Geoscience Reference
In-Depth Information
(1995) noted that some mammalian predators that hunt preferentially in
forests nonetheless often exist at lower densities in homogeneous forests than
in forests interspersed with disturbed areas. Each of these examples represents
cases in which a single-scale investigation would have failed to detect habitat
variables affecting population demography.
The previous examples all concerned spatial scale. Time scale may be
equally important. The demographic value of a habitat may become evident
only in the long term, after a population has been subjected to the stresses of a
periodic drought, severe winter, or failed food crop (Beyer et al. 1996; Pelton
and van Manen 1996).
Applications and Recommendations
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A great deal of effort continues to be invested in habitat-related studies of
wildlife. In the United States, federal land management agencies in particular
have focused on developing formalized procedures for evaluating habitat for
wildlife (Morrison et al. 1998). The procedures that have been adopted rely on
models derived by species experts, who in constructing these models tend to
rely more on experience than on empirical data (Schamberger and Krohn
1982; Thomas 1982). Therefore, the models are really hypotheses in need of
testing. However, because these models hypothesize explicit relationships
between habitat attributes and animal populations (so-called habitat suitabil-
ity indices), they cannot be rejected or accepted in normal scientific fashion;
that is, none of the relationships are likely to be exactly correct. Thus it seems
inappropriate to suggest, as is common parlance, that they should (or even
could) be “verified” or “validated.”
Brooks (1997) and Morrison et al. (1998) proposed steps for verifying or
validating habitat suitability models. Unfortunately, these authors and most
others writing on this subject have misused these terms.
Verification
means
establishment of truth and
validation
technically refers to establishment of
legitimacy (i.e., in the case of a model, showing that there are no logical or
mathematical flaws; Oreskes et al. 1994). The general misapplication of these
terms in reference to model testing is not merely a semantic issue, but rather a
real misrepresentation of accomplishment. Because of the complexity of natu-
ral systems, habitat models invariably exclude some relevant parameters and
presume relationships that are not exactly or not at all correct. There is simply
no way to perfectly model these sorts of open systems (i.e., systems in which
all variables and relationships are not known). In a well-reasoned discussion of
