in subdivided populations: kin selection meets bet-hedging

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Natural selection on fecundity variance

in subdivided populations:

kin selection meets bet-hedging

Laurent Lehmann

∗†

Fran¸cois Balloux

February 4, 2007

Department of Genetics, University of Cambridge, CB2 3EH Cambridge, UK, ll316@cam.ac.ukCorresponding author, phone +44-1223-332584, fax +44-1223-333992

Department of Genetics, University of Cambridge, CB2 3EH Cambridge, UK, fb255@mole.bio.cam.ac.uk

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Abstract

In a series of seminal papers, Gillespie (1974, 1975, 1977) challenged the notion that the “fittest” individuals are those that produce on average the highest number of offspring. He showed that in small populations, the variance in fecundity can deter-mine fitness as much as mean fecundity. One likely reason why Gillespie’s concept of within-generation bet-hedging has been largely ignored is the general consensus that natural populations are of large size. As a consequence, essentially no work has inves-tigated the role of the fecundity variance on the evolutionary stable state of life-history strategies. While typically large, natural populations also tend to be subdivided in local demes connected by migration. Here, we integrate Gillespie’s measure of selection for within-generation bet-hedging into the inclusive fitness and game theoretic mea-sure of selection for structured populations. The resulting framework demonstrates that selection against high variance in offspring number is a potent force in large, but structured populations. More generally, the results highlight that variance in offspring number will directly affect various life-history strategies, especially those involving kin interaction. The selective pressure on three key traits are directly investigated here, namely within-generation bet-hedging, helping behaviors and the evolutionary stable dispersal rate. The evolutionary dynamics of all three traits is markedly affected by variance in offspring number, though to a different extent and under different demo-graphic conditions.

Keywords: within-generation bet-hedging, variance in offspring number, helping, dispersal, kin selection, game theory, structured population, population genetics, Poisson distribution.

Introduction

Predicting the fate of an allele newly arisen through mutation is possibly the oldest and

1

most recurrent problem in population genetics. It has been known for long that when the

2

fecundity of individuals follows a Poisson distribution, the survival probability of a mutant

3

gene lineage increases with an increase in the mean fecundity of individuals (Haldane, 1927;

4

Fisher, 1930). It has been later recognized that when the variance in offspring number differs

5

from the mean, the survival probability of a mutant gene lineage decreases with an increase

6

in the variance in offspring number (Bartlett, 1955; Ewens, 1969). This can be understood

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by realizing that a random line of genes that failed to transmit itself at any generation in the

1

past is lost forever, and this independently of the mean fecundity of individuals. Intuitively,

2

we thus expect that between two competing strategies with equal mean fecundity, the one

3

with the lower variance will out-propagate its alternative.

4

In a series of insightful papers, Gillespie (1974, 1975, 1977) elucidated the conditions

5

under which natural selection will decrease the variance in offspring number in panmictic

6

populations of constant size. His key result is that the intensity of selection against the

7

variance is proportional to the inverse of population size. The selective pressure against the

8

variance thus decreases with increasing population size and vanishes for very large

popula-9

tions. This result can be understood by noting that in a population subject to regulation,

10

the expected number of recruited offspring of an individual (i.e., its fitness) depends on its

11

total random number of offspring produced relative to the total random number of offspring

12

produced by its competitors. When population size becomes large, the law of large numbers

13

informs us that the total number of offspring produced by competitors converges to a fixed

14

value, which is given by the mean fecundity and is devoid of any variance. In this situation,

15

a mutant allele has only to produce a mean number of offspring exceeding the mean of the

16

resident allele in order to invade the population. Only when the population is small enough

17

can the variance in offspring number play a role as important as the mean in determining the

18

fitness of individuals. While most natural populations are arguably large, they also tend to

19

be discontinuously distributed and typically consist of local demes connected by migration.

20

Such a spatial structure implies that demes can be of very small size while the population as

21

a whole remains large. This feature is renowned to affect the evolutionary process because it

22

alters the forces shaping the changes in gene frequencies such as selection or random genetic

23

drift (Whitlock, 2003; Cherry and Wakeley, 2003; Roze and Rousset, 2003). We thus expect

24

that the effect of the variance in fecundity on the average number of recruited offspring of

25

an individual will also depend on population structure.

26

The nature of selection on the variance in offspring number in subdivided populations

27

has been studied by Shpak (2005), who concluded that the conditions under which

high-28

variance strategies are outperformed by low-variance strategies depends on the timing of

29

the regulation of the size of the population. When density-dependent competition occurs

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before dispersal (i.e., soft selection), the variance in the number of offspring competing in

1

a deme is determined by the number of individuals reproducing locally. In this case, the

2

intensity of selection against the variance is proportional to the inverse of the size of the

3

pool of individuals contributing to this variance, that is, deme size. However, in natural

4

populations, density-dependent competition and competition between individuals can also

5

occur after dispersal. Under this process (called hard selection), individuals in each deme

6

send juveniles that will compete in other demes. Then, the individuals contributing to

7

the variance in the number of juveniles competing in a focal deme can be separated into

8

two pools: one small “resident” pool of individuals breeding in the focal deme and one large

9

“foreign” pool, consisting of all those individuals breeding in different demes and which affect

10

the demography of the focal deme through the dispersal of their progeny. This suggest that

11

the contribution to the variance in offspring number in a focal deme should mainly come

12

from the “resident” pool because the “foreign” pool, being very large, will contribute an

13

essentially fixed number of individuals to each deme. Thereby, demes affect each other

14

in a deterministic way when deme number is large (Chesson, 1981), a result that follows

15

again from the law of large numbers. The nature of selection on the variance in offspring

16

number under hard selection was recently investigated by Shpak and Proulx (2006) and their

17

formalization suggests that selection on the variance in fecundity decreases as migration rate

18

increases (i.e., the “foreign” pool of individuals becomes larger).

19

More generally, we also expect that the variance in fecundity will not only affect the

20

evolution of bet-hedging strategies but also other life-history traits. In particular, traits

21

involving kin interactions such as sex-ratio, dispersal or altruism depend on the local

relat-22

edness arising through population structure, which is itself a dynamical variable shaped by

23

the variance in fecundity. The effect of the variance in offspring number on the evolution of

24

such behaviors has been overlooked so far. Apart from sex-ratio homeostasis (Verner, 1965;

25

Taylor and Sauer, 1980) in panmicitic populations and sex-allocation (Proulx, 2000, 2004),

26

we are aware of no model investigating the role of the fecundity variance on the evolutionary

27

stable state of various life-history strategies.

28

The objective of the present paper is twofold. First, we integrate the classical measure

29

of selection for within-generation bet-hedging (Gillespie, 1974, 1975, 1977) into the

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sive fitness and game theoretic measure of selection for structured populations (e.g., Taylor,

1

1990; Taylor and Frank, 1996; Frank, 1998; Rousset and Billiard, 2000; Rousset, 2004, 2006).

2

This allows us to set up a framework for studying the effect of the mean and the variance in

3

fecundity for selection on various life-history traits and under different life-cycle assumptions

4

(e.g., timing of regulation, population structure). Second, we apply the resulting framework

5

to investigate the selective pressure on three life-history traits in subdivided populations

un-6

der soft and hard selections regime. These traits are within generation bet-hedging, helping

7

between deme-mates and dispersal. Our results for selection on the fecundity variance are

8

qualitatively consistent with those of Shpak (2005) and Shpak and Proulx (2006) but differ

9

quantitatively because the approach endorsed in this paper allows us to take the variance

10

in gene frequency between demes explicitly into account. Our results also indicate that the

11

magnitude of selection on both helping behaviors and dispersal rates is markedly affected

12

by the fecundity variance, and can even lead to outcomes qualitatively different from the

13

classical predictions considering mean fecundity only.

