Pairing multivariate data analysis and ecological modeling in the biomanipulated Lake Engelsholm, Denmark / Parvis multivariat dataanalys och ekologisk modellering i den biomanipulerade sjön Engelsholm, Danmark
A multivariate analysis was applied to monthly climatic, hydrologic and water quality series of Lake Engelsholm, a shallow lake that was subjected to biomanipulation. In this study, we focused on identifying the most important patterns of monthly time series after biomanipulation. Moreover, we tried to quantify how those variables are interconnected with each other. A multivariate data analysis was made by using Principal Component Analysis (PCA) and Cluster Analysis. A statistical model using Canonical Correlation Analysis (CCA) was developed to predict the biological variables. Its efficiency was compared to a conceptual ecological model called IPH-ECO. The predictions made by IPH-ECO presented a better performance than those predictions using multivariate analysis in the period after biomanipulation.