Delusions in Habitat Evaluation
119
ber of habitats can directly affect the results. Yet habitat distinctions often are
not clear-cut. A researcher might distinguish two general forest types, uplands
and lowlands, or might classify habitats by dominant overstory, or might divide
these further by stand age or understory, and so on. As more types are defined,
sample sizes are reduced for observed use of each type, thereby diminishing the
power of the statistical tests to distinguish differences between use and availability. Also, because the proportional use and availability of all habitats each
sum to 1, the number of habitats distinguished affects all of these proportions.
Aebischer et al. (1993a, 1993b) observed that this unit–sum constraint renders
invalid many of the statistical tests often employed to compare use and availability because the proportions are not independent. That is, if one habitat type
has a low proportional use, others will have a correspondingly high use, and if
there are only a few types, then the infrequent use of one type will lead to the
apparent selection for another. Aebischer et al.’s (1993a, 1993b) method of
compositional analysis was developed specifically to circumvent this problem.
Not just the number of types, but the criteria used to partition types may
greatly affect results. Knight and Morris (1996) were able to visually differentiate 13 habitat types on landscape photographs of their study area, but postulated that only two broad classifications were distinguished by red-backed
voles (Clethrionomys gapperi ), the subject of their study. After analysis of their
data, however, it became clear that from the voles’ perspective, at least three
functional habitats existed.
Another problem is the scale at which habitats are viewed. For example, an
animal might appear to select for a certain habitat type, defined by a dominant
cover type, whereas in reality it selected for certain specific kinds of sites that
just happened to occur more commonly in that cover type than in others. An
animal’s choice of habitat type is often called macrohabitat selection and the
choice of specific sites or patches within habitats is called microhabitat selection. These may be perfectly hierarchical in that the most preferred microhabitats always occur within the same macrohabitat, in which case an animal may
really select initially at the scale of macrohabitat, and then focus on specific
sites within it. Schaefer and Messier (1995) observed this sort of nested hierarchy across a range of scales for foraging muskoxen (Ovibos moschatus) in the
Canadian High Arctic. Alternatively, the distribution of preferred microhabitats could be largely unrelated to the broader habitats defined by the biologist;
in this case, a site attribute study might identify characteristics related to preferred microhabitats, whereas a use–availability study would detect no selection at the level of habitat type. This situation was apparently the case for
wood mice (Apodemus sylvaticus) inhabiting arable lands in Great Britain: The
119
ber of habitats can directly affect the results. Yet habitat distinctions often are
not clear-cut. A researcher might distinguish two general forest types, uplands
and lowlands, or might classify habitats by dominant overstory, or might divide
these further by stand age or understory, and so on. As more types are defined,
sample sizes are reduced for observed use of each type, thereby diminishing the
power of the statistical tests to distinguish differences between use and availability. Also, because the proportional use and availability of all habitats each
sum to 1, the number of habitats distinguished affects all of these proportions.
Aebischer et al. (1993a, 1993b) observed that this unit–sum constraint renders
invalid many of the statistical tests often employed to compare use and availability because the proportions are not independent. That is, if one habitat type
has a low proportional use, others will have a correspondingly high use, and if
there are only a few types, then the infrequent use of one type will lead to the
apparent selection for another. Aebischer et al.’s (1993a, 1993b) method of
compositional analysis was developed specifically to circumvent this problem.
Not just the number of types, but the criteria used to partition types may
greatly affect results. Knight and Morris (1996) were able to visually differentiate 13 habitat types on landscape photographs of their study area, but postulated that only two broad classifications were distinguished by red-backed
voles (Clethrionomys gapperi ), the subject of their study. After analysis of their
data, however, it became clear that from the voles’ perspective, at least three
functional habitats existed.
Another problem is the scale at which habitats are viewed. For example, an
animal might appear to select for a certain habitat type, defined by a dominant
cover type, whereas in reality it selected for certain specific kinds of sites that
just happened to occur more commonly in that cover type than in others. An
animal’s choice of habitat type is often called macrohabitat selection and the
choice of specific sites or patches within habitats is called microhabitat selection. These may be perfectly hierarchical in that the most preferred microhabitats always occur within the same macrohabitat, in which case an animal may
really select initially at the scale of macrohabitat, and then focus on specific
sites within it. Schaefer and Messier (1995) observed this sort of nested hierarchy across a range of scales for foraging muskoxen (Ovibos moschatus) in the
Canadian High Arctic. Alternatively, the distribution of preferred microhabitats could be largely unrelated to the broader habitats defined by the biologist;
in this case, a site attribute study might identify characteristics related to preferred microhabitats, whereas a use–availability study would detect no selection at the level of habitat type. This situation was apparently the case for
wood mice (Apodemus sylvaticus) inhabiting arable lands in Great Britain: The