Browsing by Author "Castle, Sarah C."

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    Community-Driven Metadata Standards for Agricultural Microbiome Research
    Dundore-Arias, Jose Pablo; Eloe-Fadrosh, Emiley A.; Schriml, Lynn M.; Beattie, Gwyn A.; Brennan, Fiona P.; Busby, Posy E.; Calderon, Rosalie B.; Castle, Sarah C.; Emerson, Joanne B.; Everhart, Sydney E.; Eversole, Kellye; Frost, Kenneth E.; Herr, Joshua R.; Huerta, Alejandra I.; Iyer-Pascuzzi, Anjali S.; Kalil, Audrey K.; Leach, Jan E.; Leonard, J.; Maul, Jude E.; Prithiviraj, Bharath; Potrykus, Marta; Redekar, Neelam R.; Rojas, J. Alejandro; Silverstein, Kevin A. T.; Tomso, Daniel J.; Tringe, Susannah G.; Vinatzer, Boris A.; Kinkel, Linda L. (2020-02-20)
    Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of "required" and "desirable" metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research.
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    Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?
    Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Bemans, J. M.; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C.; Glanville, Helen C.; Jones, Davey L.; Angel, Foey; Salminen, Janne; Newton, Ryan J.; Buergmann, Helmut; Ingram, Lachlan J.; Hamer, Ute; Siljanen, Henri M. P.; Peltoniemi, Krista; Potthast, Karin; Baneras, Lluis; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C.; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C.; Lopes, Ana R.; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S.; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindstrom, Eva S.; Basiliko, Nathan; Nemergut, Diana R. (Frontiers, 2016-02-24)
    Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.