An Ecopath model of the Baltic Sea food web
Susa NiiranenFinnish Institute of Marine Research
31st November 2007Ecem’07, Trieste
Photos: ww.fimr.fi, www.sahfos.ac.uk, www2.ecology.su.se/dbbm/images/Saduria%20KL.jpg, www.wildinbritain.co.uk, www.fmnh.helsinki.fi
Background
Aim: To create food web interaction input (sensitivities) for a
Bayesian policy tool to be implemented on the Gulf of Finland (Evagulf-project)
The response of a food web to changes in nutrient concentrations as a main focus
Methods: Food web was constructed using the Ecopath-software and its
extensions: Ecosim (temporal dynamics) Ecospace (temporal and spatial dynamics)
Baltic Sea
Characteristics
Brackish water Semi-enclosed area A wide range of physico- chemical conditions
- Salinity- Temperature- Anoxia- Seasonality
Low species richness Eutrophication
http://daac.gsfc.nasa.gov/oceancolor/images/Baltic_bloom_July24_2003.jpg
Our model
30 species/ functional groups
Main focus on open sea areas
Nutrients (N,P), DOM and POM included
External forces such as seasonality, near bottom O2
concentration, fishing pressure and advection are included in the model
Base year data (1996), as well as reference data (1997- 2005), are from FIMR and ICES (fish data)
Food web
TL >4 seals, cod*TL >3 herring*, sprat*, H.sarsiTL >2 mysids zooplankton (6 species/groups), macrozoobenthos (4 species), meiobenthos, ciliates
TL >1 bacteria (water+sediment), phytoplankton (summer + spring),cyanobacteria
Non-living: Redfield (N), P, DOM and POM *Juveniles and adults
Ecosim fish results (1996-2005)
herring sprat
cod cod (F)
Incorporating seasonality
T.longicornis Summer phytoplankton
cod
Spatial resolution (Ecospace)
habitats MPAs
Salinity <4 psu <6 psu >8 psu
Fish nursery areas
Anoxic bottoms <2ml O2/l <0ml O2/
Ecospace runs (10yrs)
Conclusions
Our simulated biomasses of sprat, herring and cod correspond well with real data
Fishing pressure is a strong force controlling the top of the Baltic Sea food web
Seasonality has a large impact on the lower trophic levels of the food web. The effect dampenes down towards top predators Is it vital to include seasonality in long term simulations?
MPAs seem to have a local effect on fish biomasses
Future prospects
Construction of sub-areal models/food webs Inclusion of coastal dynamics Testing with data of a longer time-scale Inclusion of the Baltic Sea temperature and salinity gradients The impact of invasive species and inclusion of them eg. Marenzelleria viridis and Mnemiopsis leidyi (”comb jellyfish”) The integration of FIMR biogeochemical models
Acknowledgements:
Our study has been funded by Evagulf-project (EU Interreg)
Thank you!