A model-based projection of historical state of a coastal ecosystem: relevance of phytoplankton stoichiometry
Kerimoglu, O.; Große, F.; Kreus, M.; Lenhart, H.-J.; van Beusekom, J.E.E. (2018). A model-based projection of historical state of a coastal ecosystem: relevance of phytoplankton stoichiometry. Sci. Total Environ. 639: 1311-1323. https://dx.doi.org/10.1016/j.scitotenv.2018.05.215
In: Science of the Total Environment. Elsevier: Amsterdam. ISSN 0048-9697; e-ISSN 1879-1026, meer
We employed a coupled physical-biogeochemical modelling framework for the reconstruction of the historic (H), pre-industrial state of a coastal system, the German Bight (southeastern North Sea), and we investigated its differences with the recent, control (C) state of the system. According to our findings: i) average winter concentrations of dissolved inorganic nitrogen and phosphorus (DIN and DIP) concentrations at the surface are similar to 70-90% and similar to 50-70% lower in the H state than in the C state within the nearshore waters, and differences gradually diminish towards off-shore waters; ii) differences in average growing season chlorophyll a (Chl) concentrations at the surface between the two states are mostly less than 50%; iii) in the off-shore areas, Chl concentrations in the deeper layers are affected less than in the surface layers; iv) reductions in phytoplankton carbon (C) biomass under the H state are weaker than those in Chl, due to the generally lower Chl: C ratios; v) in some areas the differences in growth rates between the two states are negligible, due to the compensation by lower light limitation under the H state, which in turn explains the lower Chl: C ratios; vi) zooplankton biomass, and hence the grazing pressure on phytoplankton is lower under the H state. This trophic decoupling is caused by the low nutritional quality (i.e., low N:C and P:C) of phytoplankton. These results call for increased attention to the relevance of the acclimation capacity and stoichiometric flexibility of phytoplankton for the prediction of their response to environmental change.
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