Barents Sea fish modelling in Uncover

Daniel Howell

Marine Research

Institute of Bergen

COD

CAPELINHERRING

MINKE WHALE

HARP SEAL

MULTISPECIES INTERACTIONSIN THE NORWEGIAN SEA - BARENTS SEA ECOSYSTEM

KRILL AMPHIPODS

Fish model

• Multispecies fish population model– Cod, capelin, herring

• Age and length structured

• To be implemented in FLR– Based on Gadget model

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

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

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

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

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

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

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

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

Model Overview

• Monthly time steps

• Several Areas (Barents Sea and other subsidary areas for migration)

• Currently only considering interactions in the Barents Sea

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

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

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)

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)

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)

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

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

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

Model Flexibility

• Choice of functions typically available – Growth– Fishing selectivity– Etc.

• Can write new functions, or modify existing ones, relatively easily if required

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

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

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

Sub Models (1.4)

• Migration– Model is large scale

• Migration is specified as percentage moving between areas in a given month (process driven)

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

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

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

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