Byron K. Williams, U.S. Geological Survey, Division of Biological Resources, 12201 Sunrise Valley Drive, Reston, VA 20192
Fred A. Johnson, Office of Migratory Bird Management, U.S. Fish and Wildlife Service, 11500 American Holly Drive, Laurel, MD 20708-4016
(* originally published in 1995 in The Wildlife Society Bulletin, Vol. 23(3):430-436)
Migratory bird hunting is an important form of outdoor recreation in the United States. Each year, approximately 3 million people engage in 22 million days of migratory bird hunting, with an expenditure of $700 million (U.S. Deps. Inter. and Commer. 1993). Although the number of waterfowl hunters has declined somewhat in recent years (Trost et al. 1987), duck and goose hunting still constitutes about one-third of all migratory bird hunting activity (Martin, E. M. and P. J. Padding, 1994, Preliminary estimates of waterfowl harvest and hunter activity in the United States during the 1993 hunting season, U.S. Fish and Wildl. Serv., Off. Migratory Bird Manage. Adm. Rep., Laurel, MD, 1994).
In response to demands for increased hunting opportunities, waterfowl harvest regulations have grown to include features such as special seasons on more abundant species, geographic zoning, and species-specific bag limits (U.S. Dep. Inter. 1988). These regulations, along with basic season lengths and bag limits, constitute a sometimes bewildering network of regulatory options. The impacts of these regulations on harvest and population status is highly uncertain, even though North American waterfowl are among the world's most thoroughly investigated biota (Nichols and Johnson 1989).
Several factors have contributed to uncertainty about the effects of waterfowl harvest regulations. One factor is the sheer number and complexity of regulatory options offered in recent years. The introduction of so many options has complicated the regulations process greatly and has made it effectively impossible to assess either their marginal or cumulative effects. A second factor is the large-scale confounding of harvest and environmental effects that occurs when regulations "chase" population and habitat conditions (i.e., liberal regulations are set whenever populations appear to be abundant, and restrictive regulations are used whenever populations are low). Such a strategy may be appropriate under conditions in which population dynamics and the effects of hunting are well understood. However, in this case the knowledge required is unavailable to waterfowl managers, and "chasing" populations makes acquiring the information needed to reduce key uncertainties virtually impossible. A third factor concerns limitations of "reductionist science" in which the behavior of a managed system is understood by dividing it into subsystems more amenable to investigation (e.g., studying the effect of harvest on annual survival rate). A reductionist approach is likely to be most useful if one is dealing with processes that are additive in their effects on population dynamics (i.e., survival, recruitment, immigration, emigration), a situation seldom encountered in ecological systems.
These and other factors have resulted in an inability to recognize effects of regulations on waterfowl population dynamics. Because this uncertainty limits our ability to make regulatory decisions consistent with long-term harvest and conservation goals, there are material benefits in its reduction. We advocate an approach known as "adaptive resource management" (Holling 1978, Walters 1986) that explicitly recognizes uncertainty about management impacts and seeks to provide useful information about system dynamics. Incorporation of uncertainty in the formulation of management strategies sets adaptive resource management apart from other, more traditional applications of strategic decision making.
We describe an adaptive approach to regulating waterfowl harvests. Our objectives are to: (1) propose adaptive harvest management as an extension of the current regulatory process, focusing on active pursuit of knowledge for informed decision-making; and (2) discuss key issues and challenges associated with adaptive harvest management.
Managing adaptively requires feedback between management and assessment, when each activity influences the other. Thus, managers must assess a managed system periodically and somehow adapt decisions to the system state while accounting for uncertainty about the effects of those decisions. Adaptive management is sometimes described as "embracing" uncertainty (Walters 1986), in that it recognizes uncertainty as an attribute of management, and uses management itself as a tool to accelerate the reduction in uncertainty. A definition we find useful is:
"Adaptive harvest management describes the ability to make a sequence of decisions, in the face of uncertainty, that is optimal with respect to a stated objective, recognizing some constraints"
(D. R. Anderson, Natl. Biol. Serv., Ft. Collins, Colo., pers. commun. 1995). Less formally, adaptive harvest management might be described as managing in the face of uncertainty, with a focus on its reduction. An adaptive approach emphasizes uncertainty about regulatory effects and incorporates uncertainty as a factor guiding management actions (Johnson et al. 1993). It also implies that the performance of management can be improved if uncertainties are reduced.
