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!