1. Implementing behaviour and life history strategies in IBMs by Geir Huse Department of Fisheries and Marine Biology, University of Bergen, Norway Lecture I, NORFA course

2. Talk outline 1 Introduction 2 Present concept for implementing adaptive traits in IBMs Strategy vectors The genetic algorithm Artificial neural networks 3 Case study Morph evolution in sticklebacks

3. Life is a lot easier without it.. But Behaviours are abundantly present in the real world Behaviour can have strong impact on spatial and population dynamics Implementing behaviour is a potential advantage of IBMs compared with state variable approaches Why do we need behaviour in IBMs?

4. 1 By applying estimated parameter values for traits 2 Through “rules” thought to represent an evolved strategyrules 3 Through evolved behaviours evaluated by an objective criterion Implementation of adaptive traits in IBMs:

5. AttributeAttribute vector: (weight, age,position,fitness,….) Chambers 1993 Strategy vector: (parity, SAM, allocation of energy, behavioural strategies,...) Huse et al. (2002) Specififying individuals in IBMs:

6. ”mum” ”dad” ”offspring” 0.560.86 0.56 6.785.01 6.78 5.15-0.25 5.15 1.651.65 1.85 -0.210.50 0.50 8.917.56 7.56 -2.93-1.05 -1.05............ Breakpoint The genetic algorithm (Holland 1975) Strategy vector or ”chromosome” Mutation

7. Reproduction -produce new strategy vectors -recombination -mutation The GA Initiate random population Problem test -update attribute vector New population Generation loop Rank individuals

8. Artificial neural networks Artificial neural networks can be used to translate strategy vectors into behaviour Input 1  Input 2 .  Input n  Behaviour InputHiddenOutput W ih W ho Weights implemented on strategy vector S

9. Determine by a fitness measure: Net reproductive rate R 0 Instantaneous rate of increase r These fitness measures are hampered by many assumptions and are often difficult to implement in IBMs Alternatively: use endogenous fitnessendogenous fitness What is a good strategy vector?

10. Do the GA find optimal solutions? While optimality models always find the best solution to problems, How about adaptation models...? Patch choice model A simple vertical migration scenario In cases were the optimal solution can be calculated, it tends to be found by the GA

11. Exploring adaptive radiation and speciation in fish by individual- based models (Huse & Hart in prep.) (Gasterosteus aculeatus)

12. Background Differentiation into limnetic and benthic is seen in pairs of threespine stickleback found in several lakes in British Columbia Hypothesis: co-existence of morphs is governed by habitat specific selection pressures on foraging, with intermediate phenotypes suffering competitive disadvantages (Schluter 1993) Sympatric speciation? Invasions?..

13. Objectives Develop individual-based model of trophic interactions between stickleback morphotypes Study the effect of diffent prey types, competition, spatial detail and invasions on speciation Evaluate individual-based modelling as a tool in studying speciation

14. The model: Feeding Two separate prey populations: Limnetic prey: Daphnia 1-2 mm Benthic prey: Asellus 7 mm Each fish gets 250 attempts per generation to get food Prey encounter proportional to relative prey abundance Outcome determined by individual morphotype using Monte Carlo simulation Random sorting of individuals per round of attempts Prey is removed from population when eaten Growth calculated by bioenergetics

15. The model: Adaptation Strategy vector: (body size, limnetic fidelity, mate selectivity, gill raker length) 11 different alleles [0,1] per locus Individuals are diploid and recombinations are performed as in meiosis Phenotype calculated as the average of the two homologues alleles Fitness criterion: Net reproductive rate R 0 = l x ·m x Offspring production in proportion to fitness

16. Simulations Four different simulations are presented: 1 Adaptation without competition 2 Adaptation with competition 3 Assortative mating without spatial detail 4 Assortative mating and spatial detail

17. General results Training decomposition: Individuals act ”silly” due to random initiation of strategies Solved by gradually making tasks more difficult

18. 1 Adaptation without competition: Phenotypic differentiation due to different prey sizes available Gill raker size Body size

19. 2 Adaptation with competition: Phenotypic differentiation from competition Reduced benthic food Reduced limnetic food

20. 3 Assortative mating without spatial detail No population divergence seen despite increased competition and assortative mating Body size Gill raker length

21. 4 Assortative mating and spatial detail 1 5 10 100 50 20

22. The model predicts phenotypic differentiation to different environmental states The model predicts that sympatric speciation can occur given that prey occur spatially distinct Assortative mating is important in maintaining differentiation and sympatric speciation The methodology may help bridge the gap between phenotypic and genotypic approaches to life history evolution Conclusions

23. ANN calculations

24. The patch choice model of Mangel and Clark 1988 by ANN (Huse, Strand & Giske 1999) The problem is to find the patch at each time step that maximises the survival to the horizon given the current state of the individualThe problem is to find the patch at each time step that maximises the survival to the horizon given the current state of the individual

25. ING model predictions and optimal solutions Pp= local predator abundance Zb = local zooplankton abundance

26. Makes decision using probability and random numbers Example IF random number < probability of getting prey THEN prey is caught Monte Carlo simulation:

27. Monte Carlo simulations

28. Simulating ”survival of the fittest” within the model domain Monte Carlo simulations Those who manage to fulfil the criteria for reproduction in the best way are the fittest Survivors at any time are the fittest No knowledge of optimal strategy   Endogenous fitness