Model ensemble for the simulation of plankton community dynamics of Lake Kinneret (Israel) induced from in situ predictor variables by evolutionary computation

Abstract

This study addresses the need for operational models in view of rapidly advancing in situ sensor technology that puts lakes into online surveillance mode. A model ensemble for simulating plankton community dynamics in Lake Kinneret (Israel) from 1988 to 1999 has been induced from electronically-measurable predictor variables (EMPV) such as water temperature, pH, turbidity, electrical conductivity and dissolved oxygen by the hybrid evolutionary algorithm HEA. It cascade wise predicts the total nitrogen to total phosphorus ratios TN/TP, concentrations of chlorophyta, baccilariophyta, cyanophyta and dinophyta, as well as densities of rotifera, cladocera and copepoda solely from EMPV. The best coefficients of determination (r2) have been achieved with 0.6 by the dinophyta model, 0.45 by the rotifera model and 0.44 by the bacillariophyta model. The worst coefficients of determination (r2) have been produced by the cladocera model with 0.24 and by the TN/TP model with 0.28. Despite the differences in the r2 values and apart from the cladocera model, the remaining models matched reasonably well seasonal and interannual plankton dynamics observed over 11 years in Lake Kinneret.The model ensemble developed by HEA also revealed ecological thresholds and relationships determining plankton community dynamics in Lake Kinneret solely based on in situ predictor variables.