Which factors affect phytoplankton biomass in shallow eutrophic lakes?

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• 作者：G谩bor Borics (1)
  Levente Nagy (2)
  Stefan Miron (3)
  Istv谩n Grigorszky (4)
  Zsolt L谩szl贸-Nagy (5)
  Bal谩zs A. Luk谩cs (1)
  L谩szl贸 G-T贸th (1)
  G谩bor V谩rb铆r贸 (1)
  
• 关键词：Phytoplankton ; Carpathian Basin ; Lake use ; Land use ; Nutrients
• 刊名：Hydrobiologia
• 出版年：2013
• 出版时间：September 2013
• 年：2013
• 卷：714
• 期：1
• 页码：93-104
• 全文大小：331KB
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• 作者单位：G谩bor Borics (1)
  Levente Nagy (2)
  Stefan Miron (3)
  Istv谩n Grigorszky (4)
  Zsolt L谩szl贸-Nagy (5)
  Bal谩zs A. Luk谩cs (1)
  L谩szl贸 G-T贸th (1)
  G谩bor V谩rb铆r贸 (1)
  
  1. MTA Centre for Ecological Research, Department of Tisza River Research, Bem sqr. 18/c, Debrecen, 4026, Hungary
  2. Romanian Water Authority, Somes-Tisa Branch, Str. Vanatorului nr. 17, 400213, Cluj-Napoca, Romania
  3. Romanian Water Authority, Prut-Barlad Branch, Str. Th. Vascauteanu nr. 10, 700462, Iasi, Romania
  4. Department of Hydrobiology, Debrecen University, Egyetem sqr. 1, Debrecen, 4032, Hungary
  5. Environmental Protection, Nature Conservation and Water Authority, Lower-Tisza Region, Fels艖 Tisza-part 17, Szeged, 6721, Hungary
  
• ISSN：1573-5117

文摘

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.