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81
Human Dimensions of Wildlife, 8:81–95, 2003
Copyright © 2003 Taylor & Francis
1087-1209/03 $12.00 + .00
DOI: 10.1080/10871200390205138
Peer-Reviewed Articles
Adaptive Impact Management: An Integrative
Approach to Wildlife Management
SHAWN J. RILEY
Department of Fisheries and Wildlife, Michigan State University,
East Lansing, Michigan, USA
WILLIAM F. SIEMER
DANIEL J. DECKER
Department of Natural Resources, Cornell University, Ithaca,
New York, USA
LEN H. CARPENTER
Wildlife Management Institute, Fort Collins, CO, USA
JOHN F. ORGAN
U.S. Fish and Wildlife Service, Hadley, MA, USA
LOUIS T. BERCHIELLI
Bureau of Wildlife, New York State Department of Environmental
Conservation, Albany, New York, USA
Wildlife professionals need better ways to integrate ecological and human
dimensions of wildlife management. A focus on impacts, guided by a struc-
tured decision process, will orient wildlife management toward rigorous,
integrative decision making. Impacts are important socially defined effects
Wildlife managers with the New York State Department of Environmental Conservation con-
ducted initial applications of AIM, and provided valuable insights that were incorporated in develop-
ment of the AIM process. L. C. Chase, N. A. Connelly, K. K. Smith, and personnel from the Colorado
Division of Wildlife offered suggestions that improved the manuscript. Preparation of this article was
supported in part by the New York State Department of Environmental Conservation, and the Cornell
University Agricultural Experiment Station through Hatch Project NYC 147-403.
Address correspondence to Shawn J. Riley, Department of Fisheries and Wildlife, 14 Natural
Resources Building, Michigan State University, East Lansing, Michigan 48824-1222, USA. Fax:
(517) 432-1699. E-mail: rileysh2@msu.edu
82 S. J. Riley et al.
of events and interactions related to wildlife that merit management. To
manage impacts we propose adaptive impact management (AIM). This
approach has seven primary components: situational analysis, objective
setting, development of system model(s), identification and selection of
management alternatives, actual management interventions, monitoring, and
refinement of models and eventually interventions. Adaptive impact manage-
ment builds upon strengths of systems thinking and conventional adaptive man-
agement, yet differs in that fundamental objectives of management are impacts
on society, rather than conditions of a wildlife population or habitat. Emphasis
is placed on stakeholder involvement in management and shared learning
among scientists, managers, and stakeholders. We describe and assess adaptive
impact management with respect to black bear management in New York.
Keywords
adaptive management, decisions, impacts, stakeholders, systems,
values, Ursus americanus, wildlife management
The sociocultural context for wildlife management has changed in recent decades,
especially with respect to availability and social acceptability of management
technology. For example, recreational hunting, a conventional tool for managing
game species, is no longer regarded as the only management technique or even
the technique of choice in some situations. Whereas this change is regrettable to
some wildlife managers, it may be inevitable in light of another change—declining
hunter numbers in some regions of the U.S. (Enck, Decker, & Brown, 2000)—
leading researchers to predict that hunting by itself will become insufficient for
management of white-tailed deer (Brown, Decker, Riley, et al., 2000). Originally
serving only a few interests, the wildlife profession now must develop management
programs acceptable to a large and growing array of stakeholders with diverse
and often competing stakes in wildlife management. Stakeholder involvement
has become a central element of contemporary wildlife management (Chase,
Lauber, & Decker, 2001). Wildlife managers are responding to these and other
changes in the management environment by adopting management approaches that
integrate biological and human dimensions and broaden stakeholder involvement
in management.
As part of this shift in management, a reorientation toward impacts of
human-wildlife interactions has been suggested (Riley, Decker, Carpenter, et al.,
2002). Impacts are significant positive and negative effects, defined in terms of
human values, that result from events or interactions involving: (a) wildlife
individuals, populations, habitats, and communities; (b) wildlife management
interventions; and (c) stakeholder interactions with respect to wildlife (Riley
et al., 2002). Events or interactions pertinent to wildlife management can be of
several general types: wildlife interactions with other wildlife, wildlife inter-
actions with their environment, interactions between wildlife and humans, inter-
actions between humans and wildlife habitat, and interactions among humans
where wildlife is a reason for the interaction. Every instance of such events or
Adaptive Impact Management 83
interactions has an effect of some type and degree. Effects are of management
concern only if people perceive them and then interpret them as producing
impacts (i.e., the effects warrant management attention). These effects may be
large or small, positive or negative, but to be considered an impact they must
be important to stakeholders.
