Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems

Abstract

We present an analysis of the global impact of microplanktonic grazers on marine phytoplankton and its implications for remineralization processes in the microbial community. The data were obtained by an extensive literature search that yielded 788 paired rate estimates of autotrophic growth () and microzooplankton grazing (m) from dilution experiments. From studies in which phytoplankton standing stock was measured in terms of carbon equivalents, we show that the production estimate from dilution experiments is a reasonable proxy (r ϭ 0.89) for production determined by the standard 14 C method. The ratio m : , the proportion of primary production (PP) consumed by micrograzers, shows that microzooplankton consumption is the main source of phytoplankton mortality in the oceans, accounting for 67% of phytoplankton daily growth for the full data set. This ratio varies modestly among various marine habitats and regions, with data averages ranging from 60% for coastal and estuarine environments to 70% for the open oceans, and from ϳ59% for temperate-subpolar and polar systems to 75% for tropical-subtropical regions. Given estimates for the metabolic requirements of micrograzers and assuming they consume most bacterial production, regionally averaged estimates of the protistan respiration are 35-43% of daily PP for the first level of consumer or 49-59% of PP for three trophic transfers. The estimated contributions of microbial grazers to total community respiration are of the same magnitude as bacterial respiration. Consequently, potential ecosystem differences in micrograzer activity or trophic structure are a large uncertainty for biogeochemical models that seek to predict the microbial community role in carbon cycling from bacterial parameters alone.

Phytoplankton community dynamics during late spring coccolithophore blooms at the continental margin of the Celtic Sea (North East Atlantic, 2006–2008)

“…Especially, coccolithophores are a prominent feature of the late spring bloom, and this has been attributed to their tolerance for high irradiances, lower nutrient requirements and/or ability to utilise organic nitrogen or phosphorus sources (Palenik and Henson, 1997;Leblanc et al, 2009 and references therein). Bloom termination follows when nutrient depletion depresses primary productivity and grazing and viral control catch up with algal growth (Brussaard, 2004;Calbet and Landry, 2004;Behrenfeld, 2010). In reality, this general NE Atlantic spring bloom scenario can be more or less scrambled due to local weather conditions and physical phenomena (Ji et al, 2010), both in the open ocean, where movements of eddies and other water masses can reset succession events (Smythe-Wright et al, 2010), and along continental margins, where vertical mixing resulting from internal tides can bring nutrient-rich deeper water into the euphotic zone (Sharples et al, 2007).…”

Role of krill versus bottom-up factors in controlling phytoplankton biomass in the northern Antarctic waters of South Georgia

“…In high latitude environments as elsewhere in the world ocean, protozoans tend to be the main grazers of phytoplankton (Calbet & Landry 2004). This also holds generally in the Southern Ocean, where the grazing effect of metazoans is often found to be small (e.g.…”

“…Boyd et al 2007). Smith & Lancelot (2004) argued further that bottom-up control was strongest for the large, bloom-forming diatoms, since their large size offered a refuge from predation from the protozoans that generally dominate the grazer assemblage (Calbet & Landry 2004). In contrast, the meso-and macroplankton that can deal with the large or chain-forming diatoms have longer generation times, and thus cannot increase their populations fast enough to keep a bloom in check (Smetacek et al 2004).…”

Analysis of phytoplankton distribution and community structure in the German Bight with respect to the different size classes

“…This needs time series data where the development of both parameters can be tracked over a longer period, or modeling approaches (e.g., Baretta et al, 1995). Of course, also other parameters like light availability (Loebl et al, 2009), zooplankton grazing (e.g., Calbet and Landry, 2004), or degradation by viruses (Brussaard, 2004;Rhodes et al, 2008) influence phytoplankton biomass development and have to be taken into account.…”