1. Chapter 5: extensions of LMC

2. What a monster…

3. Local Mate Competition - quick recap

4. More Mums = More Sons

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

6. 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

7. 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

8. 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…

9. Sequential oviposition: Superparasitism Scenario: 2 females lay eggs on the same host sequentially time 1st female2nd female

10. 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

11. 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…

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

13. 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

14. 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

15. 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?

16. 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

17. 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

18. 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