Optimal temperature for malaria transmission is dramatically lower than previously predicted

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Abstract

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission. © 2012 Blackwell Publishing Ltd/CNRS.

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Most cited references 32

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Thermal Adaptation

Michael Angilletta Jr. (2009)

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Systematic variation in the temperature dependence of physiological and ecological traits.

Anthony I. Dell, Samraat Pawar, Van Savage (2011)

To understand the effects of temperature on biological systems, we compile, organize, and analyze a database of 1,072 thermal responses for microbes, plants, and animals. The unprecedented diversity of traits (n = 112), species (n = 309), body sizes (15 orders of magnitude), and habitats (all major biomes) in our database allows us to quantify novel features of the temperature response of biological traits. In particular, analysis of the rising component of within-species (intraspecific) responses reveals that 87% are fit well by the Boltzmann-Arrhenius model. The mean activation energy for these rises is 0.66 ± 0.05 eV, similar to the reported across-species (interspecific) value of 0.65 eV. However, systematic variation in the distribution of rise activation energies is evident, including previously unrecognized right skewness around a median of 0.55 eV. This skewness exists across levels of organization, taxa, trophic groups, and habitats, and it is partially explained by prey having increased trait performance at lower temperatures relative to predators, suggesting a thermal version of the life-dinner principle-stronger selection on running for your life than running for your dinner. For unimodal responses, habitat (marine, freshwater, and terrestrial) largely explains the mean temperature at which trait values are optimal but not variation around the mean. The distribution of activation energies for trait falls has a mean of 1.15 ± 0.39 eV (significantly higher than rises) and is also right-skewed. Our results highlight generalities and deviations in the thermal response of biological traits and help to provide a basis to predict better how biological systems, from cells to communities, respond to temperature change.

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The ecology of climate change and infectious diseases

Kevin Lafferty (2009)

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Author and article information

Journal

Title: Ecology Letters

Abbreviated Title: Ecol Lett

Publisher: Wiley

ISSN: 1461023X

Publication date Created: January 2013

Publication date (Print): January 2013

Publication date (Electronic): October 11 2012

Volume: 16

Issue: 1

Pages: 22-30

Affiliations

[1 ]Ecology, Evolution, and Marine Biology Department; University of California; Santa Barbara; CA; 93106; USA

[2 ]Center for Infectious Disease Dynamics; Department of Entomology; Penn State University; Merkle Lab; University Park; PA; 16802; USA

[3 ]Department of Ecology and Evolution; University of Chicago; 1101 E 57th Street; Chicago; IL; 60637; USA

[4 ]Bren School of Environmental Science and Management; University of California; Santa Barbara; CA; 93106; USA

[5 ]Geography Department; University of California; Santa Barbara; CA; 93106; USA

[6 ]Department of Biomathematics; David Geffen School of Medicine; University of California; Los Angeles; CA; 90095-1766; USA

[7 ]Department of Environmental and Forest Biology and Division of Environmental Science; College of Environmental Science and Forestry; State University of New York; 1 Forestry Drive; Syracuse; NY; 13210; USA