Monitoring Is Not Enough: On the Need for a
Model-Based Approach to Migratory Bird
Management
James D. Nichols
Abstract—Informed management requires information about system state and about effects of potential management actions on
system state. Population monitoring can provide the needed information about system state, as well as information that can be used
to investigate effects of management actions. Three methods for
investigating effects of management on bird populations are (1)
retrospective analysis, (2) formal experimentation and constraineddesign studies, and (3) adaptive management. Retrospective analyses provide weak inferences, regardless of the quality of the monitoring data. The active use of monitoring data in experimental or
constrained-design studies or in adaptive management is recommended. Under both approaches, learning occurs via the comparison of estimates from the monitoring program with predictions from
competing management models.
Monitoring, the activity of making periodic assessments of
state variables reflecting system dynamics, has become popular among those studying ecological populations and communities. In the case of bird populations, monitoring programs in
North America include efforts to monitor population size (e.g.,
the North American Breeding Bird Survey [Sauer and Droege
1990], the Audubon Christmas Bird Counts, and numerous
other surveys); and the vital rates (survival and reproductive
rates) responsible for changes in population size (e.g., the
MAPS program [DeSante and others 1995] and BBIRD
[Conway and Martin, this proceedings]).
When faced with a difficult management decision,
decisionmakers frequently exhibit a form of displacement
behavior or stalling tactic by asking for additional information, without thinking sufficiently clearly about exactly
what kinds of information will be most useful in making the
decision. I believe that some monitoring programs have been
established as a result of such displacement behavior with
the vague hope that future retrospective looks at monitoring
results will somehow lead to a reduction in future uncertainty and to an understanding of population dynamics and
responses to management.
In this paper, I express the view that monitoring can be
very useful in helping us to understand and manage bird
populations. However, I argue that this utility is dependent
upon using monitoring results in conjunction with models
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.
James D. Nichols, Biological Resources Division, Patuxent Wildlife Research Center, Laurel, MD 20708.
USDA Forest Service Proceedings RMRS-P-16. 2000
and hypotheses about system behavior as part of a scientific
process such as experimentation or adaptive management
(Walters 1986). In the absence of a scientific context, I
believe that monitoring, by itself, is not likely to lead to good
understanding or management (Nichols 1991a). In this
sense, I tend to agree with the following provocative statement by Krebs (1991:3), “Monitoring of populations is politically attractive but ecologically banal unless it is coupled
with experimental work to understand the mechanisms
behind system changes.” I will try to provide a basis for such
views about monitoring and, in the process, identify explicit
roles for monitoring data in management and research
programs.
Management Requisites and the
Role of Monitoring ______________
I believe that four fundamental requirements exist for the
informed management of any animal population or community (Nichols and others 1995). First, the manager must
develop explicit goals for the management process. These
goals should be translated into an objective function that
specifies the relative values of different specific objectives
and the costs of management actions (Nichols and others
1995; Williams 1996; Conroy, this proceedings). Second, the
manager must have the ability to implement different management actions relevant to attainment of management
objectives. In the example of migratory bird management,
relevant actions would most likely involve habitat management, but also could include control of predators or nest
parasites. Third, at each decision point, the manager must
have access to information about the state of the managed
system and about other goal-related variables. For migratory birds, system state would likely include population
size(s) and, possibly, related quantities. The fourth requisite
for informed management is knowledge about the response
of the managed system to alternative management actions.
In an uncertain world, this “knowledge” is typically represented by hypotheses or models about the dynamics of the
managed system.
Monitoring programs are important to the third and
fourth management requisites listed above. They are clearly
important as the primary source of information about the
state of the managed system. In single-species management, we must know something about the population size at
time t to make an informed decision about how to manage at
that time. Monitoring programs directed at the population
variables of interest provide such information for management. The other role of monitoring programs is to provide
data that can be used to develop our “knowledge” of system
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dynamics and responses to management. It is with respect
to this acquisition of knowledge that I claim “monitoring is
not enough.”
Approaches to Learning __________
Retrospective Analyses
Monitoring data have been used in various ways to help us
understand the managed system. Perhaps the most commonly used approach is the retrospective analysis of timeseries data produced by monitoring programs (Nichols 1991a).
