Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River, Brazil).

The need for prediction is widely recognized in limnology. In this study, data from 25 lakes of the Upper Paraná River floodplain were used to build models to predict chlorophyll-a and dissolved oxygen concentrations. Akaike's information criterion (AIC) was used as a criterion for model selection. Models were validated with independent data obtained in the same lakes in 2001. Predictor variables that significantly explained chlorophyll-a concentration were pH, electrical conductivity, total seston (positive correlation) and nitrate (negative correlation). This model explained 52% of chlorophyll variability. Variables that significantly explained dissolved oxygen concentration were pH, lake area and nitrate (all positive correlations); water temperature and electrical conductivity were negatively correlated with oxygen. This model explained 54% of oxygen variability. Validation with independent data showed that both models had the potential to predict algal biomass and dissolved oxygen concentration in these lakes. These findings suggest that multiple regression models are valuable and practical tools for understanding the dynamics of ecosystems and that predictive limnology may still be considered a powerful approach in aquatic ecology.

Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River, Brazil) / Predição das concentrações de clorofila-a e oxigênio dissolvido em lagoas da planície de inundação do Alto Rio Paraná

The need for prediction is widely recognized in limnology. In this study, data from 25 lakes of the Upper Paraná River floodplain were used to build models to predict chlorophyll-a and dissolved oxygen concentrations. Akaike's information criterion (AIC) was used as a criterion for model selection. Models were validated with independent data obtained in the same lakes in 2001. Predictor variables that significantly explained chlorophyll-a concentration were pH, electrical conductivity, total seston (positive correlation) and nitrate (negative correlation). This model explained 52 percent of chlorophyll variability. Variables that significantly explained dissolved oxygen concentration were pH, lake area and nitrate (all positive correlations); water temperature and electrical conductivity were negatively correlated with oxygen. This model explained 54 percent of oxygen variability. Validation with independent data showed that both models had the potential to predict algal biomass and dissolved oxygen concentration in these lakes. These findings suggest that multiple regression models are valuable and practical tools for understanding the dynamics of ecosystems and that predictive limnology may still be considered a powerful approach in aquatic ecology.

O objetivo desse estudo foi o de construir modelos para predizer as concentrações de clorofila-a e oxigênio dissolvido em lagoas da planície de inundação do Alto Rio Paraná. Para tanto, foram selecionadas 25 lagoas na planície de inundação. O critério de Akaike (AIC) foi utilizado para a seleção dos modelos. Posteriormente, os modelos foram validados utilizando dados independentes obtidos nas mesmas lagoas. As variáveis que explicaram significativamente as concentrações de clorofila-a (52 por cento) foram pH, condutividade elétrica, material em suspensão (relação positiva) e nitrato (relação negativa). As variáveis que melhor explicaram as concentrações de oxigênio dissolvido (54 por cento) foram pH, área das lagoas, nitrato (relação positiva), temperatura da água e condutividade elétrica (relação negativa). A elevada capacidade preditiva desses modelos foi demonstrada através da utilização de dados independentes. Esses resultados demonstraram que a limnologia preditiva continua sendo uma importante área de pesquisa na ecologia aquática.