We propose Adaptive Impact Management (AIM) as an approach to managing
impacts. This approach builds upon strengths of adaptive environmental assessment
and management (Holling, 1978), but also addresses weaknesses attributed to
social challenges in adaptive approaches (Johnson, 1999; Lee, 1999; Walters,
1997). Citizen participation, objectives, systems models, and subsequent man-
agement interventions in an AIM process emphasize impacts to society. Habitat
or wildlife populations may be foci of management actions, but are only means to
achieve impacts, not ends in themselves. A central assumption of AIM is that
integrating knowledge from multiple disciplines and engaging key stakeholders in
participatory management will increase the probability of identifying important
impacts on which to focus management. We believe this tact will promote the
soci-
etal support necessary for rigorous experimentation needed in adaptive management.
Management of impacts can be accomplished under a variety of frame-
works, and AIM may not be appropriate for every management issue. However,
an adaptive framework, with emphasis on learning through doing and guidance of
structured decision processes, offers promise for advancement of decision-making
for the most important wildlife issues society faces. The purpose of this paper is
to describe components of AIM and discuss benefits and costs of an AIM
approach. We use examples from the early stages of an AIM approach to black
bear (Ursus americanus) management in New York to demonstrate how AIM
concepts can be applied.
Adaptive Management
Wildlife management is just one aspect of resource conservation that is experi-
encing worldwide adoption of more integrative approaches to management. Other
attempts are occurring under the rubrics of ecosystem management (Yaffee,
1999), conservation biology (Meffe & Viederman, 1995), citizen science (Light,
Carlsen, Blann, Fagrelius, Barton, & Stenquist, 1998), and community-based
conservation (Western & Wright, 1994). Important commonalities among these
ideas include: (1) a broad range of knowledge and skills from natural and social
sciences must be integrated and applied to conservation issues; and (2) effective,
lasting conservation efforts are most apt to result when definition of problems,
identification of opportunities, development of solutions, and implementation of
management are shared processes among resource agencies and citizens at scales
where effects are perceived by stakeholders (Mangel, Talbot, Meffe, et al., 1996).
Another central theme of these contemporary approaches is that change is inevit-
able, and uncertainty and unpredictability are inherent in resource management.
84 S. J. Riley et al.
Adaptive management is promoted as a way of embracing such change and
uncertainty. Drawing upon systems dynamics (Forrester, 1968) and industrial
operations theory (Ackoff, 1970), Holling (1978) and Walters (1986) established
a philosophy and techniques for adaptive environmental assessment and manage-
ment. The vast appeal of adaptive management encouraged its adoption into the
lexicon of wildlife management (Lancia, Braun, Callopy, et al., 1996). However,
in most respects adaptive management has been more significant as a concept
than a management practice (Lee, 1999).
Numerous reviews of adaptive management have been presented (Johnson,
1999; Parma, Amarasekare, Mangel, et al., 1998; Walters, 1997); we will not add
to those critiques. Shortcomings in applications of adaptive resource management
prompt the modifications proposed here. Models and approaches to conventional
adaptive management are primarily based on objectives for a condition of wild-
life populations, species, or habitat rather than impacts on society defined by
stakeholders (Walters, 1997). Frequently missing are approaches to identification
of human values and incorporation of these values in the objective functions of
adaptive management (Gilmour, Walkerden, & Scandol, 1999; Johnson, 1999;
Lee, 1999). This shortcoming unfortunately leads to a focus on means or enabl-
ing objectives rather than stakeholder-defined fundamental objectives (Keeney,
1992). AIM seeks integration of biological or human dimensions considerations
rather than exclusively emphasizing one dimension over another.
The Adaptive Impact Management Process
The logic and blueprint for AIM is a modification of adaptive environmental
assessment and management (Holling, 1978) that incorporates a value-based,
decision-making philosophy (Hammond, Keeney, & Raiffa, 1999; Keeney, 1992).
