correct is through active adaptive management (Nichols et al. 1995; Williams et al.
1996). Indeed, this may be the only realistic way to reduce the uncertainty in bio-
logical processes, at least within our lifetime.
Such an active adaptive management procedure has been implemented, despite the
inherent difficulty in coordinating agencies and resource users in a variety of juris-
dictions (Nichols et al. 1995). If this kind of coordinated model evaluation can be
conducted for waterfowl, it can be used even more readily for less mobile species.
The key may be that there has been a long-standing tradition in waterfowl manage-
ment to apply biomathematical models to the production of recruitment and harvest
management. Such models have been applied rarely to wildlife species, for which
harvesting policy is often developed in a more haphazard fashion. The adaptive
approach demonstrates a more productive option.
Statistical hypothesis testing is not always the best way to make informed decisions
about causal factors associated with wildlife population dynamics, because of pre-
occupation with rejection of null hypotheses rather than evaluation of the merits of
a suite of more plausible models. We outline an alternative approach to inference
that is based on information theory, allowing one to decide which model or suite of
models offers the best explanation for existing patterns of data. Such an approach
complements the practical need to make the best management decisions possible on
the basis of incomplete scientific information. A cornerstone of all model evaluation
procedures is some measure of goodness-of-fit between models and data. Such model
evaluation is an essential component of adaptive management regimes, where altern-
ative explanatory models are vigorously pursued using historical data or experimental
perturbation. We show how adaptive management can be used to improve manage-
ment of harvesting in migratory waterfowl populations in North America.
MODEL EVALUATION AND ADAPTIVE MANAGEMENT 267
15.6 Summary
