Ew E NEST m odel Thorsten Blenckner Maciej T. Tomczak Susa Niiranen Olle Hjerne
Baltic Nest Institute (BNI) – Linking science and management Baltic NEST = a science ‐ based decision support system (DSS) to : • Explore and synthesize ecosystem information • Evaluate the effects of eutrophication and fishery • Calculate costs of different management options Baltic NEST can be used with any computer with internet access and can be downloaded free from nest.su.se/nest . 2 DTU Aqua, Technical University of Denm ark
Atmospheric emissions and load Cost minimization model Marine modeling NEST DSS builds on six different models Drainage basin modeling Fishery management Marine and runoff data 3 DTU Aqua, Technical University of Denm ark
Nest food web model • A Baltic Proper food web model incorporating groups from phytoplankton to fishery (based on Harvey et al.,2003) • Aim to create a model that is able to: • Reproduce historical (1974 ‐ >) phenomena (e.g. regime shifts) and help to identify past food web dynamics Incorporate fishery impact • • Quantify responses to environmental variation and indicate potential changes in food ‐ web interactions to future climate change (2007 ‐ 2100) � linkage to biogeochemical models • Currently the model is parameterized to fit fish biomasses to XSA ‐ data (1974 ‐ 2007) 4 DTU Aqua, Technical University of Denm ark
Modelling approach • An Ecopath with Ecosim ‐ approach (www.ecopath.org) employed, where: ‐ Ecopath (mass ‐ balance) P = M p + F + M other + BA + migration C = P + Unass. food + R ‐ Ecosim (simulation) • 28 functional groups (23 living, 3 fleets, 2 detritus) • Multi ‐ stanza groups for 3 main species 5 DTU Aqua, Technical University of Denm ark
Cod_fleet Seals Cod Her_fleet Spr_fleet Age 2, etc Age 1 Age 0 Herring Eggs/larvae Age 2 etc.. Sprat Age 1 Age 2, etc... Age 0 Age 1 Age 0 Pseudocalanus Macrozoobenthos Acartia Mysids Temora Other... Meiobenthos Microzoopl. Bacteria Detritus sed DOM Spring phytoplankton Cyanob. Other... 6 DTU Aqua, Technical University of Denm ark
Cod_fleet Seals Cod Her_fleet Spr_fleet Age 2, etc RV Age 1 Age 0 Herring Eggs/larvae Age 2 etc.. Sprat Age 1 Age 2, etc... Age 0 Age 1 O 2 Age 0 ? Pseudocalanus Macrozoobenthos Acartia Mysids Temora Other... Meiobenthos Microzoopl. Bacteria Temp N, P Detritus sed DOM Spring phytoplankton Cyanob. Other... 7 DTU Aqua, Technical University of Denm ark
Forcing Factor Seson Forced Group Type of forcing 1 Tem p_ 0 _ 1 0 _ Aug Sum m er Sprat eggs production 2 Tem p 0 _ 5 0 m _ spring Spring Acartia sp; Them ora sp im pact on biom ass 3 CodRV Annual Cod eggs production or youngest stanza 4 macrozoobenthos Annual macrozoobenthos 5 Acartia Annual Acartia sp 6 Temora Annual Temora sp 7 Pseudocalanus Annual Pseudocalanus sp 8 PP Spring spring phytoplankton 9 PP Summer other phytoplankton 10 --------------- -------- cyanobacteria to fit relative biom ass tim e series 11 B_Sprat 1 Annual Sprat Age 1 from XSA 12 B_Ad. Sprat Annual Sprat Age 2+ 13 B_Herring 1 Annual Herring Age 1 14 B_Herring 2 Annual Herring Age 2 15 B_Ad. Herring Annual Herring Age 3+ 16 B_Cod 2 Annual Cod Age 2 17 B_Cod3 Annual Cod Age 3 18 B_Ad. Cod Annual Cod Age 3+ 19 F_ Sprat 1 Annual Sprat Age 1 20 F_ Ad. Sprat Annual Sprat Age 2 + 21 F_ Herring 1 Annual Herring Age 1 Fishing pressure on given group 22 F_ herring 2 Annual Herring Age 2 XSA 23 F_ Ad. Herring Annual Herring Age 3 + 24 F_ Cod 2 Annual Cod Age 2 25 F_ Cod3 Annual Cod Age 3 8 DTU Aqua, Technical University of Denm ark 26 F_ Ad. Cod Annual Cod Age 3 +
9 DTU Aqua, Technical University of Denm ark
14 20 18 12 SPRAT 16 10 14 Biomass (t/km2) Biomass (t/km2) 12 8 10 6 8 6 4 4 2 2 0 0 1974 1979 1984 1989 1994 1999 2004 2009 1974 1979 1984 1989 1994 1999 2004 2009 Year Year 3 3.5 14 HERRING 3 12 2.5 2.5 10 2 Biomass (t/km2) Biomass (t/km2) Biomass (t/km2) 2 8 1.5 1.5 6 1 1 4 0.5 0.5 2 0 0 0 1974 1979 1984 1989 1994 1999 2004 2009 1974 1979 1984 1989 1994 1999 2004 2009 1974 1979 1984 1989 1994 1999 2004 2009 Year Year Year 1.4 1.6 2 COD 1.8 1.4 1.2 1.6 1.2 1 1.4 Biomass (t/km2) Biomass (t/km2) Biomass (t/km2) 1 1.2 0.8 0.8 1 0.6 0.8 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 1974 1979 1984 1989 1994 1999 2004 2009 1974 1979 1984 1989 1994 1999 2004 2009 1974 1979 1984 1989 1994 1999 2004 2009 Year Year Year 10 DTU Aqua, Technical University of Denm ark
