Chapter 5: extensions of LMC

What a monster…

Local Mate Competition - quick recap

More Mums = More Sons

How are these extensions different to ‘classic’ LMC? (what makes them interesting?)

Classic LMCWhat’s the difference? Partial LMCAll 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 HaystacksInteractions 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 LMCWhat’s the difference? Partial LMCAll 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 HaystacksInteractions 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 female2nd 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…

Superparasitism in Nasonia - Graph from Werren 1980: No. offspring 2nd female/ no. offspring 1st female ESS sex ratio for 2nd female

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