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1.
Mol Ecol ; 31(7): 2013-2031, 2022 04.
Article in English | MEDLINE | ID: mdl-35124872

ABSTRACT

Emerging plant pathogens have been increasing exponentially over the last century. To address this issue, it is critical to determine whether these pathogens are native to ecosystems or have been recently introduced. Understanding the ecological and evolutionary processes fostering emergence can help to manage their spread and predict epidemics/epiphytotics. Using restriction site-associated DNA sequencing data, we studied genetic relationships, pathways of spread and the evolutionary history of Phellinus noxius, an emerging root-rotting fungus of unknown origin, in eastern Asia, Australia and the Pacific Islands. We analysed patterns of genetic variation using Bayesian inference, maximum-likelihood phylogeny, population splits and mixtures measuring correlations in allele frequencies and genetic drift, and finally applied coalescent-based theory using Approximate Bayesian computation (ABC) with supervised machine learning. Population structure analyses revealed five genetic groups with signatures of complex recent and ancient migration histories. The most probable scenario of ancient pathogen spread is movement from an unsampled population to Malaysia and the Pacific Islands, with subsequent spread to Taiwan and Australia. Furthermore, ABC analyses indicate P. noxius spread occurred thousands of generations ago, contradicting previous assumptions that this pathogen was recently introduced to multiple geographical regions. Our results suggest that recent emergence of P. noxius in eastern Asia, Australia and the Pacific Islands has probably been driven by anthropogenic and natural disturbances, such as deforestation, land-use change, severe weather events and/or introduction of exotic plants. This study provides a novel example of applying genome-wide allele frequency data to unravel the dynamics of pathogen emergence under changing ecosystem conditions.


Subject(s)
Ecosystem , Plant Diseases , Bayes Theorem , Gene Frequency , Genetic Variation , Pacific Islands , Phylogeny , Plant Diseases/microbiology , Plants
2.
Mol Ecol ; 22(24): 6033-47, 2013 Dec.
Article in English | MEDLINE | ID: mdl-24112757

ABSTRACT

The rust fungus, Puccinia psidii, is a devastating pathogen of introduced eucalypts (Eucalyptus spp.) in Brazil where it was first observed in 1912. This pathogen is hypothesized to be endemic to South and Central America and to have first infected eucalypts via a host jump from native guava (Psidium guajava). Ten microsatellite markers were used to genotype 148 P. psidii samples from eucalypts and guava plus five additional myrtaceous hosts across a wide geographic range of south-eastern Brazil and Uruguay. Principal coordinates analysis, a Bayesian clustering analysis and a minimum-spanning network revealed two major genetic clusters among the sampled isolates, one associated with guava and another associated with eucalypts and three additional hosts. Multilocus genotypes infecting guava differed by multiple mutational steps at eight loci compared with those infecting eucalypts. Approximate Bayesian computation revealed that evolutionary scenarios involving a coalescence event between guava- and eucalypt-associated pathogen populations within the past 1000 years are highly unlikely. None of the analyses supported the hypothesis that eucalypt-infecting P. psidii in Brazil originated via host jump from guava following the introduction of eucalypts to Brazil approximately 185 years ago. The existence of host-associated biotypes of P. psidii in Brazil indicates that this diversity must be considered when assessing the invasive threat posed by this pathogen to myrtaceous hosts worldwide.


Subject(s)
Basidiomycota/classification , Biological Evolution , Eucalyptus/microbiology , Plant Diseases/microbiology , Psidium/microbiology , Basidiomycota/genetics , Bayes Theorem , Brazil , Genetic Variation , Genetics, Population , Genotype , Host Specificity , Microsatellite Repeats , Models, Genetic , Multilocus Sequence Typing , Mycological Typing Techniques , Principal Component Analysis , Uruguay
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