Phenotypic variation from waterlogging in multiple perennial ryegrass varieties under climate change conditions - Frisk, Carl A.; Xistris-Songpanya, Georgianna; Osborne, Matthieu; Biswas, Yastika; Melzer, Rainer; Yearsley, Jon M. (Frontiers in Plant Science, 2022)
Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four common varieties (one diploid and three tetraploid) grown in atmospherically controlled growth chambers during two months of peak growth. The climate treatments compare ambient climatological conditions in North-western Europe to the RCP8.5 climate change scenario in 2050 (+2°C and 550ppm CO2). At the end of each month multiple phenotypic plant measurements were made, the plant harvested and then allowed to grow back. Using image analysis and PCA methodologies, we assessed how multiple predictors (phenotypic, environmental, genotypic and temporal) influenced overall plant performance, productivity and phenotypic responses. Long-term waterlogging was found to reduce leaf-color intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than the diploid one. The climate change treatment was found to reduce color intensities further. Flooding was found to reduce plant productivity via reductions in color pigments and root proliferation. These effects will have negative consequences for global food security from facing extreme weather events and flooding. Our imaging analysis approach to estimate effects of waterlogging can be adapted as plant health diagnostics tools via remote sensing and drone-technology.