For management to account for resource status as well as uncertainty about harvest effects, 3 critical components linking management, assessment, and population dynamics are required: (1) a process of decision making with clear, focused management objectives; (2) a monitoring program that periodically determines the status of the resource; (3) a process by which the effects of management decisions on the resource can be assessed.
Decision making process.--The process of regulating waterfowl harvests is well known, and involves a rather lengthy sequence of public announcements, deliberations, and decision making (Blohm 1989). Participation by the U.S. Fish and Wildlife Service (USFWS), state wildlife agencies, the Canadian and Mexican governments, and the public occurs at numerous times during the regulatory cycle. The process involves assessment of waterfowl populations, publication of Federal Register notices, and numerous meetings by the Waterfowl Flyway Councils and USFWS Regulations Committee. It culminates in selection of regulations at the flyway level (season lengths, daily bag limits, and outside dates for the earliest opening and latest closing dates for a hunting season) and special regulations at the state level (e.g., split seasons, harvest zones, special seasons, area closures). A major challenge for waterfowl biologists is to identify the effects of such a profusion of harvest regulations.
A critical component of this process should be the unambiguous specification of objectives for harvest management. Sound harvest management requires clear objectives, if only to measure how well the decision-making process has worked over time. This apparently simple requirement has been difficult to fulfill. Indeed, many of the conflicts arising in waterfowl harvest regulation can be traced back to disagreement about goals and objectives for harvest management. At various times the Flyway Councils and the USFWS Regulations Committee have promulgated regulations to maximize average harvest, minimize harvest variability, stabilize population size, and other variations. A predictable consequence of this conflicting process is that regulations have failed to meet any of the proposed objectives and thus have been unsatisfactory to many participants and stakeholders. A rational regulatory process that encourages regulations pursuant to recognized, agreed upon goals and objectives is not possible in the face of such confusion and controversy. Clarification of goals and objectives is an ongoing need, and must be seen as a key responsibility of management.
Monitoring programs.--Informed decision making consists of large-scale monitoring programs that deliver information about population status and trends, harvest levels, and other important biological attributes. This information is crucial to ascertain the impacts of harvest regulation, and thereby to establish a coherent framework for setting harvest regulations.
Key sources of information in the regulatory process are the long-term cooperative monitoring programs of the USFWS, state wildlife agencies, and federal and provincial governments in Canada. These programs yield information about breeding-population status, harvest levels, production, migration, and other population characteristics of value in regulating harvests. Data collected each year and added to long-term databases represent a foundation for harvest management of waterfowl. They are the baseline on which much waterfowl research is conducted and are necessary for modeling waterfowl populations.
Analysis and assessment.--Many organizations have played key roles in adding to knowledge about waterfowl, including the Flyway Council Technical Committees, the USFWS Office Migratory Bird Management, and wildlife research programs at Patuxent Environmental Science Center, Northern Prairie Science Center, the Cooperative Fish and Wildlife Research Units, and other research institutions. These groups have made important advances toward a sound understanding of waterfowl populations and the impacts of harvest, including investigating patterns in monitoring data, estimating key population parameters such as survivorship and reproduction, and predicting harvest impacts on population dynamics. Information accumulated through monitoring and assessment is folded into models of population size and distribution as influenced by harvest regulations. The goal of these efforts is to model the responses of a population to harvest regulation, based on long-term monitoring and research programs. By building on the databases they are designed to represent, these models provide valuable information to management, and thus represent a crucial link in the regulations process.
