scholarly journals Analysis and modelling of the interactive effects of temperature and light on phytoplankton growth and relevance for the spring bloom

2007 ◽  
Vol 30 (1) ◽  
pp. 75-91 ◽  
Author(s):  
A. Nicklisch ◽  
T. Shatwell ◽  
J. Kohler
Keyword(s):  
Spring Bloom ◽  
Phytoplankton Growth ◽  
Interactive Effects ◽  
Effects Of Temperature

Interactive effects of temperature and redox on soil carbon and iron cycling

2021 ◽  
Vol 157 ◽  
pp. 108235
Author(s):  
Nikhil R. Chari ◽  
Yang Lin ◽  
Yuan S. Lin ◽  
Whendee L. Silver
Keyword(s):  
Soil Carbon ◽  
Interactive Effects ◽  
Iron Cycling ◽  
Effects Of Temperature

Use of a Modified Gompertz Equation to Predict the Effects of Temperature, pH, and NaCl on the Inactivation of Listeria monocytogenes Scott A Heated in Infant Formula

1996 ◽  
Vol 59 (1) ◽  
pp. 16-23 ◽  
Author(s):  
R. H. LINTON ◽  
W. H. CARTER ◽  
M. D. PIERSON ◽  
C. R. HACKNEY ◽  
J. D. EIFERT
Keyword(s):  
Listeria Monocytogenes ◽  
Infant Formula ◽  
Nonlinear Regression ◽  
Survival Curve ◽  
Interactive Effects ◽  
Parameter Estimates ◽  
Survival Curves ◽  
Gompertz Equation ◽  
Nacl Concentration ◽  
Effects Of Temperature

The heat resistance of Listeria monocytogenes was determined in infant formula for all possible combinations of temperature (50, 55, and 60°C), pH level (5, 6, and 7), and NaCl concentration (0, 2, and 4%). Survival curves were fit using nonlinear regression with a Gompertz equation. The Gompertz equation was flexible enough to fit the three most commonly observed survival curves: linear curves, those with an initial lag region followed by a linear region, and sigmoidal shaped. Parameter estimates obtained by the method of nonlinear least squares were used to describe the effect(s) of different heating treatments on the lag region, death rate, and tailing region of survival curves. These estimates were further used to predict single and interactive effects of temperature, pH, and percentage of NaCl on the log of the surviving fraction (LSF) of bacteria. Interactions among these variables significantly (P ≤ .05) affected the LSF. Generally, increased pH or NaCl concentration lead to an increased LSF, whereas increased time or temperature lead to a decreased LSF. All multiple-factor interactions significantly (P ≤ .05) affected the LSF. The correlation of observed LSF versus predicted LSF (R2 = .92) indicated that the estimated Gompertz equation was in close agreement with the observation. This study demonstrated that the Gompertz equation and nonlinear regression can be used as an effective means to predict survival curve shape and response to heat of L. monocytogenes under many different environmental conditions.

scholarly journals Interactive Effects of Light Quality and Temperature on Arabidopsis Growth and Immunity

2020 ◽  
Vol 61 (5) ◽  
pp. 933-941
Author(s):  
Xiaoying Liu ◽  
Chunmei Xue ◽  
Le Kong ◽  
Ruining Li ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Blue Light ◽  
Light Quality ◽  
Red Light ◽  
Complex Interaction ◽  
Hypocotyl Elongation ◽  
Interactive Effects ◽  
Phytochrome B ◽  
Light Conditions ◽  
Effects Of Temperature

Abstract We report here the interactive effects of three light qualities (white, red and blue) and three growth temperatures (16�C, 22�C and 28�C) on rosette growth, hypocotyl elongation and disease resistance in Arabidopsis thaliana. While an increase in temperature promotes hypocotyl elongation irrespective of light quality, the effects of temperature on rosette growth and disease resistance are dependent on light quality. Maximum rosette growth rate under white, red and blue light are observed at 28�C, 16�C and 22�C, respectively. The highest disease resistance is observed at 16�C under all three light conditions, but the highest susceptibility is observed at 28�C for white light and 22�C for red and blue light. Interestingly, rosette growth is inhibited by phytochrome B (PHYB) under blue light at 28�C and by cryptochromes (CRYs) under red light at 16�C. In addition, disease resistance is inhibited by PHYB under blue light and promoted by CRYs under red light. Therefore, this study reveals a complex interaction between light and temperature in modulating rosette growth and disease resistance as well as the contribution of PHYB and CRY to disease resistance.

Inter- and intra-varietal variation in aerobic methane emissions from environmentally stressed pea plants

Botany ◽  
2018 ◽  
Vol 96 (12) ◽  
pp. 837-850 ◽  
Author(s):  
Awatif M. Abdulmajeed ◽  
Mohammad I. Abo Gamar ◽  
Mirwais M. Qaderi
Keyword(s):  
Stress Factors ◽  
Interactive Effects ◽  
Controlled Environment ◽  
Uvb Radiation ◽  
Pisum Sativum L ◽  
Temperature Regimes ◽  
Varietal Variation ◽  
Growth Chambers ◽  
Effects Of Temperature ◽  
Two Temperature

Environmental stress factors can influence methane (CH4) emissions from plants. There are a few studies on the interactive effects of stress factors on plant aerobic CH4, but none on the comparative evaluation of CH4 emissions between and among plant varieties. We examined the effects of temperature, UVB radiation, and watering regime on CH4 emissions from 10 pea (Pisum sativum L.) varieties first and then selected two varieties with the highest (237J Sundance; var. 1) and lowest (422 Ho Lan Dow; var. 2) emissions for further studies. Plants were grown in controlled-environment growth chambers under two temperature regimes (22 °C / 18 °C and 28 °C / 24 °C, 16 h light / 8 h dark), two UVB levels (0 and 5 kJ·m−2·d−1), and two watering regimes (well-watered and water-stressed) for 14 days, after one week of growth under 22 °C / 18 °C. Higher temperatures and water stress increased CH4 emissions, and increased emission was associated with stress. Pea varieties varied in growth and CH4 emissions; var. 1 was more stressed and had higher emission than var. 2. In the stressed variety, the water-stressed plants grown under higher temperatures at UVB5 had the highest CH4 emission, whereas the well-watered plants grown under lower temperatures at UVB5 had the lowest emission. We conclude that climatic stress conditions increase CH4 emissions, which vary with plant varieties.

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