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Predicting the impact of changing nutrient load and temperature on the phytoplankton of England’s largest lake, Windermere

By J. Alex Elliott

Abstract

1. Climate change and eutrophication will be two of the largest threats to lake ecosystems this century. Therefore, the effect of changing water temperature (+0 to +4oC) and nutrient load (0.5-2.0 proportional change) on the phytoplankton of Windermere was assessed using the phytoplankton community model, PROTECH. \ud 2. The following metrics were used for the analysis: annual, spring, summer and autumn mean chlorophyll a concentrations for total phytoplankton, diatoms and Cyanobacteria. Also, the timing of the spring diatom bloom was assessed and the number of days when the World Health Organisation (WHO)-derived risk threshold of 10 mg m-3 Cyanobacteria chlorophyll a was exceeded. \ud 3. The diatoms in Windermere produced their largest amount of chlorophyll a in the spring. Whilst the quantity of diatom biomass produced was relatively unaffected by the simulated changes in temperature and nutrient load, the timing of the bloom peak was 2-3 days earlier per 1 oC.\ud 4. The modelled Cyanobacteria dominated in the summer and autumn, and generally responded positively to both increasing nutrients and temperature illustrating a synergistic relationship between these two drivers. However, in the autumn this relationship was sometimes disrupted due to variations in the length of stratification.\ud 5. Temperature as a factor alone seemed to act in two ways: it affected phenology (e.g. bloom peak timing) mainly in the early part of the growing season and enhanced the dominance of Cyanobacteria in the late growing season. Furthermore, these effects were greatly reduced under the lower nutrient scenarios, suggesting that local management of nutrient inputs to the lake potentially offers a solution to the effects caused by the increase in temperature.\u

Topics: Biology and Microbiology, Ecology and Environment
Year: 2012
DOI identifier: 10.1111/j.1365-2427.2011.02717.x
OAI identifier: oai:nora.nerc.ac.uk:15943

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