las comunidades de peces del río guadiamar y el accidente minero ...

Las comunidades de peces del río Guadiamar
y el accidente minero de Aznalcóllar
PCA 2 – Factors affecting bank stability at the site scale. This analysis included
variables that inform about the erosion risk at a given site (Appendix 1). There was one
principal component that accounted for 26.8% of total variance. This axis represented a
gradient from highest to lowest risk of erosion (greatest risk at the positive end and
lowest at the negative end).
PCA 3 – Land uses at the site scale. This analysis included data about land uses
in the area right next to the sampled site (Appendix 1). It yielded a main principal
component that accounted for 37.5% of total variance. This axis represented a gradient
from greater human impact (urban and agricultural areas at the positive end) to less
humanized uses (native forests at the negative end).
PCA 4 – Land uses at the catchment scale. This PCA represents the main land
uses in the whole catchment area of each sampling site (% of each type of land use,
Appendix 1). It yielded one principal component that accounted for 40.3% of total
variance. This axis represented a gradient from greater human impact (urban and
agricultural areas at the negative end) to less humanized uses (native forests at the
positive end).
The second step to reduce the number of variables was to test for collinearity
between the remaining ones (Appendix 1) using Pearson´s correlations. Whenever the
correlation coefficient between two variables was greater than 0.75, one of the variables
was chosen for the regression models.
Once the final predictor variable list was defined (Table 1), preliminary
regression models were computed and their residuals were analyzed for outliers and/or
influential cases (Cook, 1979). No outliers were detected in the dataset, so the final
regression models were applied to a total of 22 cases (n=22) and a maximum of 6
predictor variables, since a larger number of variables would lead to a Type 2 error
(Field, 2005). The final maximum of 6 variables for each model included those that
showed a high correlation with the relevant dependent variable and low collinearity with
the other selected variables. A list of all the variables included in the 9 models is
presented in Table 1.
The final models were fitted with a normal error distribution and an identity link
function. The best models supported by the data were selected using the Akaike
Information Criterion (AIC), a model selection approach based on Information Theory
(Burham and Anderson, 2002). This allowed us to rank a set of competitive models and
decide which explained the most variance whilst being most parsimonious (Johnson and
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