14

Model

15

Life cycle and change in gene frequency

16

Consider that the population consists of haploid organisms occupying an infinite number of

17

demes of sizeN that are connected by migration (finite deme number is considered in the

18

Appendix). The life cycle is punctuated by the following events. (1) Each adult produces

19

a large number of independently distributed juveniles. The mean and the variance of the

20

fecundity distribution of an adult depend on its own phenotype and on the phenotype

21

of all other deme-mates. (2) Each juvenile disperses independently from each other with

22

probabilitymto another deme. (3) Density-dependent ceiling regulation occurs so that only

23

N juveniles reach adulthood in each deme. The size of each deme is thus assumed to be

24

constant over time.

25

The evolution of a phenotypic traitzunder selection in this population can be described

26

by a gradual, step-by-step transformation caused by the successive invasion of mutant alleles

27

resulting in different phenotypic effects than resident alleles fixed in the population. The

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invasion of a mutant allele can in turn be ascertained by the change in its frequencypover

1

one generation, which for a mutant with small phenotypic effect δ(weak selection) can be

2

written as

3

∆p=δSp(1−p) +O(δ2). (1)

4

The term O(δ2) is a remainder and S is Hamilton’s (1964) inclusive fitness effect, which

5

measures the direction of selection on the mutant and also provides the first order

pheno-6

typic effect of the mutant on its probability of fixation when the population is of finite size

7

(Rousset, 2004, p. 99, pp. 108–109 and pp. 206–207; Roze and Rousset, 2003, 2004; Rousset,

8

2006). The inclusive fitness effect (S) can be computed by the direct fitness method (Taylor

9

and Frank, 1996; Rousset and Billiard, 2000), as

10 S≡ ∂w ∂z• + ∂w ∂zD 0 FST, (2) 11

wherew ≡w(z•, z0D) is a fitness function giving the expected number of adult offspring of

12

a focal individual (FI) and FST is the relatedness between the FI and another individual

13

randomly sampled from its deme (here Wright’s measure of population structure). The

14

partial derivatives ofware the effects of actors (i.e., individuals bearing the mutant allele)

15

on the fitness of the FI. The actors being the FI itself with phenotype denoted z•, and 16

the other individuals from the focal deme with average phenotype denotedz0D. The partial

17

derivatives can be evaluated at a candidate evolutionary stable strategyz?. This phenotype

18

towards which selection leads through small mutational steps is obtained by solvingS = 0 at

19

z•=z0D=z

?. In the next section we provide expressions for both the fitness functionwand

20

the relatednessFST in terms of the means and the variances of the fecundity distributions

21

of the individuals in the population.

22

Fitness function

23

The direct fitness function of a FI can be decomposed into two terms:

24

w=wp+wd. (3)

25

This formulation emphasizes that fitness depends on the expected number of FI’s offspring

26

recruited in the focal deme (wp) and on the ones reaching adulthood in a foreign deme after

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dispersal (wd). In the Appendix we show that when deme size is large, each component of

1

fitness can be expressed as a function of only the mean and the variance of the fecundity

2

distribution of the different individuals in the population affecting the fitness of the FI.

3

Neglecting terms of order 1/N2 and higher order, the philopatric component of fitness can

4 be written as 5 wp= (1−m)f• fp 1 + σ 2 p N f2 p ! − σ 2 •,p N f2 p (4) 6

where f• is the mean fecundity of the FI and fp= (1−m)f0+mf1 is the average of the

7

mean number of juveniles coming in competition in the focal deme (eq. 38 of the Appendix).

8

This number depends on the average mean fecundityf0 of individuals breeding in the focal

9

deme and on the average mean fecundityf1of individuals breeding in different demes. The

10

fitness functionwpalso involves the variance in the number of offspring of the FI coming in

11

competition in the focal deme

12

σ2,p= (1−m)mf•+ (1−m)2σ2•, (5)

13

where σ2 is the variance of the fecundity distribution of the FI (here, the variance of the

14

number of offspring produced before the dispersal stage). The first term (1−m)mf• in this 15

equation can be interpreted as the variance in offspring number entering in competition in

16

the focal deme and caused by dispersal being a random event. The second term in eq. 5 is

17

the additional variance in offspring number entering in competition in the focal deme and

18

resulting from fecundity being a random variable. Accordingly, when fecundity is a fixed

19

number and migration is random, we are left only with the first term while if fecundity is a

20

random number but a fixed proportion of juvenile migrate, we are left only with the second

21

term. The fitness function also depends on the average variance of the number of juveniles

22

entering in competition in the focal deme, which reads

23

σ2p= (1−m)mf0+ (1−m)2σ20+mf1, (6)

24

whereσ2

0 is the average variance in fecundity of individuals breeding in the focal deme.

25

The component of the fitness of the FI resulting from dispersing progeny reads

26 wd= mf• f1 1 + σ 2 d N f2 1 − σ 2 •,d N f2 1 , (7) 27

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where

1

σ2,d=mf• (8)

2

is the variance in the number of offspring of the FI entering in competition in other demes

3

(eq. 42 of the Appendix). This variance depends only on dispersal being a random event

4

and not on the variance in fecundity. This is so because the contribution of the variance in

5

fecundity to the variance in the number of competing offspring in a given deme depends on

6

the square of the migration rate to that deme (eq. 36 of the Appendix). Since in the infinite

7

island model two offspring descending from the same parent have a probability of zero of

8

emigrating to the same deme, the effect of the fecundity variance on the variance in the

9

number of competing offspring in a non-focal deme vanishes. Finally, we need the average

10

variance of the number of juveniles entering in competition in different demes, this is

11

σ2d= (1−(1−m)2)f1+ (1−m)2σ21 (9)

12

whereσ2

1 is the average variance in fecundity of individuals breeding in different demes.

13

Equations 4 and 7 highlight that both the means (f) and the variances (σ2) of the

14

fecundity distributions of individuals in the population affect the fitness of a FI. This result is

15

an approximation that follows from the assumption that demes are of large size. Conversely,

16

if demes are small, the fitness function w will also depend on the kurtosis, skewness and

17

other measures of the dispersion of the fecundity distributions (see eq. 26 of the Appendix).

18

By contrast, when fecundity follows a Poisson distribution, fitness depends only on mean

19

fecundity. This is an exact result (see section Poisson distributed fecundity of the Appendix),

20

holding whatever the sizes of demes, which is usually invoked in applications of inclusive

21

fitness theory (e.g., Comins et al., 1980; Rousset and Ronce, 2004) or population genetics

22

(e.g., Karlin and McGregor, 1968; Ewens, 1969; Gillespie, 1975; Ewens, 2004), because it

23

greatly simplifies the calculations. In order to investigate how a distribution of fecundity

24

with a variance in excess of a Poisson distributed fecundity might affect selection, we consider

25

here the fate of mutant alleles affecting three different life-history traits:

26

(a) The mutant results in both a reduction in the mean (byC) and the variance (byD)

27

of its random number of juveniles produced before dispersal.

28

(b) The mutant causes an action that reduces its mean fecundity by some cost C but

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which increases the mean fecundity of each individual in the deme (excluding the actor) by

1

a benefitB/(N−1).

2

(c) The mutant expresses a decrease (or increase) in the dispersal rate set by the resident

3

allele.

4

Assumptions for the variance in fecundity

5

According to our assumption that the deme sizes remain constant over time, the mean

6

fecundityf of individuals in a monomorphic population has to be large in order to cancel

7

out the fluctuations of deme sizes induced by demographic stochasticity. If the variance in

8

fecundity is of the same order of magnitude as the mean, all terms involving a variance in the

9

fitness functions (eq. 4 and eq. 7) will vanish because they are divided by the mean squared

10

(i.e.,σ2/f20 whenf becomes large). We are then left with a situation where the fitness

11

of a FI depends only on its mean fecundity of that of the other actors in the population.

12

However, when the variance in fecundity is of higher order of magnitude than the mean, the

13

coefficient of variation defined as

14

σv≡

σ

f (10)

15

(e.g., Lynch and Walsh, 1998, p. 23) remains positive under large mean fecundity (i.e.,

16

σv >0 for f large). For the rest of this paper, we assume that the fecundity distribution

17

has a variance of larger order of magnitude than the mean and that the mean fecundity is

18

finite but large enough to prevent demographic stochasticity.

19

Several distributions closely related to the Poisson distribution have a variance of larger

20

order of magnitude than the mean. Such distributions can be conveniently obtained by

mix-21

ing the rate of occurrence of birth eventsf of the Poisson distribution with other distribution

22

(i.e., mixed Poisson distributions, Willmot, 1986). For instance, the Negative-Binomial

dis-23

tribution, which is advocated to be the basis of the fertility in humans and other species

24

(Caswell, 2000), can be obtained by mixing the birth ratef by a Gamma distribution. This

25

distribution has meanf and variance f+f2/α, where the parameterαallows tuning this

26

variance away from that of a Poisson distribution (Fig. 1). When the mean becomes large,

27

the coefficient of variation of this distribution remains positive (σv →1 +f /α). Similarly,

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when clutch size follows a Poisson distribution and the whole clutch has a probability (1−s)

1

of complete failure, the resulting distribution has meansf and variancef s+f2s(1s).

2

Relatedness

3

The variance in fecundity will also affect the dynamics of relatedness between two individuals

4

sampled in the same deme after dispersal. At equilibrium, relatedness satisfies the recursion

5

6

FST= (1−m)2(Pr(C) + [1−Pr(C)]FST), (11)

7

where (1−m)2is the probability that the two individuals are of philopatric origin and Pr(C)

8

is the probability that they descend from the same parent.

9

Using eq. 10 and eq. 51 from the Appendix, assuming that both deme size and mean

10

fecundity are large, and keeping only terms of leading order we find that the coalescence

11 probability is given by 12 Pr(C) =1 +σ 2 v N , (12) 13 whereσ2

v is called the effective variance by Gillespie (1975, p. 407).

14

Using eq. 4, eq. 7 and eq. 12 we investigate in the next section the selective pressure on

15

the three life-history traits discussed above.

16

Examples

17

Within-generation bet-hedging

18

In this section, we investigate the conditions under which an organism is selected to decrease

19

its mean fecundity in order to reduce its variance in fecundity. The setting is the same as

20

the one investigated by Shpak and Proulx (2006). The fecundity means and variances of

21

the various classes of actors affecting the fitness of a FI bearing a mutant bet-hedging allele

22

are evaluated as is usually done by the direct fitness method (e.g., Frank, 1998; Rousset and

23

Billiard, 2000; Rousset, 2004) and are presented in Table 1. Substituting these functionals

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into the direct fitness function (eq. 3) allows us to calculate explicitly the selective pressure

1

on the mutant allele (eq. 2). Evaluating this selective pressure at the phenotypic value of

2

the resident allele (z•=z0D=z1= 0) and taking into account only terms of leading order

3

(i.e., neglecting terms of order 1/N2, 1/f2,1/(N f) and beyond), we find that the inclusive

4

fitness effect becomes

5 S=−C 1 +σ 2 v(1−m) 2 N ! +Dσ 2 v(1−m) 2 N +C(1−m) 2 FSTR, (13) 6 where 7 FSTR = 1 N + N1 N FST (14) 8

is the relatedness between the FI and an individual randomly sampled with replacement

9

from its deme (thus including the FI with probability 1/N) and

10 FST= 1 +σ2v (1−m)2 N(2−m)m . (15) 11

The inclusive fitness effectSis composed of three terms. First, there is a direct cost to the

12

FI stemming from the reduction in its (mean) fecundity. Second, there is a direct benefit to

13

the FI stemming from the reduction in its variance in fecundity, which is proportional to the

14

probability (1−m)2/Nthat two randomly sampled offspring in the focal deme descend from

15

the FI and on the coefficient of variation squared (i.e., effective fecundity). Third, we have to

16

account for an indirect benefit resulting from the reduction in competition in the focal deme

17

faced by the FI’s offspring, which results from its own and its relativesı reduced fecundity.

18

This decrease in kin competition is proportional to the probability (1−m)2that two offspring

19

produced in the focal deme compete against each other. Since we assumed large deme size,

20

there are no effects on fitness resulting from relatives lowering their fecundity variance as such

21

effects are of order 1/N2. However, the variance in fecundity increases relatedness (eq. 15)

22

and thus has an impact on the intensity of kin competition. Rearranging the inclusive fitness

23

effect, the low-variance mutant spreads when the threshold cost to benefit ratio satisfies

24 C D < σ2 v(1−m) 2 (2−m)m (N+ 1) (2−m)m−σ2 v(1−m) 2 (1−2 (2−m)m)−1. (16) 25

This threshold decreases with an increase in migration rate and deme size and with a decrease

26

in the variance in fecundity. The inequality cannot be satisfied when the coefficient of

27

variation is vanishingly small (σ2

v= 0) or when migration is complete (m= 1).

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While the intensity of selection on the variance in fecundity is proportional to the inverse

1

of population size in panmictic populations (Gillespie, 1974, 1975, 1977; Demetrius and

2

Gundlach, 2000; Demetrius, 2001), we see from eq. 13 that the intensity of selection on this

3

variance is proportional to (1−m)2/Nin subdivided populations when regulation occurs after

4

dispersal. Thus, selection on the variance decreases as the migration rate and/or deme size

5

increases. This is so because the contribution of the variance in fecundity of a focal individual

6

to the variance in the number of offspring competing in a given deme after dispersal depends

7

on the square of the migration rate to that deme. Under infinite island model assumptions,

8

this contribution vanishes for all demes except for the focal deme (compare eq. 5 and eq. 8).

9

The finding that selection on the variance is of intensity (1−m)2/Ncorroborates the results of

10

Shpak and Proulx (2006). Indeed, the two first term of the inclusive fitness effect is consistent

11

with eq. 20 of Shpak and Proulx (2006). However, we note that the approach endorsed here

12

takes into account the effect of the variance in gene frequency between demes on the expected

13

change of allele frequency at the level of the population given by eq. 1. Indeed, the third

14

term of the inclusive fitness effect, which involves kin competition, depends on the variance

15

in gene frequency among demes (i.e. FST) and is not considered by Shpak and Proulx (2006)

16

but its presence here is consistent with previous population genetic derivation of selection

17

on the mean fecundity in subdivided populations (e.g. Roze and Rousset, 2003; Rousset,

18

2004; Roze and Rousset, 2004). In the Appendix, we complement our analyses by presenting

19

the condition under which an organism is selected to decrease its mean fecundity in order

20

to reduce its variance in fecundity when regulation occurs before dispersal (soft selection).

21

Selection on the variance in fecundity then scales as 1/N as was already established by

22

Shpak (2005).

23

Helping behaviors

24

Here, we investigate the effect of the variance in fecundity on the condition under which an

25

organism should be prepared to sacrifice a fraction of its resources to help its deme-mates.

26

The setting is exactly the same as Taylor (1992a), except the assumptions regarding the

27

distribution of fecundity. While Taylor considers an infinite fecundity, we assume here as

28

in the previous section that the fecundity is finite but large and that its distribution has a

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variance in excess of a Poisson distributed fecundity. The means and the variances of the

1

fecundities of the various classes of individuals affecting the fitness of a FI are given in Table

2

1. Evaluating the selective pressure on the helping allele at the phenotypic value of the

3

resident allele (z•=z0D=z1= 0) and taking into account only terms to leading order, the

4

selective pressure on helping becomes

5 S=−C 1 +σ 2 v(1−m) 2 N ! +BFST−(1−m)2(B−C)FSTR, (17) 6 where FR

ST and FST are given by eq. 14 and eq. 15, respectively. This inclusive fitness

7

effect S is also composed of three terms: First, the direct cost of helping; second, the

8

benefit received by the FI from its neighbors; third, the cost of the increase in competition

9

in the focal deme faced by the FI’s offspring caused by him and its neighbors expressing

10

the helping trait. In Taylor’s (1992a) original setting we have FST = (1−m)2FSTR, which

11

inserted into eq. 17 cancels out all the benefit terms. However, in the present situation we

12

have FST > (1−m)2FSTR (see eq. 12) because relatedness is inflated due to the increased

13

frequency of coalescence events stemming from the higher variance in fecundity. This results

14

in a situation where the fecundity benefitB is no longer cancelled out by the concomitant

15

increase in kin competition. Rearranging this selective pressure, we find that helping spreads

16 when 17 C B < σ2v(1−m) 2 (2−m)m (N+ 1) (2−m)m−σ2 v(1−m) 2 (1−2 (2−m)m)−1 (18) 18

is satisfied. As in the preceding section, the threshold cost to benefit ratio decreases with an

19

increase in the migration rate and deme size and with a decrease in the variance in fecundity.

20

When the coefficient of variation is vanishingly small (σ2v →0), we have C/B <0 so that

21

the direction of selection on helping is determined solely by direct fecundity benefits. The

22

behavior is then selected for if−C >0, that is, if the number of juveniles counted before any

23

competition stage, is increased (Taylor, 1992a; Rousset, 2004). Here, helping can be selected

24

for at a direct fecundity cost to the actor wheneverσv2>0. We note that Taylor’s seminal

25

hard selection model and relaxations of its life-cycle assumptions has been investigated by

26

several authors (Taylor and Irwin, 2000; Irwin and Taylor, 2001; Perrin and Lehmann, 2001;

27

Rousset, 2004; Roze and Rousset, 2004; Gardner and West, 2006; Lehmann et al., 2006;

28

Lehmann, 2006).

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Dispersal

1

Finally, we examine the effect of the variance in fecundity on the evolutionary stable (ES)

2

dispersal rate. The setting is exactly the same as in Frank (1985, 1998), at the exception

3

that we assume again as in the previous sections that fecundity is finite but large and that its

4

distribution has a variance in excess of a Poisson distributed fecundity. The fitness functions

5

wof a focal parent bearing a mutant genotype affecting the dispersal rate of its offspring is

6

more complicated than those investigated so far because we have to take into account the

7

survival rates of juveniles during dispersal. Let us designate by d the evolving dispersal

8

rate and byd•the average dispersal rate of the offspring of a FI. A proportion 1−d• of the 9

offspring produced by the FI remain in the focal deme and a fractionsd• of these offspring 10

enter in competition in another deme after dispersal. The direct fitness function for the

11

evolution of dispersal can then be obtained by setting (1−m)≡1−d• in the numerator 12

of the first ratio in eq. 4 andm≡sd• in the numerator of the first ratio of eq. 7, while all 13

the remaining terms of these fitness functions are given in Table 2. The fitness function for

14

dispersal can then be written as

15 w= 1−d• (1−dR 0) +sd1 1 + σ 2 p N f2 p ! + sd• (1−d1) +sd1 1 + σ 2 d N f12 − σ 2 •,p N f2 p − σ 2 •,d N f12, (19) 16

which, for a large mean fecundity and vanishing coefficient of variation (σv = 0), reduces

17

to the classical fitness function for dispersal (e.g., Frank, 1998; Gandon and Rousset, 1999;

18

Perrin and Mazalov, 2000). Substituting the fitness function into eq. 2, evaluating the

19

selective pressure at the candidate ES dispersal rate, that is at d• = dR0 = d1 = d, and

20

taking into account only terms to leading order, we find that the selective pressure on

21 dispersal becomes 22 S=− 1−s 1−d(1−s)+ (1−d) (1−d(1−s))2F R ST+ σ2v(1−d)(1−d(1−s) +s) N(1−d(1−s))3 , (20) 23

whereFSTR andFSTare given by eq. 14 and eq. 15 with the backward migration rate expressed

24

as m≡sd/(1−d+sd), which represents the probability that an individual sampled in a

25

deme is an immigrant. This selective pressure on dispersal is decomposed into three terms.

26

First, the classical direct cost of increasing the dispersal rate, which varies directly with

27

the survival probabilitysduring dispersal. Second, the indirect benefit stemming from the

28

reduction in competition in the focal deme faced by the FI’s offspring, which results from his

(15)

and its neighbors offspring dispersing (e.g., Hamilton and May, 1977; Taylor, 1988; Frank,

1

1998; Gandon and Michalakis, 1999; Gandon and Rousset, 1999; Perrin and Mazalov, 2000).

2

Third, a benefit resulting from the dispersing progeny of the FI reducing the variance in its

3

number of offspring reaching adulthood in the focal deme. This term is new and is driven

4

by eq. 5. By dispersing, juveniles minimize the variance in the number of offspring of a

5

focal parent entering in competition in the focal deme. Since the variance in the number

6

of offspring entering locally in competition (eq. 5) dominates the variance in the number of

7

offspring entering in competition in different demes by dispersing (eq. 8), dispersal decreases

8

the overall variance in the number of offspring entering in competition. As a consequence,

9

fitness increases through dispersing and the ES dispersal rates are boosted by higher variance

10

in fecundity. Fig. 2 compares the ES dispersal rate obtained from the present model with the

11

classical ES dispersal rate (e.g., Hamilton and May, 1977; Frank, 1998; Perrin and Mazalov,

12

2000; Gandon and Rousset, 1999) obtained under vanishing coefficient of variation (σv2= 0

13

in eq. 20 and eq. 15).

