Phytoplankton Community Interactions and Cyanotoxin Mixtures in Three Recurring Surface Blooms within One Lake

2021 ◽  
pp. 128142
Author(s):  
Victoria G. Christensen ◽  
Hayley T. Olds ◽  
Jack Norland ◽  
Eakalak Khan
Oikos ◽  
2015 ◽  
Vol 125 (8) ◽  
pp. 1134-1143 ◽  
Author(s):  
Jennifer R. Griffiths ◽  
Susanna Hajdu ◽  
Andrea S. Downing ◽  
Olle Hjerne ◽  
Ulf Larsson ◽  
...  

2018 ◽  
Vol 81 (2) ◽  
pp. 109-124 ◽  
Author(s):  
JL Pinckney ◽  
C Tomas ◽  
DI Greenfield ◽  
K Reale-Munroe ◽  
B Castillo ◽  
...  

2010 ◽  
Vol 30 (4) ◽  
pp. 453-459
Author(s):  
Liang CHEN ◽  
Xiu-Feng ZHANG ◽  
Zheng-Wen LIU

1987 ◽  
Vol 44 (12) ◽  
pp. 2155-2163 ◽  
Author(s):  
I. M. Gray

Differences between nearshore and offshore phytoplankton biomass and composition were evident in Lake Ontario in 1982. Phytoplankton biomass was characterized by multiple peaks which ranged over three orders of magnitude. Perhaps as a consequence of the three times higher current velocities at the northshore station, phytoplankton biomass ranged from 0.09 to 9.00 g∙m−3 compared with 0.10 to 2.40 g∙m−3 for the midlake station. Bacillariophyceae was the dominant group at the northshore station until September when Cyanophyta contributed most to the biomass (83%). Although Bacillariophyceae was the principal component of the spring phytoplankton community at the midlake station, phytoflagellates (49%) and Chlorophyceae (25%) were responsible for summer biomass, with the Chlorophyceae expanding to 80% in the fall. The seasonal pattern of epilimnetic chlorophyll a correlated with temperature. While chlorophyll a concentrations were similar to values from 1970 and 1972, algal biomass had declined and a number of eutrophic species (Melosira binderana, Stephanodiscus tenuis, S. hantzschii var. pusilla, and S. alpinus) previously found were absent in 1982.


Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 181
Author(s):  
Alexia D. Saint-Macary ◽  
Neill Barr ◽  
Evelyn Armstrong ◽  
Karl Safi ◽  
Andrew Marriner ◽  
...  

The cycling of the trace gas dimethyl sulfide (DMS) and its precursor dimethylsulfoniopropionate (DMSP) may be affected by future ocean acidification and warming. DMSP and DMS concentrations were monitored over 20-days in four mesocosm experiments in which the temperature and pH of coastal water were manipulated to projected values for the year 2100 and 2150. This had no effect on DMSP in the two-initial nutrient-depleted experiments; however, in the two nutrient-amended experiments, warmer temperature combined with lower pH had a more significant effect on DMSP & DMS concentrations than lower pH alone. Overall, this indicates that future warming may have greater influence on DMS production than ocean acidification. The observed reduction in DMSP at warmer temperatures was associated with changes in phytoplankton community and in particular with small flagellate biomass. A small decrease in DMS concentration was measured in the treatments relative to other studies, from −2% in the nutrient-amended low pH treatment to −16% in the year 2150 pH and temperature conditions. Temporal variation was also observed with DMS concentration increasing earlier in the higher temperature treatment. Nutrient availability and community composition should be considered in models of future DMS.


2021 ◽  
pp. 073563312199595
Author(s):  
Te-Lien Chou ◽  
Kai-Yu Tang ◽  
Chin-Chung Tsai

Programming learning has become an essential literacy for computer science (CS) and non-CS students in the digital age. Researchers have addressed that students’ conceptions of learning influence their approaches to learning, and thus impact their learning outcomes. Therefore, we aimed to uncover students’ conceptions of programming learning (CoPL) and approaches to programming learning (APL), and analyzed the differences between CS and non-CS students. Phenomenographic analysis was adopted to analyze 31 college students (20 CS-related, and 11 not) from northern Taiwan. Results revealed six categories of CoPL hierarchically: 1. memorizing concepts, logic, and syntax, 2. computing and practicing programming writing, 3. expressing programmers’ ideas and relieving pressure, 4. applying and understanding, 5. increasing one’s knowledge and improving one’s competence, and 6. seeing in a new way. Four categories of APL were also found, namely: 1. copying from the textbook, teachers, or others, 2. rote memory, 3. multiple exploration attempts, and 4. online or offline community interactions. Furthermore, we found that most CS students held higher level CoPL (e.g., seeing in a new way) than non-CS students. However, compared with non-CS students, CS students adopted more surface approaches to learning programming, such as copying and rote memory. Implications are discussed.


Author(s):  
Yekaterina Yezhova ◽  
David Capelle ◽  
Michael Stainton ◽  
Tim Papakyriakou

2021 ◽  
Vol 123 ◽  
pp. 107352
Author(s):  
Yulu Tian ◽  
Yuan Jiang ◽  
Qi Liu ◽  
Dingxue Xu ◽  
Yang Liu ◽  
...  

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