Chapter 5: extensions of LMC

What a monster…

Local Mate Competition - quick recap

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Foundress Number

Sex Ratio (proportion male)

More Mums = More Sons

How are these extensions different to ‘classic’ LMC?

(what makes them interesting?)

Classic LMC What’s the difference?

Partial LMC All mating at natal patch

Dispersal = some mating beyond patch

Variable clutch size

Equal number of offspring /female

Different f = different clutch size

Limited dispersal

Foundress females unrelated

Females may be related

Haystacks Interactions within one generation

Groups extends over multiple generations

Fertility insurance

Min no. sons = can mate all girls

May need more males to mate all

females

Classic LMC What’s the difference?

Partial LMC All mating at natal patch

Dispersal = some mating beyond patch

Variable clutch size

Equal number of offspring /female

Different f = different clutch size

Limited dispersal

Foundress females unrelated

Females may be related

Haystacks Interactions within one generation

Groups extends over multiple generations

Fertility insurance

Min no. sons = can mate all girls

May need more males to mate all

females

Extensions of LMC

- less well tested empirically

- and less good a fit of data to theory

- most commonly explained by a)information processing or

b)fertility insurance

- 1 example of each…

Sequential oviposition: Superparasitism

Scenario:

2 females lay eggs on the same host sequentially

time

1st female 2nd female

Predictions:

ESS sex ratio for 2nd female is influenced by clutch size of 1st female

If 2nd<1st, should lay less female biased sex ratio

Why?

Smaller proportion of offspring = weaker LMC

- less competition between sons

- less benefit to increasing number of daughters

Stu’s worked example

1st female: 2 males + 18 females = sex ratio of 0.1

2nd female lays only 1 egg… 2 options:

a) daughter: gains average female reprod value

b) son: gains 6 times reprod value of a female

Because of female biased sex ratio, son has 18/(2=1) =6 mates…

2nd female should ‘parasitise’ female biased SR of 1st

The larger the brood of the 2nd female, the greater LMC…

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Superparasitism in Nasonia - Graph from Werren 1980:

No. offspring 2nd female/ no. offspring 1st female

ES

S s

ex r

atio

for

2nd

fem

ale

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2 points to highlight:

On one hand, a good fit of data to theory…

On the other, % variance explained here ~ 30%

vs.

90% variance of data explained by LMC theory (last wk)

Why?

main probable reason = imperfect information processing

Further extensions: asymmetrical LMC

Sequential oviposition may lead to asynchronous offspring emergence

May affect male mating success &/or level of LMC faced

e.g. Patch of multiple hosts - Nasonia, Shuker et al.

- 1st clutch emerge & mate; females disperse, males stay

- 1st clutch males experience different level of LMC to 2nd

- predicts different optimal sex ratios…

Less female biased SR if other hosts on patch parasitised

But less biased than theory: constraints + info processing

Fertility insurance

LMC assumes the minimum predicted number of male offspring will be able to fertilise all female offspring…

Not always the case.

Malaria meets conditions for LMC - population subdivided

Expect variation in sex allocation with level of inbreeding

But much unexplained variation in sex ratio, e.g.

-through course of infection

-with level of host anaemia

-life history differences?

Fertility Insurance: Malaria

Sexual stage gametocytes taken up by vector in blood meal

Male & female gametes produced

Must leave blood cells & enter hostile environ to mate

Fertility insurance favoured for 2 reasons:

1. low number of functional male gametes produced ~ sperm limitation

Unsuccessful gamete production; poor motility; low survival

2. the number of gametes that interact is low

High mortality; low number in blood; limited search area

Theory predicts that:

-small number of interacting gametes (~small clutch size) =less female-biased sex ratio favoured: need to ensure female gametes are mated…

- these two factors can interact to favour even less female-biased sex ratio

Data so far:

- sex ratios in humans & lizards suggest low number of functional gametes

- bird malaria: less female biased SR than expected

- much variation in sex ratio taken at different stages of an infection

Predicts mean sex ratios well, even with complex individual sex ratios

2 most general reasons for data not matching theory:

1.limits on information processing &

2.constraints in small clutches ~ fertility insurance

Future directions

- quantitative tests of existing theory

- mechanistic Q’s for well-understood models e.g. assessing environ & sex ratio adjustment

- new theory for biology of less-understood systems?

LMC Summary