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Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In this paper, we present the results of applying PLS to explore relationships among biotic indicators of surface water quality and landscape conditions accounting for the above problems. Available field sampling and remotely sensed data sets for the Savannah Basin are used. We were able to develop models and compare results for the whole basin and for each ecoregion (Blue Ridge, Piedmont, and Coastal Plain) in spite of the data constraints. The amount of variability in surface water biota explained by each model reflects the scale, spatial location, and the composition of contributing landscape metrics. The landscape-biota model developed for the whole basin using PLS explains 43% and 80% of the variation in water biota and landscape data sets, respectively. Models developed for each of the three ecoregions indicate dominance of landscape variables which reflect the geophysical characteristics of that ecoregion.

Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin