On the Evolution of Phenotypic Plasticity In Spatially Structured Environments

1. Bruno Ernande, NMA Course, Bergen On the Evolution of Phenotypic Plasticity In Spatially Structured Environments Bruno Ernande Fisheries Department IFREMER Port-en-Bessin, France

2. Phenotypic plasticity Phenotype = Genotype + Environment zij = gi + Ej a single genotype can produce different phenotypes according to the environment where it develops and lives this holds for both spatial and temporal environmental variation Reaction norm the systematic profile of phenotypes zij expressed by a single genotype gi in response to a given range of environments Ej Phenotypic plasticity may be an active process allowing short term adaptation. Can it be selected for? Bruno Ernande, NMA Course, Bergen Definitions Phenotype z reaction norm degree of plasticity g1 g2 Environment E

3. Phenotype z g1 g1 g2 Ve Vp Vg g2 Ve Environment E Environment E Bruno Ernande, NMA Course, Bergen Prerequisites for phenotypic plasticity to evolve • To be selected for, phenotypic plasticity needs to • enhance fitness of plastic genotypes relative to non-plastic ones • be under genetic control • exhibit sufficient additive genetic variance in the population • Requirements are met in both plants and animals: Schlichting 1986; Sultan 1987; Scheiner 1993; Pigliucci 1996 Vp = Vg + Ve Vp = Vg + VE + VgE

4. Character-state reaction norm {zi1, zi2, zi3, zi4, zi5}: the different character-states are evolving under the constraints imposed by correlations across environments Falconer 60’s, Via and Lande 1985, Kawecki and Stearns 1993 Polynomial reaction norm {zi0 , s}: intercept and slope are considered as the evolving traits. Gavrilets and Scheiner 1993a,b z gi gi Slope, s zi0 intercept E0 E E Bruno Ernande, NMA Course, Bergen How to represent reaction norms in models? z zi5 zi4 zi3 zi2 zi1 1 2 3 4 5

5. Bruno Ernande, NMA Course, Bergen How to represent reaction norms in models? • Reaction norm as a functional trait • zi(E): reaction norm is represented by a flexible function which can evolve like a trait • Gomulkiewicz & Kirkpatrick 1992 • This of course the most flexible way to model a reaction norm z gi zi(E) E

6. Bruno Ernande, NMA Course, Bergen Previous models of phenotypic plasticity evolution • Optimality Theory:Ecologically oriented models • Geared toward identifying the selective pressures favouring or preventing the evolutionary emergence of phenotypic plasticity • from explicit ecological scenarios and • a priori trade-offs • Based on population dynamics, no genetics: phenotypic evolution • Long-term evolution but no evolutionary transients, only evolutionary equilibria • No density- nor frequency-dependent populations: interactions between individuals are not accounted for • Stearns and Koella 1986; Houston and McNamara 1992; Kawecki and Stearns 1993; Sasaki & de Jong, 1999

7. Bruno Ernande, NMA Course, Bergen Previous models of phenotypic plasticity evolution • Quantitative genetics: Genetically oriented models • Aim at identifying the implications of the underlying genetics for the evolutionary emergence of phenotypic plasticity, focusing mainly on genetic constraints such as • the lack of additive genetic variance or • genetic correlations • Based on a statistical description of the population, no detailed ecology • Evolutionary transients together with equilibria, but short term evolution (constant additive genetic (co-)variance matrix) • No density- nor frequency-dependent populations: interactions between individuals are not accounted for • Via and Lande 1985, 1987; Van Tienderen 1991, 1997;Gomulkiewicz and Kirkpatrick 1992; Gavrilets and Scheiner 1993

8. Bruno Ernande, NMA Course, Bergen Under-investigated aspects • Density-dependent population dynamics and frequency-dependent selection • Would allow to account for phenotypic plasticity triggered by interactions between individuals such as competition for food resources or mates, predation,… • Accounting for different types of costs of phenotypic plasticity • Maintenance costs: expenses incurred by maintaining the potential for being plastic • Production costs: costs paid by a plastic genotype actually producing a given phenotype in excess to those incurred by a fixed genotype producing the same phenotype • The consequence of alternative distribution patterns • Are individuals distributed randomly across environments or do they select it? • The evolutionary implications of a precise environmental setting • Frequency of the different environments, the quality of the resource they offer… How these factors are driving the potential evolution of phenotypic plasticity, how do they interact and what is their relative importance?

