scholarly journals Effect of mutation supply on population dynamics and trait evolution in an experimental microbial community

2021 ◽  
Author(s):  
Johannes Cairns ◽  
Alexandre Jousset ◽  
Lutz Becks ◽  
Teppo Hiltunen
Keyword(s):  
Population Dynamics ◽  
Microbial Community ◽  
Trait Evolution

scholarly journals Estimating Population Turnover Rates by Relative Quantification Methods Reveals Microbial Dynamics in Marine Sediment

2017 ◽  
Vol 84 (1) ◽  
Author(s):  
Richard Kevorkian ◽  
Jordan T. Bird ◽  
Alexander Shumaker ◽  
Karen G. Lloyd
Keyword(s):  
Population Dynamics ◽  
Microbial Community ◽  
16S Rrna ◽  
Sulfate Reduction ◽  
Relative Quantification ◽  
Turnover Rates ◽  
Content Type ◽  
Sulfate Reducing ◽  
Population Turnover ◽  
Read Abundance

ABSTRACT The difficulty involved in quantifying biogeochemically significant microbes in marine sediments limits our ability to assess interspecific interactions, population turnover times, and niches of uncultured taxa. We incubated surface sediments from Cape Lookout Bight, North Carolina, USA, anoxically at 21°C for 122 days. Sulfate decreased until day 68, after which methane increased, with hydrogen concentrations consistent with the predicted values of an electron donor exerting thermodynamic control. We measured turnover times using two relative quantification methods, quantitative PCR (qPCR) and the product of 16S gene read abundance and total cell abundance (FRAxC, which stands for “fraction of read abundance times cells”), to estimate the population turnover rates of uncultured clades. Most 16S rRNA reads were from deeply branching uncultured groups, and ∼98% of 16S rRNA genes did not abruptly shift in relative abundance when sulfate reduction gave way to methanogenesis. Uncultured Methanomicrobiales and Methanosarcinales increased at the onset of methanogenesis with population turnover times estimated from qPCR at 9.7 ± 3.9 and 12.6 ± 4.1 days, respectively. These were consistent with FRAxC turnover times of 9.4 ± 5.8 and 9.2 ± 3.5 days, respectively. Uncultured Syntrophaceae, which are possibly fermentative syntrophs of methanogens, and uncultured Kazan-3A-21 archaea also increased at the onset of methanogenesis, with FRAxC turnover times of 14.7 ± 6.9 and 10.6 ± 3.6 days. Kazan-3A-21 may therefore either perform methanogenesis or form a fermentative syntrophy with methanogens. Three genera of sulfate-reducing bacteria, Desulfovibrio, Desulfobacter, and Desulfobacterium, increased in the first 19 days before declining rapidly during sulfate reduction. We conclude that population turnover times on the order of days can be measured robustly in organic-rich marine sediment, and the transition from sulfate-reducing to methanogenic conditions stimulates growth only in a few clades directly involved in methanogenesis, rather than in the whole microbial community. IMPORTANCE Many microbes cannot be isolated in pure culture to determine their preferential growth conditions and predict their response to changing environmental conditions. We created a microcosm of marine sediments that allowed us to simulate a diagenetic profile using a temporal analog for depth. This allowed for the observation of the microbial community population dynamics caused by the natural shift from sulfate reduction to methanogenesis. Our research provides evidence for the population dynamics of uncultured microbes as well as the application of a novel method of turnover rate analysis for individual taxa within a mixed incubation, FRAxC, which stands for “fraction of read abundance times cells,” which was verified by quantitative PCR. This allows for the calculation of population turnover times for microbes in a natural setting and the identification of uncultured clades involved in geochemical processes.

scholarly journals Population dynamics and transcriptomic responses of Pseudomonas aeruginosa in a complex laboratory microbial community

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Yingying Cheng ◽  
Joey Kuok Hoong Yam ◽  
Zhao Cai ◽  
Yichen Ding ◽  
Lian-Hui Zhang ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Population Dynamics ◽  
Microbial Community ◽  
Transcriptomic Responses

