TY  - JOUR
A1  - Leach, Taylor H.
A1  - Beisner, Beatrix E.
A1  - Carey, Cayelan C.
A1  - Pernica, Patricia
A1  - Rose, Kevin C.
A1  - Huot, Yannick
A1  - Brentrup, Jennifer A.
A1  - Domaizon, Isabelle
A1  - Grossart, Hans-Peter
A1  - Ibelings, Bastiaan W.
A1  - Jacquet, Stephan
A1  - Kelly, Patrick T.
A1  - Rusak, James A.
A1  - Stockwell, Jason D.
A1  - Straile, Dietmar
A1  - Verburg, Piet
T1  - Patterns and drivers of deep chlorophyll maxima structure in 100 lakes
BT  - the relative importance of light and thermal stratification
JF  - Limnology and oceanography
N2  - The vertical distribution of chlorophyll in stratified lakes and reservoirs frequently exhibits a maximum peak deep in the water column, referred to as the deep chlorophyll maximum (DCM). DCMs are ecologically important hot spots of primary production and nutrient cycling, and their location can determine vertical habitat gradients for primary consumers. Consequently, the drivers of DCM structure regulate many characteristics of aquatic food webs and biogeochemistry. Previous studies have identified light and thermal stratification as important drivers of summer DCM depth, but their relative importance across a broad range of lakes is not well resolved. We analyzed profiles of chlorophyll fluorescence, temperature, and light during summer stratification from 100 lakes in the Global Lake Ecological Observatory Network (GLEON) and quantified two characteristics of DCM structure: depth and thickness. While DCMs do form in oligotrophic lakes, we found that they can also form in eutrophic to dystrophic lakes. Using a random forest algorithm, we assessed the relative importance of variables associated with light attenuation vs. thermal stratification for predicting DCM structure in lakes that spanned broad gradients of morphometry and transparency. Our analyses revealed that light attenuation was a more important predictor of DCM depth than thermal stratification and that DCMs deepen with increasing lake clarity. DCM thickness was best predicted by lake size with larger lakes having thicker DCMs. Additionally, our analysis demonstrates that the relative importance of light and thermal stratification on DCM structure is not uniform across a diversity of lake types.
Y1  - 2018
U6  - https://doi.org/10.1002/lno.10656
SN  - 0024-3590
SN  - 1939-5590
VL  - 63
IS  - 2
SP  - 628
EP  - 646
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Brentrup, Jennifer A.
A1  - Williamson, Craig E.
A1  - Colom-Montero, William
A1  - Eckert, Werner
A1  - de Eyto, Elvira
A1  - Großart, Hans-Peter
A1  - Huot, Yannick
A1  - Isles, Peter D. F.
A1  - Knoll, Lesley B.
A1  - Leach, Taylor H.
A1  - McBride, Chris G.
A1  - Pierson, Don
A1  - Pomati, Francesco
A1  - Read, Jordan S.
A1  - Rose, Kevin C.
A1  - Samal, Nihar R.
A1  - Staehr, Peter A.
A1  - Winslow, Luke A.
T1  - 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
JF  - Inland waters : journal of the International Society of Limnology
N2  - 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.
KW  - chlorophyll fluorescence
KW  - Global Lake Ecological Observatory Network (GLEON)
KW  - high-frequency sensors
KW  - PEG model
KW  - phytoplankton
KW  - profiling buoys
KW  - subsurface chlorophyll maximum
Y1  - 2016
U6  - https://doi.org/10.5268/IW-6.4.890
SN  - 2044-2041
SN  - 2044-205X
VL  - 6
SP  - 565
EP  - 580
PB  - Freshwater Biological Association
CY  - Ambleside
ER  -