The key difference between AIM and conventional adaptive management is that
AIM seeks to define objective functions in terms of desired stakeholder-identified
impacts. These objectives become performance measures of subsequent impact
management. A focus on impacts and stakeholder involvement will lead to: (1)
management of what really matters to society, which will lead to stronger polit-
ical support for experimental management often lacking in adaptive management
programs; and (2) improvements in shared learning among scientists, managers,
and stakeholders.
Identification of management goals and objectives in terms of impacts
requires early and continuous stakeholder involvement (Shindler & Cheek,
1999). Collaboration may take various forms depending on the scale of impacts
(Riley et al., 2002) and the level of stakeholder interest in the issue. Flexibility in
forums and processes that accounts for context specificity (e.g., scale of concern)
is vital for sustained citizen participation (Chase, Siemer, & Decker, 1999). An
appropriate image of the process is improvisational theatre, where the director
Adaptive Impact Management 85
(wildlife manager) guides the flow of interactions and analyses, but is capable of
adapting to include new actors and techniques as the actual plot unfolds (Payne,
Bettman, & Johnson, 1993).
Components of an AIM process, taken stepwise from the point of initializing
implementation, include situational analysis, objective setting, model development,
identification and selection of alternatives, management interventions, monitoring,
and adjustment to models and management (Figure 1).
FIGURE 1 The adaptive impact management process, which begins with
development of fundamental objectives and leads to monitoring and refinement
of systems models, and possibly to changes in fundamental objectives.
86 S. J. Riley et al.
Situational Analysis
The principal objective of situational analysis is to frame the decision-making
situation (Keeney, 1992, pp. 30–33), by delineating the decision context, identifying
potentially relevant impacts (i.e., the expression of values that should be addressed
as fundamental objectives), and describing the management environment in
which the pertinent impacts occur. Situational analyses use existing information
and often new inquiry to improve understanding of what is known about relevant
impacts. Knowledge is used to construct a first generation “map” of the manage-
ment system. This step insures managers and stakeholders are in agreement that
everyone is working with the same core data, on the same pertinent issues to be
addressed by management. Context-specific stakeholder involvement strategies
are developed at this stage (Chase et al., 1999), and stakeholders can play a role
in identifying and perhaps even obtaining needed data (Decker, Schusler, Brown,
& Mattfeld, 2000).
Decision Framing
Decision framing is a process of central importance in AIM. A public policy
decision is “framed by the alternatives and values considered in making that
decision” (Keeney, 1992, p. 30). Frames are mental structures people create to
organize and simplify the world (Entman, 1993), serving as perceptual “windows”
through which people view opportunities or interpret problems, and establish dir-
ection for successive management efforts (Hammond et al., 1999). In the absence
of interaction, stakeholders can be expected to have different perspectives that
influence how they frame decisions; no single stakeholder or decision-maker
perspective can provide a complete or comprehensive societal view (Russo &
Schoemaker, 1989). Both managers and stakeholders are prone to common
decision traps (Russo & Schoemaker, 1989), such as the tendency to focus on
means for achieving some end (e.g., how do we reduce the size of the bear popu-
lation?), without first fully considering the desired ends in terms of impacts
(e.g., what ends are we trying to achieve with respect to bear management?).
Perspectives of the management environment resulting from deliberative
stakeholder processes greatly influence formulation of fundamental objectives
(based on impacts), the management interventions chosen to achieve desired
impacts, and the social acceptability of the interventions. An AIM approach can
help people avoid some common decision-making traps because it encourages
managers and stakeholders to view issues through the same conceptual “window.”
In most cases, wildlife agency personnel familiar with the pertinent issues
have ample knowledge to conduct situational analyses; however, the assistance
of an advisory group of key stakeholders can complement agency perspectives
and ensure an open process (Margerum, 1999). Membership in an advisory
group should be tailored to the specific issue. Environmental and agricultural
Adaptive Impact Management 87
professionals, recreationists, tourism and economic development interests,
extension agents, mid-level resource administrators, and independent wildlife
scientists are candidates for advisors. The initial range of stakeholders involved
depends upon anticipated impacts.
The types of questions to be asked at this point are: (1) what is the range of
impacts occurring now and expected in the future; (2) who are the key stakeholders;
(3) what are the operational scales (geographical and temporal extent) of the
anticipated impacts; (4) what are the capacity and limits of the resource, stake-
holders, and management? These four interrelated questions are best addressed
simultaneously.
