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The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: an extension of the Plankton Ecology Group (PEG) model (COMPLETED)

A manuscript was published in Inland Waters in 2018 (

The use of high-frequency sensors on profiling buoys to investigate physical, chemical and biological processes in lakes is a rapidly growing field. Profiling buoys with automated winch and sensor packages that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 GLEON lakes, enabled a collaborative effort to examine the vertical and temporal characteristics of phytoplankton dynamics and sub-surface chlorophyll maxima (SSCM). The effectiveness of three different sampling methods was assessed including (1) manual profiles, (2) single-depth buoys, and (3) automated profiling buoys. High-frequency ChlF surface data and profiles were compared to the Plankton Ecology Group (PEG) model. The depth-integrated phytoplankton dynamics captured by the profiling buoys revealed the much greater complexity that underlies the generalized PEG model. Conventional sampling techniques would have largely missed SSCM in 7 out of 11 study lakes. While surface-only ChlF data underestimated phytoplankton abundance at times by nearly 2-fold in 4 lakes, overall there was a remarkable similarity between surface and integrated water column data. Contrary to the PEG model, which proposes a minimal role for physical control of phytoplankton dynamics during the growing season, thermal structure and light availability were closely associated with phytoplankton dynamics. Thus, an extension of the PEG model with a new conceptual framework that explicitly includes physical control metrics to better predict SSCM formation in lakes is proposed. High-resolution temporal and vertical data on phytoplankton dynamics have the potential to create critical new insights into important lake ecosystem processes, ranging from food web dynamics to lake metabolism and carbon cycling. 

2014-09-26 to 2015-05-15

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