Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012

Allison, Steven; Martiny, Jennifer B. H.

doi:10.15485/1828589

Title: Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012

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Abstract

Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities.more »« less

Authors:

;

University of California Irvine; University of California Irvine
University of California Irvine

Publication Date:: Thu May 27 04:00:00 UTC 2021

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; Can microbial functional traits predict the response and resilience of decomposition to global change?

Sponsoring Org.:: BSSD

Subject:: 54 ENVIRONMENTAL SCIENCES; EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA; ESS-DIVE File Level Metadata Reporting Format; Nitrogen addition; Precipitation; cellulose; community composition; fats; fungi; global change; grassland; hemicellulose; home field advantage; lignin; litter decomposition; microbes; nitrogen fertilization; precipitation; protein; reciprocal transplant; starch; sugars

OSTI Identifier:: 1828589

DOI:: https://doi.org/10.15485/1828589

Citation Formats


                    Allison, Steven, and Martiny, Jennifer B. H. Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012.  United States: N. p., 2021. 
        Web.  doi:10.15485/1828589.

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                    Allison, Steven, & Martiny, Jennifer B. H. Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012.  United States.  doi:https://doi.org/10.15485/1828589

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                    Allison, Steven, and Martiny, Jennifer B. H. 2021.  
"Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012".  United States.  doi:https://doi.org/10.15485/1828589.  https://www.osti.gov/servlets/purl/1828589. Pub date:Thu May 27 04:00:00 UTC 2021

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@article{osti_1828589,

  title        = {Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012},

  author       = {Allison, Steven and Martiny, Jennifer B. H.},

  abstractNote = {Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities. This data package includes ten csv files (five data files and their corresponding data dictionaries) and one file-level metadata excel file. Data files contains information about which plots were exposed to treatments related to drought and nitrogen, information about litter bags reciprocal transplants manipulation for water input and nitrogen, detail information about water addition, precipitation records, and litter variables collected. Data dictionary files include detail explanation for each column in the data files. The file-level metadata file describes each file mentioned above. All the analyses were done using the R software.},

  doi          = {10.15485/1828589},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu May 27 04:00:00 UTC 2021},

  month        = {Thu May 27 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.15485/1828589

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