Predicting invasive fungal pathogens using invasive pest assemblages: Testing model predictions in a virtual world

PLoS ONE

Volumn 6, Issue 10, 2011, Pages

  Paini, Dean R ^a,b   Bianchi, Felix J J A ^a,d   Northfield, Tobin D ^c,e   de Barro, Paul J ^a,b  

^a CSIRO   (Australia)

^b Australia Cooperative Research Centre for National Plant Biosecurity   (Australia)

^c WASHINGTON STATE UNIVERSITY   (United States)

^d WAGENINGEN UNIVERSITY   (Netherlands)

^e UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUSTRALIA; COMPUTER MODEL; CONTROLLED STUDY; FUNGAL VIRULENCE; FUNGUS IDENTIFICATION; GEOGRAPHIC DISTRIBUTION; GEOGRAPHICAL VARIATION (SPECIES); INVASIVE SPECIES; NONHUMAN; PREDICTION; PROCESS DEVELOPMENT; SELF ORGANISING MAP; SPECIES COMPARISON; SPECIES DISTRIBUTION; FACTUAL DATABASE; FUNGUS; INTRODUCED SPECIES; MICROBIOLOGY; PEST CONTROL; PHYSIOLOGY; PLANT; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS;

DATABASES, FACTUAL; FUNGI; INTRODUCED SPECIES; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PEST CONTROL; PLANTS; REPRODUCIBILITY OF RESULTS;

EID: 80053912652     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0025695     Document Type: Article

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