ABSTRACT

Heterotrophic protists are important consumers of phytoplankton biomass and productivity in marine microbial food. Despite considerable effort both in the laboratory and the field, oceanographers lack a mechanistic and predictive understanding of the foraging ecology of these predators. This lack of understanding of the population dynamics and ecology of marine planktonic food webs is rooted in a mismatch in investigative scales: uptake of nutrients and prey, that drive plankton growth and grazing rates, occur at the microscopic, individual level, whereas their ecological significance and most of our investigations occur at much larger scales. However, processes at these different scales are tightly linked because there is a causative relationship and feedback between predators, microscopic movements and behavioral responses and the ultimate, macroscopic predator population distribution and encounters of environmentally relevant variables. Using recently developed theoretical and empirical methods that overcome this mismatch in scales.

In this study the investigator will quantify planktonic predator-prey interactions in the laboratory and embed these empirical observations in a theoretically-motivated model that predicts predator distributions and consumption rates. Predator movements and population distributions will be simultaneously quantified, ranging in observation scales from micrometers and seconds to meters and hours. Coverage of this vast range of scales provides the opportunity to link processes that are separated over several orders of magnitude. Therefore, the microscopic causes of macroscopic processes can be quantified. These automated video observations will be collected within large (liters) observation volumes in which the distribution of prey is stable and manipulated to include gradients and patches. Spatially structured prey distributions will be used to test the importance of prey type, abundance and distribution on the movement behaviors and grazing rates of a taxonomically and functionally representative set of heterotrophic protists. The empirical results will be embedded in a theoretically-motivated encounter rate model that predicts predator population distributions and grazing rates. These model simulations aim to identify key factors necessary to characterize predator foraging behaviors and success. Predator grazing rates will be quantified through several, independent approaches. Comparison of these redundant but independent measures provides the opportunity to identify key factors driving the process. Tight integration of laboratory and theoretical work will improve model assumptions, validate model predictions and most importantly, provide a tool that parameterizes complex biological processes. With this theoretical framework, this research will develop a mechanistic understanding of the factors driving plankton distributions and population dynamics. Ultimately, a mechanistic understanding of protist foraging behaviors is key to predicting the ecological role of these phytoplankton predators and thus, the population dynamics of planktonic food webs.

This project will provide training for a graduate student and undergraduate interns to gain quantitative expertise through the integration of theoretical, empirical and mathematical approaches. Given the opportunity, The PI will focus recruitment efforts of both graduate and undergraduate students on minority candidates. Her continued involvement and demonstrated track record with minority serving programs will be an asset in that endeavor. For the oceanographic community, this work demonstrates a novel approach to deciphering processes in complex biological systems. Through integration of empirical data with a well established theoretical framework, the mechanistic linkages between the driving microscopic processes and their ecologically relevant, macroscopic outcomes will be established. The proposed work will provide the necessary parameters to include predator behaviors in ocean ecosystem models.

Please report errors in award information by writing to: awardsearch@nsf.gov.