Pomati, F. and Jokela, J. and Simona, M. and Veronesi, M. and Ibelings, B.W. (2011) An automated platform for phytoplankton ecology and aquatic ecosystem monitoring. Environmental Science and Technology, 45, 9658-9665. ISSN 0013-936X.
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Official URL: http://dx.doi.org/10.1021/es201934n
High quality monitoring data are vital for tracking and understanding the causes of ecosystem change. We present a potentially powerful approach for phytoplankton and aquatic ecosystem monitoring, based on integration of scanning flow-cytometry for the characterization and counting of algal cells with multiparametric vertical water profiling. This approach affords high-frequency data on phytoplankton abundance, functional traits and diversity, coupled with the characterization of environmental conditions for growth over the vertical structure of a deep water body. Data from a pilot study revealed effects of an environmental disturbance event on the phytoplankton community in Lake Lugano (Switzerland), characterized by a reduction in cytometry-based functional diversity and by a period of cyanobacterial dominance. These changes were missed by traditional limnological methods, employed in parallel to high-frequency monitoring. Modeling of phytoplankton functional diversity revealed the importance of integrated spatiotemporal data, including circadian time-lags and variability over the water column, to understand the drivers of diversity and dynamic processes. The approach described represents progress toward an automated and trait-based analysis of phytoplankton natural communities. Streamlining of high-frequency measurements may represent a resource for understanding, modeling and managing aquatic ecosystems under impact of environmental change, yielding insight into processes governing phytoplankton community resistance and resilience.
|Institutes:||Nederlands Instituut voor Ecologie (NIOO)|
|Deposited On:||10 Oct 2011 02:00|
|Last Modified:||24 Apr 2012 16:40|
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