@article{RusakTanentzapKlugetal.2018,
  author    = {Rusak, James A. and Tanentzap, Andrew J. and Klug, Jennifer L. and Rose, Kevin C. and Hendricks, Susan P. and Jennings, Eleanor and Laas, Alo and Pierson, Donald C. and Ryder, Elizabeth and Smyth, Robyn L. and White, D. S. and Winslow, Luke A. and Adrian, Rita and Arvola, Lauri and de Eyto, Elvira and Feuchtmayr, Heidrun and Honti, Mark and Istvanovics, Vera and Jones, Ian D. and McBride, Chris G. and Schmidt, Silke Regina and Seekell, David and Staehr, Peter A. and Guangwei, Zhu},
  title     = {Wind and trophic status explain within and among-lake variability of algal biomass},
  series = {Limnology and oceanography letters / ASLO, Association for the Sciences of Limnology and Oceanography},
  volume    = {3},
  journal   = {Limnology and oceanography letters / ASLO, Association for the Sciences of Limnology and Oceanography},
  number    = {6},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2378-2242},
  doi       = {10.1002/lol2.10093},
  pages     = {409 -- 418},
  year      = {2018},
  abstract  = {Phytoplankton biomass and production regulates key aspects of freshwater ecosystems yet its variability and subsequent predictability is poorly understood. We estimated within-lake variation in biomass using high-frequency chlorophyll fluorescence data from 18 globally distributed lakes. We tested how variation in fluorescence at monthly, daily, and hourly scales was related to high-frequency variability of wind, water temperature, and radiation within lakes as well as productivity and physical attributes among lakes. Within lakes, monthly variation dominated, but combined daily and hourly variation were equivalent to that expressed monthly. Among lakes, biomass variability increased with trophic status while, within-lake biomass variation increased with increasing variability in wind speed. Our results highlight the benefits of high-frequency chlorophyll monitoring and suggest that predicted changes associated with climate, as well as ongoing cultural eutrophication, are likely to substantially increase the temporal variability of algal biomass and thus the predictability of the services it provides.},
  language  = {en}
}
@article{BrentrupWilliamsonColomMonteroetal.2016,
  author    = {Brentrup, Jennifer A. and Williamson, Craig E. and Colom-Montero, William and Eckert, Werner and de Eyto, Elvira and Grossart, Hans-Peter and Huot, Yannick and Isles, Peter D. F. and Knoll, Lesley B. and Leach, Taylor H. and McBride, Chris G. and Pierson, Don and Pomati, Francesco and Read, Jordan S. and Rose, Kevin C. and Samal, Nihar R. and Staehr, Peter A. and Winslow, Luke A.},
  title     = {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},
  series = {Inland waters : journal of the International Society of Limnology},
  volume    = {6},
  journal   = {Inland waters : journal of the International Society of Limnology},
  publisher = {Freshwater Biological Association},
  address   = {Ambleside},
  issn      = {2044-2041},
  doi       = {10.5268/IW-6.4.890},
  pages     = {565 -- 580},
  year      = {2016},
  abstract  = {The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG model's proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.},
  language  = {en}
}