The current regulatory process links decision making, modeling and assessment, and population monitoring in an iterative cycle. Regulations in 1 year influence harvest, which in turn influences population status in the next year. Effects of harvest regulations on a waterfowl population are reflected in monitoring data, which add to the cumulative body of information that is used to update population models. These models in turn guide the regulatory process in the next cycle. Model updating occurs each year with new monitoring data, so that both the models and the information base they represent constantly evolve.
In this scenario regulations have both direct as well as indirect effects, and both are key to effective regulation of waterfowl harvests. First, regulations directly affect a population by influencing the amount of harvest, and through harvest, the subsequent population size. Second, regulations have indirect effects by influencing the information available for the subsequent regulatory cycle. For example, following on a period in which regulations have been relatively constant for a number of years, new insights often can be gained by deliberating changing regulations and observing the response in harvest and population size. It seems intuitive that "informative" regulations are in some sense better than regulations that are not informative.
The current regulatory scenario is itself adaptive, in that it describes a procedure whereby regulations are adapted to the available monitoring data. For the management of waterfowl harvests, this means periodic updating of databases, incorporating these data into improved population models, and using this information for setting annual harvest regulations. A typical application would involve using population models to explore the impacts of a number of different regulations to identify regulations that maximize harvest (or harvest opportunity) and limit the negative effects on populations. Regulations thus identified should guide the decision-making process.
Although the regulatory procedure described above is in some sense adaptive, the strategy is far from optimal for attaining management objectives. Its key limitations are a failure to account for uncertainty about population responses to regulations, and a failure to recognize value in attaining useful information to reduce that uncertainty. Thus, information is simply an unplanned by-product of harvest regulations, and the process is an example of passive adaptive management (Walters and Holling 1990). Although a passive adaptive approach can lead to improved management over time, improvements typically accrue very gradually. With the single exception of the period from 1980-84 when waterfowl harvest regulations were stabilized (Patterson and Sparrowe 1987), waterfowl harvest management has been (and continues to be) passively adaptive.
The use of regulations to actively pursue understanding of regulatory effects is an example of active adaptive management (Walters and Holling 1990). In this case the accretion of information and reduction of uncertainty about population responses are incorporated explicitly as an objective in the decision-making process. Some regulatory strategies are likely to be more informative than others, because they lead to more informative databases and improved models for describing the consequences of regulations. Active adaptive management entails the use of such strategies while pursuing more traditional harvest management objectives.
Hereafter, we use the phase adaptive harvest management to mean the active pursuit of information through regulations. To avoid any confusion about the role of information in adaptive harvest management, it is useful to identify its role explicitly. An adaptive approach emphasizes resource management per se, with value ascribed to information and understanding only to the extent that they contribute to the objectives of resource management. Thus, adaptive management does not recognize intrinsic value in biological monitoring, research, or scientific assessment. From a management viewpoint these activities, and the knowledge they produce, are justified only to the extent that they serve management purposes. It may be reassuring that adaptive management, by recognizing the importance of reliable information on which to base management decisions, reinforces strong cooperation between researchers and managers.
Adaptive harvest management can be described in terms of 4 components: (1) an array of potential hunting regulations that are available to decision makers for the control of waterfowl harvests; (2) a set of models representing meaningful hypotheses about population dynamics and the effects of harvest; (3) a measure of "uncertainty" for each model, expressing the relative likelihood that it appropriately describes population responses to regulations; and (4) an objective function (i.e., a mathematical expression of harvest management objectives) by which to evaluate and compare regulatory options. These components are used to identify the actively adaptive harvest strategy that is optimal with respect to management objectives.
The actual mechanics of determining optimal regulatory strategies is quite complicated and beyond the scope here (see Williams 1988, 1989; Lubow 1993, 1995). In essence, the problem is cast in the framework of constrained optimization of stochastic dynamic systems, with an objective function based on expected long-term harvests weighted by the model likelihoods. The system in this case consists of the set of population models, each describing population dynamics in terms of population size, environmental conditions, and regulations. The models represent different hypotheses about the impacts of regulations, and the likelihood weights represent uncertainty as to which hypothesis is most appropriate.
