Geoscience Reference
In-Depth Information
objective of the paper, or by uncertainty in defining the model's goals, along
with coexisting purposes of predicting or understanding (Bunnell 1989). For
instance, most of the papers based on the inductive approach deal with the def-
inition of a species-environment relationship without specifying whether they
intend to analyze the relationship of cause and effect or just use the relation-
ship as a functional description of the effect. In the first case, the goal would be
to evidence the limiting factors that are related to the species' biological needs
and that drive the distribution process; in the second, it would be the simple
use of correlated variables whose distribution is functional to the description
of the species' distribution.
Basically, we can summarize species needs as food, shelter, and adequate
reproduction sites (Flather et al. 1992; Pausas et al. 1995). When using the dis-
tribution of an environmental variable to describe the species' distribution we
implicitly assume that there is a correlation between these basic needs and the
environmental variables used. This correlation can be causal; that is, it
describes the species' basic needs. In such cases we can identify a function that
within a reasonable range of values associates each value of the environmental
variable to a measure of the fulfillment of the species' basic needs (e.g., repro-
ductive success). But it can also be a functional description; that is, we don't
really know why some ranges of values of the environmental variable are pre-
ferred by the species but we observe that the species tends to occur more fre-
quently within those ranges. The variable might influence all the species' basic
needs simultaneously or be correlated to another variable that describes one of
the species' needs.
Generally speaking, the quantity and quality of the locational data and the
GIS
layers used in analyses are not sufficient to assess cause-effect relationships
that determine the species' distribution. Furthermore, cause-effect relation-
ships spring from the interactions of biophysical factors that range through
different time and space scales (Walters 1992); few papers take scale depen-
dency into account in their analysis. Moreover in this kind of analysis causal
effects can be hidden by independent interfering variables (Piersma et al.
1993) or by the unaccounted stochasticity of natural events such as weather
fluctuations, disturbance, and population dynamics (Stoms et al. 1992) and
should be assessed in controlled environments.
We believe such uncertainties could be addressed by defining the overall goal
as the assessment of the relationship that best describe the species distribution.
In other words, even if the causal understanding of a relationship is not clear,
whenever the species-environment relationship is able to describe the distribu-
tion of a species satisfactorily, the overall goal is achieved (Twery et al. 1991).