Every event or interaction among people with respect to wildlife, between
people and wildlife or habitat, or between wildlife and their environment has an
effect of some type and degree. The effects considered most significant by stake-
holders are impacts that should attract management attention (Riley et al., 2002).
Defining impacts to be managed precisely may not be possible at this early point
in the process. Nevertheless, it is possible to anticipate the range and relative
importance of potential impacts. This early articulation of impacts is essential for
initiating identification of objectives.
Stakeholder Involvement in Situational Analysis
Stakeholder involvement is most effective when it matches the geographical,
temporal, and social scale of the issue (Chase et al., 1999). Local citizens and
local government generally are most able to address issues affecting their
communities. Similarly, regional nongovernmental organizations and state or
provincial public agencies should be engaged in issues that involve many
communities. Consideration also should be given to matching stakeholders to the
duration of expected impacts. For instance, age and duration of residency are
important characteristics of stakeholders if factors affecting anticipated impacts
occurred for long periods of time and necessitate a long-term perspective.
Determining relevant scales and relevant stakeholders for those wildlife
management issues requires careful judgments. These judgments constitute a great
deal of the art in the “art and science” of wildlife management (Lee, 1993). Scale
of management interventions should be aligned with the scale of impacts managers
seek to influence (Bissonette, 1997). Relevant scales for each impact tend to be
identified through interactions between stakeholders and managers rather than
being determined a priori (Riley et al., 2002).
The level of stakeholders’ involvement also must be appropriate to their
capacity for involvement and the biological, political, economic, and technological
limits of management (Riley et al., 2002). Level of conflict associated with an
issue may initially reduce capacity of stakeholders to work toward a common
goal (Wondolleck & Yaffee, 2000). Some level of conflict resolution is often
required early in the process and certainly prior to applying an adaptive approach
88 S. J. Riley et al.
to resource management (Lee, 1999). In some situations it may be possible for
communities to coalesce for a broad civic purpose (Schusler & Decker, 2001).
Objective Setting
Goals are statements of intent about the purpose of management, couched as
general, long-term conditions to be attained. Bear management in New York is
grounded within five major goals (e.g., “Assure that people are not caused to
suffer from wildlife or users of wildlife”) (Henry, Tripp, Gilligan, et al., 2000).
Goals, often established through legislation, lead to objectives essential for
directing and evaluating alternative actions to achieve desired outcomes (impacts).
Objectives normally are characterized by describing a decision situation, an object,
and a direction of preference (Hammond et al., 1999). Objectives form a basis for
a set of possible management interventions and evaluation of alternatives. In
complex situations, such as wildlife management, it is not always obvious who
should formulate objectives. However, objectives formulated through citizen
participation are more likely to result in sustained actions because of greater
ownership and support by stakeholders (Gregory, 2000). The process of formulat-
ing clear, acceptable objectives normally receives inadequate attention compared
to its importance (Russo & Schoemaker, 1989), although numerous techniques
exist for determining objectives (Hammond et al., 1999; Keeney, 1992).
Fundamental and Enabling Objectives
Two types of objectives are essential to AIM. Fundamental objectives characterize
the reason for management in terms of desired impacts. A set of fundamental
objectives guides development and evaluation of management alternatives and
interventions. A fundamental objective of black bear management could be to
increase the psychological well-being of a community in which negative black
bear–human interactions are frequent events. Enabling objectives state how
fundamental objectives will be achieved. An enabling objective in the black bear
example previously could be to increase the level of education about successfully
living with black bears in that particular community.
Linking Fundamental and Enabling Objectives
Keeney (1992) suggests linking fundamental and enabling objectives through
a listing of process-ends relationships. For each objective, participants should
ask, “Why is this important in the specific situation?” The answer either will be
that the objective is an essential reason for management (fundamental objective),
or the objective is important because it helps attain another objective (enabling).
Each fundamental objective should have at least one enabling objective linked
to it. Similarly, each enabling objective should be tied to at least one fundamental
Adaptive Impact Management 89
objective. A network is created with ties identified between fundamental and
enabling objectives. Enabling objectives are initially formulated with fundamen-
tal objectives, but are not galvanized until after development of system models.
A black bear management example. The black bear population has been
increasing in New York State in recent years, and so have bear harvests, bear
sightings, and bear-related complaints to DEC. In response to these and other
aspects of the management environment, DEC created a team of biologists and
managers to develop a new statewide plan for black bear management.
