| Summary: | Gametocytes, the transmission stage of malaria parasites, are generally rare in infections. I explore whether this may be a parasite adaptation that paradoxically maximises fitness. I present an optimality analysis of gametocyte investment based on a discretely formulated within-host model. I show that low gametocyte investment is predicted as a result of the trade-off that exists between producing gametocytes and increasing parasite numbers. I predict that gametocyte investment should decrease as maximum asexual density occurs later in infections and also as parasite fecundity rises. I address statistical problems with estimating gametocyte investment from blood smears. I also consider the simpler case of estimating parasitaemias and gametocytaemias. Traditional methods of counting parasites in smears can produce biased estimates of parasitaemia, gametocytaemia and gametocyte conversion. I introduce an alternative method of counting based on inverse sampling. This method is unbiased, is consistently precise and the most time-efficient method of counting. I used the inverse sampling method to estimate gametocyte conversion (the observed outcome of investment) in <i>P. chabaudi</i> infections in mice, which had been manipulated to alter the time of maximum asexual density. Gametocyte conversion showed two peaks. The timing of the peaks depended on the time of maximum asexual density. Maximum conversion decreased as the time to maximum asexual density rose as predicted by my optimality analysis. An interesting finding was that gametocyte conversion decreased after reaching a maximum. This result is counter to most life history theory. I suggest this indicates that survival of infections into a chronic phase may be an important component of fitness. Maximum conversion occurred after maximum asexual density. This leads me to propose that contrary to the common view, gametocyte investment may be suppressed at times when there is a high risk of parasite clearance. Understanding the reasons for gametocyte rarity may help to predict how <i>P. falciparum</i> will respond to intervention and suggest new methods of malaria control.
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