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Readme.md | 2019-01-15 | 2.2 kB | |
sns_vir_mu0.nc | 2019-01-15 | 2.9 GB | |
sns_zm_fa_inf0.nc | 2019-01-15 | 2.9 GB | |
snsRefResults.nc | 2019-01-15 | 4.4 GB | |
Totals: 4 Items | 10.1 GB | 0 |
Physics or biology? Persistent chlorophyll accumulation in a shallow coastal sea explained by pathogens and carnivorous grazing
Kai W. Wirtz, Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
Abstract
One of the most striking patterns at the land–ocean interface is the massive increase of chlorophyll-a (CHL) from continental shelves towards the coast, a phenomenon that isclassically linked to physical features. Here I propose that the coastal–offshore CHL gradient in a shallow sea has biological origins related to phytoplankton mortality thatare neglected in state-of-the-art biogeochemical models. I integrate a trait-based ecosystem model into a modular coupling framework that is applied to the southern North Sea (SNS). The coupled model very well reproduces daily, seasonal and inter-annual (2000-2014) dynamics and meso-scale patterns in macronutrients,zooplankton biomass, and CHL as observedin situand by remote sensors. Numerical experiments reveal that coast–offshore CHL gradients may predominantly arise from atrophic effect as resolved by an increase in carnivorous grazing towards shallow waters. This carnivory gradient reflects higher near-coast abundance of juvenile fish and benthicfilter feeders. Furthermore, the temporal evolution of CHL can be much affected by viral infection as a fast-responding loss process at intermediate to high phytoplanktonconcentrations. Viral control in the model also prevents excessive and unrealistic blooms during late spring. Herbivores as often only ecological factor considered for explaining the spatio-temporal phytoplankton distribution are in this study supplemented by pathogens as well as pelagic and benthic carnivores as powerful agents, which are barelyrepresented in current modeling but can mediate physical drivers of coastal ecosystems
Files contained in this dataset and .md5 sum
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ece4b1d147551b50da5f72e6672bc154 /h/ksedata02/data/model/MAECS_RefRun/sns_vir_mu0.nc
d5d5b26b95944f24d83787ef8f7966ef /h/ksedata02/data/model/MAECS_RefRun/sns_zm_fa_inf0.nc