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 select...

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Published inBrazilian journal of biology Vol. 69; no. 2 Suppl; pp. 491 - 500
Main Authors Rocha, R R A, Thomaz, S M, Carvalho, P, Gomes, L C
Format Journal Article
LanguageEnglish
Published Brazil Instituto Internacional de Ecologia 01.06.2009
Subjects
BIOLOGY
Brazil
Chlorophyll - analysis
chlorophyll-a
floodplain lakes
Models, Biological
Multivariate Analysis
oxygen
Oxygen - analysis
Regression Analysis
Rivers - chemistry
Seasons
Brazil
Argentina, Buenos Aires, Parana R
Brazil, Upper Parana R
oxigênio
chlorophyll-a
clorofila-a
floodplain lakes
model
modelo
lagoas
oxygen
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Summary: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.
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ISSN:1519-6984
1678-4375
1678-4375
1519-6984
DOI:10.1590/S1519-69842009000300005