14

Discussion

15

In this paper, we integrated the classical measure of selection for within-generation

bet-16

hedging (Gillespie, 1974, 1975, 1977) into the game theoretic and inclusive fitness measure

17

of selection for structured populations (e.g., Taylor, 1990; Taylor and Frank, 1996; Frank,

18

1998; Rousset and Billiard, 2000; Rousset, 2004, 2006). The resulting framework in which

19

fitness depends explicitly on both the means and the variances of the fecundity distribution

20

can be applied to study the evolution of various life-history traits (i.e., sex-ratio, helping,

21

reproductive effort), under soft and hard selection regimes. The selective pressure on three

22

traits where directly investigated here, namely within-generation bet-hedging, helping

be-23

haviors and the evolutionary stable dispersal rate. The results suggest that the evolutionary

24

dynamics of all these traits is affected by the variance in fecundity, though with variable

25

intensity and under different demographic conditions.

(16)

Within-generation bet-hedging

1

Natural selection selects against the variance in offspring number when panmictic

pop-2

ulations are not too large (Gillespie, 1974, 1975, 1977; Demetrius and Gundlach, 2000;

3

Demetrius, 2001). Our models suggest that selection against this variance in subdivided

4

populations scales as 1/N (eq. 57) when regulation occurs before dispersal (soft selection)

5

and scales as (1−m)2/N (eq. 13) when regulation occurs after dispersal (hard selection).

6

This is so because the contribution of the variance in fecundity of a focal individual to the

7

variance in its number of offspring competing in a given deme depends on the square of the

8

migration rate to that deme. Under soft selection, all the competition occurs in the focal

9

deme so that migration has no effect on selection. By contrast, under hard selection, the

10

offspring of an individual compete in different demes and, due to our infinite island model

11

assumptions, the contribution of the variance in fecundity to the various demes vanishes for

12

all demes except for the focal deme (compare eq. 5 and eq. 8). These results corroborate the

13

findings of both Shpak (2005) and Shpak and Proulx (2006) and illustrate that they can be

14

derived from the same model.

15

It is sometimes assumed in the literature that selection against the variance in offspring

16

number and its impact on various life-history trait will be of limited importance in natural

17

populations because they are of large size (e.g., Seger and Brockman, 1985). Our results and

18

those of Shpak (2005) and Shpak and Proulx (2006) demonstrate that selection against this

19

variance can perfectly operate in large but subdivided populations in the presence of both

20

soft and hard selection regimes. That selection can target the fecundity variance in large

21

populations may be relevant for the evolution of bet-hedging strategies in general and for the

22

theory of sex-ratio homeostasis (Verner, 1965; Taylor and Sauer, 1980) in particular. Indeed,

23

the results suggest that the demographic parameters of the population will condition whether

24

strategies involving probabilistic sex-ratio determination (i.e. each individual develops into

25

a male or a female with a given probability) will resist invasion by strategies involving a

26

rigid determination of it (i.e. a fixed proportion of offspring develop into either sex).

27

More generally, the fecundity variance might also play a role for the evolution of systems of

28

phenotypic determination. Systems of adaptive polymorphism are expected in the presence

29

of fluctuating environments or resource specialization and involve, among other mechanisms,

(17)

the random determination of an individual’s phenotype or its genetic determination (Grafen,

1

1999; Leimar, 2005). Since probabilistic phenotype determination can introduce additional

2

variance in the number of offspring entering in competition, the conditions under which

3

mixed evolutionary stable strategies are favored over pure genetic polymorphism are likely

4

to be dependent on the variance in the number of offspring sent into competition by each

5

strategy.

6

Helping behaviors

7

While the preceding examples of bet-hedging strategies resulted in directional selection

8

against the variance in fecundity, the selective pressure on other strategies can depend on

9

the magnitude of this variance. Helping behaviors involving kin interaction fall into this

cat-10

egory because their evolution depends on the relatedness between interacting individuals,

11

whose dynamic is determined by the probability of common ancestry within deme, itself a

12

function of the fecundity distribution (Ewens, 2004; Rousset, 2004). An increase in

fecun-13

dity variance decreases the number of effective ancestors within demes and thus increases

14

the relatedness between deme-mates possibly leading to an increase in the selective pressure

15

on helping behaviors.

16

Taylor (1992a,b) has demonstrated that when demes are of constant size and when

fe-17

cundity is infinite (i.e., no variance in fecundity), selection on helping is determined solely

18

by direct fecundity benefits. Here, Taylor’s assumption on fecundity was replaced by the

as-19

sumption that fecundity follows a distribution with finite but large mean and with a variance

20

exceeding the mean. Allowing for a non-vanishingly small coefficient of variation increases

re-21

latedness between deme members with the consequence that all kin selected effects, whether

22

positive or negative, are increased. Here, this translates in helping being selected for at a

23

direct fecundity cost to the actor with the intensity of selection on helping decreasing when

24

the variance in fecundity decreases (eq. 18). This result suggests that selection on helping is

25

also bound to depend on the mating system because the variance in the number of mating

26

partners obtained by males will increase relatedness within demes.

27

The finding that the magnitude of the variance in fecundity qualitatively affects the

28

outcome of selection on costly helping might help us to understand why different authors

(18)

have repeatedly reached contradictory results when investigating selection on helping under

1

apparently identical life cycles. While several authors have emphasized that unconditional

2

costly helping is counter selected when social interactions occur between semelparous

indi-3

viduals living in structured populations of constant size (Wilson et al., 1992; Taylor, 1992a,b;

4

Rousset, 2004; Lehmann et al., 2006), other authors have suggested that costly helping is

5

selected for in that case (Nowak and May, 1992; Killingback et al., 1999; Hauert and Doebeli,

6

2004). The results by the former group of authors are based on analytical models assuming

7

infinite and/or Poisson distributed fecundities. The results by the latter group are mostly

8

based on simulations. In this case, it remains unclear how the updating rules used by the

9

authors to simulate the transmission of strategies from one generation to the next in the

pop-10

ulation impact on relatedness and may thus result in significant departure from the Poisson

11

distributed fecundity assumption. For instance, copying the fittest neighbor will result in a

12

different dynamics of relatedness than copying neighbors with a probability proportional to

13

their fitness. These specific features might help to explain why some authors observed the

14

emergence of costly helping behaviors while others did not.

15

Dispersal

16

The present paper emphasizes that the variance in the number of offspring sent into

com-17

petition can be as important in determining fitness as the mean of such offspring (eq. 4 and

18

eq. 7). This variance will depend among other factors on the probability that an individual

19

reproduces, its offspring disperse, migrants survive dispersal and successful migrants survive

20

competition. While a near endless chain of stochastic events may affect individuals as they

21

move along their life cycle, we focused here only on the stochasticity induced by reproduction

22

and migration. Migration has a striking and non-intuitive impact on the total variance in

23

the number of offspring entering competition because it results in two distinct effects on this

24

variance. First, migration redistributes the variance in fecundity of an individual over the

25

whole population. Since the variance in the number of offspring competing in a given deme

26

after dispersal depends on the square of the migration rate to that deme, the contribution

27

of the variance in fecundity to the variance in competing offspring in that deme vanishes for

28

all demes except for the focal one, whenever there is a large number of demes (compare eq. 5

(19)

and eq. 8). Second, we assumed that migration itself is a probabilistic event, which thus

1

generate a variance in the number of competing offspring even for fixed fecundity schedules

2

(first term in eq. 5 and eq. 8). These two effects of migration on fitness will have two distinct

3

consequences for the evolution of stable dispersal rates.

