Quantifying Nutrient Trade in the Arbuscular Mycorrhizal Symbiosis Under Extreme Weather Events Using Quantum-Dot Tagged Phosphorus - van ’t Padje, Anouk; Bonfante, Paola; Ciampi, Luisa Tartaglia; Kiers, E. Toby (Frontiers in Ecology and Evolution, 2021)
Given the current trends in climate change, extreme weather events are expected to increase in strength and frequency. Such events can impact species survival and species interactions. One of the most ubiquitous symbioses on earth is the nutrient exchange partnership between arbuscular mycorrhizal fungi and their host plants. While past work has shown that mycorrhizal fungi can help alleviate stress, it is unknown how phosphorus transfer from plants to fungi is affected by extreme weather events, such as flooding and heat waves. To test this response, we grew Medicaco truncatula host plants with or without mycorrhizal fungi and then exposed them to extreme weather treatments: increasing soil temperature by 12 °C, or by flooding the plant roots for 7 days. We measured plant and fungal performance, and quantified phosphorus (P) uptake before and after extreme weather treatments using a technique in which we tagged apatite, a form of rock phosphorus, with fluorescing quantum-dots (QDs) nanoparticles. We then measured fluorescence in root and shoot tissue at harvest. We found that plants and arbuscular mycorrhizal fungi were affected by soil flooding, with plant survival, fungal colonization and QD-apatite uptake decreasing under flooded conditions. We did not see these negative effects in the heat treatment. While the presence of arbuscular mycorrhizal fungi affected plant biomass allocation, leading to an increase in shoot biomass, the symbiosis did not increase plant survival, total biomass nor QD uptake in either treatment. More generally, we found host tissue contained roughly 80% more QD-apatite from the pre-treatment compared to the post-treatment nutrient injection. Future studies should focus on various plant-fungal combinations to create databases on which predictive models to extreme weather events can be constructed.