Species Interactions in the Baltic Sea -An age structured model approach PhD Student Thomas Talund Thøgersen

Purpose - To assess the biological and economic effects of an age-structure model approach, compared to a “traditional” approach. - To see the effects of species interaction in a bio-economic management model. - To compare the biological and economic effects of including different stock-recruitment relationships in bio- including different stock-recruitment relationships in bio- economic models. economic models. More specific, this is done by: 1. Including an age structured module in the bio-economic management model “FISHRENT” 2. Including salinity as a proxy for environmental factors affecting the stock-recruitment relationship for cod 3. Include stochasticity in the stock-recruitment functions.

Purpose The inclusions is not a purpose in itself!! Applied: The models should be generally applicable to fisheries within the EU waters. The data requirements should therefore not be more detailed than DCF. Flexible The models should be flexible so that the assessments can easily be applied to different multi-fleet and multi-species fisheries.

Overview of presentation Theoretical background….will be skipped Theoretical background….will be skipped Characteristics of the Eastern Baltic Sea Characteristics of the Eastern Baltic Sea Overview of the fleets fishing in the Eastern Baltic Sea Overview of the fleets fishing in the Eastern Baltic Sea FISHRENT – A bio-economic management model FISHRENT – A bio-economic management model Stock-recruitment relationships Stock-recruitment relationships The age structured model The age structured model Species interaction in the model Species interaction in the model Conclusions Conclusions Where to go next? Where to go next? If time…show the FISHRENT model If time…show the FISHRENT model

The Baltic Sea Characteristics: - Semi-closed Sea - Low salinity level - High nutrient levels due to many adjoining countries - Species poor Sea, but highly productive - Main species is cod (Gadus morhua), herring (Clupea harengus) and sprat (Sprattus sprattus)

The ICES subdivisions of the Baltic Sea

Cod relative availability in the Baltic Sea Table 1. Cod relative availability (%) by age in ICES Subdivisions 25–28 in the eastern Baltic, by semester and fish age. Age 1. semester All semester All100 Kilde: Bastardie et al _2009

The Fishing Fleets of the eastern Baltic Sea The fleet is dominated by two types of vessels - Vessels with active gears (trawl or seine fishing). - Vessels with passive gears (nets, traps or longline) The cost structure of the vessel is assumed to depend on whether it is uses ACTIVE or PASSIVE gears. The cost structure is furthermore assumed to be dependent on the SIZE of the vessel

Net and longline Typical small vessels such as the one - long line is often used for salmon fishery 7 m vessel using net and longline

Demersal trawlers Specialized in catching demersal species, such as cod and flatfish. Pelagic Trawlers Specialised in catching pelagic species, such as sprat and herring.

Purse seiners/trawlers Purse seiners are vessels equipped with a net long enough to surround the fish stock. Purse seiners are specialized in the catch of pelagic species such as herring and sprat, used for fish meal These vessels are typically equipped with both seine and trawl gear. Purse seining are not allowed in the Baltic Sea.

Fishing Segments in the Baltic Fishing Segments in the Baltic - Segments are merged by countries, that is believed to have the same cost structure. - Demersal and pelagic trawlers are merged, since this segmentation can be arbitrary. - The 10 economically most important segments are chosen - These segments account for 96% of the total catch of cod, sprat and herring in the Baltic Sea. SegmentCountryValueValue share Trawl and seine12-24mDEU/POL103% Trawl and seine12-24mDNK /SWE5318% Trawl and seine12-24mEST/FIN104% Trawl and seine 24-40mDEU/POL269% Trawl and seine 24-40mDNK /SWE6021% Trawl and seine 24-40mEST/FIN4516% Trawl and seine 24-40mLTU/LVA197% Trawl and seine >40mDNK /SWE4014% Passive gears < 12mDEU/POL83% Passive gears < 12mDNK /SWE155% Total %

The value of cod, sprat and herring CodDEU/POLDNK /SWEEST/FINLTU/LVAI alt Trawl and seine12-24m Trawl and seine 24-40m Trawl and seine >40m Passive gears < 12m I alt SpratDEU/POLDNK /SWEEST/FINLTU/LVAI alt Trawl and seine12-24m Trawl and seine 24-40m Trawl and seine >40m Passive gears < 12m0.0 I alt HerringDEU/POLDNK /SWEEST/FINLTU/LVAI alt Trawl and seine12-24m Trawl and seine 24-40m Trawl and seine >40m Passive gears < 12m I alt All Three SpeciesDEU/POLDNK /SWEEST/FINLTU/LVAI alt Trawl and seine12-24m Trawl and seine 24-40m Trawl and seine >40m Passive gears < 12m I alt

FISHRENT MODEL

Quota limitations

Effort limitations

Open Access

1. Minimum effort to catch the quotas (fishermen decision) 2. Maximum effort to catch the quotas (fishermen decision) 3. Minimum effort limitations based on F (manager decision) 4. Maximum effort limitations based on F (manager decision) 5. Open access (effort is maximized in order to catch the most) Summary Policy Options

Some quick and dirty economic considerations - Landings value is calculated as landings times fish price - Fish prices change as the landings changes - Fuel cost and other variable costs is a function of effort - Crew share depends on the landings value as well as the fuel costs - Fixed and capital costs depends on the amount of vessels - The number of fleets depends on the investments - The investments depends on the profit - The profit is landings value – (variable costs, fixed costs and capital costs) - NPV = sum of discounted profits over time Net Present Value

Stock-recruitment models

Stock-recruitment model The five recruitment is calculated using non- linear regression in a way that they minimize the sum of squared residuals. Maximum likelihood estimation is an alternative, and has measures to indicate if the regression results are robust - Akaike Information Criterion (AIC) - Akaike Information Criterion (AIC) - Bayesian Information Criterion (BIC) - Bayesian Information Criterion (BIC)

Age structured model

Species interaction First attempt: Include a matrix containing the coefficients between the predator species at age and the prey species at age and multiply that with the abundance of species at age. Problem: - It does not exist !! - It will assume a linear relationship between species abundance and predation

Three types of functional responses Type 1: Linear relationship between prey density and predator food intake Type 2: Marginal decreasing relationship between prey density and predator food intake (assumes that the food processing time is of importance) Type 3: Assumes that the marginal relationship between prey density and predator food intake is increasing at low densities and decreasing at high densities. This is explained by marginal increasing learning time (hunting efficiency) at low densities.

Functional Response

- An age structured bio-economic model is constructed - Stochasticity in stock-recruitment has been added - Salinity has been added to stock-recruitment model - Lack of data to include ALL species interactions - Predation mortality for cod has been included. - The model is not as flexible as intended. This is a result of many area-specific relationships. result of many area-specific relationships. - Results of comparing age structured models and species interaction models with the baseline non-structured model interaction models with the baseline non-structured model has not been performed yet…to be continued has not been performed yet…to be continued Conclusions

- The literature has to be searched to find useful info to include the effect of cod, when the abundance of herring include the effect of cod, when the abundance of herring and sprat changes. and sprat changes. - Fish prices should depend on the size of the fish. Lack of ambiguous relationship between age and size of ambiguous relationship between age and size complicates this. complicates this. - Estimate Maximum likelihood of stock-recruitments relationships instead of minimizing “sum of least squares” relationships instead of minimizing “sum of least squares” - Calculate useful Information Criteria - Include the increased variation of predicted climate changes in the recruitment models changes in the recruitment models Where to go next