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Quantification of Landscape Indicators/Aquatic Resource Associations in the Savannah River Basin
STATISTICAL ANALYSES
Two multivariate analyses, canonical correlation and partial least square (PLS) regression, were conducted to study the relationships between landscape metrics and surface water quality variables. Canonical correlation is well known in biological and ecological studies, but to our knowledge, this is the first use of PLS to explore relationships in ecological data. Although PLS is unfamiliar to many statisticians, it has been used extensively in chemistry to describe relationships between chemical structures and activity. We wanted to test PLS as a potentially frugal method for landscape ecologists, faced with issues of collinearity and small sample size, to explore relationships which may be used to assess the quality and vulnerability of an ecosystem. We ran PLS only for the biota landscape data to permit detailed exploration of the relationships.
In canonical correlation analyses, collinearity, missing observations,
multinormality, and the ratio of number of variables to number of observations
are important issues that need to be dealt with prior to analysis. PLS
is less subject to these constraints. For the canonical correlation analyses,
landscape metrics were selected based on pairwise correlation and discriminant
analyses resulting in a total of four landscape metrics. For the PLS analyses,
all landscape variables (26) were initially considered in the model and
the most important ones were included based on their VIP (> 0.8) in
the final model. VIP provides information not only as to how important
each landscape variable is (e.g. Table 7
)
but how similar the contribution is to that of other variables. For example,
landscape metrics such as Crop_slp_mod, Ag_slp_mod, and Ag_mod had the
same VIP as Soil_er (Table 13)
indicating equal contributions of soil erodibility and crop/agriculture
on slopes with moderately erodible soil in predicting biota. Therefore,
for this group of landscape variables, one variable (e.g. soil erodibility)
alone may be used in any model.
Area on slope > 3 has the largest canonical coefficient in the land-biota
analysis and has the largest VIP in PLS. The only other landscape variable
common to canonical correlation and the initial PLS model is Pct_bar which
is not greatly important in either method. Pct_bar did not make it to
the final model because of its low VIP value. In contrast to the canonical
model, the PLS model indicated that landscape metrics such as stream density,
percent forest and agriculture on moderately erodible soil were the second
important landscape variable group. The other intriguing feature, PLS
allowed analysis by ecoregion which revealed different landscape metrics
that relate to surface water biota based on their spatial association
(ecoregion, Table 15).
It is evident that the importance of any landscape metric is a function
of its spatial location in the study area. The importance of percent forest
and percent pasture were the highest in Blue Ridge and decreased consistently
across the adjacent ecoregions of the study area; the opposite relationship
was found for Crop_slp and soil erodibility. Crops on area with slope
> 3% with moderately erodible soil, percent crop, percent of areas
on slopes > 3% and soil erodibility are the most important landscape
variables in the Piedmont. The relative importance of percent pasture
on slopes > 3% increased in the Coastal Plain.
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