theoretical modelling in biology (g0g41a ) pt i. analytical models iv. optimisation and inclusive...
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![Page 1: Theoretical Modelling in Biology (G0G41A ) Pt I. Analytical Models IV. Optimisation and inclusive fitness models Tom Wenseleers Dept. of Biology, K.U.Leuven](https://reader035.vdocuments.us/reader035/viewer/2022062516/56649e425503460f94b35209/html5/thumbnails/1.jpg)
Theoretical Modelling in Biology (G0G41A )
Pt I. Analytical Models
IV. Optimisation and inclusive fitness models
Tom WenseleersDept. of Biology, K.U.Leuven
28 October 2008
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Aims
• last week we showed how to do exact genetic models• aim of this lesson: show how under some limiting cases
the results of such models can also be obtained using simpler optimisation methods (adaptive dynamics)
• discuss the relationship with evolutionary game theory (ESS)
• plus extend these optimisation methods to deal with interactions between relatives (inclusive fitness theory / kin selection)
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General optimisation method: adaptive
dynamics
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Optimisation methods• in limiting case where selection is weak
(mutations have small effect) the equilibria in genetic models can also be calculated using optimisation methods (adaptive dynamics)
• first step: write down invasion fitness w(y,Z) = fitness rare mutant (phenotype y)fitness of resident type (phenotype Z)
• if invasion fitness > 1 thenfitness mutant > fitness resident and mutant can spread
• evolutionary dynamics can be investigated using pairwise invasibility plots
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Pairwise invasibility plots= contour plot of invasion fitness
Resident trait Z
Mut
ant t
rait
y
invasion possible fitness rare mutant > fitness resident type
invasion impossible fitness rare mutant > fitness resident type
one trait substitution
evolutionary singular strategy ("equilibrium")
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Evolutionary singular strategy
• Selection for a slight increase in phenotype is determined by the selection gradient
• A phenotype z* for which the selection differential is zero we call an evolutionary singular strategy. This represents a candidate equilibrium.
Zyy
ZywZD
),(
)(
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Reading PIPs: Evolutionary Stabilityis a singular strategy immune to invasions by neighbouring phenotypes? yes → evolutionarily stable strategy (ESS)i.e. equilibrium is stable(local fitness maximum)
Resident trait z
Mut
ant
trai
t y
yes
Resident trait z
Mut
ant
trai
t y
no
inv
inv
no inv
no inv
0),(
when true*
2
2
zZyy
ZywB
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Reading PIPs: Invasion Potentialis the singular strategy capable of invading into all its neighbouring types?
Resident trait Z
Mut
ant
trai
t y
yes
Resident trait Z
Mut
ant
trai
t y
no
no inv
no inv
invinv
inv
inv
no inv
no inv
0),(
when true*
2
2
zZyZ
ZywA
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Reading PIPs: Convergence Stabilitywhen starting from neighbouring phenotypes, do successful invaders lie closer to the singular strategy?i.e. is the singular strategy attracting or attainable
D(Z)>0 for Z<z* and D(Z)<0 for Z>z*, true when A>B
Resident trait Z
Mut
ant
trai
t y
yes
Resident trait Z
Mut
ant
trai
t y
no
no inv
no inv
invinv
inv
inv
no invno inv
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Reading PIPs: Mutual Invasibilitycan a pair of neighbouring phenotypes on either side of a singular one invade each other?
w(y1,y2)>0 and w(y2,y1)>0, true when A>-B
Resident trait Z
Mut
ant
trai
t y
yes
Resident trait Z
Mut
ant
trai
t y
no
no inv
no inv
invinv
inv
inv
no inv
no inv
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Typical PIPs
Resident trait Z
Mut
ant
trai
t y
ATTRACTOR
no inv
no inv
invinv
Resident trait Z
Mut
ant
trai
t y inv
inv
no inv no inv
REPELLOR
stable equilibrium "CONTINUOUSLY STABLE STRATEGY"
unstable equilibrium
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Two interesting PIPsGARDEN OF EDEN BRANCHING POINT
evolutionarily stable,but not convergence stable(i.e. there is a steady statebut not an attracting one)
convergence stable,but not evolutionarily stable
"evolutionary branching"
Resident trait z
Mut
ant
trai
t y inv
inv
no inv
no inv
Resident trait z
Mut
ant
trai
t y
inv
inv
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(1) evolutionary stable, (2) convergence stable, (3) invasion potential, (4) mutual invasibility
repellorrepellor"branching point"attractorattractorattractor"garden of eden" repellor
Eightfold classification(Geritz et al. 1997)
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Application: game theory
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Game theory
• "game theory": study of optimal strategic behaviour, developed by Maynard Smith
• extension of economic game theory, but with evolutionary logic and without assuming that individuals act rationally
• fitness consequences summarized in payoff matrix
hawk-dove game
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Two types of equilibria
• evolutionarily stable state: equilibrium mix between different strategies attained when fitness strategy A=fitness strategy B
• evolutionarily stable strategy (ESS):strategy that is immune to invasion by any other phenotype- continuously-stable ESS: individuals express a continuous
phenotype- mixed-strategy ESS: individuals express strategies
with a certain probability (special case of a continuous phenotype)
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Calculating ESSs• e.g. hawk-dove game
earlier we calculated that evolutionarily stable state consist of an equilibrium prop. of V/C hawks • what if individuals play mixed strategies?
