As an important fuel type in California ecosystems, grasses play a critical role in determining fire behavior and fire effects on coexisting plants and ecosystems. Current vegetation demographic models (e.g. Functional Assembled Terrestrial Ecosystem Simulator, FATES: https://github.com/NGEET/fates/wiki) have included both C4 and C3 grasses as two different plant functional types that enable us to examine if species specific difference could alter the outcomes of plant-climate-fire interactions. To accurately represent plant-environment (including both abiotic and biotic factors) interactions, understanding in how different grass species allocate their carbon to different plant organs that include leaf, stem, reproductive structures, and belowground organs is important. However, data on grass allometry is rare in current literatures. We thus aim to collect data on biomass allocation for 12 different grass species that are dominant in California open-canopy ecosystems in order to improve grass allometry simulations in FATES.
To do so, we will grow 12 different grass species from seeds in greenhouse at University of California, Berkeley. We will randomly assign grass seedings to different treatments. At the end of the growing period, plants will be harvested and the biomass separated into leaf, stem, reproductive structure if present, and root prior to taking measurements of biomass and plant size.
With biomass and plant size data, we will develop allometry equations for each grass species. We then will use derived grass allometry parameters to perform single point, C3 or C4 grassland simulations in FATES.
The undergraduate research assistants will help transplant the plants, take measurements on biomass and plant size, and participate and learn how to process data using languages such as R or Python. Students with programming experience who are interested in modelling can learn now to run simulations using FATES.
Undergraduate students with background in plant ecology or biology, basic understanding in plant growth, and highly organized are preferred. Experiences with R and Excel are desired but not mandatory.