Structured decision making as a framework for large-scale wildlife harvest management decisions KELLY F. ROBINSON,1,2, ANGELA K. FULLER,3 JEREMY E. HURST,4 BRYAN L. SWIFT,4 ARTHUR KIRSCH,5 JAMES FARQUHAR,4 DANIEL J. DECKER,6 AND WILLIAM F. SIEMER6 1
New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, Ithaca, New York 14853 USA 2 Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan 48824 USA 3 U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, Ithaca, New York 14853 USA 4 Division of Fish and Wildlife, New York State Department of Environmental Conservation, Albany, New York 12233 USA 5 Division of Fish and Wildlife, New York State Department of Environmental Conservation, Avon, New York 14414 USA 6 Department of Natural Resources, Cornell University, Ithaca, New York 14853 USA Citation: Robinson, K. F., A. K. Fuller, J. E. Hurst, B. L. Swift, A. Kirsch, J. Farquhar, D. J. Decker, and W. F. Siemer. 2016. Structured decision making as a framework for large-scale wildlife harvest management decisions. Ecosphere 7(12): e01613. 10.1002/ecs2.1613
Abstract. Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decisionaiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns. Key words: decision analysis; Odocoileus virginianus; population demographics; structured decision making; uncertainty; white-tailed deer. Received 29 July 2016; revised 12 October 2016; accepted 1 November 2016. Corresponding Editor: James W. Cain III. Copyright: © 2016 Robinson et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. E-mail:
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INTRODUCTION
game species across large spatial scales, such as within and among states or other political boundaries, large tracts of ocean, or thousands of miles of coastline. For example, migratory waterfowl
Fish and wildlife management agencies have the often difficult task of managing the harvest of ❖ www.esajournals.org
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harvests are managed across flyways that encompass multiple states, as well as international borders (Williams and Johnson 1995, Johnson et al. 2015), and panmictic marine fish stocks in the North Atlantic Ocean can experience fishing pressure from multiple islands and continents (Sedberry et al. 1996, Ball et al. 2000). Harvest management can be difficult at these scales because of variation in both ecological and social dimensions across the management area. Ecologically, populations frequently differ in demography across a large spatial scale, warranting management decision making partitioned at finer scales for optimal results (Holling 1992, Peterman and Peters 1998). Socially, stakeholders often have differing views about how game species should be managed, and these views may vary spatially (Enck and Brown 2008, Leong et al. 2012). Stakeholders may also differ in their opinions regarding what constitutes a desirable hunting or fishing experience. Although stakeholder views on harvest management are influenced partially by the demographics of the target population, stakeholder values are complex, with satisfaction comprised of many non-harvest related components that can be specific to a given geographic region (Hendee 1974, Decker et al. 1980, Hammitt et al. 1990). Traditional methods of decision making for natural resource management often do not adequately account for the complexities inherent in harvest management decisions at large spatial scales. In addition to ad hoc or informal decision processes used for decades, Gregory et al. (2012) classified some of the more formal traditional decision-making methods, such as science-based, consensus-based, and economic- or multi-criteriabased analyses. Science-based decision making assumes that biological or ecological science is the only relevant source of knowledge, ignoring other social and economic considerations and lacking means for making values tradeoffs, which are necessary for harvest management (Gregory et al. 2012). Consensus-based decisions tend to ignore important tradeoffs and insights, as groups emphasize coming to a consensus rather than creating more informed decisions (Gregory et al. 2001), and can often lead to “group think,” in which contrary information is disregarded in favor of adopting a common viewpoint (Janis and Mann 1977, Gregory et al. 2001). Finally, economic ❖ www.esajournals.org
and multi-criteria methods alone do not provide the deliberative process necessary for decomposing the complexity inherent in natural resource decisions. Economic methods, such as cost–benefit analysis and contingent valuation, require that all aspects of a decision problem be measured in terms of a single currency (e.g., dollars). These methods, although useful for evaluating economic objectives, require stakeholders to construct their preferences for environmental resources and social dimensions in an unfamiliar way, which can exceed cognitive capabilities (Gregory et al. 1993). Likewise, multi-criteria analyses can provide rigorous methods for evaluating multiple objectives within a decision-making process, but do not describe methods for the formal expression of the objectives or the creation of alternative management actions for evaluation (Gregory