assume individual 1 plays hawk with prob. y1 and social interactant plays hawk with prob. y2, fitness of individual 1 is then w1(y1, y2)=w0+(1-y1).(1- y2).V/2+y1.(1- y2).V+y1. y2.(V-C)/2
• invasion fitness, i.e. fitness of individual playing hawk with prob. y in pop. where individuals play hawk with prob. Z is w(y,Z)=w1(y,Z)/w1(Z,Z)
• ESS occurs when
• true when z*=V/C, i.e. individuals playhawk with probability V/CThis is the mixed-strategy ESS. 0
),()(
Zy
y
ZywZD
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Extension for interactions between relatives:
inclusive fitness theory
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Problem
• in the previous slide the evolutionarily stable strategy that we found is the one that maximised personal reproduction
• but is it ever possible that animals do not strictly maximise their personal reproduction?
• William Hamilton: yes, if interactions occur between relatives. In that case we need to take into account that relatives contain copies of one's own genes. Can select for altruism (helping another at a cost to oneself) = inclusive fitness theory or "kin selection"
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Inclusive fitness theory
• condition for gene spread is given by inclusive fitness effect = effect on own fitness + effect on someone else's fitness.relatedness
• relatedness = probability that a copy of a rare gene is also present in the recipient
• e.g. gene for altruism selected for when
B.r > C = Hamilton's rule
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Calculating costs & benefits in Hamilton's rule
• e.g. hawk-dove gameassume individual 1 plays hawk with prob. y1 and social interactant plays hawk with prob. y2, fitness of individual 1 is then w1(y1, y2)=w0+(1-y1).(1- y2).V/2+y1.(1- y2).V+y1. y2.(V-C)/2and similarly fitness of individual 2 is given byw2(y1, y2)=w0+(1-y1).(1- y2).V/2+y2.(1- y1).V+y1. y2.(V-C)/2
• inclusive fitness effect of increasing one's probability of playing hawk
• ESS occurs when IF effect = 0z*=(V/C)(1-r)/(1+r) 0.
),(),(
1
212
1
211
ry
yyw
y
yyw
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Calculating relatedness
• Need a pedigree to calculate r that includes both the actor and recipient and that shows all possible direct routes of connection between the two
• Then follow the paths and multiply the relatedness coefficients within one path, sum across paths
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r = 1/2 x 1/2 = 1/4
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r = 1/2 x 1/2 + 1/2 x 1/2 = 1/2
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r = 1/2 x 1/2 + 1 x 1/2 = 3/4
(c) Full-sister in haplodiploid social insects
Queen Haploid father
1AB C
AC, BCAC
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Class-structured populations• sometimes a trait affects different classes of
individuals (e.g. age classes, sexes)• not all classes of individuals make the same
genetic contribution to future generations• e.g. a young individual in the prime of its life will
make a larger contribution than an individual that is about to die
• taken into account in concept of reproductive value. In Hamilton's rule we will use life-for-life relatedness = reproduce value x regression relatednesss
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E.g. reproductive value of males and females in haplodiploids
M Qx
MQ
frequency of allele in queens in next generation pf’=(1/2).pf+(1/2).pm frequency of allele in males in next generation pm’=pf
if we introduce a gene in all males in the first generation then we initially have pm=1, pf=0; after 100 generations we get pm=pf=1/3if we introduce a gene in all queens in the first generation then we initially have pm=0, pf=1; after 100 generations we get pm=pf=2/3
From this one can see that males contribute half as many genes to the future gene pool as queens. Hence their relative reproductive value is 1/2. Regression relatedness between a queen and a son e.g. is 1, but life-fore-life relatedness = 1 x 1/2 = 1/2
Formally reproductive value is given by the dominant left eigenvector of the gene transmission matrix A (=dominant right eigenvector of transpose of A).