The concept of adaptive management has existed for decades (Holling, 1978; Walters, 1986; Reever Morghan et al., 2006, McCarthy and Possingham, 2007; Williams et al., 2009, Williams, 2011; Allen et al., 2013; Allen and Garmestani, 2015). As indicated in the previous National Research Council (NRC) (1998) report, “an adaptive management approach that has research designed to provide data to reduce areas of current uncertainty should eventually give a more realistic assessment of the feasibility of eradication of B. abortus in the GYA [Greater Yellowstone Area].” Although resource managers are generally aware of the approach, the term continues to be misused and misunderstood (Williams, 2011). In addressing brucellosis, the term “adaptive management” is used in different ways and its meaning has not always been clear. In this chapter, the committee reexamines adaptive management in the context of addressing brucellosis in the GYA and offers clarification for correction.
Adaptive management is most clearly and succinctly defined as “a systematic approach for improving resource management by learning from management outcomes” (Williams et al., 2009). Adaptive management is a form of structured decision making that is carried out iteratively over time, as opposed to a process that is applied only once (Martin et al., 2009). Structured decision making enables decision makers to focus on what, why, and how actions will be taken. It involves stakeholder engagement, problem identification, specification of objectives, identifying alternative approaches, projecting the consequences, and identifying uncertainties (Williams et al., 2009). The following definition of adaptive management is cited by the U.S. Department of the Interior’s (DOI’s) technical guide (Williams et al., 2009) and adopted from the 2004 NRC report Adaptive Management for Water Resources Project Planning:
Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process. Adaptive management also recognizes the importance of natural variability in contributing to ecological resilience and productivity. It is not a “trial and error” process, but rather emphasizes learning while doing. Adaptive management does not represent an end in itself, but rather a means to more effective decisions and enhanced benefits. Its true measure is in how well it helps meet environmental, social, and economic goals, increases scientific knowledge, and reduces tensions among stakeholders. (NRC, 2004)
According to the original authors of the concept (Walters and Holling, 1990), there are three ways to structure management as an adaptive process (Walters, 1986): (1) evolutionary, or “trial and error,” in which early choices are essentially haphazard, while later choices are made from a subset that gives better results; (2) passive adaptive, where historical data available at each time are used to construct a single best estimate or model for response, and the decision choice is based on assuming this model is correct; or (3) active adaptive, where data available at each time are used to structure a range of alternative response
models, and a policy choice is made that reflects some computed balance between expected short-term performance and long-term value of knowing which alternative model (if any) is correct. Active adaptive management seeks to increase the rate of learning by applying two or more management actions simultaneously, which are in turn based on alternate hypotheses or models of system function. When it is possible to carry out an active approach, it is possible to decide which experimental approaches should be optimally tested based on what is already known about the likelihoods of system responses and associated risks (Walters and Holling, 1990). As in any scientific experimentation, it is necessary to pay attention to principles of statistical design such as controls, randomization, replication, and stratification.
Williams and colleagues (2009) put forth six steps of adaptive management: (1) assessing the problem; (2) designing a management approach; (3) implementing the management approach; (4) monitoring the responses to the management actions; (5) evaluating the responses; and (6) adjusting the management approach based on what was learned (Williams et al., 2009). These six steps are then repeated over time. Westgate and colleagues (2013) outlined an alternative set of six steps: (1) identification of management goals in collaboration with stakeholders; (2) specification of multiple management options, one of which can be “do nothing;” (3) creation of a rigorous statistical process for interpreting how the system responds to management interventions, which typically involves creation of quantitative models and/or a rigorous experimental design; (4) implementation of management action(s); (5) monitoring of system response to management interventions (preferably on a regular basis); and (6) adjust management practice in response to results from monitoring (Westgate et al., 2013). Step 3 from the latter is key to active adaptive management. It is essentially equivalent to the scientific method of hypothesis formulation (conceptual modeling) and hypothesis testing (using well-formulated experimental designs). Experimentation is used not only to support or refute hypotheses but also to provide new knowledge that can be used to incrementally refine or replace the hypotheses and the model.
Modeling is essential to the process of adaptive management, as models provide the basis for making predictions of how the system will respond to management actions as well as other environmental variations. A model, its structure, and its parameters embody a set of hypotheses about how the system works. A model can be conceptual or quantitative, but it always embodies current understanding and can be used to make informed predictions of system dynamics in response to the environment or management actions. Model predictions are compared with data, and the hypothesis is then rejected, supported, or revised.
