Evidence that implicit assumptions of ‘no evolution’ of disease vectors in changing environments can be violated on a rapid timescale
Author(s) -
Andrea Egizi,
Nina H. Fefferman,
Dina M. Fonseca
Publication year - 2015
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2014.0136
Subject(s) - climate change , vector (molecular biology) , context (archaeology) , adaptation (eye) , biology , temporal scales , selection (genetic algorithm) , habitat , ecology , evolutionary biology , computer science , machine learning , genetics , gene , paleontology , neuroscience , recombinant dna
Projected impacts of climate change on vector-borne disease dynamics must consider many variables relevant to hosts, vectors and pathogens, including how altered environmental characteristics might affect the spatial distributions of vector species. However, many predictive models for vector distributions consider their habitat requirements to be fixed over relevant time-scales, when they may actually be capable of rapid evolutionary change and even adaptation. We examine the genetic signature of a spatial expansion by an invasive vector into locations with novel temperature conditions compared to its native range as a proxy for how existing vector populations may respond to temporally changing habitat. Specifically, we compare invasions into different climate ranges and characterize the importance of selection from the invaded habitat. We demonstrate that vector species can exhibit evolutionary responses (altered allelic frequencies) to a temperature gradient in as little as 7-10 years even in the presence of high gene flow, and further, that this response varies depending on the strength of selection. We interpret these findings in the context of climate change predictions for vector populations and emphasize the importance of incorporating vector evolution into models of future vector-borne disease dynamics.
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