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Two Approaches to Modelling

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... so we are starting with a simpler (Icelandic) haddock model ... Still to get assessments working for the haddock model. FLXSA. Then get it working with cod ... – PowerPoint PPT presentation

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Title: Two Approaches to Modelling


1
Two Approaches to Modelling
  • Bruce McAdam
  • The University of Aberdeen
  • With Daniel Howell, Christian von Dorrien and
    Tara Marshall

2
UNCOVER Models
  • We want to explore
  • The effects of different management strategies on
    fish populations
  • The effects of different aspects of
    biology/ecology/environment on fish populations
  • To answer these questions we need
  • Models with different degrees of realism
  • Models implementing different management
    strategies
  • We compare the outcomes of different models

3
For Example
  • How will climate change affect chances of stock
    recovery?
  • Model with two different climate scenarios
  • Requires a model that depends realistically on
    climate
  • Does improved understanding of SR relationship
    affect expectations of recovery?
  • Model a simple SR relationship
  • And a more complex one
  • Compare projections.

4
Practical Matters
  • We need to go from our understanding of the
    fisheries
  • And create working models, in the form of
    computer programs.
  • Generally we will use FLR to do this, but there
    are many design decisions to make

5
Two Approaches
  • Start with a simple model of biology
  • Make it more complex
  • This could be done in R
  • This lets us compare different models of ecology
  • FLR also makes adding different management
    strategies easy
  • Start with a (preexisting) complex model
  • Integrate it into FLR in order to add different
    management strategies etc.

6
Examples
  • You want to know whether using a more detailed SR
    relation matters
  • start with a simple model in R (simple SR
    relation, e.g. current ICES assumptions)
  • Make it more complex (a different SR relation,
    e.g. incorporating temperature)
  • Compare results
  • You want to compare two HCRs using the best
    possible biological information
  • And you already have a detailed model of the
    stock
  • Call the existing model from FLR
  • Write R code for the different harvest rules and
    compare results.

7
Which to Use?
  • It depends on
  • What question you are asking
  • What prior work has been performed
  • What have you already got?
  • What skills are available

8
An Example
  • There are Gadget models of the Barents Sea
    ecosystem
  • These run from a stand-alone application
  • They are too complex to reimplement in R
  • We would like to experiment with different
    environmental scenarios and management strategies
  • So we need to use the Gadget model from within R

9
Gadget
  • Its a model that can run simulations
  • Given files describing the stocks, and giving
    parameters for the model
  • It can also be used for model fitting
  • Given description of model, and observations of
    the system, work out the parameters
  • We are using it to run simulations only
  • Previous work (other projects) have done the
    model fitting
  • It runs as a stand-along application (.exe file)
    that reads and writes text files.

10
Youve seen this before
11
We use it like this
Gadget
FLR
12
Implementation
  • Implement in R
  • But, whenever we need a biological simulation
  • Create Gadget input files
  • Run the Gadget command
  • Read data from Gadget output files
  • This contains actual stock and catch data
  • Gadget is treated as a black box
  • R knows nothing about the sort of model it is.

13
Algorithm (written in R)
  • Start with a folder containing all of Gadgets
    input files
  • Run Gadget to generate output files
  • Main Loop (repeat for some number of years)
  • Read output files
  • Create necessary FLR objects
  • Run an assessment (e.g. XSA), apply a harvest
    control rule (HCR)
  • Update Gadget input files with next years
    catch/environment (and anything else needed)
  • Run Gadget with new input, and repeat the loop
  • After finishing, plot graphs etc.

14
Preliminary Results
  • The cod model is complex, so we are starting with
    a simpler (Icelandic) haddock model
  • We model a constant recruitment scenario, with
    different levels of TAC (fixed percentage of
    SSB).
  • This is all just experimentation to check it will
    be possible to implement useful models

15
Pretty Pictures
TAC10 of SSB
TAC50 of SSB
16
Technical Issues
  • Weve done about 2 person-weeks of work
  • We had to implement
  • Reading data from Gadget
  • Writing data to Gadget
  • Running Gadget
  • Converting Gadget data into an FLR form (FLStock
    objects)
  • Running an assessment on the FLStocks
  • Surprisingly, we found it much easier to
    communicate with Gadget, than to do the
    assessment in FLR (work in progress)

17
Where Next?
  • Still to get assessments working for the haddock
    model
  • FLXSA
  • Then get it working with cod
  • Shouldnt be too much work
  • And look (a bit) at efficiency

18
Summary
  • There are two different approaches to modelling
  • Write a model in R, and build it up
  • Start with a complex model, and call it from R
  • FLR can call external models
  • Its not too difficult
  • You need to do quite a lot of programming in R
    but not as much as implementing the model
  • The code you write will be case specific
  • The model doesnt have to be Gadget.

19
Remember
  • My job is to help people customise models like
    this
  • But you need to keep me informed about your
    objectives etc.
  • And make sure you use me as a resource
  • Otherwise, the project will not make efficient
    use of my time (I work 100 on UNCOVER).
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