9. Bruno Ernande, NMA Course, Bergen The modelling approach • We use adaptive dynamics theory (Metz et al. 1992; Dieckmann & Law 1996; Metz et al. 1996; Geritz et al. 1998) and its recent extension to function-valued traits • Properties and assumptions: • Selection gradient derived from explicit ecological scenarios • Phenotypic model (clonal model), no genetics • Long term evolution of phenotypic plasticity: mutation driven (slow mutation rate, small mutational steps) • Describes adaptive transient states together with evolutionary equilibria • Allows to account for interactions between individuals • density-dependent population dynamics and • frequency-dependent selection Ernande & Dieckmann 2004 JEB

10. Bruno Ernande, NMA Course, Bergen The basics • Individuals are living across a range of environments e that can represent: • abiotic parameters (temperature, salinity, amount of nitrates…) • biotic characteristics (species or densities of preys, of predators, types of competitors ) • The phenotype p can vary across environmental types e according to a function p(e)which is a reaction norm • Determinants of environmental heterogeneity: • How frequent are the different environmental types? Frequency of occurenceo(e) • What is the quality of the different environments? Intrinsic carrying capacityk(e) • How sensitive to phenotypic variation is the performance of organisms in each type of environment? Sensitivity to maladaptations(e) Ernande & Dieckmann 2004 JEB

11. Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment Population Growth rate + REACTION NORM Costs of Phenotypic Plasticity Maintenance, production FITNESS Long term growth rate of a rare mutant in a resident population Bruno Ernande, NMA Course, Bergen Model structure Ernande & Dieckmann 2004 JEB

12. Phenotype Environment REACTION NORM Bruno Ernande, NMA Course, Bergen Resource utilization efficiency Resource utilization efficiency Ernande & Dieckmann 2004 JEB

13. along an environmental gradient sensitivity s(e) matching,m(e) s(e) p(e) Bruno Ernande, NMA Course, Bergen Resource utilization efficiency • In each environment e, a matching phenotype m(e) maximizes efficiency of resource utilization Ep(e)(harvesting, handling, digestibility,…) in a given environmente 1 Efficiency,Ep(e) 0 m(e) Phenotype,p(e) Ernande & Dieckmann 2004 JEB

14. Bruno Ernande, NMA Course, Bergen Resource competition Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Environment REACTION NORM Ernande & Dieckmann 2004 JEB

15. 2 k(e) a=0 a<1 E>0 a=1 Competition coefficient, A(E) 1 Realized carrying capacity,kp(e) E<0 a>1 degree of asymmetry 0 0 0 0 1 Difference in efficiency,E Efficiency,Ep(e) Bruno Ernande, NMA Course, Bergen Resource competition • Competition for resources • logistic density-dependence with a coefficient of competition A(E) and a realized carrying capacity kp(e), both depending on the resource utilization efficiency. Ernande & Dieckmann 2004 JEB

16. Bruno Ernande, NMA Course, Bergen Alternative distribution strategies Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment REACTION NORM Ernande & Dieckmann 2004 JEB

17. Occurrence,o(e) • Ideal Free Distribution: • Individuals can detect intrinsic quality of the different environments Quality,k(e) Efficiency,Ep(e) Distribution,dp(e) Occurrence,o(e) • Optimal Foraging: • Individuals can both detect intrinsic quality of the different environments and distribute according to theirefficiency. Quality,k(e) Efficiency,Ep(e) Distribution,dp(e) Bruno Ernande, NMA Course, Bergen Alternative distribution strategies Occurrence,o(e) • Random Distribution: • No selective control over local habitat Quality,k(e) Efficiency,Ep(e) Distribution,dp(e) Environment, e Ernande & Dieckmann 2004 JEB

18. Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment Population Growth rate REACTION NORM Bruno Ernande, NMA Course, Bergen Population growth rate Ernande & Dieckmann 2004 JEB

19. Bruno Ernande, NMA Course, Bergen Costs of phenotypic plasticity Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment Population Growth rate REACTION NORM + Costs of Phenotypic Plasticity Maintenance, production Ernande & Dieckmann 2004 JEB

