@misc{BlockDenfeldStockwelletal.2019,
  author    = {Block, Benjamin D. and Denfeld, Blaize A. and Stockwell, Jason D. and Flaim, Giovanna and Grossart, Hans-Peter and Knoll, Lesley B. and Maier, Dominique B. and North, Rebecca L. and Rautio, Milla and Rusak, James A. and Sadro, Steve and Weyhenmeyer, Gesa A. and Bramburger, Andrew J. and Branstrator, Donn K. and Salonen, Kalevi and Hampton, Stephanie E.},
  title     = {The unique methodological challenges of winter limnology},
  series = {Limnology and Oceanography: Methods},
  volume    = {17},
  journal   = {Limnology and Oceanography: Methods},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1541-5856},
  doi       = {10.1002/lom3.10295},
  pages     = {42 -- 57},
  year      = {2019},
  abstract  = {Winter is an important season for many limnological processes, which can range from biogeochemical transformations to ecological interactions. Interest in the structure and function of lake ecosystems under ice is on the rise. Although limnologists working at polar latitudes have a long history of winter work, the required knowledge to successfully sample under winter conditions is not widely available and relatively few limnologists receive formal training. In particular, the deployment and operation of equipment in below 0 degrees C temperatures pose considerable logistical and methodological challenges, as do the safety risks of sampling during the ice-covered period. Here, we consolidate information on winter lake sampling and describe effective methods to measure physical, chemical, and biological variables in and under ice. We describe variation in snow and ice conditions and discuss implications for sampling logistics and safety. We outline commonly encountered methodological challenges and make recommendations for best practices to maximize safety and efficiency when sampling through ice or deploying instruments in ice-covered lakes. Application of such practices over a broad range of ice-covered lakes will contribute to a better understanding of the factors that regulate lakes during winter and how winter conditions affect the subsequent ice-free period.},
  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}
}