ProjectPredicting phytoremediation services through functional traits
Faced with more than 30 000 contaminated sites in Canada, phytoremediation represents an economic and ecological technique, using plants’ capacities and microorganisms to decontaminate soils. However, one of the main challenges in phytoremediation remains to identify the best candidates to perform key remediation services (e.i. soil decontamination, metal accumulation in aboveground tissues, etc). The selection of species in phytoremediation often requires preliminary screening and field experiments, which are long, expensive and non-generalizable processes. To improve plant selection, we aim to develop a framework to predict phytoremediation services of many plant species based on their functional traits. Indeed, functional ecology approaches base on functional traits can predict other ecosystem services, such as litter decomposition or carbon sequestration. However, for these widely measured traits to be useful and generalizable in phytoremediation, we need to test for correlations between these and phytoremediation services for hundreds of plant species. To do so, we will perform a systematic review of the primary literature in phytoremediation and correlate it to the international database TRY, counting thousands of species’ traits. The phytoremediation capacities of each species will be correlated to their mean traits through linear mixed models. This should identify which and how traits can predict phytoremediation services to further evaluate the potential of thousands of other species. Furthermore, we will test this application with well know and efficient genus in phytoremediation, Salix. We will evaluate the influence of willow diversity on this predictive framework since diversity is often used in phytoremediation for its many advantages. Indeed, plant interaction might modify plant traits and phytoremediation services. A greenhouse experiment with five willows species will determine if and how species interactions need to be taken into account in the predictive framework we will develop. This research project aims to improve plant selection and theoretical knowledge in phytoremediation to increase the efficiency of this technology for soil remediation treatments.