Our research over the past years focussed on the use of HTS technology for ecological genomics. We managed to become on the world’s leading teams developing metabarcoding and metagenomic approaches for biodiversity research.
We seek not only information on how communities are composed but also how its members are interconnected and interdependent. The novelty in our approach is to seek this deep level of understanding within individual samples by utilizing modern genomics approaches, a goal that clearly needs to ensure that the tools to be used are fully understood and developed. Over the past few years we explored the feasibility of metabarcoding for large-scale bio-surveillance of terrestrial arthropods and evaluated the suitability of Malaise traps as standardized probes of a global arthropod biomonitoring network. We also demonstrated the applicability of the approach for freshwater invertebrate assessments. At the same time we attempted to improve several aspects of metabarcoding/metagenomic data analysis, such as taxon and haplotype assignment, sequence alignment, data processing, and joint species distribution modelling. All these activities and results set the stage for the next logical step in our endeavour to explore community surveys to audit biota across the entire tree of life. In addition to simply counting and registering we want to explore relationships between community members, better understand the functional competence of communities, and model responses to changes in the environment.
Here some selected projects:
The observation and quantification of change in ecosystems are fundamental tools for assessing the response of species communities to environmental alterations. Past studies have typically monitored the response of a few indicator species through repeated surveys of sites to measure impacts on the entire community, e.g. quantified by shifts in abundance or by variation in alpha and beta diversity. Although such studies can deliver a basic understanding of biodiversity, they fall short of providing the observational data needed to manage and protect it at larger scales. New computationally demanding ecosystem models have the potential to greatly enhance our capacity to predict community responses to change, but they demand more comprehensive spatial distribution information, creating the need for new approaches to gather and synthesize biodiversity data. Metabarcoding, metagenomics, and total RNA-seq are capable of generating the required comprehensive biodiversity data sets at species-level resolution.
This project teams up with 200 high school classrooms each year to provide critical information on the changing geographic distributions of plant-pollinator interactions across Canada, and be of considerable benefit to everyone as pollinator-dependent foods make up about a third of our diet. By combining state-of-the-art DNA barcoding of bees, and the pollen they carry, with distribution and climate change data, we will show how distributions of Canada’s bee species are changing along with climate. The project will also determine how pollination services shift across Canada, with impacts on food production and landscape management advice to improve vital species chances of persisting in agricultural landscapes and alleviating pollination deficits. This program will provide exciting, hands-on, technologically-savvy, and scientifically-relevant educational experiences for high school students to inspire the next generation to attend to, appreciate, and benefit from pollinators and pollination.
We don’t even know all species living on our farmlands. Only functional trait information in a landscape context will allow it to better target measures in a way they will be most likely to conserve beneficial insects, provide pest control and retain pollination services. For instance, molecular techniques provide an effective means of assessing trophic relationships, particularly in systems where these are difficult to observe with conventional nonmolecular methods. A major advantage of using DNA for gut content analysis is that it allows precise identification of species-specific predator–prey relationships. Moreover, apart from screening high numbers of consumers simultaneously, HTS-based approaches allow the analysis of concomitant predation on multiple prey species.