Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi

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Standard

Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi. / Sheikh, Sanea; Khan, Faheema Kalsoom; Bahram, Mohammad; Ryberg, Martin.

I: Scientific Reports, Bind 12, 22043, 2022.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Sheikh, S, Khan, FK, Bahram, M & Ryberg, M 2022, 'Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi', Scientific Reports, bind 12, 22043. https://doi.org/10.1038/s41598-022-26514-2

APA

Sheikh, S., Khan, F. K., Bahram, M., & Ryberg, M. (2022). Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi. Scientific Reports, 12, [22043]. https://doi.org/10.1038/s41598-022-26514-2

Vancouver

Sheikh S, Khan FK, Bahram M, Ryberg M. Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi. Scientific Reports. 2022;12. 22043. https://doi.org/10.1038/s41598-022-26514-2

Author

Sheikh, Sanea ; Khan, Faheema Kalsoom ; Bahram, Mohammad ; Ryberg, Martin. / Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi. I: Scientific Reports. 2022 ; Bind 12.

Bibtex

@article{2fb033a9a7ac4d909a46a05cc7882285,
title = "Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi",
abstract = "Ectomycorrhiza (ECM) is a symbiotic relation between plant and fungi that is essential for nutrient uptake of many stand forming trees. There are two conflicting views about the evolution of ECM in fungi suggesting (1) relatively few transitions to ECM followed by reversals to non-ECM, or (2) many independent origins of ECM and no reversals. In this study, we compare these, and other, hypotheses and test the impact of different models on inference. We assembled a dataset of five marker gene sequences (nuc58, nucLSU, nucSSU, rpb1, and rpb2) and 2,174 fungal taxa covering the three subphyla: Agaricomycotina, Mucoromycotina and Pezizomycotina. The fit of different models, including models with variable rates in clades or through time, to the pattern of ECM fungal taxa was tested in a Bayesian framework, and using AIC and simulations. We find that models implementing variable rates are a better fit than models without rate shift, and that the conclusion about the relative rate between ECM and non-ECM depend largely on whether rate shifts are allowed or not. We conclude that standard constant-rate ancestral state reconstruction models are not adequate for the analysis of the evolution of ECM fungi, and may give contradictory results to more extensive analyses.",
author = "Sanea Sheikh and Khan, {Faheema Kalsoom} and Mohammad Bahram and Martin Ryberg",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
doi = "10.1038/s41598-022-26514-2",
language = "English",
volume = "12",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi

AU - Sheikh, Sanea

AU - Khan, Faheema Kalsoom

AU - Bahram, Mohammad

AU - Ryberg, Martin

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022

Y1 - 2022

N2 - Ectomycorrhiza (ECM) is a symbiotic relation between plant and fungi that is essential for nutrient uptake of many stand forming trees. There are two conflicting views about the evolution of ECM in fungi suggesting (1) relatively few transitions to ECM followed by reversals to non-ECM, or (2) many independent origins of ECM and no reversals. In this study, we compare these, and other, hypotheses and test the impact of different models on inference. We assembled a dataset of five marker gene sequences (nuc58, nucLSU, nucSSU, rpb1, and rpb2) and 2,174 fungal taxa covering the three subphyla: Agaricomycotina, Mucoromycotina and Pezizomycotina. The fit of different models, including models with variable rates in clades or through time, to the pattern of ECM fungal taxa was tested in a Bayesian framework, and using AIC and simulations. We find that models implementing variable rates are a better fit than models without rate shift, and that the conclusion about the relative rate between ECM and non-ECM depend largely on whether rate shifts are allowed or not. We conclude that standard constant-rate ancestral state reconstruction models are not adequate for the analysis of the evolution of ECM fungi, and may give contradictory results to more extensive analyses.

AB - Ectomycorrhiza (ECM) is a symbiotic relation between plant and fungi that is essential for nutrient uptake of many stand forming trees. There are two conflicting views about the evolution of ECM in fungi suggesting (1) relatively few transitions to ECM followed by reversals to non-ECM, or (2) many independent origins of ECM and no reversals. In this study, we compare these, and other, hypotheses and test the impact of different models on inference. We assembled a dataset of five marker gene sequences (nuc58, nucLSU, nucSSU, rpb1, and rpb2) and 2,174 fungal taxa covering the three subphyla: Agaricomycotina, Mucoromycotina and Pezizomycotina. The fit of different models, including models with variable rates in clades or through time, to the pattern of ECM fungal taxa was tested in a Bayesian framework, and using AIC and simulations. We find that models implementing variable rates are a better fit than models without rate shift, and that the conclusion about the relative rate between ECM and non-ECM depend largely on whether rate shifts are allowed or not. We conclude that standard constant-rate ancestral state reconstruction models are not adequate for the analysis of the evolution of ECM fungi, and may give contradictory results to more extensive analyses.

U2 - 10.1038/s41598-022-26514-2

DO - 10.1038/s41598-022-26514-2

M3 - Journal article

C2 - 36543862

AN - SCOPUS:85144578888

VL - 12

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 22043

ER -

ID: 340107872