Transcript Genetic Basis for the plasticity of growth and survival in
On the Evolution of Phenotypic Plasticity In Spatially Structured Environments
Bruno Ernande Fisheries Department IFREMER Port-en-Bessin, France
Bruno Ernande, NMA Course, Bergen
Bruno Ernande, NMA Course, Bergen
Definitions
degree of plasticity
g
1
g
2 Environment
E
∎
Phenotypic plasticity
Phenotype = Genotype + Environment
z ij = g i + E j
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
z ij
expressed by a single genotype
g i
response to a given range of environments
E j
in
∎
Phenotypic plasticity may be an active process allowing short term adaptation. Can it be selected for?
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 Bruno Ernande, NMA Course, Bergen
V p V g V e g
1
g
2
V e g
1
g
2 Environment
E V p = V g + V e
Environment
E V p = V g + V E + V g
E
∎
Requirements are met in both plants and animals: Schlichting 1986; Sultan 1987; Scheiner 1993; Pigliucci 1996
Bruno Ernande, NMA Course, Bergen
How to represent reaction norms in models?
z z z
i5
z
i4
z
i3
z
i2
z
i1
g i
1 2 3 4 5
E
∎
Character-state reaction norm
{
z
i1
, z
i2
, z
i3
, z
i4
, z
i5 }: 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
g i
Slope,
s z
i0 intercept
E
0
E
∎
Polynomial reaction norm
{
z
i0
, s
}: intercept and slope are considered as the evolving traits.
Gavrilets and Scheiner 1993a,b
How to represent reaction norms in models?
z g i z i
(E) Bruno Ernande, NMA Course, Bergen
E
∎
Reaction norm as a functional trait
z i
(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
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
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
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?
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
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
p(e)
can vary across environmental types which is a reaction norm
e
according to a function
∎
Determinants of environmental heterogeneity:
How frequent are the different environmental types? Frequency of occurence o(e)
What is the quality of the different environments? Intrinsic carrying capacity k(e)
How sensitive to phenotypic variation is the performance of organisms in each type of environment? Sensitivity to maladaptation s(e) Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Model structure
Phenotype Resource utilization efficiency Environment Competition:
-Asymmetry -Realized carrying capacity
REACTION NORM Population Growth rate
+
Costs of Phenotypic Plasticity Maintenance, production Environment Distribution strategy of the individuals FITNESS Long term growth rate of a rare mutant in a resident population Ernande & Dieckmann 2004 JEB
Resource utilization efficiency
Phenotype Resource utilization efficiency Environment REACTION NORM Bruno Ernande, NMA Course, Bergen Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Resource utilization efficiency
∎
In each environment
e
, a matching phenotype
m(e)
resource utilization
E
p
(e)
maximizes efficiency of (harvesting, handling, digestibility,…) in a given environment
e
along an environmental gradient
1
sensitivity s(e)
0
p(e) m(e) Phenotype, p(e) s(e) Ernande & Dieckmann 2004 JEB
Resource competition
Phenotype Resource utilization efficiency Environment Competition:
-Asymmetry -Realized carrying capacity
Bruno Ernande, NMA Course, Bergen Environment REACTION NORM Ernande & Dieckmann 2004 JEB
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
k p
(e) , both depending on the resource utilization efficiency.
2
k(e)
a=0 a<1
E>0
1
E<0
a=1 a>1
degree of asymmetry
0 0
Difference in efficiency,
E
0 0 1
Efficiency,
E p
(e) Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Alternative distribution strategies
Phenotype Resource utilization efficiency Competition:
-Asymmetry -Realized carrying capacity
Environment Environment Distribution strategy of the individuals REACTION NORM Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Alternative distribution strategies
Occurrence, o(e) Quality, k(e) Efficiency,
E p
(e) Distribution, Environment,
d p
(e)
e
∎
Random Distribution:
No selective control over local habitat Occurrence, o(e) Quality, k(e) Efficiency,
E p
(e) Distribution,
d p
(e)
∎
Ideal Free Distribution:
Individuals can detect intrinsic quality of the different environments Occurrence, Quality, k(e) o(e) Efficiency,
E p
(e) Distribution,
d p
(e)
∎
Optimal Foraging:
Individuals can both detect intrinsic quality of the different environments and distribute according to their efficiency.
Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Population growth rate
Phenotype Resource utilization efficiency Environment Competition:
-Asymmetry -Realized carrying capacity
Population Growth rate REACTION NORM Environment Distribution strategy of the individuals Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Costs of phenotypic plasticity
Phenotype Resource utilization efficiency Competition:
-Asymmetry -Realized carrying capacity
Environment REACTION NORM Population Growth rate
+
Costs of Phenotypic Plasticity Maintenance, production Environment Distribution strategy of the individuals Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Costs of phenotypic plasticity
∎
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.
Distribution, Maintenance
d p
(e) Production Environment,
e
∎
Three types of costs
maintenance costs independent of the distribution of the individuals
production costs depending fully on the distribution
mixed cost Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Invasion fitness of a mutant
Phenotype Resource utilization efficiency Competition:
-Asymmetry -Realized carrying capacity
Environment REACTION NORM Population Growth rate
+
Costs of Phenotypic Plasticity Maintenance, production Environment Distribution strategy of the individuals FITNESS Long term growth rate of a rare mutant in a resident population Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Canonical equation
∎
Fitness of a rare mutant
p’
in a resident population
p
:
f
p
,
p
Growth rate
p
,
p
Costs
p
,
p
frequency-dependence
∎
Adaptive dynamics of a function valued trait p are are given by:
d dt p
(
e
) 1 2
p
p
2 (
e
,
e
)
g p
(
e
)
d e
Dieckmann & Heino 2001 with
p
(e,e’)
: the mutational variance-covariance function,
g
p
(e)
: the selection gradient in environmental type
e
derivative of the fitness function
f(p’,p)
is the functional at trait
p’ = p
.
g p
lim 0
f
p
e
,
p
f
p
,
p
f
p
e
,
p
0
Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Evolutionary trajectories
Phenotype Resource utilization efficiency Environment Competition:
-Asymmetry -Realized carrying capacity
REACTION NORM Population Growth rate
+
Costs of Phenotypic Plasticity Maintenance, production Environment Distribution strategy of the individuals FITNESS Long term growth rate of a rare mutant in a resident population Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Evolutionary equilibria
∎
Evolutionary equilibria
p*
or evolutionary singularities are attained when:
p
* 2 (
e
,
e
)
g p
* (
e
)
d e
0 ∎
This is possible when
the selection gradient vanishes at
p*
,
g p*
(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
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
p
* (
e
)
p
m
with a weight
g
[
m
(
e
)
m
(
e
)
g
(
e
)
p
*] /[
m
(
e
)
g
(
e
)] ∎
The weights of the two forces depend on the distribution strategy of the individuals:
R.D.
m
(
e
) ( 1 2
a
)
r w x w
(
e
)
s
(
e
) I.F.D.
m
(
e
) ( 1 2
a
)
r w x K x w
(
e
)
K
(
e
)
s
(
e
) O.F.
m
(
e
) ( 1 2
a
)
r w x K x E x p
*
w
(
e
)
K
(
e
)
s
(
e
)
g
(
e
)
c w
/
KE p
*
w x
(
e
)
K
(
e
)
g
(
e
)
c wK
/
E p
*
w x
(
e
)
K x
(
e
)
g
(
e
)
c wKE p
*
w x
(
e
)
K x
(
e
)
Ernande & Dieckmann 2004 JEB
Evolutionary effect of the different types of costs
Bruno Ernande, NMA Course, Bergen
p
∎
As costs are shifting from maintenance to production type (i.e.
increases), the effects of:
the frequency of occurence o(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.
Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Evolutionary effect of the distribution strategies
p
∎
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 Ernande & Dieckmann 2004 JEB
Bruno Ernande, NMA Course, Bergen
Evolutionary branching of reaction norms
∎
If costs of plasticity and sensitivity are higher
Monomorphic Dimorphic1
1. Directional selection
monomorphic maladapted reaction norm 2. Selection turns disruptive
evolutionarily non-stable 3. Protected dimorphism in reaction norm:
Evolution of Trophic Specialization.
Dimorphic 2
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.