Staff from DEC is using a sequence of public outreach efforts to identify
impacts that will become the basis of fundamental objectives within the statewide
bear management plan. The management team began by generated a preliminary
list of impacts. They based their preliminary list on insights from an outreach
process conducted between 1992 and 1994 to get input on proposed changes in
bear hunting and dog training regulations. As a next step, the management plan-
ning team worked with human dimensions (HD) specialists, who designed and
implemented a series of regional meetings to obtain input on the range of impacts
recognized by stakeholders in 2001. Stakeholder informants identified a range of
impacts that the researchers organized into six categories. The categories of
impacts identified by stakeholder representatives was similar to that identified
previously by the bear management planning team based on stakeholder input
between 1992 and 1994.
Human dimensions staff used findings from the small group meetings to
design a scale to assess bear impacts. This scale was included in a self-administered,
mail-back questionnaire being used as the data collection instrument for a
statewide black bear management survey implemented in spring, 2002. Among
other things, the stakeholder survey will allow researchers to quantify how
people are impacted by black bears by state region (e.g., upstate vs. downstate,
Catskills vs. Adirondacks), stakeholder group (e.g., hunters vs. nonhunters) and
value orientation (e.g., protection orientation vs. use orientation). As a final step
in the sequence of outreach efforts, DEC staff will conduct regional, qualitative
processes to further refine understanding of stakeholder-defined impacts generated
through the statewide mail survey.
Figure 2 displays a partial fundamental–enabling network for addressing
black bear management in New York State. A network diagram such as Figure 2
focuses on the means that enable managers to achieve one fundamental objective:
maximizing human safety. This fundamental objective has its origins in the
safety impacts recognized by stakeholders (i.e., managers understand from recent
interactions that stakeholders are concerned about their safety if they are
involved in a bear-related vehicular accident, or if they are confronted by a bear).
The fundamental objective of maximizing safety has three primary subdimensions:
safety of motorists, safety of outdoor recreationists, and safety of people at home.
Every element outside the fundamental objective box represents a means to
90 S. J. Riley et al.
achieve the end of human safety. Arrows in the figure describe which enabling
objectives are believed to influence achievement of specific subdimensions of the
human safety.
Figure 2 represents only a partial ends–means matrix for decision making
with respect to black bear management in New York. In reality, wildlife managers
and stakeholders must consider a comprehensive and hierarchical set of funda-
mental objectives to identify and evaluate a comprehensive range of enabling
objectives, and eventually, to identify and evaluate a full range of action alternatives.
Creating a complete articulation of means–ends relationships is essential to
create an effective decision-making frame (Keeney, 1992, p. 92).
Model Development
Wildlife management involves “messy problems” (Vennix, 1999, p. 380). Wildlife
management takes place within ecological and social systems that are highly
dynamic and nonlinear (Holling, Berkes, & Folke, 1998). Wildlife management
is also contentious because of the diversity of values associated with decisions
about wildlife resources. Many attributes under consideration in wildlife man-
agement, such as human attitudes, beliefs, and values typically are described
qualitatively. Systems dynamics offers three important strengths for developing
FIGURE 2 A partial means–end network for management of safety-related
impacts associated with New York State black bears.
Adaptive Impact Management 91
AIM: (1) better structure to guide and communicate thinking (Walters, 1986);
(2) increased decision-making capacity (Forrester, 1968); and (3) increased rates
of learning (Senge & Sterman, 1994).
Lee (1999, p. 5) indicated, “The essence of managing adaptively is having
an explicit vision or model of the ecosystem one is trying to guide.” Stakeholders
seldom have a common understanding of ecosystems or an understanding that
can be communicated in a common language. Modeling, especially when done in
a group setting, helps organize and communicate the complexity of management
systems to managers and stakeholders (Vennix, 1999). Model development also
exposes important uncertainties about the management system. With few excep-
tions, managers perform poorly at making accurate decisions within a multifaceted
system such as wildlife management (Kahneman, Slovic, & Tvesrsky, 1982).
Many facets of the management system may not even be recognized, let alone
understood. Models become highly useful tools for describing and managing a
wildlife management system, integrating ecological and human dimensions.
Models encourage examination of proposed management interventions, and
help define acceptable sets of management options carried forward through the
policy process. Assumptions behind policy changes will be explicit and subject
to additional evaluation and improvement. Modeling also leads to systematic
identification of information deficiencies that can become addressed by research.