4

First, assuming that each juvenile disperses independently from each other and that the

5

environment is constant, the evolutionary stable dispersal rate depends on a balance between

6

two opposing forces. There is a cost of dispersal resulting from reduced survival during

7

migration opposed by a benefit leading to local decrease in kin competition (e.g., Hamilton

8

and May, 1977; Frank, 1985, 1998; Gandon and Michalakis, 1999; Gandon and Rousset, 1999;

9

Perrin and Mazalov, 2000). Here, this balance is displaced in favor of increased dispersal

10

because the cost of dispersal is reduced as migration reduces the total variance in the number

11

of offspring of an individual sent into competition. By dispersing, the offspring of the focal

12

individual decrease the impact of the variance in fecundity on fitness. A large variance in

13

fecundity can result in a two-fold increase of the evolutionary stable dispersal rate (Fig. 2).

14

That the variance in fecundity can affect the candidate ESS strategy has already been shown

15

for models of sex-allocation (e.g., Proulx, 2000, 2004). Our result that the ES dispersal rate

16

is affected by the variance in fecundity might be of practical importance when evaluating

17

the causes of dispersal in natural populations and it suggests that the fecundity distribution

18

might explain a part of the observed variance in dispersal rates.

19

The second consequence of subdivided populations for the evolution of dispersal rates

20

stem from the fact that if migration is a probabilistic event, it increases the variance in

21

offspring number of an adult coming into competition. One might then wonder whether a

22

strategy resulting in a fixed proportion of individual dispersing would not be favored over a

23

strategy involving probabilistic dispersal, in complete analogy with the theory of sex-ratio

24

homeostasis (Taylor and Sauer, 1980), where the production of a fixed proportion of males

25

and females is favored over probabilistic sex-ratio determination. In the section entitled

26

“Dispersal homeostasis” of the Appendix, we constructed a model precisely along these lines

27

(see eq. 63), which demonstrates that dispersal homeostasis can indeed evolve but that the

28

selective pressure on it is only of order 1/(f N) wheref is the mean fecundity. This result

29

is in line with work by (Taylor and Sauer, 1980), who showed that selection on “sex-ratio

(20)

homeostasis” in panmictic populations is proportional to the inverse of the total number

1

f N of offspring born in a group. According to our assumption that both deme size and

2

fecundity is large, terms of such order were generally dismissed in the calculation of the

3

various selective pressures (eq. 13, eq. 17 and eq. 20). However, selection on both dispersal

4

and sex-ratio homeostasis might be effective in subdivided population when both the mean

5

fecundity and deme size are not too large.

6

Limitations and extensions of the model

7

All results derived in this paper are based on five main assumptions that greatly simplified

8

the analyses and that we now discuss. First, we assumed an island model of migration. This

9

assumption was introduced in order to highlight in a simple way the effects of the fecundity

10

variance on fitness in subdivided populations, although the model derived in the Appendix

11

is more general and allows for isolation by distance (e.g., stepping-stone dispersal). In this

12

case we expect selection on the variance in fecundity to be increased because the variance

13

in the number of offspring competing in a given deme after dispersal depends on the square

14

of the migration rate to that deme (eq. 36). This probability will not be vanishingly small

15

for migration at short spatial distances so that selection against the variance will generally

16

exceed the intensity (1−m)2/N established here for infinite island situations. Second,

17

we assumed that demes are of large size (terms of order 1/N2 are neglected, see section

18

Regulation of the Appendix). This assumption was introduced in order to focus primarily

19

on selection on the variance in fecundity and to neglect selection on other measures of

20

dispersion of the fecundity distribution and which are likely to matter when demes are of

21

small size. As can be noted by inspecting eq. 26, natural selection will not only reduce

22

the variance in fecundity but also favor positively skewed fecundity distributions that are

23

platykurtic. That is, distributions that are asymmetric with a fatter right tail and that are

24

less peaked than a normally distributed variable. This is intuitively expected because it

25

results in a shape of the fecundity distribution that reduces the likelihood of producing no

26

offspring at all. Exactly how natural selection will shape the fecundity distribution given

27

the constraints on reproductions remains to be investigated. Third, we assumed that the

28

mean fecundity is finite but large. This assumption was introduced in order to be able to

(21)

neglect demographic stochasticity and to obtain analytical expressions as simple as possible.

1

Under ceiling regulation, demographic stochasticity is completely prevented from occurring

2

for demes of large size as soon as individuals produce more than two offspring (Lehmann

3

et al., 2006). However, other forms of population regulation occur in nature, such as

density-4

dependent survival with no specific ceiling effect, which might result in wider population

5

fluctuations. Overall, allowing for low fecundities and small deme sizes should in principle

6

lead to situations where selection on the measures of statistical dispersions (e.g., variance,

7

skewness) is exacerbated. Fourth, we assumed that the variance in fecundity is of larger order

8

of magnitude than the mean so that the coefficient of variation (σv) is not vanishingly small

9

even when the mean fecundity is large. This assumption was introduced to ensure that the

10

variance of the fecundity distribution still impacts on fitness under our hypotheses of large

11

fecundities and demes sizes. A typical example of such a fecundity distribution is given by the

12

Negative-Binomial distribution, which follows from many models of reproduction (Caswell,

13

2000, p. 455), and has been advocated to be characteristic of the fertility in humans and

14

other species (Anscombe, 1949; Cerda-Flores and Davila-Rodriguez, 2000). That organisms

15

have a variance in fecundity in excess to the mean has also been reported in studies of

16

reproductive success (Crow and Kimura, 1970, Table 7.6.4.2.; Clutton-Brock, 1988). Fifth,

17

we assumed an infinite number of demes so that the effect of drift at the level of the total

18

population on gene frequency change is not taken into account. However, our direct fitness

19

derivation presented in the Appendix is framed within a finite population so that the inclusive

20

fitness effectS also allows us to evaluate the first order phenotypic effect of a mutant allele

21

on its probability of fixation (Rousset, 2004, 2006). The evaluation of the probability of

22

fixation itself deserves further formalizations that might be achieved by following along

23

the lines of the direct fitness approach to diffusion equations of Roze and Rousset (2003,

24

2004). We finally mention that we investigated here only three phenotypic traits, whose

25

evolution depends on the magnitude of relatedness (i.e.,FST) that is itself of function of the

26

variance in fecundity. However, the evolution of many other traits including sex-ratio,

mate-27

choice, harming behaviors such as spiteful acts, niche-constructing phenotypes or resource

28

exploitation curves depend on relatedness (e.g., Hamilton, 1971; Boomsma and Grafen,

29

1991; Taylor and Getz, 1994; Gandon and Michalakis, 1999; Ajar, 2003; Lehmann, 2006).

30

Consequently, the expression of all these traits is likely to be indirectly influenced by the

(22)

variance in fecundity.

1

Based on all these comments and the premise that there is as much genetic variation for

2

the variance in fecundity as there is for the mean, we expect that the results presented in this

3

paper are of relevance for natural systems. As a final note, we can thus only re-emphasize

4

Gillespie’s (1977) view that the role of the variance deserves a more prominent place in our

5

thinking about evolutionary processes.

6

Acknowledgements

7

We thank one anonymous reviewer for helpful comments on this manuscript. LL

acknowl-8

edges financial support from from the Swiss NSF.

