University of California Santa Barbara, United States
Abstract: Giant kelp, Macrocystis pyrifera, is an important and ubiquitous foundation species found in temperate coastal regions worldwide. Giant kelp forests are highly dynamic systems, with variable seasonal and annual fluctuations that make predicting biomass challenging. To improve our understanding of the spatiotemporal dynamics of giant kelp, we have constructed an agent-based demographic model at a specific kelp forest site off Santa Barbara, California. The model tracks individual giant kelp plants and their fronds on a daily timescale and is parameterized with environmental forcings and demographic processes that are roughly consistent with observations from our site. While the model captures the seasonal cycles of total and canopy biomass consistent with both observed in situ and remotely sensed data, the model tends to overestimate biomass. To address this issue, we investigate life history processes of giant kelp that were not as well-constrained in our model; specifically, the recruitment of giant kelp individuals, frond initiation, frond elongation, and frond and individual biomass losses. To better constrain these processes, we leverage the availability of datasets collected and curated by the Santa Barbara Coastal Long-Term Ecological Research (SBC LTER). Using in situ frond growth data, we can better estimate juvenile and adult kelp frond elongation rates that are relevant for our kelp forest site of interest. Additionally, we have developed a multiple linear regression model that is able to predict giant kelp recruitment at other sites well. The addition of these relationships of the demographic transitions of giant kelp into our model should improve the model’s ability to predict giant kelp biomass and production. Our model will assist with monitoring and management of these important ecosystems and improve our understanding of how giant kelp forests respond to perturbations and climate change forcings.