TY  - JOUR
A1  - Rusak, James A.
A1  - Tanentzap, Andrew J.
A1  - Klug, Jennifer L.
A1  - Rose, Kevin C.
A1  - Hendricks, Susan P.
A1  - Jennings, Eleanor
A1  - Laas, Alo
A1  - Pierson, Donald C.
A1  - Ryder, Elizabeth
A1  - Smyth, Robyn L.
A1  - White, D. S.
A1  - Winslow, Luke A.
A1  - Adrian, Rita
A1  - Arvola, Lauri
A1  - de Eyto, Elvira
A1  - Feuchtmayr, Heidrun
A1  - Honti, Mark
A1  - Istvanovics, Vera
A1  - Jones, Ian D.
A1  - McBride, Chris G.
A1  - Schmidt, Silke Regina
A1  - Seekell, David
A1  - Staehr, Peter A.
A1  - Guangwei, Zhu
T1  - Wind and trophic status explain within and among-lake variability of algal biomass
JF  - Limnology and oceanography letters / ASLO, Association for the Sciences of Limnology and Oceanography
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.1002/lol2.10093
SN  - 2378-2242
VL  - 3
IS  - 6
SP  - 409
EP  - 418
PB  - Wiley
CY  - Hoboken
ER  - 
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  - Mantzouki, Evanthia
A1  - Beklioglu, Meryem
A1  - Brookes, Justin D.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Dugan, Hilary A.
A1  - Doubek, Jonathan P.
A1  - Grossart, Hans-Peter
A1  - Nejstgaard, Jens C.
A1  - Pollard, Amina I.
A1  - Ptacnik, Robert
A1  - Rose, Kevin C.
A1  - Sadro, Steven
A1  - Seelen, Laura
A1  - Skaff, Nicholas K.
A1  - Teubner, Katrin
A1  - Weyhenmeyer, Gesa A.
A1  - Ibelings, Bastiaan W.
T1  - Snapshot surveys for lake monitoring, more than a shot in the dark
JF  - Frontiers in Ecology and Evolution
KW  - multi-lake snapshot surveys
KW  - lake monitoring
KW  - Nyquist-shannon sampling theorem
KW  - space-for-time substitution
KW  - phytoplankton ecology
Y1  - 2018
U6  - https://doi.org/10.3389/fevo.2018.00201
SN  - 2296-701X
VL  - 6
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Tang, Alan T.
A1  - Sullivan, Katie Rose
A1  - Hong, Courtney C.
A1  - Goddard, Lauren M.
A1  - Mahadevan, Aparna
A1  - Ren, Aileen
A1  - Pardo, Heidy
A1  - Peiper, Amy
A1  - Griffin, Erin
A1  - Tanes, Ceylan
A1  - Mattei, Lisa M.
A1  - Yang, Jisheng
A1  - Li, Li
A1  - Mericko-Ishizuka, Patricia
A1  - Shen, Le
A1  - Hobson, Nicholas
A1  - Girard, Romuald
A1  - Lightle, Rhonda
A1  - Moore, Thomas
A1  - Shenkar, Robert
A1  - Polster, Sean P.
A1  - Roedel, Claudia Jasmin
A1  - Li, Ning
A1  - Zhu, Qin
A1  - Whitehead, Kevin J.
A1  - Zheng, Xiangjian
A1  - Akers, Amy
A1  - Morrison, Leslie
A1  - Kim, Helen
A1  - Bittinger, Kyle
A1  - Lengner, Christopher J.
A1  - Schwaninger, Markus
A1  - Velcich, Anna
A1  - Augenlicht, Leonard
A1  - Abdelilah-Seyfried, Salim
A1  - Min, Wang
A1  - Marchuk, Douglas A.
A1  - Awad, Issam A.
A1  - Kahn, Mark L.
T1  - Distinct cellular roles for PDCD10 define a gut-brain axis in cerebral cavernous malformation
JF  - Science Translational Medicine
N2  - Cerebral cavernous malformation (CCM) is a genetic, cerebrovascular disease. Familial CCM is caused by genetic mutations in KRIT1, CCM2, or PDCD10. Disease onset is earlier and more severe in individuals with PDCD10 mutations. Recent studies have shown that lesions arise from excess mitogen-activated protein kinase kinase kinase 3 (MEKK3) signaling downstream of Toll-like receptor 4 (TLR4) stimulation by lipopolysaccharide derived from the gut microbiome. These findings suggest a gut-brain CCM disease axis but fail to define it or explain the poor prognosis of patients with PDCD10 mutations. Here, we demonstrate that the gut barrier is a primary determinant of CCM disease course, independent of microbiome configuration, that explains the increased severity of CCM disease associated with PDCD10 deficiency. Chemical disruption of the gut barrier with dextran sulfate sodium augments CCM formation in a mouse model, as does genetic loss of Pdcd10, but not Krit1, in gut epithelial cells. Loss of gut epithelial Pdcd10 results in disruption of the colonic mucosal barrier. Accordingly, loss of Mucin-2 or exposure to dietary emulsifiers that reduce the mucus barrier increases CCM burden analogous to loss of Pdcd10 in the gut epithelium. Last, we show that treatment with dexamethasone potently inhibits CCM formation in mice because of the combined effect of action at both brain endothelial cells and gut epithelial cells. These studies define a gut-brain disease axis in an experimental model of CCM in which a single gene is required for two critical components: gut epithelial function and brain endothelial signaling.
Y1  - 2019
U6  - https://doi.org/10.1126/scitranslmed.aaw3521
SN  - 1946-6234
SN  - 1946-6242
VL  - 11
IS  - 520
PB  - American Assoc. for the Advancement of Science
CY  - Washington
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  -