(23)

References

1

Ajar, E. 2003. Analysis of disruptive selection in subdivided populations. Bmc Evolutionary 2

Biology 3.

3

Anscombe, F. A. 1949. The statistical analysis of insect counts based on the Negative

4

Binomial. Biometrics 5:165–173.

5

Bartlett. 1955. An introduction to stochastic processes. Cambridge University Press,

Cam-6

bridge.

7

Boomsma, J. J. and A. Grafen. 1991. Colony-level sex-ratio selection in the eusocial

hy-8

menoptera. Journal of Evolutionary Biology 4:383–407.

9

Caswell, H. 2000. Matrix population models. Sinauer Associates, Massachusetts.

10

Cerda-Flores, R. M. and M. I. Davila-Rodriguez. 2000. Natural fertility in northeastern

11

Mexico. Archives of Medical Research 31:599–604.

12

Cherry, J. L. and J. Wakeley. 2003. A diffusion approximation for selection and drift in a

13

subdivided population. Genetics 163:421–428.

14

Chesson, P. L. 1981. Models for spatially distributed populations: the effect of within-patch

15

variability. Theoretical Population Biology 19:288–325.

16

Clutton-Brock, T. H. (ed.). 1988. Reproductive success. The University of Chicago Press,

17

Chicago.

18

Comins, H. N., W. D. Hamilton, and R. M. May. 1980. Evolutionarily stable dispersal

19

strategies. Journal ot Theoretical Biology 82:205–230.

20

Crow, J. F. and M. Kimura. 1970. An introduction to population genetics theory. Harper

21

and Row, New York.

22

Demetrius, L. 2001. Mortality plateaus and directionality theory. Proceedings of the Royal 23

Society of London Series B-Biological Sciences 268:2029–2037.

24

Demetrius, L. and V. M. Gundlach. 2000. Game theory and evolution: finite size and

25

absolute fitness measures. Mathematical Bioscences 168:9–38.

(24)

Ewens, W. J. 1969. Population genetics. Methuen, London.

1

Ewens, W. J. 2004. Mathematical Population Genetics. Springer-Verlag, New York.

2

Fisher, R. A. 1930. The genetical theory of natural selection. Clarendon Press, Oxford.

3

Frank, S. A. 1985. Dispersal polymorphism in subdivided populations.Journal of Theoretical 4

Biology 122:303–309.

5

Frank, S. A. 1998. Foundations of social evolution. Princeton University Press, Princeton,

6

NJ.

7

Gandon, S. and Y. Michalakis. 1999. Evolutionary stable dispersal rate in a metapopulation

8

with extinction and kin competition. Journal of Theoretical Biology 199:275–290.

9

Gandon, S. and F. Rousset. 1999. Evolution of stepping-stone dispersal rates. Proceedings 10

of the Royal Society of London Series B-Biological Sciences221:2507–2513.

11

Gardner, A. and S. A. West. 2006. Demography, altruism, and the benefits of budding.

12

Journal of Evolutionary Biology 19:1707–1716.

13

Gillespie, J. H. 1974. Natural selection for within-generation variance in offspring number.

14

Genetics 76:601–606.

15

Gillespie, J. H. 1975. Natural selection for within-generation variance in offspring number

16

2. Discrete haploid models. Genetics 81:403–413.

17

Gillespie, J. H. 1977. Natural selection for variances in offspring numbers - new evolutionary

18

principle. American Naturalist 111:1010–1014.

19

Grafen, A. 1999. Formal Darwinism, the individual-as-maximizing-agent analogy and

bet-20

hedging.Proceedings of the Royal Society of London Series B-Biological Sciences 266:799–

21

803.

22

Grimmet, G. and D. Stirzaker. 2001. Probability and Random Processes. Oxford University

23

Press, Oxford.

24

Haldane, J. B. S. 1927. A mathematical theory of natural and artificial selection. V. Selection

25

and mutation. Proceedings of the Cambridge Philosophical Society 23:838–844.

(25)

Hamilton, W. D. 1964. The genetical evolution of social behaviour, I.Journal of Theoretical 1

Biology 7:1–16.

2

Hamilton, W. D. 1971. Selection of selfish and altruistic behaviour in some extreme models.

3

In Eisenberg, J. and W. Dillon (eds.), Man and beast: comparative social behavior, pp.

4

59–91. Smithsonian Institutions Press, Washington, DC.

5

Hamilton, W. D. and R. M. May. 1977. Dispersal in stable habitats. Nature 269:578–581.

6

Hauert, C. and M. Doebeli. 2004. Spatial structure often inhibits the evolution of cooperation

7

in the snowdrift game. Nature 428:643–646.

8

Irwin, A. J. and P. D. Taylor. 2001. Evolution of altruism in stepping-stone populations

9

with overlapping generations. Theoretical Population Biology 60:315–325.

10

Karlin, S. and J. McGregor. 1968. The role of the Poisson progeny distribution in population

11

genetic models. Mathematical Bioscences 2:11–17.

12

Karlin, S. and H. M. Taylor. 1981. A second course in stochastic processes. Academics

13

Press, New York.

14

Killingback, T., M. Doebeli, and N. Knowlton. 1999. Variable investment, the continuous

15

Prisoner’s Dilemma, and the origin of cooperation. Proceedings of the Royal Society of 16

London Series B-Biological Sciences 266:1723–1728.

17

Lehmann, L. 2006. The evolution of trans-generational altruism: kin selection meets niche

18

construction. Journal of Evolutionary Biology In press.

19

Lehmann, L., N. Perrin, and F. Rousset. 2006. Population demography and the evolution

20

of helping behaviors. Evolution 60:1137–1151.

21

Leimar, O. 2005. The evolution of phenotypic polymorphism: randomized strategies versus

22

evolutionary branching. American Naturalist 165:669–681.

23

Lynch, M. and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer,

24

Massachusetts.

25

Nowak, M. A. and R. M. May. 1992. Evolutionary games and spatial chaos.Nature 359:826–

26

829.

(26)

Perrin, N. and L. Lehmann. 2001. Is sociality driven by the costs of dispersal or the benefits of

1

philopatry? A role for kin discrimination mechanisms. American Naturalist 158:471–483.

2

Perrin, N. and V. Mazalov. 2000. Local competition, inbreeding and the evolution of

sex-3

biased dispersal. The American Naturalist 155.

4

Proulx, S. R. 2000. The ESS under spatial variation with application to sex allocation.

5

Theoretical Population Biology 58:33–47.

6

Proulx, S. R. 2004. Sources of stochasticity in models of sex allocation in spatially structured

7

populations. Journal of Evolutionary Biology 17:924–930.

8

Rousset, F. 2003. A minimal derivation of convergence stability measures. Journal of 9

Theoretical Biology 221:665–668.

10

Rousset, F. 2004. Genetic structure and selection in subdivided populations. Princeton

11

University Press, Princeton, NJ.

12

Rousset, F. 2006. Separation of time scales, fixation probabilities and convergence to

evolu-13

tionarily stable states under isolation by distance.Theoretical Population Biology69:165–

14

179.

15

Rousset, F. and S. Billiard. 2000. A theoretical basis for measures of kin selection in

sub-16

divided populations: finite populations and localized dispersal. Journal of Evolutionary 17

Biology 13:814–825.