Identification and Selection of Alternatives
In this phase types of potential management interventions are identified and the
critical processes and indicators of management performance are explicitly articu-
lated. In formulating alternatives, managers tend to maintain status quo or rely on
rules of thumb (Russo & Schoemaker, 1989). To counter this tendency, options
should not be limited to those believed to be available. Stakeholders often have
creative ideas for alternatives and personal perspectives about expected impacts
(Gregory, 2000). The key consideration is to continually analyze any proposed
intervention (enabling objective) in terms of the fundamental objectives
(impacts) expressed by stakeholders.
Management Interventions
It is unrealistic to develop a priori the single best model for a management system.
An adaptive mode accepts uncertainties and invites more than one model and
management approach. Managers and stakeholders can develop alternative,
competing models about the structure of the management system. Probabilities
about which may be the “true” model or approach to achieve desired impacts are
assigned to each competing model, with a strength of belief depicted by values
ranging from 0 (no belief in the model) to 1 (complete belief). Management
interventions are then conducted based upon the competing models.
92 S. J. Riley et al.
Monitor
An important step, often lacking in adaptive management, is rigorous evaluation
of impacts that result from interventions (White, 2001). Primary performance
measures are the fundamental objectives identified in the objective-setting step of
AIM. Model probabilities are updated through Bayesian analyses based on what
is learned after management interventions are conducted (Anderson, 1998).
A goal of adaptive impact management is to provide compelling evidence that
refines belief probabilities, based on monitoring of management interventions, of
one model towards a probability of 1.0. The purpose of this process is to focus
impact management under direction of the model believed to be the best repre-
sentation of the system.
Adjust
System models are adjusted through time with increased knowledge about the
management system or as changes occur in the system. With time and experience,
confidence systematically improves in the “surviving” model. Management
alternatives predicted by models to be viable sometimes fail because of poor
implementation. Adjustments must then be made to the implementation process,
not to the model structure.
Discussion
Benefits of AIM
By focusing on impacts, AIM is expected to have several advantages over
current adaptive management approaches: (1) increased relevancy of wildlife
management to society; (2) greater stakeholder satisfaction; (3) managers more
apt and capable of embracing change and uncertainty rather than avoiding it;
and (4) learning becomes a motivator as well as a product throughout the man-
agement system. Because relevant impacts are the primary focus, an adaptive
approach—experimental management—should be more readily adopted and
implemented by decision makers such as wildlife commissions than current
adaptive management efforts (Walters, 1997). The inclusion of stakeholders in
the development and refinement of AIM models, as well as in implementation
and evaluation of management interventions, should put wildlife management
in a favorable political atmosphere (Chase, Lauber, & Decker, 2001). Less use
of legislative referenda processes can be an outcome of greater stakeholder
participation and achievement of desired impacts (Loker, Decker, & Chase,
1998). Most importantly, this approach will help managers stay agile in an
ever-changing management environment by discouraging a static, one-model-
fits-all approach.
Adaptive Impact Management 93
Costs of AIM
The adaptive or experimental portion of AIM may not always be chosen for
many justifiable reasons (Walters & Green, 1997). An adaptive approach is not
likely to be cost-effective if the opportunities for making major changes to policy
are not favorable. Potential gains in learning are not achieved if the only changes
made in management interventions are incremental or slight (Walters & Holling,
1990). The potential for learning increases with the magnitude of management
interventions.
Some management costs may increase with AIM. There is a learning curve
and new expertise will be needed. Agencies will need to strengthen expertise in
socioeconomic disciplines and employ effective facilitators to maintain a neutral
position in decisions. Whereas systems modeling aspects may be a barrier to
some potential users, availability of user-friendly software such as Stella II (High
Performance Systems, Hanover, NH) should increase understanding and facilitate
modeling. More important than modeling skills are positive attitudes and willing-
ness among managers to experiment and take risks.
Future effectiveness of wildlife professionals may depend upon their ability
to discover and adopt new ways to facilitate stakeholder involvement in impact-
oriented management. Structured decision processes, such as AIM, have much to
offer wildlife managers as operational approaches. A critical difference offered
by AIM over previous methods is employment of stakeholder-identified impacts
in fundamental objectives and an emphasis on learning shared among scientists,
managers, and stakeholders. Paradoxically, increased stakeholder involvement in
decisions may help professional managers maintain, rather than lose, leadership
in wildlife management.
References
Ackoff, R. L. (1970).
A concept of corporate planning
. New York: Wiley.
Anderson, J. L. (1998). Embracing uncertainty: The interface of Bayesian statistics and
cognitive psychology.
Conservation Ecology
[on-line], 2(2). <http://www.consecol.
org/vol2/iss1/art2>.
Bissonette, J. A. (1997). Scale-sensitive properties: Historical context, current meaning. In
J. A. Bissonette (Ed.),
Wildlife and landscape ecology: Effects of pattern and scale
(pp. 3–31). New York: Springer-Verlag.
Brown, T. L., Decker, D. J., Riley, S. J., Enck, J. W., Lauber, T. B., & Mattfeld, G. F.
(2000). The future of hunting as a mechanism to control white-tailed deer popula-
tions.
Wildlife Society Bulletin, 28
(4), 797–807.
Chase, L. C., Siemer, W. F., & Decker, D. J. (1999).
Designing strategies for stakeholder
involvement in wildlife management: Insights from case studies in Colorado and New
York
. Human Dimensions Research Unit Publication 99-9. Department of Natural
Resources, Cornell University, Ithaca, New York.
Chase, L. C., Lauber, T. B., & Decker, D. J. (2001). Citizen participation in wildlife
management decisions. In D. J. Decker, T. L. Brown, & W. F. Siemer (Ed.),
Human
94 S. J. Riley et al.
dimensions of wildlife management in North America 2001
(pp. 153–170). Washington,
DC: The Wildlife Society.
Decker, D. J., Krueger, C. C., Baer, R. A., Jr., Knuth, B. A., & Richmond, M. E. (1996).
From clients to stakeholders: A philosophical shift for fish and wildlife management.
Human Dimensions of Wildlife, 1
, 70–82.
Decker, D. J., Schusler, T. M., Brown, T. L., & Mattfeld, G. F. (2000). Co-management:
An evolving process for the future of wildlife management.
Transactions of the 65th
North American Wildlife and Natural Resources Conference, 65
, 262–277.
Enck, J. W., Decker, D. J., & Brown, T. L. (2000). Status of hunter recruitment and reten-
tion in the United States.
Wildlife Society Bulletin, 28
(4), 817–824.
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm.
Journal of
Communication, 43
, 51–58.
Forrester, J. W. (1968).
Principles of Systems, 2nd
edition. Portland, OR: Productivity.
Gilmour, A., Walkerden, G., & Scandol, J. (1999). Adaptive management of the water
cycle on the urban fringe: Three Australian case studies.
Conservation Ecology,
3
(11). [on-line] <http://www.consecol.org/vol3/iss1/art11>.
Gregory, R. (2000). Using stakeholder values to make smarter environmental decisions.
Environment, 42
, 34–44.
Hammond, J. S., Keeney, R. L., & Raiffa, H. (1999). Smart choices: A practical guide to
making better decisions. Boston, MA: Harvard Business School.
Henry, R., Tripp, N., Gilligan, V., Smith, E., Pratt, G., Fodge, J., et al. (2000).
New York
State’s black bear standard operating procedures manual
. Albany, NY: New York
State Department of Environmental Conservation.
Holling, C. S. (Ed.). (1978).
Adaptive environmental assessment and management
. New
York: Wiley.
Holling, C. S., Berkes, F., & Folke, C. (1998). Science, sustainability, and resource man-
agement. In F. Berkes, C. Folke, & J. Colding (Eds.),
Linking social and ecological
systems 1998
(pp. 342–362). Cambridge, UK: Cambridge University Press.
Johnson, B. L. (1999). Adaptive management—Scientifically sound, socially challenged?
Conservation Ecology
, 3(10) [on-line] <http://www.consecol.org/vol3/iss1/art10>.
Kahneman, D., Slovic, P., & Tversky, A. (1982).
Judgment under uncertainty: Heuristics
and biases
. Cambridge, UK: Cambridge University.
Keeney, R. L. (1992).
Value-focused thinking: A path to creative decisionmaking
.
Cambridge, MA: Harvard University.
Lancia, R. A., Braun, C. E., Callopy, M. W., Dueser, R. D., Kie, J. G., Martinka, C. J.,
et al. (1996). ARM! For the future: Adaptive resource management in the wildlife
profession.
Wildlife Society Bulletin, 24
, 436–442.
Lee, K. N. (1993). Greed, scale mismatch, and learning.
Ecological Applications, 3
,
560–564.
Lee, K. N. (1999). Appraising adaptive management.
Conservation Ecology, 3
(3).
[on-line] <http://www.consecol.org/vol3/iss2/art3>.
Light, S., Carlsen, E., Blann, K., Fagrelius, S., Barton, K., & Stenquist, B. (1998).
Citizen
science, watershed partnerships, and sustainability: The case in Minnesota
. St. Paul,
MN: Surdna Foundation.
Loker, C. A., Decker, D. J., & Chase, L. C. (1998). Ballot initiatives—Antithesis of
human dimensions approaches or catalyst for change?
Human Dimensions of Wild-
life, 3
, 8–20.
Adaptive Impact Management 95
Mangel, M., Talbot, L. M., Meffe, G. K., Tundi Agardy, M., Alverson, J. B., Barlow, J.,
et al. (1996). Principles for the conservation of wild living resources.
Ecological
Applications, 6
(2), 338–362.
Margerum, R. D. (1999). Integrated environmental management: The foundations for
successful practice.
Environmental Management, 24
, 151–166.
Meffe, G. K., & Viederman, S. (1995). Combining science and policy in conservation
biology.
Wildlife Society Bulletin, 23
, 327–333.
Parma, A. M., Amarasekare, P., Mangel, M., Moore, J., Murdock, W. W., Noonburg, E.,
et al. (1998). What can adaptive management do for our fish, forests, food, and
biodiversity?
Integrative Biology, 1
, 16–26.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New
York: Cambridge University Press.
Riley, S. J., Decker, D. J., Carpenter, L. H., Organ, J. F., Siemer, W. F., Mattfeld, G. F.
(2002). The essence of wildlife management.
Wildlife Society Bulletin, 30
(2), in press.
Russo, J. E., & Schoemaker, P. J. H. (1989).
Decision traps: Ten barriers to brilliant
decision-making and how to overcome them
. New York: Fireside.
Schusler, T. M., & Decker, D. J. (2001).
Engaging local communities in wildlife manage-
ment planning: An evaluation of the Lake Ontario Islands search conference
. Human
Dimensions Research Unit Publication 01-5. Department of Natural Resources,
Cornell University, Ithaca, New York.
Senge, P. M., & Sterman, J. D. (1994). System thinking and organizational learning: Acting
locally and thinking globally in the organization of the future. In J. D. W. Morecroft
& J. D. Sterman,
Modeling for learning organizations
(pp. 195–216). Portland, OR:
Productivity.
Shindler, B., & Cheek, K. A. (1999). Integrating citizens in adaptive management: A propos-
itional analysis.
Conservation Ecology
, 3(9), [on-line] URL: <http://www.consecol.
org/vol3/iss1/art9>.
Vennix, J. A. M. (1999). Group model-building: Tackling messy problems.
System Dynamics
Review, 15
, 379–401.
Walters, C. J. (1986).
Adaptive management of renewable resources
. New York:
McGraw-Hill.
Walters, C. J. (1997). Challenges in adaptive management of riparian and coastal ecosys-
tems.
Conservation Ecology, 1
(1), [on-line:] <http://www.consecol.org/vol1/iss2/
art1>.
Walters, C. J., & Green, R. (1997). Valuation of experimental management options for
ecological systems.
Journal of Wildlife Management, 61
, 987–1006.
Walters, C. J., & Holling, C. S. (1990). Large-scale management experiments and learning
by doing.
Ecology, 71
, 2060–2068.
Western, D., & Wright, R. M. (1994). The background to community-based conservation.
In D. Western & R. M. Wright (Eds.),
Natural connections: Perspective in
community-based conservation
(pp. 1–12). Washington, DC: Island Press.
White, G. C. (2001). Why take calculus? Rigor in wildlife management.
Wildlife Society
Bulletin, 29
, 380–386.
Wondolleck, J. M., & Yaffee, S. L. (2000).
Making collaboration work: Lessons from
innovation in natural resource management
. Washington, DC: Island Press.
Yaffee, S. L. (1999). Three faces of ecosystem management.
Conservation Biology, 13
,
713–725.
