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Trait-Based Model Reproduces Patterns of Population Structure and Diversity of Methane Oxidizing Bacteria in a Stratified Lake

Zimmermann, Matthias and Mayr, Magdalena J. and Bouffard, Damien and Wehrli, Bernhard and Bürgmann, Helmut (2022) Trait-Based Model Reproduces Patterns of Population Structure and Diversity of Methane Oxidizing Bacteria in a Stratified Lake. Frontiers in Environmental Science, 10 . Art. No. 833511. ISSN 2296-665X. doi:10.3389/fenvs.2022.833511. https://resolver.caltech.edu/CaltechAUTHORS:20220729-722100000

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Abstract

In stratified lakes, methane oxidizing bacteria are critical methane converters that significantly reduce emissions of this greenhouse gas to the atmosphere. Efforts to better understand their ecology uncovered a surprising diversity, vertical structure, and seasonal succession. It is an open question how this diversity has to be considered in models of microbial methane oxidation. Likewise, it is unclear to what extent simple microbial traits related to the kinetics of the oxidation process and temperature optimum, suggested by previous studies, suffice to understand the observed ecology of methane oxidizing bacteria. Here we incorporate niche partitioning in a mechanistic model of seasonal lake mixing and microbial methane oxidation in a stratified lake. Can we model MOB diversity and niche partitioning based on differences in methane oxidation kinetics and temperature adaptation? We found that our model approach can closely reproduce diversity and niche preference patterns of methanotrophs that were observed in seasonally stratified lakes. We show that the combination of trait values resulting in coexisting methanotroph communities is limited to very confined regions within the parameter space of potential trait combinations. However, our model also indicates that the sequence of community assembly, and variations in the stratification and mixing behavior of the lake result in different stable combinations. A scenario analysis introducing variable mixing conditions showed that annual weather conditions and the pre-existing species also affect the developing stable methanotrophic species composition of the lake. Both, effect of pre-existing species and the environmental impact suggest that the MOB community in lakes may differ from year to year, and a stable community may never truly occur. The model further shows that there are always better-adapted species in the trait parameter space that would destabilize and replace an existing stable community. Thus, natural selection may drive trait values into the specific configurations observed in nature based on physiological limits and tradeoffs between traits.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3389/fenvs.2022.833511DOIArticle
https://doi.org/10.3929/ethz-b-000350091DOIPhysico-chemical data
http://doi.org/10.5281/zenodo.3274379DOISource code
https://github.com/zimmermm/FiniteVolumeRDS.jlRelated ItemJulia package
https://github.com/zimmermm/MOBDiversityModelRelated ItemMOBDiversity model
Additional Information:© 2022 Zimmermann, Mayr, Bouffard, Wehrli and Bürgmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 11 December 2021; Accepted: 23 May 2022; Published: 24 June 2022. We thank Karin Beck, Andreas Brand, Jason Day, Patrick Kathriner, Miro Meyer, Michael Plüss and Serge Robert for their help and advice in the field and the lab. We acknowledge the helpful discussions and feedback by Carsten Schubert, Blake Matthews and Anita Narwani. We thank Andreas Brand for his advice and support during the first phase of the project. The Swiss National Science Foundation (grant CR23I3_156759), ETH Zurich and Eawag funded this research. Open access funding provided by Swiss Federal Institute of Aquatic Science and Technology. Author Contributions. MZ, MM, HB, and BW conceptualized the study. MZ and DB conceptualized the model. MZ developed the model under supervision of DB and with inputs by HB, MM, and BW. MM and MZ acquired the data. MZ wrote the first draft of the manuscript and created the figures. All authors contributed text during revisions of the manuscript and were involved in commenting and editing the paper. HB and BW acquired the funding. Data Availability Statement. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Physico-chemical data is available at the ETH Research Collection (https://doi.org/10.3929/ethz-b-000350091). The source code of the physical model is available on GitHub (http://doi.org/10.5281/zenodo.3274379). The source code of the numerical discretization of the reaction-diffusion equation used for the biogeochemical model is available as a Julia package (https://github.com/zimmermm/FiniteVolumeRDS.jl). The actual implementation of the biogeochemical model as well as the microbial growth model for Lake Rotsee is available on GitHub (https://github.com/zimmermm/MOBDiversityModel). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funders:
Funding AgencyGrant Number
Swiss National Science Foundation (SNSF)CR23I3_156759
ETH ZurichUNSPECIFIED
EawagUNSPECIFIED
Swiss Federal Institute of Aquatic Science and TechnologyUNSPECIFIED
Subject Keywords:niche partitioning, microbial kinetics, community assembly, methane affinity, temperature optimum, growth model, tradeoffs, competitive exclusion
DOI:10.3389/fenvs.2022.833511
Record Number:CaltechAUTHORS:20220729-722100000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220729-722100000
Official Citation:Zimmermann M, Mayr MJ, Bouffard D, Wehrli B and Bürgmann H (2022) Trait-Based Model Reproduces Patterns of Population Structure and Diversity of Methane Oxidizing Bacteria in a Stratified Lake. Front. Environ. Sci. 10:833511. doi: 10.3389/fenvs.2022.833511
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115964
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Aug 2022 15:12
Last Modified:01 Aug 2022 15:12

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