Development of a robust marine ecosystem model to predict the role of iron in biogeochemical cycles: A comparison of results for iron-replete and iron-limited areas, and the SOIREE iron-enrichment experiment
Abstract
A new mixed layer multi-nutrient ecosystem model, incorporating diatoms, non-diatoms and zooplankton, is described that models the role of iron in marine biogeochemical cycles. The internal cell biochemistry of the phytoplankton is modelled using the mechanistic model of Flynn [2001. A mechanistic model for describing dynamic multi-nutrient, light, temperature interactions in phytoplankton. Journal of Plankton Research 23, 977-997] in which the internal cell concentrations of chlorophyll, nitrogen, silica, and iron are all dynamic variables that respond to external nutrient concentrations and light levels. Iron stress in phytoplankton feeds back into chlorophyll synthesis and changes in photosynthetic unit (PSU) size, thereby reducing their growth rate. Because diatom silicon metabolism is inextricably linked with cell division, diatom population density (cell m−3) is modelled as well as C biomass. An optimisation technique was used to fit the model to three time-series datasets at Biotrans (47°N, 20°W) and Kerfix (50°40′S, 68°25′E) and the observations for the Southern Ocean Iron-Release Experiment (SOIREE) iron-enrichment experiment (61°S, 140°E). The model gives realistic simulations of the annual cycles of nutrients, phytoplankton, and primary production at Biotrans and Kerfix and can also accurately simulate an iron fertilisation experiment. Specifically, the model predicts the high values of diatom Si:N and Si:C ratios observed in areas where iron is a limiting factor on algal growth. In addition, the model results at Kerfix confirm previous suggestions that underwater light levels have a more limiting effect on phytoplankton growth than iron supply. The model is also used to calculate C budgets and C and Si export from the mixed layer. The implications of these results for developing biogeochemical models incorporating the role of iron are discussed.