Major research areas:
Molecular assessment of pollination networks
Pollinator-plant associations are crucial for most terrestrial natural and agricultural systems as they maintain primary production, biodiversity, and ecosystem functions. It is eminent that different environments affect taxonomic and functional diversity and abundance of plants and bees, and by that also their interactions, fitness, nutrition of bees and pollination processes. We are interested in the ecological and evolutionary mechanisms that shape local pollination networks, and their dynamics, adaptability and resiliencies along natural environmental gradients and under local or global changes. Past and ongoing DFG and EU funded works are placed in Germany and South America, but we are nationally and globally involved in further collaborations. One major technological breakthrough for these works was that our lab pioneered, published and applies high-throughput sequencing (HTS) and related bioinformatics to assess pollination networks. This has been a great advance for us and the research community due to high-throughput scalability of data generation as well as comparability of data between work groups.
The Plant-Pollinator-Microbe triangle
Due to the tight mutualism between pollinators and flowers, this system is also highly interesting as a model to investigate and understand microbial transmissions between hosts, their tri-partite coevolution and potential arising conflicts. Microbes can contribute positively or negatively to host health, development and fecundity. Detrimental effects on pollinators and plants are caused by pathogens and competitors, while beneficial symbionts enhance nutrition, detoxification, spoilage inhibition and pathogen defense. Insights into the occurrence, evolution, and implications of these associations strongly contribute to our understanding of the current risk factors threatening pollinator and plant populations. In DFG funded projects, we found that pollinator microbiomes can exhibit a wide range from strongly conserved and stable associations to being rather environmentally driven. Life-history strategies like e.g. sociality can promote stability in bees, as well as diverse developmental factors, nest materials used and phylogenetic predisposition. We found that landscape, environment and agriculture can strongly impact wild bee microbiomes, particularly for solitary bee species. We also found that such environmentally driven microbiomes are susceptible to pathogens and microbiome shifts due to anthropogenic changes. Further, works included genomic study of relevant microbes and their consequences on bee health. For plants, we found that floral microbiome composition is strongly co-evolutionary driven by host phylogeny and their metabolome profiles, but yet dependent of the local microbial species pool and highly relevant for floral functions. We found that land-use intensification results in reduced diversity and homogenization of floral microbiomes due to reduced plant diversity and interactions with pollinators.
With increasing knowledge on hosts-microbiome interactions in the pollination system, the field now undergoes a paradigm shift: Microbe-host associations in pollination systems are not isolated, but highly intertwined with each other and with the pollination network itself. For example, we found that flower microbes affect pollinator visitation by changing attractiveness for specific pollinators. On the other hand pollination network structure influences which microbes are dispersed between pollinators via flowers. Investigating the resiliences of the three parties and spill-over of pathogens and beneficial microbes between species with respect to anthropogenic land-use changes is particularly intruiguing. We are currently at the very beginning of understanding these reciprocal effects in these multipartite networks, how land-use intensification modifies this complex system and the consequences of changes on host health.
Recent articles (more here):
Molecular and bioinformatic tools in Ecology and Evolution
A central aspect of our research is the computational analysis of high-throughput data, both experimentally determined in projects and such collected from other sources. In particular, the lab generates HTS data for assessment of pollination networks, microbiomes, phylogenies and genomes, which we map to and blend with complementary data from public databases (e.g. traits, maps, remote sensing). We also perform large-scaled modelling simulations and conceptual meta-analyses. We are challenged with large, overwhelming and heterogenous datasets; extracting biologically meaningful information requires the right tools and methods, but also awareness of major threats. We apply rigorous statistics and constantly expand the toolbox of analytical methods for data processing and pattern recognition with cutting-edge methods, e.g. classification and feature selection by machine learning. We are dedicated to software tool development for quantitative analytic processes, to create and connect public databases and to support transparent FAIR principles in data sciences.
Projects in other research areas with our involvement