Predicting which alien plant species will become invasive is key to mitigating their impact. Plant-soil feedbacks (PSF) between plant species and soil microbes are crucial for controlling plant abundance. It has been suggested that positive PSF could promote alien plant invasion but that negative PSF could help their control. However, a framework to predict which alien species will experience positive feedback, and under what conditions, is lacking.
My project will combine fundamental ecological theory, state-of-the-art knowledge of PSF, carefully designed experiments, and cutting-edge techniques in data analysis and high-throughput sequencing to gain a predictive understanding of the role of PSF in plant invasion. I will test the novel hypothesis that close relatedness between alien and native plant species will promote invasion when nutrients are limiting by allowing alien plants to integrate into native soil mutualistic networks, but prevent invasion under high nutrient conditions by increasing the susceptibility of alien plants to native soil pathogens. For alien and native plant species that vary in relatedness I will: 1) quantify the strength of PSF under differing nutrient conditions; 2) characterise mutualistic associations in the roots; and 3) characterise the soil microbial communities they cultivate. Moreover, I will develop an open access database of collated PSF data to facilitate further global collaborations.
Results will reveal how native plant communities and their local environment influence alien plant invasions. They will lay the foundation for important applied research on the impact of plant invasions on native PSF, and how PSF knowledge might be used in ecosystem restoration worldwide. By targeting EU Regulation 1143/2014 on Invasive Alien Species and Goal 15 of the UN Sustainable Development Goals, this research will contribute to applied outcomes for the EU and substantially raise my research profile within Europe and internationally.
Field of science
- /social sciences/other social sciences/social sciences interdisciplinary/sustainable development
- /natural sciences/computer and information sciences/data science/data analysis
Call for proposal
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