Algal Community Response to Multiple Stressors in North Temperate Lakes Over the Past Three Decades

Session: Poster session

Katrina Gaibisels, York University, [email protected]
Thomas Van Zuiden, York University, [email protected]
Ryan Sorichetti, Ontario Ministry of the Environment and Climate Change, [email protected]
Andrew Paterson, Ontario Ministry of the Environment and Climate Change, [email protected]
James Rusak, Dorset Environmental Sci. Ctr., [email protected]
Huaxia Yao, ON Ministry of Environment and Climate Change, [email protected]
Sapna Sharma, York University, [email protected]

Abstract

There has been a significant increase in reports of algal blooms across Ontario since 1994. Interacting multiple stressors have impacted water quality during recent decades, including acid rain, calcium declines, brownification, and climate change.  We analyzed long-term records of lake chemistry, climate conditions, and teleconnections indices to identify the local and regional drivers of phytoplankton biomass and variability in eight study lakes in south-central Ontario between 1984 and 2013. Diatoms and chrysophytes were the most abundant during the study period. Each reached about 80% of the relative biomass, and exhibited the highest variability in biomass. Redundancy analysis explained between 24% and 44% of the variation in phytoplankton biomass, with the greatest proportion explained by lake chemistry. For the most abundant groups, we found that acid rain and brownification were the most important drivers at both the regional and lake levels. Regionally, diatom biomass increased with lower concentrations of sulphate (r=0.18) and calcium (r=0.31), and more alkaline pH levels (r=0.27), and chrysophyte biomass increased with lower calcium concentrations (r=-0.22) and more acidic pH levels (r=-0.18). By understanding how multiple stressors influence phytoplankton communities, we will be better able to anticipate harmful and nuisance blooms.

1. Keyword
phytoplankton

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nutrients

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multiple stressors

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community variability

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long-term trends