The optimization procedure accounts for both the current population status and the degree of uncertainty about system dynamics in assessing the influence of regulatory decisions on future population status (Williams 1996a,b). It chooses regulations at each point in time based on the expected sum of present and future harvests, recognizing that future yields are influenced by regulatory decisions in the present. The goal, of course, is to choose regulations at each point in time that are optimal, in that they produce a maximum expected value of present and future harvests. The process recognizes that optimal management can be realized only by eventually identifying the most appropriate model of population dynamics.
The key to an actively adaptive regulations process involves the active pursuit of information. Regulations influence the removal of individuals from the population through harvest and hence affect the size of the population the next year. Monitoring programs record data on harvest and population status from year to year, which then are used to improve the models under consideration and update the likelihoods associated with each model. This information is incorporated in an objective function consisting of predicted long term harvests for each model, with model-specific harvests weighted by the updated likelihoods. The optimization procedure identifies harvest strategies that maximize the weighted average of harvests, and these strategies subsequently are used to set regulations. This sequence is repeated each year in an ongoing cycle of monitoring, model updating, analysis and optimization, and regulations setting. Information that discriminates among models accrues with each cycle so that the most appropriate model for describing population dynamics is identified over time.
International treaties relating to the conservation of migratory birds clearly mandate that the opportunity to harvest waterfowl is of lesser importance than the protection and maintenance of populations. Although these priorities are useful to managers, the potentially competing objectives (i.e., size of the harvest vs. size of the population) leave room for debate about appropriate harvest strategies. Even if all management strategies under consideration provide sustainable harvests, average population size still can vary under the alternative strategies.
Waterfowl population goals have been identified in the North American Waterfowl Management Plan (U.S. Dep. Inter. and Environ. Can. 1986). These goals were established to ensure satisfactory levels of hunting opportunity, but also for ecological and aesthetic purposes. Though the Plan has a strong focus on waterfowl habitats, its population goals have been formally endorsed by the federal governments of Canada, Mexico, and the U.S. Therefore, waterfowl managers are obligated to consider these goals in the development of harvest strategies. However, we believe that the Plan goals are insufficient by themselves to identify unambiguous harvest objectives and optimal harvest strategies, particularly under conditions in which habitat cannot adequately support such population levels.
We suggest an objective that attributes relatively high value to hunting opportunity when Plan goals are met, and lower value to hunting opportunity as populations fall short of Plan goals. Decline in the value of hunting opportunity may be linear or non-linear, with the rate of decline dependent on the relative importance of providing hunting opportunity and achieving the Plan’s goals. In developing such an objective, managers must specify the minimum population level at which hunting opportunity can be allowed.
Managers also may wish minimize the temporal variability in hunting regulations for sociological and administrative reasons (U.S. Dep. Inter. 1988). This can be accomplished most easily by either: (1) altering the frequency of regulatory decisions from 1 to multiple years; or (2) constructing a set of regulatory options with large differences in predicted harvest rates. It is likely that either of these options would lead to smaller amounts of hunting opportunity over the long-term and more variability in population size than if a temporal constraint on regulations were not imposed (Hilborn and Walters 1992).
A array of regulatory options representing, for example, restrictive, moderate, and liberal seasons, must be developed and agreed upon. The number of options must be limited to facilitate their assessment. In addition, regulatory options should: (1) elicit different harvest and population responses; (2) produce predictable harvest rates; (3) be consistent with hunter preferences to the extent possible; and (4) facilitate law enforcement.
The development of limited regulatory options provides an opportunity to address the problems associated with the current regulatory complexity, which is criticized by managers and hunters alike (Babcock and Sparrowe 1989). The complexity of migratory bird hunting regulations, particularly those designed to increase hunting opportunities for lightly-harvested species, was probably an inevitable by-product of the increasing information available about waterfowl populations and demands for higher levels of consumptive use. Goals to simplify regulations and to increase harvest opportunities on lightly-harvested species conflict. Another consideration is whether species-specific regulations have distributed harvest pressure in the desired manner. Recent analyses (e.g., Rexstad et al. 1991) suggest that the capability to shift harvest pressure among various stocks of migratory birds may be limited. Finally, the ability to provide expanded harvest opportunity for any species is dependent on the quality of resource monitoring programs and on the ability to target the species of interest with management actions. Thus, it is unrealistic to expect to maximize harvest opportunities for every species.
