Biology subjects

Fussi, B.

Publications and source records attributed to Fussi, B..

3 recordsLinked to original sources

bioRxiv2025

Predicting forest tree leaf phenology under climate change using satellite monitoring and population-based GWAS

Leaf phenology, a critical determinant of plant fitness and ecosystem function, is undergoing rapid shifts due to climate change, yet its complex genetic and environmental drivers remain incompletely understood. Understanding the genetic basis of phenological adaptation is crucial for forecasting forest responses to a changing climate. Here, we integrate multi-year satellite-derived phenology from 46 Fagus sylvatica (European beech) populations across Germany with a population-based genome-wide association study to dissect the environmental and genetic drivers of leaf-out day (LOD) and leaf shedding day (LSD). We show that environmental factors, particularly temperature forcing and water availability, are the primary drivers of LOD variation, while LSD is influenced by a more complex suite of climatic cues. Our genomic analysis identifies candidate genes associated with LOD and LSD, primarily linked to circadian rhythms and dormancy pathways, respectively. Furthermore, genomic prediction models incorporating these loci accurately reconstruct past phenological dynamics, providing a powerful framework to forecast forest vulnerability and adaptation to future climate change.

evolutionary biology↗

bioRxiv2023

Multiple, single trait GWAS and supervised machine learning reveal the genetic architecture of Fraxinus excelsior tolerance to ash dieback in Europe

Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at epidemic levels throughout Europe. Previous studies have found significant genetic variation among clones in ash dieback susceptibility and that host phenology, such as autumn yellowing, is correlated with susceptibility of ash trees to H. fraxineus; however, the genomic basis of ash dieback tolerance in F. excelsior remains poorly understood. Here, we integrate quantitative genetics and genome-wide association analyses with machine learning to reveal the genetic architecture of ash dieback tolerance and its relationship to phenological traits in F. excelsior populations in six European countries (Austria, Denmark, Germany, Ireland, Lithuania, Sweden). We use whole-genome sequencing of 486 F. excelsior genotypes to confirm the genotypic correlation between crown damage caused by ash dieback and intensity of autumn leaf yellowing within multiple sampling sites. Although, our results suggest that the examined traits are polygenic, a relatively small number of single nucleotide polymorphisms (SNPs) explained a large proportion of the variation in both disease tolerance and autumn leaf yellowing. We could explain up to 63% (based on 9155 unlinked SNPs) of variation in individual response to ash dieback crown damage and up to 72% (based on 3740 unlinked SNPs) of variation in autumn yellowing. We identified eight SNPs encoding non-synonymous substitutions, of which those with the highest predictive power were located within genes related to plant defence (pattern triggered immunity, pathogen detection) and phenology (regulation of flowering and seed maturation, auxin transport). Overall, our results provide insights of a multifaceted defence response, according to which a combination of direct defence mechanisms and phenological avoidance of pathogen spread constitute tolerance to ash dieback.

plant biology↗

bioRxiv2023

Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing

Current climate change species response models usually not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree European beech (Fagus sylvatica L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016-2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.

evolutionary biology↗