Oceanic heterotrophic bacterial nutrition by semilabile DOM as revealed by data assimilative modeling
Virginia Institute of Marine Science
Aquatic Microbial Ecology
Previous studies have focused on the role of labile dissolved organic matter (DOM) (defined as turnover time of similar to 1 d) in supporting heterotrophic bacterial production, but have mostly neglected semilabile DOM (defined as turnover time of similar to 100 to 1000 d) as a potential substrate for heterotrophic bacterial growth. To test the hypothesis that semilabile DOM supports substantial amounts of heterotrophic bacterial production in the open ocean, we constructed a 1-dimensional epipelagic ecosystem model and applied it to 3 open ocean sites: the Arabian Sea, Equatorial Pacific and Station ALOHA in the North Pacific Subtropical Gyre. The model tracks carbon, nitrogen and phosphorus with flexible stoichiometry. This study used a large number of observations, including measurements of heterotrophic bacterial production rates and standing stocks, and DOM concentration data, to rigorously test and constrain model output. Data assimilation was successfully applied to optimize the model parameters and resulted in simultaneous representation of observed nitrate, phosphate, phytoplankton and zooplankton biomass, primary production, heterotrophic bacterial biomass and production, DOM, and suspended and sinking particulate organic matter. Across the 3 ocean ecosystems examined, the data assimilation suggests semilabile DOM may support 17 to 40% of heterotrophic bacterial carbon demand. In an experiment where bacteria only utilize labile DOM, and with more of the DOM production assigned to labile DOM, the model poorly represented the observations. These results suggest that semilabile DOM may play an important role in sustaining heterotrophic bacterial growth in diverse regions of the open ocean.
Luo, YW; Friedrichs, M. A.M.; Doney, SC; Church, MJ; and Ducklow, HW, Oceanic heterotrophic bacterial nutrition by semilabile DOM as revealed by data assimilative modeling (2010). Aquatic Microbial Ecology, 60(3), 273-287.