Many waterfowl models have been developed for purposes other than harvest management (Williams and Nichols 1990), but there is a dearth of models that describe mortality and reproductive processes over the annual cycle. In adaptive harvest management, models must not only describe the effect of hunting (e.g., additive vs. compensatory hunting mortality), but also the effect of the environment (which may include population abundance) on changes in population size. In addition, candidate models should meet 3 other criteria: (1) models must describe different harvest strategies (or there is no value in learning which model is best); (2) models must describe different responses to harvest that are detectable by the monitoring program (or the process will fail to identify the most appropriate model); and (3) models should be consistent with historical experience (i.e., empirical).
It is important to recognize uncertainties, other than those associated with biological mechanisms, that further complicate the regulation of harvest. A lack of knowledge about underlying biological mechanisms (often called structural uncertainty) is exacerbated by our inability to precisely observe population status and trends, and our inability to completely control harvest with regulations. A limited ability to recognize status and trends is described generically by the term partial observability, to emphasize the fact that (1) observability of a population is tied to monitoring precision and (2) population status can be measured only within the limits of that precision. Limitations in the control of harvest are described by the term partial controllability, which expresses the fact that regulations can be used to target actual harvest rates (and harvest impacts) only as precisely as control limits allow.
Partial observability, partial controllability, and a lack of knowledge about biological mechanisms all contribute to the uncertainties waterfowl managers face in their attempts to regulate harvests. When simultaneously operative, their joint effects may well be greater than the sum of their individual effects. For example, monitoring imprecision diminishes one's ability to "see" the system, and thus impedes the rate of learning about underlying biological structures. Similarly, an inability to recognize and control harvest rates in the presence of structural uncertainty slows learning rates, essentially because population dynamics can be attributable to: (1) an assumed rate of harvest; (2) an assumed relationship between harvest and biological processes; or (3) both factors. Finally, monitoring imprecision in the presence of partial controllability can effectively mask the effect of partial control and further confound the recognition of biological structure.
Implementation of an actively adaptive harvest strategy for waterfowl faces formidable obstacles. In addition to the technical difficulties discussed above, there are a number of potential institutional impediments. Adaptive harvest management requires an explicit admission of ignorance or disagreement (i.e., uncertainty about biological mechanisms) and involves the use of mathematical methods (i.e., stochastic optimization) not easily explained (Walters 1986). Competing interests will make it extremely difficult to reach agreement on explicit objectives and constraints. Administrators and politicians often have difficulty focusing on long-term objectives, and even short-term sacrifices of lost hunting opportunity or impacted resources may be unacceptable. Ultimately, success in adaptive harvest management will require, more than anything else, an institutional framework that embraces patience, persistence, and commitment.
Despite potential pitfalls, we believe adaptive harvest management offers considerable benefits from both technical and administrative perspectives. Some of these advantages are: (1) the opportunity to resolve long-standing controversies about the effects of hunting regulations, while pursuing more traditional management goals; (2) increased objectivity and integrity in the decision-making process; (3) a clearer focus on long-term management (i.e., sustainability); (4) a better understanding of harvest management objectives through identification of difficult trade- offs; (5) a clearly-defined role of data-gathering programs in the regulations process; (6) a stronger link between migratory bird management and research; (7) explicit accounting for all sources of uncertainty (environmental, structural, partial management control, partial system observability); and (8) treatment of management as an adaptive process, which is more appropriate for dynamic systems than a static strategy (i.e., managers make adjustments to hunting regulations based on changing resource status and their understanding of population dynamics).
