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Predicting optimal transmission investment in malaria parasites
Author(s) -
Greischar Megan A.,
Mideo Nicole,
Read Andrew F.,
Bjørnstad Ottar N.
Publication year - 2016
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/evo.12969
Subject(s) - biology , investment (military) , transmission (telecommunications) , resource allocation , malaria , production (economics) , parasite hosting , plasmodium (life cycle) , obligate parasite , ecology , host (biology) , economics , microeconomics , immunology , computer science , market economy , telecommunications , politics , world wide web , political science , law
In vertebrate hosts, malaria parasites face a tradeoff between replicating and the production of transmission stages that can be passed onto mosquitoes. This tradeoff is analogous to growth‐reproduction tradeoffs in multicellular organisms. We use a mathematical model tailored to the life cycle and dynamics of malaria parasites to identify allocation strategies that maximize cumulative transmission potential to mosquitoes. We show that plastic strategies can substantially outperform fixed allocation because parasites can achieve greater fitness by investing in proliferation early and delaying the production of transmission stages. Parasites should further benefit from restraining transmission investment later in infection, because such a strategy can help maintain parasite numbers in the face of resource depletion. Early allocation decisions are predicted to have the greatest impact on parasite fitness. If the immune response saturates as parasite numbers increase, parasites should benefit from even longer delays prior to transmission investment. The presence of a competing strain selects for consistently lower levels of transmission investment and dramatically increased exploitation of the red blood cell resource. While we provide a detailed analysis of tradeoffs pertaining to malaria life history, our approach for identifying optimal plastic allocation strategies may be broadly applicable.