One of the aquatic systems that have been changing with the climate change is the Baltic Sea. Climate changing predictions indicate that increased rainfall in northerly regions will result in a further elevation of land run off. Consequently, compounds including organic matter and inorganic nutrients (C, N and P) will be transferred to the sea. These will be incorporated differently into the food web, thus influencing food web length, efficiency and function.
During early summer 2012 we experimentally studied how the river inflow from two different Baltic Sea Basins (the Baltic Proper – Daugava river and the Bothnian Sea - Öre River) with different stoichiometric (ratio C:N:P) ratio can affect marine food web efficiency under a climate change scenario.
Riverine inflow was simulated by the daily addition of natural soil extract to indoor mesocosms, resulting in a 50% increase in carbon. These mesocosms contained a natural food web from the Bothnian Sea (including bacteria, phytoplankton and zooplankton) and fish, as highest trophic level. Four different treatments were created – two controls, one from each river, without soil addition (but equivalent N and P as their respective soil treatment) and two treatments with soil addition representative of each river.
Results/Conclusions
Our preliminary data showed that mesocosms that received the highest soil dosage (Daugava) were characterized by high light inhibition, high concentration of humic substances, low primary production, decreased abundances of cladocerans, increased abundances of rotifers and had poor fish growth. Based on an estimate of 30% gross growth efficiency our FWE calculations suggest that a five level food chain is present in most treatments. The higher FWE in the Daugava control (especially compared to the low FWE in the Daugava treatment) would however suggest a possible 4 trophic levels food chain and thus a more efficient energy and carbon transfer in this system. Understanding changes in food web efficiency and food web structure will be vital in understanding the resilience of systems under future conditions.