12 10 Shift in zooplankton community 8 Biomass (t/km2) 6 7 4 6 5 2 0 4 1974 1979 1984 1989 1994 1999 2004 2009 Year 3 10 2 9 8 1 7 Biomass (t/km2) 6 0 1974 1979 1984 1989 1994 1999 2004 5 4 6 3 2 5 1 0 1974 1979 1984 1989 1994 1999 2004 2009 4 Year 9 3 8 7 2 6 Biomass (t/km2) 5 1 4 3 0 2 1974 1979 1984 1989 1994 1999 2004 1 0 1974 1979 1984 1989 1994 1999 2004 2009 Year 11 DTU Aqua, Technical University of Denm ark
Catch vs. TL… fishing dow n to the food w eb?? 200 200 3.9 3.85 1996 1986 3.8 1976 1987 1984 Mean TL Catch 1980 1985 3.75 1977 1983 1982 1991 1988 1989 1974 2003 1990 3.7 1981 1997 1978 1979 1975 19941995 2004 3.65 1992 2006 200 2007 1999 1998 1993 2001 3.6 2000 2002 3.55 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 Log (Total Catches) 12 DTU Aqua, Technical University of Denm ark
Simulation secanrio Fisheries and Climat Change on Cod biomass 1.6 1.4 1.2 Biomass (t x km-2) 1 0.8 0.6 0.4 F= 0.9 0.2 0 1974 1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 Years Scenarios Fishing Climate BAU=means no CC 13 DTU Aqua, Technical University of Denm ark
Simulation secanrio Fisheries and Climat Change on Cod biomass 1.6 1.4 1.2 Biomass (t x km-2) 1 0.8 0.6 0.4 F= 0.9 0.2 0 1974 1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 Years Scenarios Fishing Climate BAU=means no CC BAU=means CC 14 DTU Aqua, Technical University of Denm ark
Simulation secanrio Fisheries and Climat Change on Cod biomass 2.5 F= 0.3 2 Biomass (t x km-2) 1.5 1 0.5 F= 0.9 0 1974 1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 Years Scenarios Fishing Climate BAU=means no CC BAU=means CC F=0.3 no CC F=0.3 CC 15 DTU Aqua, Technical University of Denm ark
Simulation secanrio Fisheries and Climat Change on Cod biomass 4 F= 0 3.5 3 Biomass (t x km-2) 2.5 F= 0.3 2 1.5 1 0.5 F= 0.9 0 1974 1984 1994 2004 2014 2024 2034 2044 2054 2064 2074 2084 2094 Years Scenarios Fishing Climate BAU=means no CC BAU=means CC F=0.3 no CC F=0.3 CC F=0 no CC F=0 CC 16 DTU Aqua, Technical University of Denm ark
Scenario test - Conclusions • Strong effect of fisheries management actions on cod stock recovery • Model predict a no return of cod SSB to the medium level observed in 1990’s if the fishing continues at current level • ”Business As Usual” scenario will hinder a recovery of the Eastern Baltic cod stock • Reduction in fishing pressure has a smaller positive effect on the cod stock in a future changing climate then if climate change is not occur • Climate change scenarios gave minor effect on cod stock recovery, but large effects on the food web, however more scenarios have to be test • More scenarios can be found in ICES/ HELCOM Working Group on Integrated Assessment of the Baltic Sea Report 2009 17 DTU Aqua, Technical University of Denm ark
Overall sum m ary ( pros) • Address and answer ecological questions and hypothesis: – Reproduce past dynamic of whole food-web at all trophic levels – Quantifies the ”regime-shift” in the Baltic sea ecosystem – Quantifies top-down vs. bottom-up control • Evaluate effect of environmental changes – Simulate the ecosystem response to climate change and changes in primary productivity • Evaluate ecosystem effect of fishing – on fish stocks – on whole ecosystem structure (fish down to the food web) – estimate fisheries related trophodynamic indicators (e.g TL) 18 DTU Aqua, Technical University of Denm ark
Overall sum m ary ( cons) • Only main commercial fish groups are included – lack other important components (salmons, flat fishes) – lack of interaction with coastal zones • Annual model step (no seasonality) • Only 3 fishing fleets related to fish species 19 DTU Aqua, Technical University of Denm ark
Overall sum m ary ( to do...) • True coupling between Nest food web model and a biogeochemical model (BALTSEM) applied in the NEST to test effect of HELCOM Baltic Sea Action Plan on entire food-web including fisheries Explore management policy options to produce policy-relevant information on combined future (present-2100) impacts of CC and industrial + agricultural practices on Baltic Sea ecosystem • Evaluate impact and placement of marine protected areas • Link other NEST components to food-web model Uncertainty • Depend on data quality and quantity (monitoring data extremely important !) • Unknown food-web interactions • Comparable with other Baltic model 20 DTU Aqua, Technical University of Denm ark
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