Ari Jumpponen

Dr. Ari Jumpponen


Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2001 in Mycorrhiza
Verified
Journal Article
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AJAri Jumpponen
Ari Jumpponen

29 April 2019 in Molecular Ecology
Verified
Journal Article
Open access
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LZLucie Zinger
Lucie Zinger
ABAurélie Bonin
Aurélie Bonin
IAInger Alsos
Inger Alsos
MBMiklós Bálint
Miklós Bálint
HBHolly Bik
Holly Bik
PTPierre Taberlet
Pierre Taberlet

25 September 2009 in New Phytologist
Verified
Journal Article
Open access
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AJAri Jumpponen
Ari Jumpponen
KJK. L. Jones
K. L. Jones
DOI: 10.1111/j.1469-8137.2009.03042.xCommentary p 279

26 July 2018 in Journal of Avian Biology
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Journal Article
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KGKirsten Grond
Kirsten Grond
BSBrett K. Sandercock
Brett K. Sandercock
AJAri Jumpponen
Ari Jumpponen
LZLydia H. Zeglin
Lydia H. Zeglin

1 October 2016 in Phytopathology®
Verified
Journal Article
Open access
RPRavin Poudel
Ravin Poudel
AJAri Jumpponen
Ari Jumpponen
DSDaniel Schlatter
Daniel Schlatter
TPTimothy Paulitz
Timothy Paulitz
BGB. B. McSpadden Gardener
B. B. McSpadden Gardener
KGKaren A. Garrett
Karen A. Garrett
Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. “General network analysis” identifies candidate taxa for maintaining an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. “Pathogen-focused analysis” identifies taxa with direct or indirect associations with taxa known a priori as pathogens. “Disease-focused analysis” identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

17 February 2010 in New Phytologist
Verified
Journal Article
Closed access
PDF file
AJAri Jumpponen
Ari Jumpponen
KJK. L. Jones
K. L. Jones