ADAPTIVE MANAGEMENT AND THE REGULATION OF
WATERFOWL HARVESTS*
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
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.
Operational Components
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.
Regulating Waterfowl Harvests
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.
The Pursuit of Information With Regulations
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
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.
IMPLEMENTATION ISSUES
Management Objectives
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).
The Array of Regulatory Options
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.
Alternative Models of Population Dynamics
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).
Other Considerations
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.
CONCLUSIONS
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).
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Adaptive
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