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Open AccessFeature PaperEditor’s ChoiceArticle

Exploring How Cyanobacterial Traits Affect Nutrient Loading Thresholds in Shallow Lakes: A Modelling Approach

1
Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), P.O. Box 50, 6700 AB Wageningen, The Netherlands
2
Department of Aquatic Ecology and Water Quality Management, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
3
PBL, Netherlands Environmental Assessment Agency, P.O. Box 30314, 2500 GH The Hague, The Netherlands
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Institute of Marine Sciences, The University of North Carolina at Chapel Hill, Morehead City, NC 28557, USA
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Water Systems and Global Change Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2020, 12(9), 2467; https://doi.org/10.3390/w12092467
Received: 10 July 2020 / Revised: 26 August 2020 / Accepted: 28 August 2020 / Published: 2 September 2020
Globally, many shallow lakes have shifted from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state due to eutrophication. Such shifts are often accompanied by toxic cyanobacterial blooms, with specialized traits including buoyancy regulation and nitrogen fixation. Previous work has focused on how these traits contribute to cyanobacterial competitiveness. Yet, little is known on how these traits affect the value of nutrient loading thresholds of shallow lakes. These thresholds are defined as the nutrient loading at which lakes shift water quality state. Here, we used a modelling approach to estimate the effects of traits on nutrient loading thresholds. We incorporated cyanobacterial traits in the process-based ecosystem model PCLake+, known for its ability to determine nutrient loading thresholds. Four scenarios were simulated, including cyanobacteria without traits, with buoyancy regulation, with nitrogen fixation, and with both traits. Nutrient loading thresholds were obtained under N-limited, P-limited, and colimited conditions. Results show that cyanobacterial traits can impede lake restoration actions aimed at removing cyanobacterial blooms via nutrient loading reduction. However, these traits hardly affect the nutrient loading thresholds for clear lakes experiencing eutrophication. Our results provide references for nutrient loading thresholds and draw attention to cyanobacterial traits during the remediation of eutrophic water bodies. View Full-Text
Keywords: harmful algal blooms; regime shift; alternative stable state; resilience; hysteresis; light limitation; nutrient limitation; critical nutrient loading harmful algal blooms; regime shift; alternative stable state; resilience; hysteresis; light limitation; nutrient limitation; critical nutrient loading
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MDPI and ACS Style

Chang, M.; Teurlincx, S.; Janse, J.H.; Paerl, H.W.; Mooij, W.M.; Janssen, A.B.G. Exploring How Cyanobacterial Traits Affect Nutrient Loading Thresholds in Shallow Lakes: A Modelling Approach. Water 2020, 12, 2467. https://doi.org/10.3390/w12092467

AMA Style

Chang M, Teurlincx S, Janse JH, Paerl HW, Mooij WM, Janssen ABG. Exploring How Cyanobacterial Traits Affect Nutrient Loading Thresholds in Shallow Lakes: A Modelling Approach. Water. 2020; 12(9):2467. https://doi.org/10.3390/w12092467

Chicago/Turabian Style

Chang, Manqi; Teurlincx, Sven; Janse, Jan H.; Paerl, Hans W.; Mooij, Wolf M.; Janssen, Annette B.G. 2020. "Exploring How Cyanobacterial Traits Affect Nutrient Loading Thresholds in Shallow Lakes: A Modelling Approach" Water 12, no. 9: 2467. https://doi.org/10.3390/w12092467

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