Monitoring programs may yield annual estimates of quantities such as population size over some relatively long time
period (e.g., 20 years), and it is commonly thought that such
trajectories lead almost immediately to an understanding of
underlying population dynamics. It is a common practice to
use such data with correlation and regression analyses to
investigate possible relationships between population size
and various environmental and management variables. The
problem with this approach is that it is unlikely to yield
“reliable knowledge” (Romesburg 1981), because there typically will be multiple a posteriori stories or models that
provide reasonable explanations for any observed time series. Indeed, Pirsig (1974:107) suggested that “The number
of rational hypotheses that can explain any given phenomenon is infinite.”
The potential for being misled by retrospective analysis
exists for all kinds of observations (Platt 1964; Romesburg
1981) but is probably especially large for time series of
estimates of population size and related variables. One
reason for this is that population size is not observed but is
estimated, often with large sampling variances and sometimes with bias. Temporal variation in point estimates of
population size is thus not equivalent to temporal variation
in the underlying population (Link and Nichols 1994). Stories and hypotheses developed to explain the variation in a
series of population estimates may not be adequate to
explain the real variation in the underlying quantity of
interest.
Another difficulty in drawing inferences from retrospective
analyses of population trajectory data involves the stochastic
nature of population processes. Death, for example, is typically viewed as a simple stochastic process. If a population has
100 animals at time t and if each of these animals has a
probability of 0.3 of dying during the interval (t, t + 1), then
we do not expect exactly 70 animals to be alive at time t + 1.
Instead the number of survivors will be a binomial random
variable with expected value 70, but with likely realized
values of 68, 73, 65, etc. Reproductive processes and movement also are viewed as stochastic in nature, leading to the
view of a population trajectory as a single realization of a
(likely complicated) stochastic process. We should not expect
to be able to observe a single realization of an unknown
stochastic process and infer much about the process (Nichols
1991a). This is analogous, in some sense, to being handed a
loaded coin, being permitted to flip it once, and then being
asked to specify the probability of obtaining heads.
Another difficulty associated with inferences from retrospective studies of population monitoring data involves
problems with using correlation analysis to draw inferences
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about the functional relationship between variables represented by time series. A clear example of such problems
involves the existence of trends and monotonicity in many
environmental covariates that potentially influence bird
populations. Metrics of human-related environmental variables such as habitat fragmentation, habitat degradation,
and pollutant levels will frequently tend to show an increasing trend over time. Correlation analyses involving two
variables, each of which shows a time trend, will tend to
indicate association, although this may have nothing to do
with any functional relationship between the variables. In
fact, the problem of conducting association analyses of two
time series extends beyond the case of monotonic trends, and
such analyses frequently lead to inappropriate inferences
(Yule 1926; Barker and Sauer 1991).
The conclusion I draw from this consideration of problems
associated with inferences from retrospective studies is that
such analyses should not form the basis for our knowledge of
population dynamics and responses to management. Instead, I suggest that we restrict our use of retrospective
analyses of monitoring data to the generation of hypotheses
about population dynamics and responses. We can then use
scientific processes such as experimentation and adaptive
management to distinguish between competing models.
For those who do not find these arguments convincing, I
recommend readings on the history of efforts to investigate
the effects of hunting regulations on waterfowl populations
in North America (Anderson and Burnham 1976; Nichols
and others 1984; Nichols 1991b; Smith and Reynolds 1992).
These efforts employed retrospective analyses of estimates
of population size, survival rates, hunting mortality rates,
and reproductive rates produced by some of the best largescale animal monitoring programs in the world, yet have not
led to the general acceptance of a single model of waterfowl
population dynamics and responses to hunting (Nichols and
others 1995).
Experiments and Constrained Studies
Monitoring data also can be used in conjunction with
studies designed specifically to test hypotheses about responses of animal populations to management actions. We
begin with competing hypotheses or models about population dynamics and responses to management. We try to
discriminate among them by applying different treatments
(e.g., different management actions) to birds inhabiting
different portions of the landscape. If monitoring programs
are in place for the treated areas, then differences in response variables (e.g., population size) between areas treated
in different ways may provide the basis for inference about
management effects (i.e., may help us decide which model
provides the best approximation to reality).
Inferences are strongest for true manipulative experiments (Hurlburt 1984; Skalski and Robson 1992). Such
experiments are characterized by replication and randomization in the assignment of different treatments to experimental units. Replication refers to the application of treatments to multiple experimental units as a means of
estimating the experimental error or error variance (the
variance among experimental units to be expected in the
absence of treatment differences; i.e., the variance associated
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with all factors except the different treatments). Randomization refers to random assignment of treatments to experimental units. Randomization protects against systematic differences among experimental units receiving
different treatments and represents an effort to insure that
any systematic post-treatment differences among experimental units treated differently can be attributed to the
treatments themselves.