Modeling has often been beneficially used to inform bison and elk management in the GYA. For example, models have been built of bison movements (Bruggeman et al., 2007; Geremia et al., 2011, 2014a), bison population dynamics (Coughenour, 2005; Geremia et al., 2011, 2014b; Hobbs et al., 2015), elk population dynamics (Coughenour and Singer, 1996; Taper and Gogan, 2002; Lubow and Smith, 2004; Eberhart et al., 2007), elk-wolf dynamics (Varley and Boyce, 2006), elk spatial distributions (Mao et al., 2005; Cross et al., 2010b), brucellosis transmission and seroprevalence (Cross et al., 2010a; Hobbs et al., 2015), and ecosystem dynamics (Coughenour, 2002, 2005). These models simulate and predict system responses to various management actions, and researchers and resource managers use the models’ insights and predictions to make informed management decisions.
New modeling approaches have recently been used to incorporate epidemiological, demographic, and ecological processes across space and time. These include improved epidemiological models, spatially explicit population models, Bayesian models, ecosystem models, and linked epidemiological-demographic models (Cross et al., 2010a; Hobbs et al., 2015). Spatial modeling has advanced markedly in the last two decades, and spatial heterogeneity and processes have increasingly been recognized as critical for understanding wildlife ecology and ecosystem dynamics. Land use change and its drivers have also been modeled with increasingly sophisticated approaches over the past two decades (e.g., Agarwal et al., 2002; Basse et al., 2014). Such landscape models could be usefulin addressing brucellosis, as these models can incorporate animal disease dynamics and the effects of land use and wildlife management across spatially heterogeneous ecosystems (Millspaugh et al., 2008; Sandifer et al., 2015).
Models also address uncertainty, another hallmark of adaptive management. It is essential to explicitly acknowledge uncertainties arising in model formulation, parameterization, and environmental variability. Once the sources of uncertainty are quantitatively identified, experiments can be designed to reduce uncertainties in parameter estimation and more attention can be given to key aspects of model formulation. Uncertainty can be stated qualitatively or quantitatively. There are a number of different ways to quantify uncertainty, including simple statistics, information theoretic statistics, uncertainty analysis, sensitivity analysis, model verification, and validation. Bayesian statistics was suggested early on to be particularly well suited for adaptive management (Walters, 1986), and this approach has been useful in modeling the best options for managing brucellosis in GYA bison (Hobbs et al., 2015).
Adaptive management plans for bison, elk, and brucellosis in the Greater Yellowstone Ecosystem (GYE) could make greater uses of models in identifying and evaluating management actions. Models serve as formal hypotheses of the ways that populations, disease, and ecosystems function and respond to management actions. Models could also be used to a greater extent as focal points for multi-stakeholder involvement and understanding.
2.1 The Interagency Bison Management Plan
Adaptive management is employed with the Interagency Bison Management Plan (IBMP) and the U.S. Fish & Wildlife Service (USFWS) management plan for elk in the southern GYE (USDOI and USDA, 2000a,b; USFWS and NPS, 2007), but there are areas for improvement. The IBMP calls for an adaptive management program that “includes intensive monitoring and coordination, as well as research projects with specified resultant management actions responding to the research results” (USDOI and USDA, 2000b). This was also specified in the modified preferred alternative in the Environmental Impact Statement (USDOI and USDA, 2000a):
In the context of the bison management plan and the modified preferred alternative, adaptive management means testing and validating with generally accepted scientific and management principles the proposed spatial and temporal separation risk management and other management actions. Under the adaptive management approach, future management actions could be adjusted, based on feedback from implementation of the proposed risk management actions.… By its nature, a plan using adaptive management requires monitoring and adjustments as new information is obtained.
Response to 2008 GAO Report
In 2008, the U.S. Government Accountability Office (GAO) issued a review that was critical of the IBMP’s implementation and pointed out essential components of adaptive management that were lacking (GAO, 2008). According to the GAO, the implementation of the IBMP lacked: (1) linkages among key steps, including identifying measurable management objectives, a monitoring program about the impacts of management actions, and decision making based on lessons learned from past management actions; (2) key agency partner collaborations; and (3) engagement of key stakeholders (GAO, 2008). In response, agencies involved in implementing the IBMP made significant improvements in their approach. Adjustments to the IBMP were based on the adaptive management framework and principles outlined in the DOI’s technical guide on adaptive management (Williams et al., 2009). Beginning in 2008, the IBMP has produced annual report updates describing adaptive adjustments to the IBMP, and these reports are posted online. In particular, the adjustments to the IBMP included the creation of measurable objectives and the development of a specific monitoring program to assess important scientific and management questions (IBMP, 2008).
