Different types of synchrony in chaotic and cyclic communities

Becks, Lutz; Arndt, Hartmut

doi:10.1038/ncomms2355

Cited by 26 publications

(23 citation statements)

References 61 publications

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“…Long-term data sets of such interactions become increasingly available, both from field observations, and highly controlled large-scale and laboratory experiments (Fussmann et al, 2000;Tirok and Gaedke, 2007;Becks et al, 2010Becks et al, , 2012Magurran et al, 2010;Weigelt et al, 2010;Boit and Gaedke, 2014). To mechanistically understand these trophic interactions, models are employed with the goal of reproducing the observed population dynamics, which can either settle to a steady state, or include more complex patterns like limit cycles or chaos (Fussmann et al, 2000;Boit et al, 2012;Becks and Arndt, 2013;Barraquand et al, 2017). Obtaining an agreement of such non-static experimental observations and modeled time series remains a demanding task.…”

Section: Introductionmentioning

confidence: 99%

Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

et al. 2019

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“…Bacterial systems are dynamic, often facing altering selection pressures due to evolution (Buckling & Rainey ; Hiltunen & Becks )), ecology (Hibbing et al . ; Becks & Arndt ) or the interplay of these at one timescale (Harrington & Sanchez ; Hiltunen et al . ; Friman et al .…”

Section: Discussionmentioning

confidence: 99%

Genomic evolution of bacterial populations under coselection by antibiotics and phage

et al. 2017

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Bacteria live in dynamic systems where selection pressures can alter rapidly, forcing adaptation to the prevailing conditions. In particular, bacteriophages and antibiotics of anthropogenic origin are major bacterial stressors in many environments. We previously observed that populations of the bacterium Pseudomonas fluorescens SBW25 exposed to the lytic bacteriophage SBW25Φ2 and a non-inhibitive concentration of the antibiotic streptomycin (co-selection) achieved higher levels of phage resistance compared to populations exposed to the phage alone. In addition, the phage became extinct under coselection while remaining present in the phage alone environment. Further, phenotypic tests indicated that these observations might be associated with increased mutation rate under coselection. In this study, we examined the genetic causes behind these phenotypes by wholegenome sequencing clones isolated from the end of the experiments. We were able to identify genetic factors likely responsible for streptomycin resistance, phage resistance and hypermutable (mutator) phenotypes. This constitutes genomic evidence in support of the observation that while the presence of phage did not affect antibiotic resistance, the presence of antibiotic affected phage resistance. We had previously hypothesized an association between mutators and elevated levels of phage resistance under co-selection. However, our evidence regarding the mechanism was inconclusive, since although with phage mutators were only found under co-selection, additional genomic evidence was lacking and phage resistance was also observed in non-mutators under co-selection. More generally, our study provides novel insights into evolution between univariate and multivariate selection (here two stressors), as well as the potential role of hypermutability in natural communities.

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“…To mechanistically understand these trophic interactions, models are employed with the goal of reproducing the observed population dynamics, which can either settle to a steady state, or include more complex patterns like limit cycles or chaos (G. F. Fussmann et al 2000; Boit et al 2012; Becks and Arndt 2013; Barraquand et al 2017). Obtaining an agreement of such non-static experimental observations and modelled time series remains a demanding task.…”

Section: Introductionmentioning

confidence: 99%

Estimating parameters from multiple time series of population dynamics using Bayesian inference

Rosenbaum

Raatz

Weithoff

et al. 2018

Preprint

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1 1. Empirical time series of interacting entities, e.g. species abundances, are highly 2 useful to study ecological mechanisms. Mathematical models are valuable tools to 3 further elucidate those mechanisms and underlying processes. However, obtaining 4 an agreement between model predictions and experimental observations remains a 5 demanding task. As models always abstract from reality one parameter often sum-6 marizes several properties. Parameter measurements are performed in additional 7 experiments independent of the ones delivering the time series. Transferring these 8 parameter values to different settings may result in incorrect parametrizations. On 9 top of that, the properties of organisms and thus the respective parameter values 10 may vary considerably. These issues limit the use of a priori model parametriza-11 tions. 12 2. In this study, we present a method suited for a direct estimation of model parame-13 ters and their variability from experimental time series data. We combine numer-14 ical simulations of a continuous-time dynamical population model with Bayesian 15 inference, using a hierarchical framework that allows for variability of individual 16 parameters. The method is applied to a comprehensive set of time series from a 17 laboratory predator-prey system that features both steady states and cyclic pop-18 ulation dynamics. 19 3. Our model predictions are able to reproduce both steady states and cyclic dynamics 20 of the data. Additionally to the direct estimates of the parameter values, the 21Bayesian approach also provides their uncertainties. We found that fitting cyclic 22 population dynamics, which contain more information on the process rates than 23 steady states, yields more precise parameter estimates. We detected significant 24 variability among parameters of different time series and identified the variation 25 in the maximum growth rate of the prey as a source for the transition from steady 26 2 states to cyclic dynamics. 27 4. By lending more flexibility to the model, our approach facilitates parametrizations 28 and shows more easily which patterns in time series can be explained also by 29 simple models. Applying Bayesian inference and dynamical population models in 30 conjunction may help to quantify the profound variability in organismal properties 31 in nature. 32 Keywords 33 Bayesian inference, chemostat experiments, hierarchical model, ordinary differential 34 equation, parameter estimation, population dynamics, predator prey, time series analy-35 sis, trait variability, Stan 36

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Different types of synchrony in chaotic and cyclic communities

Cited by 26 publications

References 61 publications

Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

Genomic evolution of bacterial populations under coselection by antibiotics and phage

Estimating parameters from multiple time series of population dynamics using Bayesian inference

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