- Tree diversity impacts microbial community
- Link between pathogen establishment, microbial community and tree diversity
- Tree species diversity for a resilient woodland.
Understanding why plants resist pathogens is complex (e.g. host genotype and environment), but one novel area of discovery is the recognition that leaf microbiomes play a critical role in protecting plants from pathogens. This raises the question, can increased tree species diversity enhance ecosystem functionality through increased microbial diversity and thus conferring greater pathogen resistance?
Increased plant diversity can drive increased ecosystem functionality at macro-scales. For example, increased crop diversity results in a greater range of biological control species being present, driving a reduction in pests and pathogens, and hence increased resilience. However, it is unknown if increased plant diversity also drives increased ecosystem functionality at the microbial scale. Evidence is emerging that microbial diversity is key to plant health and that the resident microbiomes of plant leaves (the microbes on the leaves) are important contributors to the plant’s defences against foliar pathogens. For example, in ash the microbiome influences susceptibility to ash dieback. Understanding the influence of increased tree diversity on the leaf microbiome, and whether this leads to the establishment of a genetically and functionally more diverse population of microbes that can enhance resistance to pathogen infection, facilitates work towards a nature-based solution to tree pathogens.
Many UK woodlands, both semi-natural and plantations, are dominated by a few tree species. Tree species differ in the biodiversity they support and their ecosystem functioning. However, we lack collated lists of the biodiversity supported by different tree species and information on how tree species differ in their ecosystem functioning. We require this information to identify which tree species can substitute for each other, supporting similar biodiversity and providing similar functioning, and thus, increasing functional redundancy. We therefore hypothesise that mixed species woodlands support highly diverse leaf microbial populations that can outcompete pathogens compared to a monoculture woodland microbiome. This will determine whether mixed species planting results in a more diverse and functionally useful microbiome to protect against foliar pathogens which is essential to inform management strategies for diversification of woodlands.
Figure 1. Dissimilarities in the bacterial (top) and fungal (bottom) community between oak (Quercus petraea and Q. robur) and walnut (Juglans nigra and J. regia) tree host species. The NMDS score computed using the Bray-Curtis index. The ellipsis represents the 95% confidence interval (Roy, 2019).
HostUniversity of Birmingham
- Climate and Environmental Sustainability
- Organisms and Ecosystems
- Does leaf microbial diversity increase with tree diversity? Leaves will be collected from different woodland settings. Microbial communities will be collected, DNA extractions and PCR carried out. Amplicon sequencing of bacteria/fungi using 16S/ITS rRNA gene will be done to assess the diversity of leaf microbiomes.
- Does the microbial biodiversity play a role in preventing pathogens establishment? To understand the presence, distribution, and abundance of the pathogen for each tree species, PCR and Droplet Digital PCR for DNA samples collected in Obj. 1 will be undertaken. The effect of individual or combinations of culturable bacteria and fungi on pathogen suppression will be tested.
- Which tree species will provide resilience for the biodiversity associated with woodlands? To understand which tree species can be planted to increase the woodland resilience to pathogens, mathematical modelling will be used to correlate the diversity of microbial community, abundance of pathogens and tree species.
Training and skills
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
The student will receive specialist training for this multidisciplinary project, encompassing fieldwork, microbiology, genomics, metabolomics, data management and statistical analysis and interpretation of large and complex data sets.
The student will be supported to develop these skills within the School of Biosciences and BIFoR (Birmingham) and at the University of Warwick (metagenomic and mathematical modelling), allowing the student to excel in all of these aspects of data acquisition, analysis and dissemination and to build important networks.
The supervisory team is multi-disciplinary and highly experienced, based in excellent, well-equipped institutions, and will provide comprehensive support for the student across all aspects of the project.
Partners and collaboration
The University of Warwick is one of the world’s leading and internationally excellent in the Research Excellent Framework. School of Life Sciences at the University of Warwick is at the forefront of interdisciplinary research and teaching. Prof Murray Grant, base at Life Sciences at Warwick, is a world leading expert in plant-microbe interactions, metagenomic and metabolomics. Prof Grant will bring expertise in metagenomic to the project. The Alan Turing Institute in Warwick bring expertise in mathematical modelling. The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence with expertise across the mathematical sciences and modelling.