The IBMP annual adaptive management reports are highly structured and are based on principles of structured decision making with stated overarching goals and a series of management objectives. For each objective, a series of management actions are described; and for each action, a set of corresponding monitoring metrics and management responses are outlined. This framework ensures that the objectives are clearly defined and that there are clear linkages between the objectives and the other components of the IBMP. The approach can be illustrated using the example of Goal #2 from their 2014 report, with the IBMP specifying other actions aimed at increasing the understanding of bison genetics and the ecological role of bison to inform adaptive management (IBMP, 2014).
Goal #2: Conserve a wild, free-ranging bison population.
Objective 2.1. Manage the Yellowstone bison population to ensure the ecological function and role of bison in the Yellowstone area and to maintain genetic diversity for future adaptation.
Management action 2.1.a. Increase the understanding of bison population dynamics to inform adaptive management and reduce sharp increases and decreased in bison abundance.
Monitoring metric: Conduct aerial and ground surveys to estimate the annual abundance of Yellowstone bison each summer. Management response: If abundance estimates decrease to <2,300 bison, then the agencies will increase the implementation of non-lethal management measures.
The structure of this framework appropriately links management actions to management objectives, specifies monitoring metrics to measure the responses to the actions, and specifies management responses to monitoring results. The annual reports are available online, with opportunity for public feedback. The approach has proven to be successful because the goals and objectives are agreed on by the IBMP agencies and because the proposed management actions are based on practical knowledge, experience, scientific research, and creative thought. Importantly, the objectives are stated and results of management actions taken to achieve the objective are monitored and reported. This provides the transparency and accountability that the GAO had previously noted was needed (GAO, 2008).
Need for Clarification Due to Varied Usage and Application
The term “adaptive management” is used in three different ways in the IBMP. The most pervasive use of the term is in reference to “adaptive management changes,” such as incrementally expanding the zone of tolerance for bison outside of Yellowstone National Park (YNP) or allowing limited hunting. Incremental changes are predicated on what has been learned through management about actions that are successful, and the assumption is that more learning will occur through applying the adaptive changes. However, there is no stated intention for carrying out adaptive management for the purpose of learning more about the system in a scientific sense.
A second way the term is used is by inference: because management actions are part of a larger adaptive management plan, these actions are considered as adaptive management actions. However, many of the stated IBMP management actions are merely statements of actions to be taken, without any apparent use of prior knowledge or intent to gain knowledge through the action. The following examples are excerpted from the IBMP (2012, 2014):
- Management Action 1.1a: Allow untested female/mixed groups of bison to migrate onto and occupy the Horse Butte peninsula and the Flats each winter and spring in Zone 2.
- Management Action 1.3c: Annually, the Gallatin National Forest will ensure conflict-free habitat is available for bison and livestock grazing on public lands, as per management objectives of the IBMP.
- Management Action 2.2a: Use slaughter only when necessary; attempt to use other risk management tools first.
- Management Action 3.1a: Continue bison vaccination under prevailing authority.
These actions would be considered passive, not active, adaptive management, as the focus is on achieving management objectives and learning becomes an untargeted byproduct (Williams et al., 2009). Some of these actions are also examples of “management based on resource status,” which is also not adaptive management (Williams et al., 2009).
The third way the term is used is more aligned with the original definition: to make decisions based on what has been learned and to carry out research to inform management. Several management actions explicitly call for knowledge and research to inform adaptive management. The following examples are drawn from the IBMP (2012, 2014):
- Management Action 1.1b: Use adaptive management to gain management experience regarding how bison use Zone 2 in the Gardiner basin, and provide space/habitat for bison in cattle-free areas.
- Management Action 1.1c: Use research findings on bison birth synchrony and fetal and shed Brucella abortus field viability and persistence to inform adaptive management.
- Management Action 2.1a: Increase the understanding of bison population dynamics to inform adaptive management and reduce sharp increases and decreases in bison abundance.
- Management Action 2.1b: Increase the understanding of genetics of bison in YELL to inform adaptive management.
- Management Action 2.1c: Increase understanding of the ecological role of bison to inform adaptive management by commissioning a comprehensive review and assessment.