Soil Microbial Community and Their Population Dynamics: Altered Agricultural Practices

2017 ◽  
pp. 383-415
Author(s):  
Santosh Kumar ◽  
Ravi R. Kumar ◽  
Mahendra Singh ◽  
Erayya ◽  
Mahesh Kumar ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Microbial Community ◽  
Soil Microbial Community ◽  
Agricultural Practices ◽  
Soil Microbial

scholarly journals Stochasticity in the Parasite-Driven Trait Evolution of Competing Species Masks the Distinctive Consequences of Distance Metrics

2017 ◽  
Author(s):  
Christian Alvin H. Buhat ◽  
Dylan Antonio S.J. Talabis ◽  
Anthony L. Cueno ◽  
Maica Krizna A. Gavina ◽  
Ariel L. Babierra ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Carrying Capacity ◽  
Evolutionary Dynamics ◽  
Evolutionary Optimization ◽  
Quantitative Modeling ◽  
Distance Metrics ◽  
Competing Species ◽  
Trait Evolution

Various distance metrics and their induced norms are employed in the quantitative modeling of evolutionary dynamics. Minimization of these distance metrics when applied to evolutionary optimization are hypothesized to result in different outcomes. Here, we apply the different distance metrics to the evolutionary trait dynamics brought about by the interaction between two competing species infected by parasites (exploiters). We present deterministic cases showing the distinctive selection outcomes under the Manhattan, Euclidean and Chebyshev norms. Specifically, we show how they differ in the time of convergence to the desired optima (e.g., no disease), and in the egalitarian sharing of carrying capacity between the competing species. However, when randomness is introduced to the population dynamics of parasites and to the trait dynamics of the competing species, the distinctive characteristics of the outcomes under the three norms become indistinguishable. Our results provide theoretical cases when evolutionary dynamics using different distance metrics exhibit similar outcomes.

scholarly journals Inferring the Effect of Species Interactions on Trait Evolution

2020 ◽  
Author(s):  
Liang Xu ◽  
Sander Van Doorn ◽  
Hanno Hildenbrandt ◽  
Rampal S Etienne
Keyword(s):  
Population Dynamics ◽  
Species Interactions ◽  
Approximate Bayesian Computation ◽  
Fitness Function ◽  
Species Interaction ◽  
Competitive Interactions ◽  
Summary Statistics ◽  
Summary Statistic ◽  
Trait Evolution ◽  
Approximate Bayesian

Abstract Models of trait evolution form an important part of macroevolutionary biology. The Brownian motion model and Ornstein–Uhlenbeck models have become classic (null) models of character evolution, in which species evolve independently. Recently, models incorporating species interactions have been developed, particularly involving competition where abiotic factors pull species toward an optimal trait value and competitive interactions drive the trait values apart. However, these models assume a fitness function rather than derive it from population dynamics and they do not consider dynamics of the trait variance. Here, we develop a general coherent trait evolution framework where the fitness function is based on a model of population dynamics, and therefore it can, in principle, accommodate any type of species interaction. We illustrate our framework with a model of abundance-dependent competitive interactions against a macroevolutionary background encoded in a phylogenetic tree. We develop an inference tool based on Approximate Bayesian Computation and test it on simulated data (of traits at the tips). We find that inference performs well when the diversity predicted by the parameters equals the number of species in the phylogeny. We then fit the model to empirical data of baleen whale body lengths, using three different summary statistics, and compare it to a model without population dynamics and a model where competition depends on the total metabolic rate of the competitors. We show that the unweighted model performs best for the least informative summary statistic, while the model with competition weighted by the total metabolic rate fits the data slightly better than the other two models for the two more informative summary statistics. Regardless of the summary statistic used, the three models substantially differ in their predictions of the abundance distribution. Therefore, data on abundance distributions will allow us to better distinguish the models from one another, and infer the nature of species interactions. Thus, our framework provides a conceptual approach to reveal species interactions underlying trait evolution and identifies the data needed to do so in practice. [Approximate Bayesian computation; competition; phylogeny; population dynamics; simulations; species interaction; trait evolution.]

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