This project will develop innovative ways to link reliable field estimates of organismal abundance with evidence of biodiversity obtained through metabarcoding, eDNA and image analysis of bulk samples.
One of the most pressing challenges in monitoring ecosystem services across vast agricultural landscapes is how to rapidly gather reliable data on organismal diversity and relative abundance over space and time. This information is essential in discriminating between leading models of species diversity over time and dynamics as well as monitoring how biodiversity varies in relation to natural processes and human disturbance. The overarching goal of this ecosystem genomics project is to develop innovative ways to link reliable field estimates of organismal community richness and abundance with indirect evidence of biodiversity obtained through species identification using DNA.
This project aims to develop standardized techniques for precisely comparing biodiversity across space. The project will also culminate in tools for impact assessment and early warning of insect pest outbreaks. The team will show how such measures vary seasonally and across years. Finally, the project will demonstrate the feasibility of an automated genomic biodiversity monitoring tool capable of detecting subtle changes in biodiversity and the natural consequence of ecosystem services over time.
By gathering these data across 30 sites in southern Ontario that span the gradient from intense and highly productive corn-soybean farming to conservation areas and Alternative Land Use Services (ALUS) Canada farms with varying amounts natural restoration of marginal lands, our work will provide one of the deepest datasets available on the full suite of factors influencing arthropod and aquatic insect biodiversity in relation to farming practices, climatic variation, physical features, and landscape heterogeneity.
This project is a continuation of a project called ‘Metabarcode analysis of terrestrial biodiversity’, which provided the proof-of-concept for this project. This project will develop innovative ways to link reliable field estimates of organismal abundance with evidence of biodiversity obtained through metabarcoding, eDNA and image analysis of bulk samples.
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