et al. 2012). Structured decision making (SDM) is a valuesbased process (Keeney 1992) that guides the decision maker(s) through the steps of formulating the decision context, establishing objectives, generating a set of creative management alternatives, identifying key uncertainties that might affect the decision, and making tradeoffs among objectives (Hammond et al. 1999, Gregory et al. 2012). The SDM framework breaks complex decisions into component parts and results in decisions that are more defensible (Gregory and Keeney 2002), transparent, and robust (Runge 2011) than decisions made without a formal structure. Examples of SDM include decisions pertaining to recreational fisheries (Peterson and Evans 2003, Irwin et al. 2008), threatened and endangered species protection (Conroy et al. 2008), invasive species management (Runge et al. 2011a), migratory bird harvest (Williams and Johnson 1995), wildlife health (Sells et al. 2016), and estuarine habitat management (Robinson and Jennings 2012). By breaking the decision into logical components, SDM provides a framework to contend with the complexities of harvest management decisions at large spatial scales, such as creating multiple scales of objectives (McDaniels et al. 2006, Wilson and McDaniels 2007), formally analyzing tradeoffs (Keeney 1992, Gregory and Keeney 1994), and integrating social and ecological data (Gregory et al. 1993, McDaniels et al. 2006, Failing et al. 2007). In addition to these benefits, SDM allows stakeholders to express formally their subjective 2
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judgments and preferences. Furthermore, SDM seeks an optimal solution to the problem that balances these competing stakeholder interests, rather than simply choosing winners and losers through a majority preference or consensus decision (Gregory et al. 1993, 2001). For the natural resource agency charged with managing a public trust resource, SDM provides policy makers a tool to objectively consider multiple viewpoints rather than be swayed by special interest groups. Thus, SDM may build and sustain credibility with trustees (i.e., elected officials) and the trust beneficiaries (i.e., public). We present a case study describing how we used SDM to incorporate the numerous stakeholder values present in large-scale harvest management decisions, predict the ability of different harvest management scenarios to achieve stakeholders’ objectives, and make tradeoffs among the objectives to reach a decision. The goal of the SDM case study was to aid the New York State Department of Environmental Conservation (NYSDEC) in setting regulations for Odocoileus virginianus (Zimmermann) (white-tailed deer) buck harvest throughout the state. This case study provides a decision framework for determining the optimal management strategy for managing game species when multiple competing biological and social objectives and uncertainties exist, and describes the benefits of this structured approach to harvest management.
land cover and use, crop productivity; Kelly and Hurst 2016). The zones encompassed all of New York, except for Buffalo, New York City, and Nassau County (Fig. 1), where deer hunting was prohibited by state law.
Problem statement The 2012 Deer Management Plan for NYSDEC stated that the agency should “encourage various strategies to reduce harvest of young (≤1.5 yr old) bucks in accordance with hunter desires” and that “objective criteria” should be used to evaluate these strategies (Big Game Management Team 2011). This part of the plan was created because some hunting groups requested that the agency implement additional mandatory antler restrictions, requiring that bucks have a designated number of antler points to be legally harvested, with the goal of reducing harvest of young, small-antlered bucks and thereby potentially increasing the number of older, larger-antlered bucks subsequently available for harvest. Other hunters voiced concerns that this regulation would limit their freedom to harvest any buck of their choice. Based on this direction from the Deer Management Plan, our problem statement was to “develop a decision framework that uses objective criteria to evaluate optimal strategies for reducing harvest of yearling bucks, including mandatory antler restrictions.”
Objectives
MATERIALS AND METHODS
For our case study, we worked with a group of NYSDEC biologists and managers to create fundamental objectives that reflected the needs of NYSDEC and deer hunters. Through an iterative process, the group created a set of fundamental objectives and sub-objectives that reflected NYSDEC’s concerns about costs associated with implementation of deer harvest regulations, population growth (the probability of the population exceeding acceptable levels), and hunter satisfaction. The group created objectives to address deer hunters’ concerns about what constituted a satisfying hunting experience by incorporating the social science literature about hunters’ preferences (Hendee 1974, Decker et al. 1980, Hammitt et al. 1990, Ward et al. 2008), expert knowledge of the social scientists in the working group, and the results of previous surveys of deer hunters in New York (Enck and Brown 2008, Enck et al.