18

Rousset, F. and O. Ronce. 2004. Inclusive fitness for traits affecting metapopulation

demog-19

raphy. Theoretical Population Biology 142:1357–1362.

20

Roze, D. and F. Rousset. 2003. Selection and drift in subdivided populations: A

straightfor-21

ward method for deriving diffusion approximations and applications involving dominance,

22

selfing and local extinctions. Genetics 165:2153–2166.

23

Roze, D. and F. Rousset. 2004. The robustness of Hamilton’s rule with inbreeding and

24

dominance: Kin selection and fixation probabilities under partial sib mating. American 25

Naturalist 164:214–231.

(27)

Seger, J. and H. J. Brockman. 1985. What is bet-heging? In Dawkins, R. and M. Ridley

1

(eds.),Oxford Surveys in Evolutionary Biology in evolutionary biology. Oxford Unversity

2

Press, Oxford.

3

Shpak, M. 2005. Evolution of variance in offspring number: The effects of population size

4

and migration. Theory in Biosciences 124:65–85.

5

Shpak, M. and S. R. Proulx. 2006. The role of life cycle and migration in selection for

6

variance in offspring number. Bulletin of Mathematical Biology In press.

7

Taylor, P. 1990. Allele-frequency change in a class-structured population. American Natu-8

ralist 135:95–106.

9

Taylor, P. and A. Sauer. 1980. The selective advantage of sex ratio homeostasis. American 10

Naturalist 116:305–310.

11

Taylor, P. D. 1988. An inclusive fitness model for dispersal of offspring.Journal of Theoretical 12

Biology 130:363–378.

13

Taylor, P. D. 1992a. Altruism in viscous populations - an inclusive fitness model. Evolution-14

ary Ecology 6:352–356.

15

Taylor, P. D. 1992b. Inclusive fitness in a homogeneous environment. Proceedings of the 16

Royal Society of London Series B-Biological Sciences 240:299–302.

17

Taylor, P. D. and S. A. Frank. 1996. How to make a kin selection model. Journal of 18

Theoretical Biology 180:27–37.

19

Taylor, P. D. and W. M. Getz. 1994. An Inclusive fitness model for the evolutionary

advan-20

tage of sibmating. Evolutionary Ecology 8:61–69.

21

Taylor, P. D. and A. J. Irwin. 2000. Overlapping generations can promote altruistic behavior.

22

Evolution 54:1135–1141.

23

Verner, J. 1965. Selection for the sex ratio. American Naturalist 99:419–21.

24

Wade, M. J. 1985. Soft selection, hard selection, kin selection, and group selection.American 25

Naturalist 125:61–73.

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Whitlock, M. C. 2003. Fixation probability and time in subdivided populations. Genetics 1

164:767–779.

2

Willmot, G. 1986. Mixed compound poisson distributions. Astin Bulletin 16:59–79.

3

Wilson, D., G. Pollock, and L. Dugatkin. 1992. Can altruism evolve in purely viscous

4

populations. Evolutionary Ecology 6:331–341.

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Number J of juveniles produced

P

ro

b

ab

ili

ty

5 10 15 20 25 30 0.02 0.04 0.06 0.08 0.1 0.12 0.14

Figure 1: Negative-Binomial fecundity probability distribution Pr(J) =

Γ(α+J) Γ(α)J! f α J 1 +αf −(α+J)

for the number J of juveniles produced by an individual. The mean is given byf and the variance by f +f2/α. For all curves we have a mean of f = 10, whileα=∞for the curve with the highest peak (i.e., Poisson distribution), α= 5 for the curve with intermediate peak andα= 1.5 for the curve with the lowest peak.

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Patch size

ES

d

is

p

er

sa

l r

at

e

20 40 60 80 100 0.2 0.4 0.6 0.8 1

Figure 2: Evolutionary stable dispersal rate graphed as a function of deme size N with survival rate set to s = 0.9. The plain line is the classical ES dispersal rate obtained by assuming that the progeny distribution is Poisson or has an infinite mean. The dotted line (curve in between both other curves) corresponds to the ES dispersal rate whenσ2v = 1/5

while the dashed line corresponds to the ES dispersal rate when σ2

v = 1/1.5. Fecundity

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Table 1

1

Symbol Value for within-generation bet-hedging Value for Helping

f• f(1−Cz•) f(1 +Bz0D−Cz•) f0 f(1−CzR0) f(1 + (B−C)zR0) f1 f(1−Cz1) f(1 + (B−C)z1) σ2 • σ2(1−Dz•) σ2 σ2 0 σ2(1−DzR0) σ2 σ21 σ2(1−Dz1) σ2

f Baseline mean fecundity of each individual in a monomorphic population.

σ2 Baseline fecundity variance of each individual

in a monomorphic population. z• Phenotype of a focal individual.

zR

0 Average phenotype of individuals from

the focal deme (zR= 1 Nz•+ N−1 N zD 0). zD

0 Average phenotype of individuals.

from the focal deme but excluding the FI.

z1 Average phenotype of individuals from different demes.

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Table 2

1

Symbol Value for dispersal

f• f fp f(1−dR0) +sd1 f1 f[(1−d1) +sd1] σ2 •,p (1−d•)d•f+ (1−d•)2σ2 σ2 •,d sd•f σ2p (1−dR0)d R 0f+ (1−d R 0) 2σ2+sd 1f σ2 d (1−d1)d1f+ (1−d1)2σ2+sd1f

d• Dispersal rate of the offspring of the focal individual.

dR

0 Average dispersal rate of the offspring of individuals

from the focal deme (dR= 1 Nd•+ N−1 N dD 0).

d1 Average dispersal rate of the offspring of individuals

from different demes.

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Appendix

1

Fitness function

2

In this appendix, we derive an expression for the direct fitness function w (eq. 2), which

3

involves both the mean and the variance of the number of offspring produced by a focal

4

individual and which generalizes the derivation of Gillespie (1975) to subdivided populations

5

(see also Shpak and Proulx, 2006). We assume that the mean fecundity of individuals is

6

finite but sufficiently large to prevent any demographic stochasticity. Accordingly, each

7

deme will be treated as being of constant sizeN. This assumption implies that the model

8

is not exact but provides a balance between accuracy and complexity. An exact model can

9

be obtained by recasting the forthcoming derivation within the framework of the general

10

metapopulation model of Rousset and Ronce (2004) by conditioning the expressions for

11

fitness on demographic states. For completeness, we first consider that the population is

12

constituted of a finite numberndof demes and then take the infinite island limit (nd→ ∞).

13

We now follow the events of the life cycle presented in the main text in reverse order.

14

Regulation

15

The expectation of the random numberNijxof offspring surviving regulation in demeiand

16

descending from individualxbreeding in demej can be written as

17 E[Nijx] = X Ji X Jijx

E[Nijx|Jijx, Ji] Pr(Jijx|Ji) Pr(Ji), (21)

18

where Pr(Jijx | Ji) Pr(Ji) is the joint probability that Jijx offspring of individual xenter

19

in competition in deme iamong a total number ofJi offspring and E[Nijx|Jijx, Ji] is the

20

conditional expectation ofNijx givenJijxand

21 Ji= nd X j=1 N X y=1 Jijy, (22) 22

where each random variableJijy is assumed to be independently distributed.

23

Density-dependent regulation is assumed to affect each individual independently and

24

equally. According to this assumption, the conditional expectation of the number Nijx of

Figure

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References

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