BABCOCK, K. M., AND R. D. SPARROWE. 1989. Balancing expectations with reality in duck harvest management. Trans. North Am. Wildl. and Nat. Resour. Conf. 54:594-599.
BLOHM, R. J. 1989. Introduction to harvest - Understanding surveys and season setting. Proc. Inter. Waterfowl Symp. 6:118-133.
HILBORN, R., AND C. J. WALTERS. 1992. Quantitative fisheries stock assessment: choice, dynamics & uncertainty. Routledge, Chapman & Hall, Inc., New York, NY. 570pp.
HOLLING, C.S., EDITOR. 1978. Adaptive Environmental Assessment and Management. John Wiley and Sons, London, UK. 363pp.
JOHNSON, F.A., B.K. WILLIAMS, J.D. NICHOLS, J.E. HINES, W.L. KENDALL, G.W. SMITH, AND D.F. CAITHAMER. 1993. Developing an adaptive management strategy for harvesting waterfowl in North America. Trans. North Am. Wildl. Nat. Resour. Conf. 58:565-583.
LUBOW, B. 1993. Stochastic Dynamic Programming (SDP): User's Guide. Version 1.04. Coop. Fish and Wildl. Research Unit, Colorado State University. 119 pp.
LUBOW, B. 1995. SDP: Generalized software for solving stochastic dynamic optimization problems. Wildl. Soc. Bull. 23:738-742.
NICHOLS, J. D., AND F. A. JOHNSON. 1989. Evaluation and experimentation with duck management strategies. Trans. North Am. Wildl. Nat. Resour. Conf. 54:566-593.
PATTERSON, J. H., AND R. D. SPARROWE. 1987. Background and objectives on stabilized duck hunting regulations: Canadian and U.S. perspectives. Trans. North Am. Wildl. Nat. Resour. Conf. 52:177-185.
REXSTAD, E. A., D. R. ANDERSON, AND K. P. BURNHAM. 1991. Lack of sex-selectivity in harvest of mallards under the point system. J. Wildl. Manage. 55:586-592.
TROST, R. E., D. E. SHARP, S. T. KELLEY, AND F. D. CASWELL. 1987. Duck harvests and proximate factors influencing hunting activity and success during the period of stabilized regulations. Trans. North Am. Wildl. Nat. Resour. Conf. 52:216-232.
U.S. DEPARTMENT OF THE INTERIOR. 1988. Supplemental environmental impact statement: Issuance of annual regulations permitting the sport hunting of migratory birds. U.S. Fish and Wildl. Serv., Washington, D.C. 340pp.
U.S. DEPARTMENT OF THE INTERIOR AND ENVIRONMENT CANADA. 1986. North American waterfowl management plan. U.S. Fish and Wildl. Serv., Washington, D.C. 19pp.
U.S. DEPARTMENT OF THE INTERIOR AND U.S. DEPARTMENT OF COMMERCE. 1993. 1991 National survey of fishing, hunting, and wildlife-associated recreation. U.S. Govt. Print. Off., Washington, D.C. 124pp.
WALTERS, C.J. 1986. Adaptive management of renewable resources. MacMillan Publ. Co., New York, NY. 374pp.
WALTERS, C.J. AND C.S. HOLLING. 1990. Large-scale management experiments and learning by doing. Ecology 71:2060-2068.
WILLIAMS, B.K. 1988. MARKOV: a methodology for the solution of infinite time horizon Markov decision processes. Applied Stochastic Models and Data Analysis 4:253-271.
WILLIAMS, B.K. 1989. Review of dynamic optimization methods in renewable natural resources management. Natural Resource Modeling 3:137-216.
WILLIAMS, B.K. AND J.D. NICHOLS. 1990. Modeling and the management of migratory birds. Nat. Resour. Modeling 4:1-89.
WILLIAMS, B.K. 1996a. Adaptive optimization and the harvest of biological populations. Mathematical Biosciences 136:1-20.
WILLIAMS, B.K. 1996b. Adaptive optimization of renewable natural resources: solution algorithms and a computer program. Ecological Modelling 93:101-111.