NSF Award Abstract:
Macroalgae, commonly known as seaweeds, are among the most important and diverse primary producers in coastal marine ecosystems. Macroalgae range in form from >30-m high giant kelp to mm-scale filamentous turfs. They shape aquatic ecosystems worldwide, providing food and habitat to legions of species including many important to coastal fisheries and recreation. At the same time, the diversity of macroalgal species and forms makes it challenging to understand the processes affecting their abundance and distribution. Past efforts to define functional groups based on gross morphology – for example, finely branching vs. large-bladed or erect vs. prostrate - have had some success in interpreting ecological patterns, yet key information is lost and quantitative tests of predictions often fail with such broad groupings. Alternatively, trait-based modeling is a promising approach to incorporate more complexity and define relationships between quantifiable characteristics, such as blade mass per area, and the distribution and abundance of species. This project combines field, laboratory, and modeling components to measure macroalgal traits and validate models of species distribution in the coastal ocean. The outcome will be a framework for macroalgal communities that can be used to predict how the distribution of groups and species change across space and time. The project will provide training for undergraduate and graduate students in field and laboratory research. Educational outreach efforts will leverage collaboration with the Santa Barbara Coastal Long Term Ecological Research project to reach K-12 students and teachers, including urban Los Angeles. Other outreach includes interaction with the media, the Santa Barbara Sea Center, and agencies such as the Santa Barbara Channel National Marine Sanctuary and Channel Islands National Park.
Community ecology is often mired in case histories explaining distribution and abundance of select species, yet we need a more holistic understanding of the forces driving marine ecosystems to predict change due to climate and human impacts. In temperate marine ecosystems, macroalgae serve as the base of food webs and provide habitat, but we lack a framework for understanding macroalgal ecology beyond decades-old gross morphological generalizations. Trait-based modeling is a promising approach to incorporate complexity and define relationships between quantifiable traits and the distribution and abundance of species. The investigators are combining field and laboratory measurements with modeling to assess how well the depth distribution of common macroalgal species in southern California is predictable based on measured functional traits. The project objectives include: 1) define and measure key functional traits within and across key species of macroalgae; 2) create a trait-based simulation model for the assemblage of nearshore macroalgal communities as a function of depth; 3) test the model’s predictions by quantifying the depth distribution of macroalgal species, grazers, and environmental parameters in the Santa Barbara Channel; and 4) use individual-based models to test mechanistic hypotheses for interspecific competition. The investigators are accomplishing the first objective by measuring a suite of functional traits related to performance at multiple sites (and depths) for at least 20 species of common macroalgae in the Santa Barbara Channel, including mass-specific photosynthetic rate, nitrogen uptake rate, specific blade area, canopy height, crown area, blade thickness, mechanical and material properties, lifespan, and grazing resistance. For objective 2 the investigators are creating an ecosystem model that represents a large number of potentially viable macroalgal “species” with stochastically determined physiological characteristics. Initialized species will interact with one another and their environment, evolving into an ecosystem where community structure and diversity are not imposed, but are emergent properties. The third objective involves an extensive field program to quantify the distribution of all identifiable macroalgal species across a depth gradient from 0 m to the terminus of hard substrate (up to 40m depth) at six sites in the Santa Barbara Channel. Physical properties (light, temperature, wave forces) are also measured along each cross-depth transect. In the fourth objective, the investigators are using results to improve their model, adding interspecific competition in an individual-based framework.
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.
Lead Principal Investigator: Robert Miller
University of California-Santa Barbara (UCSB-MSI)
Principal Investigator: Brian Gaylord
University of California-Davis BML (UC Davis-BML)
Co-Principal Investigator: Holly V. Moeller
University of California-Santa Barbara (UCSB)
Contact: Robert Miller
University of California-Santa Barbara (UCSB-MSI)
DMP_OCE-2146924_2146925.pdf (67.90 KB)
07/11/2023