Effect ofArtemia salineon experimental population dynamics ofSkeletonema costatumunder interspecies competition condition

2014 ◽  
Author(s):  
Ruinan Chen ◽  
Jinwei Gao ◽  
Yong Dou ◽  
Xiuting Qiao ◽  
Wenli Zhou
Keyword(s):  
Population Dynamics ◽  
Experimental Population ◽  
Interspecies Competition ◽  
Competition Condition

scholarly journals Quantifying the effect of genetic, environmental and individual demographic stochastic variability for population dynamics in Plantago lanceolata

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ulrich K. Steiner ◽  
Shripad Tuljapurkar ◽  
Deborah A. Roach
Keyword(s):  
Population Dynamics ◽  
Population Growth ◽  
Population Change ◽  
Adaptive Dynamics ◽  
Plantago Lanceolata ◽  
Environmental Parameters ◽  
Experimental Population ◽  
Stochastic Variation ◽  
Survival And Reproduction ◽  
Stochastic Events

AbstractSimple demographic events, the survival and reproduction of individuals, drive population dynamics. These demographic events are influenced by genetic and environmental parameters, and are the focus of many evolutionary and ecological investigations that aim to predict and understand population change. However, such a focus often neglects the stochastic events that individuals experience throughout their lives. These stochastic events also influence survival and reproduction and thereby evolutionary and ecological dynamics. Here, we illustrate the influence of such non-selective demographic variability on population dynamics using population projection models of an experimental population of Plantago lanceolata. Our analysis shows that the variability in survival and reproduction among individuals is largely due to demographic stochastic variation with only modest effects of differences in environment, genes, and their interaction. Common expectations of population growth, based on expected lifetime reproduction and generation time, can be misleading when demographic stochastic variation is large. Large demographic stochastic variation exhibited within genotypes can lower population growth and slow evolutionary adaptive dynamics. Our results accompany recent investigations that call for more focus on stochastic variation in fitness components, such as survival, reproduction, and functional traits, rather than dismissal of this variation as uninformative noise.

scholarly journals Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis

2019 ◽  
Author(s):  
Hadrien Delattre ◽  
Jing Chen ◽  
Matthew Wade ◽  
Orkun S Soyer
Keyword(s):  
Population Dynamics ◽  
Microbial Communities ◽  
Thermodynamic Model ◽  
Community Dynamics ◽  
Energy Requirement ◽  
Minimum Energy ◽  
Building Blocks ◽  
Thermodynamic Modelling ◽  
Energy Requirements ◽  
Experimental Population

ABSTRACTMicrobial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions, is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics accounting explicitly for metabolic activities of composing microbes, system pH, and chemical exchanges. We calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing experimental population dynamics of these synthetic communities that feature relevant species utilising low-energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis and elaborates on previous estimates of lactate fermentation by sulfate reducers. The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.

scholarly journals Quantifying genetic, environmental and individual stochastic variability inPlantago lanceolata

2018 ◽  
Author(s):  
Ulrich K. Steiner ◽  
Shripad Tuljapurkar ◽  
Deborah A. Roach
Keyword(s):  
Population Dynamics ◽  
Population Growth ◽  
Plantago Lanceolata ◽  
Environmental Parameters ◽  
Experimental Population ◽  
Stochastic Variation ◽  
Structured Matrix ◽  
Survival And Reproduction ◽  
Stochastic Events ◽  
Lower Population

AbstractPredicting ecological and evolutionary population dynamics requires understanding how genetic and environmental parameters influence variation in survival and reproduction among individuals. However such a focus often neglects the stochastic events that individuals experience throughout their lives that also influence survival and reproduction. With an illustrative example, we quantify and illustrate the influence of such non-selective demographic variability on population dynamics using size-structured matrix models of an experimental population ofPlantago lanceolata. Our analysis shows that variation in survival and reproduction among individuals explained by environment, genes, and their interaction was modest compared to the stochastic variation in lifespan and reproduction. We illustrate how expectations on population growth, based on expected lifetime reproduction and generation time, can be misleading when variance in reproduction among individuals of the same genotype (full sibs) was large. Such large within genotype variance can lower population growth, fitness. Our results accompany recent investigations that call for more focus on stochastic variation in survival and reproduction, rather than dismissal of this variation as uninformative noise.

scholarly journals Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis

2020 ◽  
Vol 17 (166) ◽  
pp. 20200053 ◽  
Author(s):  
Hadrien Delattre ◽  
Jing Chen ◽  
Matthew J. Wade ◽  
Orkun S. Soyer
Keyword(s):  
Population Dynamics ◽  
Microbial Communities ◽  
Thermodynamic Model ◽  
Community Dynamics ◽  
Energy Requirement ◽  
Minimum Energy ◽  
Building Blocks ◽  
Thermodynamic Modelling ◽  
Energy Requirements ◽  
Experimental Population

Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol −1 ) and elaborate on previous estimates of lactate fermentation by sulfate reducers (in the range of −30 to −17 kJ mol −1 depending on the culture conditions). The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.

Sign in / Sign up

Export Citation Format

Share Document