20. Costs increase with departure from the developmental base-line. The total costs of the reaction norm are proportional to its variance around the developmental base-line. Three types of costs maintenance costs independent of the distribution of the individuals production costs depending fully on the distribution mixed cost Distribution,dp(e) Maintenance Production Bruno Ernande, NMA Course, Bergen Costs of phenotypic plasticity Phenotype, p(e) Environment, e Ernande & Dieckmann 2004 JEB

21. Bruno Ernande, NMA Course, Bergen Invasion fitness of a mutant Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment Population Growth rate REACTION NORM + Costs of Phenotypic Plasticity Maintenance, production FITNESS Long term growth rate of a rare mutant in a resident population Ernande & Dieckmann 2004 JEB

22. Bruno Ernande, NMA Course, Bergen Canonical equation • Fitness of a rare mutant p’ in a resident population p: • Adaptive dynamics of a function valued trait p are are given by: Dieckmann & Heino 2001 with p(e,e’): the mutational variance-covariance function, gp(e): the selection gradient in environmental type eis the functional derivative of the fitness function f(p’,p) at trait p’ = p. frequency-dependence Ernande & Dieckmann 2004 JEB

23. Bruno Ernande, NMA Course, Bergen Evolutionary trajectories Resource utilization efficiency • Competition: • Asymmetry • Realized carrying capacity Phenotype Environment Distribution strategy of the individuals Environment Population Growth rate REACTION NORM + Costs of Phenotypic Plasticity Maintenance, production FITNESS Long term growth rate of a rare mutant in a resident population Ernande & Dieckmann 2004 JEB

24. Bruno Ernande, NMA Course, Bergen Evolutionary equilibria • Evolutionary equilibria p* or evolutionary singularities are attained when: • This is possible when • the selection gradient vanishes at p*, gp*(e’) = 0  Selection induced-equilibria. • the mutational variance-covariance function p*2(e,e’) is singular at p*  Covariance induced equilibria. Ernande & Dieckmann 2004 JEB

25. R.D. I.F.D. O.F. Bruno Ernande, NMA Course, Bergen Selection-induced equilibria • Evolutionary singularities are characterized by a balance between two opposing forces: • one toward the matching phenotype m(e) with a weight • the other toward the cost-free generalist phenotype with a weight • The weights of the two forces depend on the distribution strategy of the individuals: Ernande & Dieckmann 2004 JEB

26. As costs are shifting from maintenance to production type (i.e.  increases), the effects of: the frequency of occurenceo(e) of the different environmental types in case of all distribution strategies, the intrinsic carrying capacity k(e) in case of Ideal Free Distribution and Optimal Foraging. on the shape of the reaction norm disappear. Bruno Ernande, NMA Course, Bergen Evolutionary effect of the different types of costs m(e) Ernande & Dieckmann 2004 JEB

27. The effect of carrying capacity differs according to the kind of distribution strategy considered: in case of Random Distribution, better matching evolve in poor environments in of Ideal Free Distribution and Optimal Foraging, better matching evolves in good environmental types Bruno Ernande, NMA Course, Bergen Evolutionary effect of the distribution strategies m(e) Ernande & Dieckmann 2004 JEB

28. If costs of plasticity and sensitivity are higher Directional selection monomorphic maladapted reaction norm Selection turns disruptive evolutionarily non-stable Protected dimorphism in reaction norm: Evolution of Trophic Specialization. Bruno Ernande, NMA Course, Bergen Evolutionary branching of reaction norms Monomorphic Dimorphic1 Dimorphic 2

29. Bruno Ernande, NMA Course, Bergen Conclusions • Evolution of phenotypic plasticity can be driven by frequency-dependent interaction between conspecifics allow for branching of reaction norms and apparition of polymorphism in the degree of phenotypic plasticity. • Considering different type of costs of phenotypic plasticity have a drastic effect on the shape of reaction norm: interact in an intricate manner with the environmenal setting; • Distribution strategy of the individuals is a crucial factor: changes the effect of the quality of the environments and the susceptibility for branching in reaction norms • Promising developments: • the systematic exploration of branching points in reaction norms, • the evolutionary competition between generalist, specialist and “plasticist”: the coevolution between distribution patterns and phenotypic plasticity, • development of a model in case of a temporally fluctuating environment.