Which factors affect phytoplankton biomass in shallow eutrophic lakes?

• Published in: Hydrobiologia Vol. 714; no. 1; pp. 93 - 104
• Main Authors: Borics, Gábor, Nagy, Levente, Miron, Stefan, Grigorszky, István, László-Nagy, Zsolt, Lukács, Balázs A., G-Tóth, László, Várbíró, Gábor
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2013; Springer; Springer Nature B.V
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Biomass; Biomedical and Life Sciences; Ecology; Eutrophic lakes; Eutrophication; Fresh water ecosystems; Freshwater; Freshwater & Marine Ecology; Fundamental and applied biological sciences. Psychology; General aspects; Lakes; Land use; Life Sciences; lowlands; Nutrient concentrations; nutrient content; nutrients; Phytochemicals; Phytoplankton; Plankton; Prediction models; Primary Research Paper; regression analysis; Standing waters; Synecology; variance; Water depth; Zoology; Hungary; Eastern Europe; Europe; Romania; Phytoplankton; Nutrients; Lake use; Land use; Carpathian Basin; Freshwater environment; Aquatic environment; Lakes; Nutrient; Biomass; Shallow water; Eutrophication
• ISSN: 0018-8158; 1573-5117
• DOI: 10.1007/s10750-013-1525-6

The restoration and management of shallow, pond-like systems are hindered by limitations in the applicability of the well-known models describing the relationship between nutrients and lake phytoplankton biomass in higher ranges of nutrient concentration. Trophic models for naturally eutrophic small, shallow, endorheic lakes have not yet been developed, even though these are the most frequent standing waters in continental lowlands. The aim of this study was to identify variables that can be considered as main drivers of phytoplankton biomass and to build a predictive model. The influence of potential drivers of phytoplankton biomass (nutrients, other chemical variables, land use, lake use and lake depth) from 24 shallow eutrophic lakes was tested using data in the Pannonian ecoregion (Hungary and Romania). By incorporating lake depth, TP, TN and lake use as independent and Chl- a as dependent variables into different models (multiple regression model, GLM and multilayer perception model) predictive models were built. These models explained >50% of the variance. Although phytoplankton biomass in small, shallow, enriched lakes is strongly influenced by stochastic effects, our results suggest that phytoplankton biomass can be predicted by applying a multiple stressor approach, and that the model results can be used for management purposes.