consideration of the funding opportunities during the planning stage can enhance the likelihood of implementation.

The second challenge to implementing CyanoHAB management is the regulatory and permitting requirements that may need to be met. For example, the OPRHP often needs permits from the NYSDEC, as well as other State and Federal regulatory authorities, to implement in-lake treatments. Acquiring appropriate permits might be particularly challenging for novel treatments. By working with the permitting agency to frame the problem, the regulatory requirements can be anticipated and accounted for in an implementation plan.

Third, some of the alternatives considered for management of CyanoHABs might require or benefit from the cooperation of partners. It is valuable to engage critical partners early in the decision-making process, so they have a vested interest in the outcome, can help craft creative alternatives, and can identify sources of support for implementation.

Fourth, clear communication is an important element of CyanoHABs management. Communication between agencies allows them to coordinate their actions, and sharing clear and timely information about threats and mitigation increases the cooperation of the public.

Fifth, implementation of complex treatments may take longer than other actions, and the results may not be evident for even longer, so a sustained continuity of vision may be needed. Employees can change jobs and agencies can be reorganized during a multiyear implementation of CyanoHAB management, possibly disrupting the continuity of vision. A transparent decision framework not only memorializes decisions but also identifies long-term roles and responsibilities.

An implementation plan, with a clear timeline for actions and expected outcomes, identification of responsible parties, a funding plan, and a process for regulatory compliance can help to overcome implementation challenges. At the same time, such a plan provides a way to communicate with stakeholders and the public.

To initiate an SDM process, it is valuable to convene a core group, ideally including the decision makers and a few technical staff, to begin framing the problem and to discuss the merits of engaging a larger group in an SDM process. Typically, such a core team would jointly identify the problem it is trying to solve and sketch a rough PrOACT prototype, dedicating 2–6 hours to coarsely outline the problem, use this report and templates to identify potential objectives and some alternatives, and then consider what it would take to analyze the consequences and navigate the tradeoffs. In some cases, that structured conversation might be enough to identify a course of action. In other cases, reflection on the prototype might reveal that more work is needed. Considerations at this stage include the following: do more stakeholders and partners need to be involved in the analysis, is more detailed scientific expertise needed to understand the problem and evaluate the consequences, and is the problem complicated or contentious enough to require help from an outside facilitator? The answers to these questions can be used to plan the depth and timing of the process.

If the core team identifies the need for more work, then it is valuable to prepare a draft problem statement: a description of the decision setting from the team’s viewpoint. This problem statement captures the initial prototype, documents the relevant scientific and policy context, and can be used to communicate with stakeholders, partners, and experts. When inviting people into the process, it helps also to be clear about the role they are being asked to play and the time commitment it will involve.

The process itself will involve a series of meetings, whether virtual or in person, and individual or small group work between each meeting. The number and kind of meetings will depend on the problem, but a useful process often involves two to three preparatory conference calls (of 60–90 minutes in length) to review the problem statement, come to a common understanding of the issue, and prepare for subsequent steps, followed by an in-person meeting (of 1–5 days in length) at which the bulk of the deliberation occurs. In preparation for a meeting, the core team will need to develop a facilitation plan and identify a lead facilitator who can lead the discussion without the perception of undue influence or bias toward a certain outcome. Sometimes, internal staff can serve in this role, but, at other times, an outside facilitator will be better positioned to navigate contentious topics.

The PrOACT sequence (fig. 2) provides a structured way to proceed through a series of topics and can be used to develop an agenda for the calls and meetings, with the acknowledgment that insights at later steps often lead to the desire to revise earlier steps. The different steps in the process require different expertise. For example, policy experts and social scientists might have information to share when the objectives are being deliberated, regarding the statutory setting, the mission of the agency, or the desires of the public. Experts in cyanobacteria and harmful algal blooms might need to be consulted to evaluate the consequences. The goals of the process are to establish the fundamental objectives, develop a set of alternative strategies, evaluate the strategies against the objectives, and propose a preferred alternative that navigates the tradeoffs in the way that reflects the values of the decision maker.

The PrOACT framework also provides a way to document the decision-making process that makes it transparent to the public. Appendix 1 is a blank template for a structured decision-making report, which can be used during and after the process to document how the decision was framed, how the alternatives were evaluated, and how a preferred alternative was identified. It is often helpful to designate members of the core team to develop the documentation throughout the process. Appendixes 2 and 3 provide examples of completed reports following the template provided in appendix 1.