Strategy of Modelling

1. Strategy of Modelling Part of a short course by William Silvert Senior Research Fellow IPIMAR / DAA Lisboa, Portugal

2. Modelling Strategy • To build a model you must plan your strategy carefully. • Modelling is like a military campaign, or at least like designing a major experiment - your cleverness pitted against the subtleties of nature. • Raffiniert is der Herrgott, aber boshaft ist er nicht (Nature is subtle, but not malicious - Einstein).

3. Why are we doing this? • The first thing you have to ask when you build a model, is what is it for? • Models help you understand, they can answer questions, they can predict what will happen - but if you do not know what you want out of a model, you won’t get it! • Building a model for the sake of building a model is not very useful.

4. What is the Objective? • Ask what is the point of the model. • Make sure that you are asking the model a question that makes sense. • Often people ask models questions that have obvious answers. For example: • Is there density dependence? • How many eggs do we get from zero spawners?

5. Alternative Directions • Models can be built from the ground up, or from the top down. • Ground-up models are constructed by assembling submodels of all the processes going on in the system. • Top-down models go from system behaviour to model formulations that describe this behaviour.

6. Ground-up Models • Ground-up Models (modelos picados) can be very complicated, as everything that is known about the system can be included. • The more experts who contribute to the model, the bigger the model. • These models are very comprehensive, but seldom give good results.

7. Top-down Models • Models built from the top down, (“data-driven models”) focus on describing system behaviour. • Statistical models are purely phenomenological and do not (in general) reflect the system structure. • Good top-down modelling leads to an understanding of how the system works.

8. Working from the Top • Statistical analysis does not produce good models, but it can identify patterns that point the way. • Path Analysis and other quasi-statistical methods are helpful. • Once you see what the system is doing, you should try to understand it, not just describe it.

9. Modelling Software • There is no single solution to the question of which modelling software to use, or what language to use for programming ecosystem models. • All software and languages has features that may be useful in some cases, but you have to make sure that what you are using can do what you want it to do.

10. OOM Approahes • Object-oriented modelling (OOM) is a popular route to ecosystem models. • OOM uses the property of “inheritance” - a model of a deer population might be based on a more general model of herbivorous mammals. • Most modern programming languages and some software support OOM.

11. Formulating the Model • The most important part of modelling is formulating the problem and identifying what the model should look like. • Once you have done this, the actual construction of the model is not hard. • The best way to get all the information you need from the available experts is to get them together in a workshop.

12. Workshops • A model is only as good as the knowledge and understanding that goes into it. • That is why you should try to involve experts in the development of the model. • A workshop environment often works better than separate expert consultations.

13. Workshop Structure • The best way to run a workshop is to assemble a team of experts and have them develop a model on the spot. • One week is a good period. • Each group has an expert programmer to translate ideas into code. • Keep the model running at any cost - otherwise the participants get confused and lose interest.

14. What not to do • We can learn by studying other people’s mistakes. • Here are four examples of models that show what you should not do: • Port Hacking Estuary • Stability-Complexity Theory • Salt Mines • Isotropic Cod

15. Port Hacking Estuary • In 1973 CSIRO started a 5-year project to study and model the Port Hacking Estuary in Australia. • A modeller with a good background in pharmacological modelling was hired. • His model was nonsense. • It was linear!

16. Food Chain Models Linear Model dZ/dt = aP - bZ Lotka-Volterra Model dZ/dt = aPZ -bFZ This is non-linear.

17. Stability or Complexity • Hundreds of papers have been written on the stability-complexity question - • Are complex systems more or less stable than simple ones? • All of these models use Lotka-Volterra interactions to describe the predator-prey relationship.

18. Omnivory • One result of these studies is the conclusion by Stuart Pimm that systems with omnivores are rarely stable. • For comparison, ask a field biologist about the role of omnivores. • The answer will almost surely be: • Omnivores are stabilising, because if one prey becomes scarce, the omnivore switches to eating something else.

19. L-V Omnivory • Is this consistent with the Lotka-Volterra model? • No - in L-V models a predator spends the same effort chasing every prey type, so it can drive a vulnerable prey exponentially to extinction. • There is no element of choice or of selectivity in L-V models.

20. Down to the Salt Mines • A very distinguished ecological modeller received a large (~ €1.000.000) contract to study the effect of a small increase in salinity (~ 2‰) over an area of 2 km2. • This would be the result of hollowing out a salt dome in the Gulf of Mexico for the storage of radioactive waste.

21. Death to B. tyrannus! • He concluded that the small increase in salinity would wipe out the menhaden population (Brevoortia tyrannus) as zooplankton populations in the area would be depleted and the menhaden would starve. • But menhaden can swim - why would they stay in an area of 2 km2 and starve to death?

22. Spatial Distributions • This result seems silly, but it is actually very common. • For example, I reviewed a paper on landscape ecology in which foxes were chasing rabbits over a grid 100 m on each side. • If the fox was in a square without rabbits it starved to death, even if there were rabbits 100 m away!

23. The Isotropy of Cod • A mathematical modeller described the distribution of cod (Gadus morhua, bacalhau) on the Canadian coast by a model which included the assumption that cod swim in any direction (isotropy). • He explained that codfish are stupid and do not know about the 200 mile Extended Economic Zone.

24. The F.A.S.T. Theorem • But codfish do know about the continental shelf, which is why they swim along the coast and not offshore where there is no food. • This led me to formulate the F.A.S.T. Theorem, which states that: • Fish Are Smarter Than … • (insert the name of your favourite ecological modeller here!)