Study area We conducted our study from 2012 to 2015, with the goal of recommending appropriate harvest-regulation changes throughout New York for 2016. NYSDEC managed white-tailed deer populations within the state to balance stakeholder interests within ecological constraints related to managing the species. We applied our decision framework to seven buck management zones in New York State. Each of these zones represented unique combinations of deer population characteristics that indicate differences in population growth and harvest (e.g., yearling antler beam diameter, mean fawn to doe ratio, buck kill/mi.2) and environmental characteristics that can influence survival and population growth (e.g., depth and duration of snow cover, ❖ www.esajournals.org
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Fig. 1. The seven buck management zones of New York. Each zone was evaluated separately for the decision problem of white-tailed deer buck-harvest management. Each numbered area (n = 93) corresponds to a wildlife management unit. Hunting was not legal in units 1A, 2A, and 9C.
2011). The resulting objectives took into account economic, social, and ecological values that are common in harvest management (McDaniels et al. 2006). After creating the objectives, the hierarchy was presented to a diverse stakeholder group including hunters and conservationists, to obtain their feedback and suggestions, which were incorporated into the final objectives set. The complete objectives hierarchy, as well as the attributes used to measure each of the objectives, is listed in Table 1.
Enck et al. 2011), the group created a set of six management alternatives (i.e., regulation packages and non-regulatory approaches) that could achieve the fundamental objectives in different ways (Table 2). The management alternatives were as follows: (1) Status Quo (current harvest regulations—a buck with one antler that was at least 7.6 cm (3 in.) long was legally harvestable, and hunters could harvest up to two bucks per season), (2) reduce total allowable harvest per hunter from two bucks to one buck per season (“One-Buck”), (3) full-season mandatory antler restrictions (“Mandatory Antler Restrictions”; a buck with one antler that has at least three points (at least four points in the Lake Plains) would be legally harvestable), (4) mandatory antler restrictions for the archery season and the first one to two weeks of
Alternatives Based largely on previous input from hunters and by considering hunter responses to questions regarding buck-harvest-regulation changes in previous statewide surveys (Enck and Brown 2008, ❖ www.esajournals.org
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ROBINSON ET AL. Table 1. Fundamental objectives, sub-objectives, and measurable attributes for the white-tailed deer buckharvest management decision problem in New York. Fundamental objective Minimize costs incurred by NYSDEC† associated with implementation of deer harvest regulations (“Costs”)
Sub-objective
Measurable attribute
Minimize costs of outreach and education (“Outreach”) Minimize costs of compliance and enforcement (“Compliance”)
Number of ≥2.5-year-old bucks on the landscape and legally available for harvest Number of ≥2.5-year-old bucks harvested
Maximize opportunity to encounter and shoot a larger-antlered, older (≥2.5 yr) buck (“Big Bucks”; component of hunter satisfaction) Maximize opportunity to encounter and shoot any (≥1.5 yr) buck (“Any Buck”; component of hunter satisfaction)
Maximize opportunity to encounter and shoot any deer (antlerless or buck, “Any Deer”; component of hunter satisfaction) Maximize other hunter-related satisfactions (“Other Satisfactions”; component of hunter satisfaction)
Number of ≥1.5-year-old bucks on the landscape and legally available for harvest Number of ≥1.5-year-old bucks harvested Inherent freedom of choice (relative amount of freedom that hunters would have to choose to harvest any antlered buck that they encounter), scale 0–1 Number of deer on the landscape and legally available for harvest Number of deer harvested Minimize complexity of regulations
Maximize overall hunting opportunity
Minimize probability of the population exceeding objective levels (“Population”)
Relative amount of outreach and education necessary for each management alternative, scale 0–1 Relative amount of challenge caused for law enforcement officers, scale 0–1
Minimize effect on the index used to detect population growth (“Detect Growth”) Maximize ability to manage population growth through harvest of adult females (“Manage Growth”)
Hunter rating of the value placed on the ease of ability to discern if a buck is legal: 0–4 Hunter rating of the value placed on having changes in harvest regulations during regular firearms season: 0–4 Hunter rating of value placed on the number of days in regular firearms season: 0–4 Hunter rating of value placed on the number of buck tags available in a season: 0–4 Relative change in precision of the Buck Take Index to detect population growth under each alternative, scale 0–1 Population growth can be mitigated with increased deer management permit (antlerless harvest) issuance: Yes/No
† New York State Department of Environmental Conservation.
