MCA: New approaches to studying mast-seeding dynamics

Understanding the patterns and drivers of plant reproduction is a long-standing challenge in ecology. The number of seeds produced by perennial plants over time shows incredible variation across species and locations, from consistent levels of annual reproduction to boom and bust years of seed production exhibiting extremely high temporal variability, known as ‘mast seeding’. Linking relationships among plant reproduction, environmental drivers, and species interactions has primarily focused on aboveground factors such as pollinators and weather conditions; while belowground plant traits, such as root attributes and symbiotic relationships with mycorrhizal fungal networks, have been largely ignored. The purpose of this project is to bring together the aboveground phenomenon of mast seeding, with belowground traits and belowground species interactions using multiple global databases and long-term field data. This study has the potential to reveal new drivers of annual fluctuations in reproduction in natural and managed forestry systems. In addition to exploring the impact of mycorrhizae on reproductive dynamics, this project may inform forestry practices. Many forestry trees are inoculated with mycorrhizal fungi, and this work could allow foresters to develop more efficient tree regeneration techniques. The project will provide a mid-career scientist protected time and interdisciplinary training. Training opportunities for an undergraduate student and a postdoctoral researcher from diverse backgrounds will also be enable by this interdisciplinary study. This work will use five global databases on mast seeding, and mycorrhizal fungal associations and plant-traits (e.g., TRY, FRED, MycoBD, FungalRoot), with data from a ten-year field study of mast seeding. The investigator has expertise in mast seeding and will work with a collaborative partner with expertise in plant-soil-microbe interactions and structural equation modeling. The project will test hypotheses about how belowground plant traits and mycorrhizal associations influence the range of mast-seeding conditions (e.g., temporal variability, time-lags) observed globally across taxa, over a range of environmental conditions. Multivariate statistical approaches will be applied through structural equation modelling to generate new insight into drivers of the patterns of mast seeding. An undergraduate student will assist with exploring databases, and a postdoctoral researcher will work with the investigator to modernize lab workflows using GitHub and creating Shiny Apps. This work will facilitate new conceptual and analytical possibilities to the field of mast seeding and to plant reproduction studies more broadly. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.