Mapping phyllosphere microbial communities of temperate European forests using environmental DNA and remote sensing

In this thesis, I set out to investigate the phyllosphere microbial communities of temperate European forests, focusing on both bacterial and fungal assemblages, their drivers, and their potential roles in forest ecosystem functioning. Drawing on environmental DNA (eDNA) metabarcoding, integrated with cutting-edge remote sensing tools, and coupled with measurements of host and environmental variables, , I aimed to bridge gaps in our understanding of microbial diversity patterns and their broader ecological significance.
From the outset (Chapter 2), my research demonstrated that European forest canopies harbor diverse bacterial communities that are not only shaped by host species identity but also vary within the same host species and along gradients such as elevation. Demonstrating the close associations phyllosphere bacteria form with their host trees and the influence of the environment.
Chapter 3 expanded this perspective to fungal communities (i.e. the mycobiome) in the canopy. I found that elevation was again a key driver of fungal composition and diversity in both beech and spruce stands. Moreover, stand-level canopy water content strongly influenced fungal communities, linking host physiological status to the distribution of fungal taxa. This establishes a link between canopy traits, water balance, and fungal community assembly. These findings parallel those in the bacterial domain but highlight some differences as well. While bacterial communities were strongly linked to host species identity, the fungal assemblages also showed strong links to environmental gradients like canopy water content, a common stress indicator.
Chapter 4 focused on plant-pathogenic fungi, a subset of the canopy mycobiome with direct implications for forest health. Using host tree traits commonly used indicators of tree vitality (e.g., canopy water and chlorophyll content), I predicted patterns in these pathogenic communities. Lower canopy water content predicted higher percentages of plantpathogenic ASVs and, at times, reduced pathogen diversity—signaling that stressed trees may be more susceptible to a narrower but potentially more harmful group of pathogens. This work supports the notion that pathogen pressure is intimately connected to tree condition: stressed, water-limited trees might be easier targets for fungal pathogens. Such relationships have direct management implications. Monitoring canopy water content (via direct measurement or remote sensing) could provide early indicators of elevated pathogen risk, enabling forest managers to target interventions or conduct closer monitoring under specific stress conditions.
In Chapter 5, I moved from local-scale analyses to landscape-level assessments. By integrating eDNA-inferred microbial data with satellite remote sensing (DESIS hyperspectral imagery), I showed that spatial patterns in relative abundance and diversity of canopy fungal plantpathogen communities could be modeled and mapped with high accuracy. Key vegetation indices (e.g., those reflecting canopy water, chlorophyll, and structure) emerged as effective predictors of pathogen abundance and diversity. This step was transformative: it enabled the creation of spatial redictions at 30-meter resolution, effectively scaling up local microbial surveys to entire landscapes.
The successful coupling of molecular data with remote sensing highlights the feasibility of a new paradigm: using large-scale earth observation data to predict and monitor microbial community patterns. This approach not only addresses the Wallacean shortfall (gaps in species distribution knowledge) by extending biodiversity survey data into continuous spatial maps but also holds potential for the development of an early warning tool, laying the groundwork for the effective monitoring of environmental microbial communities.