because this zone was already restricted to bow hunting only.
the gun season, depending on the buck management zone (“Partial Mandatory Antler Restrictions”; Appendix S1), (5) shorten regular firearms season by closing the last one to two weeks of the gun season, depending on the buck management zone (“Shorter Season”; Appendix S1), and (6) encourage hunters to voluntarily refrain from harvesting yearling bucks through educational outreach (“Voluntary Restraint”). Differences in the Partial Mandatory Antler Restrictions and Shorter Season alternatives among zones were a result of the differences in the Status Quo season lengths between the northern and southern portions of New York. In addition, these two alternatives were not considered in the Westchester/Suffolk zone
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Consequences The consequences stage of SDM predicts the outcomes (measurable attributes) of each of the alternatives on each of the objectives (Tables 1 and 2). We used a combination of expert elicitation techniques, empirical data and statistical models, population simulation models, and hunter survey data to predict the consequences of the management actions on the objectives. Relative population effects.—Many of the objectives in our decision problem relied heavily on understanding how white-tailed deer populations
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ROBINSON ET AL. Table 2. Set of alternative harvest management actions created for the white-tailed deer buck management decision analysis in New York. Harvest alternative Status Quo One-buck bag limit (“One-Buck”) Mandatory Antler Restrictions Partial Mandatory Antler Restrictions Shorter Season Voluntary Restraint
Description Any buck with an antler at least 7.5 cm (3 in.) long is legal for harvest, up to two bucks may be harvested per season Reduce the number of bucks that may be harvested in a season from two to one Bucks must have a designated number of points (≥2.5 cm [1 in.]) on an antler to be legal for harvest. Four-point rule in Lake Plains buck management zone, three points in all other zones Mandatory antler restriction regulations in place from beginning of archery season through the first one to two weeks of regular firearms season, depending on the buck management zone Eliminate the last one to two weeks of the regular firearms season, depending on the buck management zone Encourage voluntary restraint from harvesting yearling bucks through educational outreach
decrease in the harvest rate of yearling bucks that could be expected under the Voluntary Restraint alternative. Where possible, we estimated the harvest rates for each alternative based on harvest data from NYSDEC or other states in which a harvest regulation had been implemented (Appendix S1: Table S2). When data were not available for harvest rate estimation (e.g., if results of an action were never documented empirically), we used formal expert elicitation methods and results from our hunter survey to calculate ranges of harvest rates. The methods for harvest rate estimation for each alternative are described in detail in Appendix S1. We used expert elicitation, deer harvest data from New York, and population model output to predict the consequences of the harvest alternatives on the agency’s ability to manage population growth (Appendix S1). Deer population growth is managed by regulating the amount of adult female harvest that occurs each year (Robinson et al. 2014). We evaluated whether population growth in a buck management zone could be counteracted by issuing more permits to harvest antlerless deer (Low, Medium, or High ability to counteract growth; Appendix S1). After five years under a given harvest alternative, the team believed that predicted adult female harvest could not counteract population growth in a “Medium” buck management zone with population growth ≥20%, or a “Low” buck management zone with growth ≥5%. Uncertainties considered.—There was uncertainty regarding four aspects of the demography of deer populations in New York (survival rates
and harvest rates would be affected by the proposed management actions. Therefore, we needed to quantify the effects of each of the alternatives on different aspects of the deer population. We used a stochastic age-based simulation model of white-tailed deer (Collier and Krementz 2007a, Robinson et al. 2014) to measure the relative ability of the harvest alternatives to achieve the Big Buck, Any Buck, and Any Deer objectives (Table 1). The model predicted relative age- and sex-specific harvest, age- and sex-specific availability of deer on the landscape, and population growth under each harvest alternative. We parameterized the population model with data from NYSDEC, data from other published studies of white-tailed deer in the northeastern United States, and the expert opinion of NYSDEC biologists (see Robinson et al. 2014 for a full description of the model; Appendix S1: Tables S1 and S2). The model simulated the population dynamics of juveniles (
