magnitude than previous experience; it may not be palatable to apply the more
aggressive treatments to the more vulnerable population units; and the potential
effects of some of the treatments might be irreversible. Further, it may be difficult
to determine whether separate populations are truly independent—substantial
and perhaps density-dependent movement of animals among the populations
could invalidate the experiment.
For management experiments designed to estimate parameters needed to
determine sustainable harvest levels, the key quantities to control are the popu-
lation density and the harvest rate. The key quantities to measure will depend on
the life-history of the particular species, but will likely include adult survival rate,
reproductive rate, and juvenile survival rate; integrated measures of population
dynamics, such as population growth rate and the annual harvest achieved, could
also be measured directly.
13.7.3 Adaptive management
The goal of a management experiment is short-term learning, even if the learning
comes at the expense of the management objectives, with the assumption that the
knowledge acquired could then be applied to subsequent management decisions
for the long-term benefit of the resource. Adaptive management is an alternative
approach that seeks the reduction of uncertainty (i.e. learning) in the context of
meeting the management objectives (Walters 1986) (Box 13.3). Thus, the prima-
ry goal is the management of the resource, with learning pursued only insofar as it
will improve such management. There are several advantages of adaptive man-
agement: it can be applied to only one experimental unit, if necessary; concerns
about conservation risk are built into the approach; and the appropriate balance
between learning and management can be found. The disadvantage is that adap-
tive management may take longer to yield useful knowledge than experiments.
In its most formal application, adaptive management links a decision theoretic
approach to resource management with an explicit method for tracking (and
reducing) uncertainty. There are four elements required for this approach: explicit
management objectives, a list of alternative management actions, multiple models
that capture the uncertainty about the dynamics of the population, and a moni-
toring system to provide feedback (Nichols et al. 1995; Williams 1997). Each year
(or whatever the time frame of management decisions is), a choice is made from the
list of possible management actions that maximizes achievement of the manage-
ment objectives, given the current state of the population, and the current state of
knowledge, where this knowledge state is specified as a set of model “weights”
reflecting the relative degrees of faith in the different models. The action is taken,
and the consequences are monitored. The results of the monitoring are then used
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