PhD defence: Ecological competence in the rhizosphere - Genetic determinants, physiological plasticity, and ecological inference of root-associated bacteria

PLEASE NOTE: The candidate gives a layman's talk, therefore the livestream will start fifteen minutes earlier.

Modern agriculture increasingly requires biological strategies to enhance crop productivity while reducing reliance on synthetic fertilizers and pesticides. Microbial bioinoculants, beneficial microorganisms applied to plants or soils, offer a promising alternative. Yet their performance in natural soils is often inconsistent.

This variability reflects a fundamental reality: bioinoculation is an ecological process.Introduced microbes must establish, persist, and function within soils that are already densely populated by resident communities and shaped by fluctuating resources and active host regulation. Bioinoculant success therefore depends not only on intrinsic genetic traits, but also on physiological plasticity and ecological compatibility.

This thesis investigates bioinoculant performance as an ecological invasion problem. It shows that microbial behaviour is highly context-dependent. Transcriptional analyses reveal that a plant growth-promoting bacterium can adopt distinct physiological states depending on host phenotype, with beneficial effects emerging only under specific host–microbe configurations. Comparative genomics further identifies the conserved iol gene cluster in Pseudomonas as a determinant of rhizosphere competence, linking inositol metabolism to motility, siderophore production, and competitive fitness in soil.

Finally, a new computational framework, ASVNet, is introduced to improve ecological inference from 16S rRNA sequencing data by integrating sequence similarity with ecological co-abundance patterns, enabling more biologically meaningful reconstruction of microbial communities. 

Together, these findings demonstrate that bioinoculant efficacy is an emergent property of genetic potential, physiological responsiveness, and ecological context, providing a foundation for more predictive microbiome-based solutions in sustainable agriculture.