1. Barents Sea fish modelling in Uncover Daniel Howell Marine Research Institute of Bergen

3. Fish model Multispecies fish population model –Cod, capelin, herring Age and length structured To be implemented in FLR –Based on Gadget model

4. Gadget Simulation model Create a virtual population within the model Follow the fish through their lives –Fishing, mortality, growth, maturation, etc. Process driven –E.g. percentage becoming mature, not percentage mature at age

5. Gadget Age&length based Multiple: species, stocks, fleets, areas Separation of model and data –No data required for the simulation run Statistical functions used to compare model and data

6. Gadget Specify a model with: Choice of equations for growth, reproduction, fishing selection... Parameters in those equations –fixed or estimated Data Statistical functions measuring fit between model and data

7. Gadget Simulation model is made, without using the data Uses specified stocks, fleets, growth equations... Produces a virtual population through time, and virtual catches from that population

8. Gadget Compared model results against the real- world data Statistical functions assign a numerical score to each data set Combined in a weighted sum to give a single likelihood score

9. Gadget A one dimensional measure of the ‘success’ of the model Can be used to optimize the model Repeat runs are made using different values of key parameters Attempting to find the lowest score –the best match to the data

10. Optimization Optimize parameters Structure of model is fixed E.g. Select a dome shaped fishing selectivity –Will remain dome shaped –Exact shape will be optimized

11. Model Overview Very complex for a fisheries model Need to avoid adding any more complexity that is necessary Extra complexity/flexibility needs either: –Data to optimize to –Externally derived parameters

12. Model Overview Monthly time steps Several Areas (Barents Sea and other subsidary areas for migration) Currently only considering interactions in the Barents Sea

13. Model components Multiple ‘stocks’ –Different species –Different stocks of one species –Split by maturity or sex –Different genetic components? Fish can move between stocks –Maturation –Physically move between two distinct stocks

14. Model components Growth Mean length growth can be a combination of : –length, age, weight, ‘condition’, water temperature –Other physical factors? Actual growth –mean growth for each timestep is converted in to a distribution, with estimable parameters

15. Model components Migration –Multiple areas –Movement pre-specified or modelled –Modelled as % moving from one area to another –Can vary over time (needs to be pre-specified or have enough data to estimate changes)

16. Model Components Reproduction –Can simply estimated on a yearly basis to best fit the data –Or based on mature population characteristics –Can also include other factors (e.g. temperature)

17. Model Components Reproduction –Closed life cycle is possible –Can be based on SSB, or on the length, weight and possibly ”condition” of adult fish –Can be based on length distribution of fish, not just overall SSB –Simple modelling of fish larvae possible –But has to be on the same time scale as the fish model (monthly time step)

18. Model Components Predation –fish can eat other stocks –predation is length based (predator and prey) –cannibalism is possible –“desired” diet of a predator is spread over available prey, with preference factors Fishing –fleets are treated as predators

19. Model Components Fishing –Fleets can have their own selection function –typically selecting on length Can either model (or specify) catch in tons Or model fishing mortality

20. Model Flexibility Parameters can be pre-specified or estimated Estimate: –once for all years –separately for each year –split years into blocks, and estimate for each block

21. Model Flexibility Choice of functions typically available –Growth –Fishing selectivity –Etc. Can write new functions, or modify existing ones, relatively easily if required

22. Modelled population The program keeps track of the details of the virtual population, and outputs summary statistics –e.g. –Numbers, biomass, weight at length and age –Catches in numbers and weight –Predation by one species on another, by length

23. New or improved process models Improvements can come as either: Processes modelled within the program Processes implemented as fixed parameters Need to keep complexity to a minimum

24. Task To look at ways in which the outputs from the different parts of WP1 and WP2 can be incorporated into the fish population model

25. Sub Models (1.4) Migration –Model is large scale Migration is specified as percentage moving between areas in a given month (process driven)

26. Sub Models (1.3) Genetic/behavioural changes through time Have the possibility to have time dependant effects in most parameters: –e.g. Migration, recruitment, growth Not yet stock-size dependant

27. Sub Models (1.1, 2.4) Fecundity Have: –Number of fish by age and length –Weight of fish in age/length cell –Condition of fish

28. Sub Models (2.1, 2.2, 2.3) Larval growth/survival –Can include simple larval growth –Can have eggs produced in one area appearing as fish in another –Can include environmental factors (e.g. Temperature) –Cannot include very small time steps –Needs data or pre-specified parameters

29. Recovery Scenarios Can set up quite detailed scenarios –Time dependant –”good” and ”bad” years Don’t have much time to actually do this –Need to prioritize