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dc.contributor.authorMakiola, Andreas
dc.date.accessioned2019-02-08T02:22:02Z
dc.date.available2019-02-08T02:22:02Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/10182/10478
dc.description.abstractPlant pathogens play a critical role for global food security, conservation of natural ecosystems and future resilience and sustainability of ecosystem services in general. Thus, it is crucial to understand the large-scale processes that shape plant pathogen communities. The recent drop in DNA sequencing costs offers, for the first time, the opportunity to study multiple plant pathogens simultaneously in their naturally occurring environment effectively at large scale. In this thesis, my aims were (1) to employ next-generation sequencing (NGS) based metabarcoding for the detection and identification of plant pathogens at the ecosystem scale in New Zealand, (2) to characterise plant pathogen communities, and (3) to determine the environmental drivers of these communities. First, I investigated the suitability of NGS for the detection, identification and quantification of plant pathogens using rust fungi as a model system. I compared two fundamentally different metabarcoding methods along with traditional cloning approaches. I found a phylogenetic bias driven by metabarcoding primer design, but no effect of sequencing method per se. This result supports the usage of metabarcoding for large scale detection and quantification of plant pathogens. At the same time it underpins the importance of the primer choice for metabarcoding, which can result in the failure to detect particular plant pathogens. After confirming the semi-quantitative nature of metabarcoding for the large scale detection of rust fungi, I expanded the approach to fungi, oomycete and bacteria plant pathogens across a wide range of different land use types, sampling soil, roots and leaf substrates. I found a higher species richness of plant pathogens in agricultural than in natural systems across substrate and pathogen taxa. In contrast, there was almost no variation in composition among plant pathogen communities from site-to-site, suggesting a similar species turnover within land uses. I detected plant pathogen groups in the substrate types and land use categories as expected based on known ecology or literature. This indicates that the metabarcoding approach worked well for the overwhelming majority of fungi, oomycete and bacteria plant pathogens. Next, I quantified the relative importance of environmental drivers for plant pathogen communities and richness. The composition of plant species (plant community at site) could generally explain most of the variance in pathogen community and richness, even after accounting for other environmental parameters such as geomorphology, climate, land use and soil. These results suggest an unequal relationship among plant pathogen, plant and environment, and furthermore that any changes in plant pathogen communities as well as richness will mostly be related to changes in plant communities, rather than direct effects of the abiotic environment. Lastly, I applied network analysis in order to identify non-random and predictable co-occurrence patterns of plant pathogens. I demonstrated that the overwhelming complexity of above and belowground plant pathogens across different ecosystems can be reduced into distinct plant pathogen communities which in turn can be more easily studied than the vast number of individual plant pathogens. The taxonomic identity of the pathogen seemed to play a far greater role in the formation of these plant pathogen communities than the substrate. How these plant pathogen communities will shift in a changing world remains subject to future research. However, predictable and defined plant pathogen communities will greatly help us anticipate future impacts on food and ecosystem production. The overall results of this thesis showed that NGS metabarcoding and network theory can successfully be applied to gain new insights about plant pathogens at an ecosystem scale. NGS metabarcoding emerged as an appropriate tool particularly for studying and predicting entire plant pathogen communities. The ecological community approach to studying plant pathogens has the potential to bring us one step closer to sustainable solutions to global food security and ecosystem services in the immediate future.en
dc.language.isoenen
dc.publisherLincoln Universityen
dc.rights.urihttps://researcharchive.lincoln.ac.nz/page/rights
dc.subjectenvironmental DNAen
dc.subjectIlluminaen
dc.subjectplant pathogensen
dc.subjectmetabarcodingen
dc.subjectnext-generation sequencingen
dc.subjectfood securityen
dc.titleCharacterising plant pathogen communities and their environmental drivers at a national scaleen
dc.typeThesisen
thesis.degree.grantorLincoln Universityen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen
lu.thesis.supervisorDickie, Ian
lu.thesis.supervisorGlare, Travis
lu.contributor.unitBio-Protection Research Centreen
dc.subject.anzsrc060504 Microbial Ecologyen
dc.subject.anzsrc06 Biological Sciencesen
dc.subject.anzsrc0605 Microbiologyen


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