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Partitioning the influence of ecology across scales on parasite evolution
Author(s) -
Greischar Megan A.,
BeckJohnson Lindsay M.,
Mideo Nicole
Publication year - 2019
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.13840
Subject(s) - biology , evolutionary ecology , ecology , transmission (telecommunications) , host (biology) , vector (molecular biology) , trait , population , investment (military) , evolutionary dynamics , malaria , population ecology , evolutionary biology , demography , genetics , gene , immunology , recombinant dna , sociology , politics , computer science , law , political science , electrical engineering , programming language , engineering
Vector‐borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human‐induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data‐driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life‐history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade‐off emerges between early and late infectiousness, and the optimal resolution of that trade‐off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.