However, with the possible exception of action 1.1b, the actions listed do not use management by experiment. As stated in the previous NRC report, adaptive management has “research designed to provide data to reduce areas of current uncertainty,” and it means “conducting management activities as hypothesis tests.” This corresponds to active adaptive management, which uses experimentalmanagement that focuses directly on learning, or “quasi-experimental management that focuses simultaneously on learning and achievement of management objectives” (Williams et al., 2009). Both approaches carry out management in ways that aim to increase learning about processes that control system dynamics, and they both involve “management by experiment.” While the other actions aim to use research findings to inform management decisions, they do not use management to learn and therefore cannot be considered as adaptive management. Whether passive or active, the hallmark of adaptive management is the intent to use management to learn about the system in order to inform future management.
2.2 Vaccination of Feedground Elk
The vaccination of feedground elk populations is an example of adaptive management applied to reducing brucellosis. At the time of the previous NRC review (1998), there was recognition that better vaccines were needed. It was known at the time that syringe vaccination was deemed inappropriate and cost-ineffective, but the lyophilization of B. abortus strain 19 (S19) vaccine and its incorporation into hydroxypropyl cellulose biobullets allowed remote vaccination where dense populations of elk could be closely approached on feedgrounds in winter (NRC, 1998). Thus, with the relatively new approach for vaccinating elk with S19, it was an available adaptive management tool that could be used in the short term and its successes and failures could be monitored (Thorne et al., 1981).
From the outset, success and failure was measured by trends in seroprevalence and, in some places and some years, by close observation of rates of abortion (Herriges, 1989). S19 biobullet vaccination was implemented as the primary short-term adaptive management tool for reducing brucellosis in feedground
elk in the mid-1980s, with the hope it would reduce disease prevalence in elk and thus reduce risk of cattle exposure. At the time of the previous NRC review, declining seroprevalence suggested it might indeed be a key to reducing rates of infection in feedground elk.
Biobullet vaccination of elk with S19 continued to be monitored for 30 years, making it now one of the longest-lasting examples of adaptive management of a wildlife disease. Seroprevalence rates initially declined (Herriges et al., 1989), but long-term studies over the last decade have shown increasing seroprevalence and no decline over three decades (Schumaker, 2015; Maichak et al., 2017). B. abortus challenge trials revealed single calfhood vaccination with S19 had low efficacy in preventing infection; would likely have only little-to-moderate effect on Brucella prevalence in elk; and was unlikely to eradicate the disease in wildlife of the GYA (Roffe et al., 2004). Immunology studies revealed that vaccination of elk with S19 and B. abortus strain RB51 induces poor protection against brucellosis (Olsen et al., 2006). Kauffman and colleagues (2013) note that “Since 1985, nearly 100,000 elk have been inoculated. However, efficacy of S19 in preventing abortions in elk is low (25%) (Roffe et al., 2004), and reductions in brucellosis prevalence among elk attending vaccinated feed-grounds have not been observed.” Furthermore, in addition to the weight of scientific evidence against S19 vaccination of elk, it appears that the Wyoming Game & Fish Department is halting the vaccination program due to logistical constraints associated with the manufacturer discontinuing production of biobullets (Scurlock, 2015).
What started as a short-term adaptive management effort became a long-term effort and, effectively, a long-term experiment. By making adjustments along the way based on continued observation and data collection, the experiment has provided useful information on efficacy and cost-effectiveness (Maichak et al., 2017). However, a number of aspects could have led to a faster learning process and more rapid management changes. First, replicate control feedgrounds could have been used during the initiation of the program. Second, more continuous assessment of the program’s efficacy and scientific peer-review could have been conducted periodically through the process. Third, the cessation of vaccination on feedgrounds could have been implemented across different groups of feedgrounds in different years so as to control for other temporal changes.
This example shows how long-term commitment to adaptive management can reveal strengths and limitations of the applications of a particular tool or manipulation that intuitively seem likely to work. Although long-term collection of data incurs labor and analysis costs, the results can be used to inform potential decisions regarding application of S19 biobullet vaccination not only for feedground elk but also for free-ranging elk without risks and expenditures inherent in such an effort (Kauffman et al., 2013).
Other short-term (but available) adaptive management tools to reduce brucellosis infection in cattle—such as phasing out or eliminating some feedgrounds, using targeted elk population reductions, reducing spatial and temporal overlap of elk and cattle on ranges, applying physical barriers around feed—are now being tested and learning will take place through monitoring. To the extent they are efficacious and cost-effective, they may become longer-term tools or manipulations until a better option becomes feasible.
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