In ecological studies in general, and studies of managed
populations and communities in particular, manipulative
experiments are frequently difficult to perform. In such
instances, we may be forced to use study designs that lack
replication, randomization, or both of these features. Such
constrained designs are considered in some detail by Green
(1979) and Skalski and Robson (1992). Inferences resulting
from such studies will typically be stronger than those
resulting from retrospective analyses, yet not as strong as
those based on manipulative experimentation (see examples
in Nichols and Johnson 1989).
The important point is that studies, whether they are true
manipulative experiments or constrained studies, can be
designed to take advantage of monitoring programs. We
begin with different a priori hypotheses about population
dynamics and responses to management. The monitoring
programs provide the estimates of potential response variables that are used to distinguish among competing hypotheses, and test for effects of the studied treatments (typically
management actions). In such studies the short-term objectives of management are temporarily set aside in an attempt
to learn something about the response of the studied system
to management practices (see Conroy, this proceedings and
Cooper and others, this proceedings for examples of such
studies). However, any knowledge of system response will be
useful in making subsequent management decisions and,
through the reduction of uncertainty, will be beneficial to
long-term management objectives.
Adaptive Management
Adaptive management (Walters 1986) presents another
approach to using monitoring data to help us distinguish
among competing hypotheses. Under this approach, however, we do not temporarily set aside short-term management objectives to learn something about the managed
system. Instead, adaptive management represents an approach to dealing with the so-called “dual control problem”
of simultaneously meeting short-term management objectives and learning about the managed system to reduce
uncertainty so that future objectives can be better met.
Adaptive management requires five components that are
closely related to the four requisites for informed management listed earlier in this paper:
1. The objective function is simply a formal specification of
management objectives.
2. Management options are actions that are available to
the manager and that are potentially relevant to attainment
of management objectives.
3. The monitoring program provides information on system state variables and other goal-related variables.
USDA Forest Service Proceedings RMRS-P-16. 2000
4. The model set is a set of competing models about system
dynamics and response to management actions.
5. A measure of uncertainty is required for each model in
the model set. These measures are probabilities that sum to
one and reflect our relative faith in the different models.
At a decision point, t, the monitoring program provides
information about the state of the managed system. Given
this state, given the objective function, and given the members of the model set with their respective measures of
uncertainty, we derive the management action that is optimal with respect to the weighted (by the measures of uncertainty) predictions of the different models. This action is
implemented and drives the system to a different state at the
next decision point, t + 1. This new state is estimated via the
monitoring program, and this estimate is used to update or
revise our measures of uncertainty. If the new state of the
system corresponds closely to the prediction of a particular
model, then the probability associated with that model is
increased. If the new state of the system deviates substantially (relative to other models) from the prediction of a
model, then the probability associated with that model is
decreased. At time t + 1, the optimal decision is again
derived given the new state of the system and the new
model weights. The process of adaptive management proceeds in this iterative fashion. Our measures of uncertainty
change through time and reflect learning about system
responses. This learning leads to a reduction in uncertainty that improves our ability to meet management
objectives (Conroy, this proceedings; Nichols and others
1995; Walters 1986; Williams 1996).
Conclusions ____________________
Population monitoring has two explicit roles in the process
of managing an animal population. First, it provides information about the state of the managed population at each
time period for which a management decision is needed.
Second, monitoring can contribute to our knowledge of
effects of management actions on populations. Three different approaches to the use of monitoring data to aid understanding of animal population dynamics and responses to
management were discussed. It was concluded that the most
common approach, using monitoring data in retrospective
analyses, is not likely to yield “knowledge” in which we can
be confident and on which we will be willing to base management decisions. Instead, retrospective analyses of monitoring data can be used to develop hypotheses and models of
animal populations and management responses.
Monitoring data can then be used in conjunction with
either experimental or constrained studies, or adaptive
management, to estimate population responses to management. These estimated responses can be compared with
predictions of competing models, and results of these predictions can then be used to distinguish among the different
models. The key to the use of monitoring data with experimental or constrained studies, or adaptive management, is
the existence of a priori hypotheses about system dynamics
and response. When the roles of monitoring data are made
explicit in this manner, it becomes easier to design monitoring programs to meet management needs.
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