If you wish to apply to the project, applications should include:
- A CENTA application form, downloadable from: CENTA application
- A CV with the names of at least two referees (preferably three and who can comment on your academic abilities)
- Submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm. and go to Apply Now in the PhD Bioscience (CENTA) section. Please quote CENTA23_B5 when completing the application form.
Applications to be received by the end of the day on Wednesday 11th January 2023.
Additional information for international applicants
- All international applicants must ensure they can fulfil the University of Birmingham’s international student entry requirements, which includes English language requirements. For further information please visit https://www.birmingham.ac.uk/postgraduate/pgt/requirements-pgt/international/index.aspx.
- Please be aware that CENTA funding will only cover University fees at the level of support for Home-fee eligible students. The University is only able to waive the difference on the international fee level for a maximum of two successful international applicants.
Field experimental design and field data/sample collection training will be done. Samples will be collected from Norbury Park and University of Birmingham managed woodland. Following DNA extraction and PCR, samples will be sent for next generation sequencing at Novogene.
Mining the sequence data of year 1 for phytopathogen distribution. Subsequently, PCR and Digital Droplet PCR, using specific primers, will be done to understand the presence, distribution, and abundance of the pathogens for each tree species. Whenever a pathogen less abundant in specific samples, resident fungi and bacteria will be isolated and cultured to test if they can suppress the pathogens if used individually or in combinations.
Data analysis of the year 1 and 2 experiments undertaken. The results will be used for mathematical modelling to understand the link between the diversity of microbial community, the abundance of pathogens and diversity of tree species.
Year 3.5: Final analysis of the data, writing up of manuscripts and thesis completion.
Field work will be done at the Norbury park and University of Birmingham managed woodland which has a monoculture and diverse woodland management. Microbial biodiversity assessment of tree species will include DNA metabarcoding and metagenomics which will be analysed using R and BEAR platforms. Isolation and propagation of bacteria and fungi will be performed and assays designed to assess whether they can supress the pathogens of each tree species. These data then will be used to build a statistical model to link the pathogen and microbial diversity and tree species. Collectively, this multidisciplinary approach will impart a unique collection of skills and research experience that will enhance student career opportunities.
- Aguilera, G., Roslin, T., Miller, K., Tamburini, G., Birkhofer, K., Caballero-Lopez, B., Lindström, S.A.M., Öckinger, E., Rundlöf, M., Rusch, A., Smith, H.G., and Bommarco, R. (2020) ‘Crop diversity benefits carabid and pollinator communities in landscapes with semi-natural habitats’. Journal of Applied Ecology, 57: 2170. Doi: 10.1111/1365-2664.13712
- Berg, G., Köberl, M., Rybakova, D., Müller, H., Grosch, R., Smalla, K. (2017) ‘Plant microbial diversity is suggested as the key to future biocontrol and health trends’. FEMS Microbiology Ecology, 93(5): fix050. doi:10.1093/femsec/fix050.
- Munir S, Li Y, He P, He P, He P, Cui W, Wu Y, Li X, Li Q, Zhang S, Xiong Y, Lu Z, Wang W, Zong K, Yang Y, Yang S, Mu C, Wen H, Wang Y, Guo J, Karunarathna SC, He Y. (2022) ‘Defeating Huanglongbing pathogen Candidatus Liberibacter asiaticus with indigenous citrus endophyte Bacillus subtilis L1-21. Frontier in Plant Science. 12:789065. doi: 10.3389/fpls.2021.789065.
- Roy, S.R. (2019) ‘Evaluating the impact of tree provenance, tree phenotype and emergent disease on microbial and insect populations in tree ecosystems’, PhD Thesis, University of Reading.
- Vogel, C.M., Potthoff, D.B., Schäfer, M. (2021) ‘Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen’. Nature Microbiology, 6, 1537–1548. Doi: 10.1038/s41564-021-00997-7.
In case of a lockdown, bioinformatic analysis and statistical modelling will be employed. Searching the database and publications, 16S and ITS rRNA sequence data for the tree species that are present at Norbury park and UoB managed woodland will be collected. These data will be analysed and used to create a statistical model to correlate diversity and abundance or microbial community and pathogens with tree species diversity and woodland wellbeing. BEAR and R will be used to collate and analyse the data. Access to online training for statistical analyses, modelling and BEAR will be provided at Birmingham.