Optimal temperature for malaria transmission is dramatically lower than previously predicted
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....
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| Published in | Ecology letters Vol. 16; no. 1; pp. 22 - 30 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.01.2013
Blackwell |
| Subjects | |
| Online Access | Get full text |
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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. |
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| AbstractList | 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 degree 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 degree C (6 degree C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 degree C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 degree C optimum and the decline above 28 degree C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission. 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. 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. 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. [PUBLICATION 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 A frica 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. |
| Author | Paaijmans, Krijn P. Smith, Thomas C. Balzer, Christian Ryan, Sadie J. Johnson, Leah R. Pawar, Samraat Ben-Horin, Tal Lafferty, Kevin D. McNally, Amy Mordecai, Erin A. de Moor, Emily |
| Author_xml | – sequence: 1 givenname: Erin A. surname: Mordecai fullname: Mordecai, Erin A. email: Mordecai@lifesci.ucsb.edu organization: Ecology, Evolution, and Marine Biology Department, University of California, CA, 93106, Santa Barbara, USA – sequence: 2 givenname: Krijn P. surname: Paaijmans fullname: Paaijmans, Krijn P. organization: Department of Entomology, Merkle Lab, Center for Infectious Disease Dynamics, Penn State University, University Park, PA, 16802, USA – sequence: 3 givenname: Leah R. surname: Johnson fullname: Johnson, Leah R. organization: Department of Ecology and Evolution, University of Chicago, 1101 E 57th Street, IL, 60637, Chicago, USA – sequence: 4 givenname: Christian surname: Balzer fullname: Balzer, Christian organization: Ecology, Evolution, and Marine Biology Department, University of California, CA, 93106, Santa Barbara, USA – sequence: 5 givenname: Tal surname: Ben-Horin fullname: Ben-Horin, Tal organization: Bren School of Environmental Science and Management, University of California, CA, 93106, Santa Barbara, USA – sequence: 6 givenname: Emily surname: de Moor fullname: de Moor, