An Adaptive Approach to Habitat
Management for Migratory Birds in the
Southeastern United States
Michael J. Conroy
Abstract—Modern tools for habitat management of migratory
birds include models describing habitat-population relationships,
coupled with remote sensing and geographic information systems
(GIS). These approaches implicitly assume some degree of underlying understanding about the functioning of bird populations and
communities in response to habitat modifications. First I discuss
some general principles in modeling, with emphasis on the use of
models as tools for generating testable predictions from our provisional understanding. I then describe some approaches for modeling
bird-forest habitat relationships, with emphasis on recent mechanistic models based on individual bird behavior. I discuss a specific
application of modeling in the management of habitats for Wood
Thrush (Hylocichla mustelina) populations in Georgia, and how a
conceptual model of habitat-population dynamics led to a management experiment designed to test underlying hypotheses of the
model. I then discuss some difficulties in parameterization of
spatially explicitly models, and some recent work on statistical
models for providing habitat-specific estimates of survival and
movement rates. Finally, I argue that scientific management only
happens when management is treated as “experimentation” and
models and other predictions about management impacts as testable hypotheses. Research and monitoring must be integrated with
management to assure that the data gathered are relevant to
decision making and used to inform decision makers. An adaptive
management (Walters 1986) paradigm provides a unifying framework for accomplishing these goals.
Biologists and managers responsible for conservation of
migratory birds are faced with a complex array of often
confusing information about the potential impacts of forest
management on these species. Here “management” includes
both manipulation of habitats and populations, such as
forest cutting, and the “preservation” of habitats through
reserves or other means. A plethora of GIS and other analytical tools, including models, that provide forecasts of bird
abundance and species composition under various alternative scenarios of management is now available. Some forest
planning tools, traditionally limited to predictions about
timber and other commodity production, have or will be
modified to include the conservation of biodiversity, often
In: Bonney, Rick; Pashley, David N.; Cooper, Robert J.; Niles, Larry,
eds. 2000. Strategies for bird conservation: The Partners in Flight planning process; Proceedings of the 3rd Partners in Flight Workshop; 1995
October 1-5; Cape May, NJ. Proceedings RMRS-P-16. Ogden, UT: U.S.
Department of Agriculture, Forest Service, Rocky Mountain Research
Station.
Michael J. Conroy, U.S. Geological Survey, Biological Resources Division,
Georgia Cooperative Fish and Wildlife Research Unit, D. B. Warnell School
of Forest Resources, University of Georgia, Athens, GA 30602.
USDA Forest Service Proceedings RMRS-P-16. 2000
birds, as a component of the objective function (e.g., Smith
and others 1981). Other tools such as Gap Analysis (Scott
and others 1993) are intended to provide managers with a
comprehensive landscape-level approach, with biodiversity
conservation as the principal goal. Like other planning tools,
these rely on the use of models to make projections about the
status of populations, communities, and ecosystems under
alternative future conditions, including management.
These uses of models have several common features and
potential difficulties. First, unlike many applications of
models in the physical sciences, the true nature of the
processes that ecologists seek to model is typically both
highly variable and incompletely understood. Therefore,
models cannot reasonably be expected either to exactly
describe a particular system (e.g., a community of forest
birds) at a given point in time, or to predict very well its state
or condition into the future. Further, because of inherent
stochasticity (e.g., because of randomly changing weather
conditions), difficulties in quantifying the system (e.g., estimating abundance, demographic parameters, movement
rates), and uncertainty about the nature of functional relationships (e.g., is the population resource limited? Is it limited
by dispersal or the availability of habitats?), it is even less
likely that forecasts about the future will be accurate. In
some instances, the state of knowledge of the organisms and
their habitats may be so poor that any model constitutes at
best a crude guess. For the above reasons, and many more,
conservation biologists frequently view models and modeling with distrust and disdain, and question the value of
modeling as a part of management.
Much of this skepticism is warranted. In my view, models
in ecology, perhaps especially in wildlife management, frequently have been used in an inappropriate and uncritical
manner. However, the problem lies not with models or modeling per se, but with the failure to use models as a component
in a scientific process leading both to more rationale (i.e.,
better informed) decisions, and to improved understanding.
The moment that a modeler/biologist behaves as if his or her
model is the truth, and then uses the model to dictate what
a management policy should be, he or she has exited the
realm of scientific natural resource management and embarked on a pilgrimage of faith.
On the other hand, judicious use of models as part of a
scientific process may prove fruitful, as long as models are
recognized simply as the mathematical expressions of our
provisional understanding of how a system might work.
Models should be treated as hypotheses, and their output as
predictions of how the world might look under some management scenario, instead of prescriptions determining how it
will look (Conroy 1993b). And in fact, most managers have
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their own conceptual “models” of how forests, populations,
and ecosystems function, and therefore how to manage
them, although few will have formally described or written
down a model (let alone attached equations to one), and most
would not see themselves as “modelers.”
In this paper I show how such a process might work, first
by describing modeling in a bit more detail, then by describing some types of models which have potential use for
managing migratory birds. I then will describe a conceptual
model that colleagues and I have used to orient field research
directed at the impacts of forest management practices on a
breeding population of a Neotropical migratory bird (NTMB),
and show how our experiments should provide a refined
model that may improve decisionmaking. Next, I will discuss the critical issue of the relationship of habitat to
demographic processes, and describe some work on methods
for empirically estimating these relationships. Finally, I will
describe some very recent work, in which model and other
sources of uncertainty are explicitly accounted for in conservation decisionmaking. Dynamic animal and forest growth
models are imbedded in an adaptive optimization procedure
to provide for optimal decisionmaking to achieve long-term
goals, and to orient research and monitoring programs to
meet these objectives.
Approaches to Modeling
Bird-Forest Habitat
Relationships ___________________
Critical Components to Model
Development
In considering the development of a model relating forest
habitat to bird populations, clearly stating the objectives of
the model is important. I assume that conservation biologists will principally be interested in models that relate in
some manner to management, either directed toward conservation of bird populations, or perhaps more commonly,
toward some other primary goal, with conservation of birds
either a secondary objective or a constraint.
The value of modeling to the manager will be determined
by whether it helps the manager better understand or
predict the consequences of management. However, because
models are abstractions of reality, these predictions will be
imperfect; in some instances the model will provide little
more than an educated guess about what might happen to
populations under various management scenarios. Thus, it
is critical that modeling not be viewed as a recipe for what
will happen (and thus, what should be done), but rather as
a mathematical means of expressing what might happen, if
the model is true (Conroy 1993b). Because a single model is
unlikely to be completely true, it is crucial for managers to
consider alternative models, including some that may be
diametrically opposed to the model the biologist believes
most likely. The consideration of alternative models is both
prudent in terms of hedging management options, and
consistent with the scientific method. Closely related, for a
model to have scientific credibility, both it and its competitors must produce testable predictions, i.e., must be capable
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of generating statements that can be supported or refuted
with observations. When models become ensconced as articles
of faith (or matters of governmental policy) and are not
subject to testing, they no longer are a part of science.
Also crucial is the issue of observability. For the most part,
useful models must produce predictions about observable
phenomena, and must be constituted of parameters that
have biological and physical meaning. If predictions cannot
be compared to observations, then there is little prospect for
verification or falsification of the model, or of discrimination
between competing models. For example, habitat suitability
models sometimes are claimed to predict the “potential
occurrence” of animals, a phenomenon that does not appear
to be observable (Conroy 1993b).
The realism of a model is the degree to which it contains
the essential elements of the system of interest. For birds in
forest systems, realism suggests that the model include key
components of environmental and biotic factors (e.g., habitat) and relate in a meaningful way to bird abundance and
distribution through time, ideally in terms of demographic
parameters (survival and reproduction rates) and behavior
(e.g., movements and dispersal). If model parameters have
no relation to biology, then the model has little realism
(Levins 1966) and is unlikely to provide predictions beyond
a narrow range of current conditions, even if empirically
validated (Conroy 1993b; Conroy and others 1995). Realism
may but does not necessarily require individually based and/
or spatially explicit models (e.g., Dunning and others 1992,
1995), recognizing that these approaches carry a heavy
burden in terms of model parameterization, validation, and
updating (Conroy and others 1995). The point to remember
is that the “realism” of a model depends on the purpose for
which it is being constructed: i.e., the research questions
that are being asked and the management decisions that
will be made using the model. A model that is sufficiently
detailed (“realistic”) for one purpose may be totally inadequate for a second, and unnecessarily complicated for a
third (see also Levins 1966).
As alluded to earlier, an additional, desirable feature of a
model is its generality, i.e., its ability to describe or predict
over a broad range of conditions. Usually (but not always),
models based on biological mechanisms, which have been
validated under broad conditions, will have greater generality than models that are strictly empirical or validated
under narrow conditions. However, as with realism, the
generality needed depends on the application; if the application is narrow, the model need not be general, so long as
future applications remain narrow, and conditions remain
similar to those under which the model was constructed and
validated.
Examples
I will illustrate some of the above principles by means of
two types of models used in wildlife management, including
management of NTMBs. In the first of these, animal abundance or (more typically) presence or absence, is predicted on
the basis of vegetation type, structure, configuration, or
other attributes (see Van Horne and Wiens 1991 for a critical
review of these models for birds). This type of model includes
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Habitat Suitability Index (HSI) and Wildlife Habitat Relational (WHR) models developed by biologists in, respectively,
the U.S. Fish and Wildlife Service (USFWS) and U.S. Forest
Service (USFS). These models are typically based on literature surveys or “expert opinion”; they often have limited
field validation; and they usually do not have parameters
with biological interpretation. A further difficulty, alluded
to earlier, is that these models usually predict habitat
“potential” rather than occupancy or other observable phenomenon, making validation problematic (Conroy 1993b).
Despite these limitations, HSI and WHR models may provide some qualitative statements about habitat suitability,
and may be all that are available for many species.
A second type of model is based on mechanisms operating
at the individual or local population level, and includes
single population, patch dynamic (Levins 1969) including
source-sink (Pulliam 1988), and spatially explicit (SEPMs;
Dunning and others 1992, 1995; McKelvey and others 1993)
models. For example, the BACHMAP model (Dunning and
others 1992) used a spatially explicit approach in which the
movement and fate (survival, reproductive status) of individual Bachman’s sparrow (Aimophila aestivalis) in a dynamic (i.e., undergoing succession and management) landscape is simulated. By summarizing birds’ fates across the
population, it is possible to assess the effects of various
landscape configurations and compositions on population
viability. Similarly, McKelvey and others (1993) used a
spatially explicit, individual-based model (OWL) to simulate impacts of forest management on Northern Spotted Owl
(Strix occidentalis) populations.
Models such as BACHMAP and OWL tend to have a strong
theoretical base and parameters that have biological interpretation. Further, they generally predict observable phenomena such as animal abundance and spatial distribution,
and thus are apparently more subject to empirical validation
(or refutation). However, this complexity can make these
models difficult to parameterize, for example, values for
survival, reproduction, movement, and other parameters.
Also, the resolution of observations is frequently insufficient
to truly validate such models; for example, predictions from
spatially explicit models that are aggregated to correspond
to broader scale field observations may not be useful in
validating the underlying model (Conroy and others 1995).
Nonetheless, in my opinion, the mechanistic nature of
these models makes them more likely to perform over a
range of conditions, and more amenable to validation and
testing against alternatives, than purely empirical or ad hoc
models.
Parameterizing, Validating, and
Applying Models for the
Management of Neotropical
Migratory Birds _________________
In my research group, we currently are taking several
simultaneous approaches to the problem of model development and application in the management of NTMBs. I will
describe these briefly, and try to show how they fit together
into a comprehensive and adaptive framework.
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Effects of Forest Management on
Wood Thrush Populations ________
Populations of Wood Thrush have declined in recent
decades, as have a number of other forest nesting NTMBs
(Sauer and Droege 1992). Forest fragmentation on the
breeding grounds may be a causal factor, but in significant
portions of the range, the population is declining during
concurrent periods of increase in the total acreage and
average stand size of forests (Turner and Ruscher 1988;
Odum and Turner 1990), suggesting that if an impact occurs
through forest management on the breeding grounds, it is
more complex than simple area or compositional effects. One
possibility is that increased patchiness (internal fragmentation) of managed stands may have resulted in decreased
reproductive success and survival and changes in dispersal
rates, potentially resulting in landscape-level population
impacts (Powell and others 1995). However, we view these
potential impacts as predictions emanating from our underlying view of what makes Wood Thrush populations work,
and freely acknowledge that we may be wrong. We have used
our hypotheses about functional relationships between habitat features and Wood Thrush populations, together with
general notions of population dynamics, to construct a conceptual model that is spatially explicit and contains alternative
hypotheses as specific submodels within an overarching
model structure.
The basic components of our model (fig. 1) involve (1) compartment level representation of breeding season survival
(adult and juvenile) and reproduction rates; (2) movement
rates between-compartment (intra- and inter-year); (3) proportional (temporary) effects of treatments on demographic
and movement parameters; and (4) between-year survival and
fidelity rates (possible impacts on the latter by treatments).
Figure 1—Conceptual model for Wood Thrush breeding population,
Piedmont National Wildlife Refuge, GA. A population of size Nt arrives
(comprised of individuals breeding or produced on this landscape last
year plus “immigrants” from other local breeding populations). Of Nt, E
individuals select other landscapes, leaving a local breeding population
of Nt - E. Shaded ellipses represent local habitat conditions (stand size,
structure, composition) influencing mortality losses (M) and reproductive gains (B). Landscape-level events (e.g., movement among stands
to search for food or new nest sites, represented by double-headed
arrows) also influence M. Summation symbol represents integration of
landscape and local effects on net number of individuals entering fall
migratory population. See table 1 for hypothesized local and landscape-level effects of management.
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We recently have completed a field experiment that will
help us to parameterize our conceptual model, and give us at
least a preliminary understanding of the potential effects of
landscape changes through management on Wood Thrush
population dynamics (Powell and others 1995, 1997; Conroy
and Krementz 1997). Our study site is located on the Piedmont National Wildlife Refuge and adjoining Oconee National Forest in Georgia (fig. 2). Management on these
federal lands is directed toward a variety of purposes,
including production of timber and sustainable harvest of
wildlife populations. An important additional component of
management is the maintenance of viable populations of
Red-cockaded Woodpeckers (RCW, Picoides borealis). RCW
management includes the thinning of loblolly pine (Pinus
taeda) stands to encourage the growth of large, older trees as
potential nest sites, and the removal of understory and
midstory vegetation mechanically and through prescribed
fire. Because such management removes potential nesting
and foraging structure for Wood Thrushes, we were interested in evaluating its impact on Wood Thrush population
parameters, and using our conceptual model of Wood Thrush
populations, we developed specific predictions regarding
these possible impacts (table 1). These hypotheses and
predictions operate at two levels of spatio-temporal resolution. Local effects are those that are thought to directly affect
survival and reproduction at the stand or finer scale through
alteration of the habitats. For example, the number and
success rates of nests would presumably be lowered by the
removal of trees structurally suited to contain nests; survival might be lowered by the removal of fruiting trees and
shrubs. Landscape effects are those that affect the population at a broader population scale, for example, nest site
fidelity of returning birds, immigration of birds from other
local breeding populations, dispersal of juveniles, or intraseason movement among desirable habitat patches (stands).
Again, alteration of the landscape through management is
hypothesized to play a role here, but detecting these effects
Figure 2—Location of Wood Thrush study area in the
Piedmont of Georgia.
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Table 1—Hypothesized effect of treatments for Red-cockaded
Woodpeckers on Wood Thrush populations.
Local effects
Nest Success
Survival
Density
Landscape effects
Survival
Fidelity
Density
Juvenile Dispersal
Control
Treatment
Higher
Higher
Higher
Lower
Lower
Lower
Higher
Higher
??
Lower
Lower
Lower
Higher
requires a broader scale analysis and the explicit consideration of the spatial configuration of treatments. The bottom
line for the population, though, is the integration of the local
and landscape effects in the production of a fall migratory
population, of which a fraction will survive and return the
following breeding season, thus completing the annual cycle.
Our experimental design (Powell and others 1995; Conroy
and Krementz 1997) (fig. 3 and 4) allowed us to test the
compartment-level effects directly, by the comparison of
density, survival rates, reproduction rates, and other parameters on treatment (subjected to RCW management)
and control (not recently treated) compartments, two years
prior to and following the treatment application. In addition,
monitoring conducted within compartments will allow evaluation (albeit nonexperimentally) of gradients in fragmentation, for example, due to uneven application of the treatments. The results of the experimental study are being used
in conjunction with the SEPM and GIS to make provisional
projections and recommendations regarding the impact of
RCW management on Wood Thrush (Powell and others
1997).
Figure 3—Schematic of experimental design for study of impacts of
forest management on Wood Thrush breeding population, Piedmont
National Wildlife Refuge, GA. Treatments are comprised of thinning
and prescribed burning for Red-cockaded Woodpecker management
(see text).
USDA Forest Service Proceedings RMRS-P-16. 2000
comparison for complex models, especially SEPMs, remains
problematic (Conroy and others 1995).
Development of an Adaptive Management
Framework for Research and Monitoring
Figure 4—Location of treatment and control compartments for study of
impacts of forest management on Wood Thrush breeding population,
Piedmont National Wildlife Refuge and Oconee National Forest, GA.
Letters represent treatment-control pairs.
Estimation of Habitat-Specific Survival
and Movement Rates From Radio
Telemetry
As noted earlier, mechanistic models such as patch dynamic and SEPMs require the estimation of (or assumptions
about) demographic parameters such as survival rates and
reproduction rates, and additional parameters such as movement rates or probability of movement among habitat patches.
Empirical estimation and hypothesis testing is needed for
these models, for example to evaluate whether a complex
model such as a SEPM really is needed (Conroy and others
1995). A number of studies have quantified reproduction
rates of NTMBs, through nest searching and monitoring and
other methods. Survival and movement rates tend to be
more problematic, usually requiring the use of banding,
color marking, or radio telemetry. Design and analysis for
survival estimation is straightforward when individual birds
occupy single habitat patches over study periods of interest.
For example, mark-recapture experiments (Pollock and
others 1990) may be used to provide estimates of survival
for samples of birds stratified by habitat type. However, if
movements of birds among habitat patches and/or types
occurs during the study, extensions of mark-recapture models may be needed to allow separate estimation of patchspecific survival and movement probabilities (Hestbeck and
others 1991; Brownie and others 1993). If movements are
very frequent, methods based on radio telemetry may prove
more useful. Conroy (1993b) and Conroy and others (1996)
evaluated methods for analysis of radio marking data, in
which birds freely move (e.g., on a daily basis) among
habitats, for testing hypotheses about habitat-specific survival and movement rates. Unfortunately, it appears that
large marked samples (100 or more) are needed to detect
all but the largest habitat specific differences in survival.
Because most study designs would fall short of such
sample sizes, the issue of parameter estimation and model
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As indicated above, models are frequently used to predict
the effects of human impacts on habitats—such as loss
through agriculture or urban development or alterations
through management practices—on bird populations. Models relating bird and other animal populations to habitats
are now included as part of planning tools such as Gap
Analysis (Scott and others 1993) and forest growth models
(e.g., Smith and others 1981), and have been used in conjunction with optimization procedures (e.g., linear programming) to derive strategies for maximization of an objective
function incorporating constraints or tradeoffs, for instance,
balancing timber revenue with species diversity. Approaches
such as these typically have failed to consider one or more
difficulties inherent in wildlife models. Commonly the system (e.g., bird population) is treated as a deterministic
response to habitat or other effects, and usually only a single
model describing habitat-population relationships is used.
Models are frequently invoked that have not been properly
validated, and for which there are no built in “reality checks.”
Given that bird populations, forest landscapes, and other
natural resource systems are inherently unpredictable—or
predictable only with great uncertainty—and that our understanding of these systems is far from complete, many
biologists are tempted to treat modeling efforts with disdain.
Another reaction is simply to pretend that these sources of
uncertainty do not exist, and to proceed with making management decisions based on models or other information as
“the best we can do.” An alternative to either approach is to
explicitly deal with uncertainty: Not just as a modeling
exercise, but as part of the process of making decisions
(Lindley 1985). Further, it should be possible to reduce some
types of uncertainty through monitoring and research, providing better understanding of how the system under management functions, and how to manage it (Walters 1986).
Johnson and others (1993) applied stochastic dynamic
optimization procedures (Williams 1989; Lubow 1993;
Puterman 1994) to the management for the optimal longterm harvest of waterfowl in the face of uncertainty. Crocker
and Conroy (1995a,b) extended this approach to the problem
of managing for multiple species in a dynamic landscape,
with species conservation as a component of the objective
function. This approach is explicitly stochastic and allows
for model uncertainty and competing models as hypotheses.
Previous approaches have included some applications of
optimization methods (e.g., Bedward and others 1992; Pressey
and Nicholls 1989) and decision theory (Lindley 1985) (e.g.,
Maguire 1986; Haight 1995; Conroy and Noon, in press) but
so far none has integrated the achievement of a long-term
optimum in an uncertain system, with the iterative incorporation of knowledge, in a truly adaptive sense (Walters
1986).
Consider a simple representation of the species persistence problem, where the “system state” is the number of
species persisting (S = 0,1,2) (fig. 5). Given the present state
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Decision-Outcome-Utility
P = 0.8
?
u = 0.5
P = 0.1
u = 0.25
a = 0.5
P = 0.1
u=0
E(u) = 0.425
S=2
P = 0.25
a = 0.1
E(u) = 0.338
?
P = 0.25
P = 0.5
u = 0.9
u = 0.45
u=0
Figure 5—Diagram of a hypothetical decision tree for a species conservation problem. Boxes
represent decision nodes, circles represent uncertain events. In illustrated scenario, the
decisionmaker observes that there are 2 species present (S = 2) of a hypothetical 2 total, and
is faced with making 1 of 2 decisions: conserve 50% (a = 0.5) or 10% (a = 0.1) of the landscape.
For each decision, there are 3 possible outcomes: S = 0, 1, or 2, with probabilities of occurrence
denoted by P, dependent on the decision. Each of the 6 decision-outcome combinations has
a value or utility (u) to the decisionmaker, in this case taken as S/2(1 - a), reflecting (1) the
desirability of conserving as many of the 2 species as possible, but (2) imposing a penalty for
the proportion of the landscape “removed” from other uses.
(e.g., S = 2) we would like to make a decision so as to maximize
the average (over uncertain future events) of some objective
or “utility,” which expresses in this case the tradeoff between
species preservation (S = 2 having highest utility) and the use
of resources for other purposes. In this example the possible
decisions are to conserve either 50% or 10% of the total
landscape. The utility of the decision-outcome combination
represents a value (1 highest, 0 lowest) received by that
outcome. For instance, the best outcome would be to conserve both species by reserving 10%, and has utility 0.9; two
decision-outcome combinations have utility 0, (no species
persist regardless of the decision to conserve). For each
decision, we need to make a prediction about the future state
of the system. For example, species-habitat relationship
models might predict that each of the outcomes S = 0,1,2
have different probabilities of occurring depending on the
amount of habitat conserved. Finally, the expected utility of
each decision is obtained by averaging over all the uncertain
outcomes, and the optimal decision is the one that maximizes
this average or expectation. In this example, the optimal
decision clearly is a = 0.5.
The schematic illustrates a simple, one-iteration process: A decision is made one time. In reality, at each
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decision-outcome (6 combinations here) the decision maker
would again be faced with making a decision, and the
optimal policy is a vector of decisions through time. For
instance, the optimal decision may be to conserve 75%
habitat at t = 1, and the rest at t = 2, or to conserve none now,
but 90% of the remaining habitat at equally spaced intervals, noting that these are not necessarily one year apart.
The optimal decision will depend on, among other things,
system dynamics (e.g., loss of nonconserved habitats) and
economic considerations (e.g., commodity and amenity values, discount rates).
We have applied such an approach to a simple system
containing four species and two habitat types, with an
inherent loss of habitats not conserved, to evaluate the
effects of model uncertainty (three simple model structures) and observability (coefficients of variation on the
observed system states of 0-100%). The results of preliminary analyses suggest that both model uncertainty and
observability have great potential for influencing what the
optimal policy is (e.g., how much habitat to conserve, of
what type, and when), and in turn have ramifications for
the optimal design of survey and research programs for
meeting resource management objectives (Crocker and
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Conroy 1995a,b). Our next steps are to expand this approach
to allow more realism, including spatial explicitness and
more complex landscapes and species assemblages. We also
will work toward a formally adaptive procedure, in which
the relative belief in alternative models is updated through
monitoring and research (Johnson and others 1993; Crocker
and Conroy 1995a,b).
adaptation
hypothesis/policy
model
prediction
Summary ______________________
Models of bird population dynamics and movements can
be a useful component of management, research, and monitoring programs. However, model users (and modelers)
must recognize that models are vast simplifications of the
real world. Further, at best, models represent mathematically our present knowledge about how populations function: No model ever has produced a single additional
scientific fact about a species or a system. Nonetheless,
models can be useful in summarizing extant knowledge,
providing testable predictions based on underlying assumptions, and directing future research efforts, for example, toward understanding key parameters or functional relationships.
On the other hand, models need not be extremely complex or completely realistic to prove useful to management. By simplifying a system to a few key components
and predictions, models sometimes can crystallize a problem in a way not possible by other methods. Further,
models offer the possibility of examining multiple combinations of possible management scenarios by the mathematical combination of inputs and parameter values.
However, users must recognize that these so called “experiments” are simply manipulations of underlying assumptions, are not better than the model and the assumptions on which they are based, and are no substitute for
the real thing.
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management/experiment
observation
Figure 6—Diagram of adaptive learning process showing the roles of
modeling, research and monitoring, and management.
By integrating research and monitoring with management,
we can assure that the information gathered is relevant to
decision making, and can discriminate between information
that is essential for reaching our resource management goal,
versus that which would simply be nice to have. Likewise,
conservation decisions should continually be reevaluated in
the light of new information, and modified as old assumptions
change in the light of new data (fig. 6). Further, the actions of
managers will themselves frequently provide new information, particularly if management is done in an experimental
manner, e.g., with suitable controls and replication. Viewed
in this manner, models are simply one component in a
continual process of scientific management.
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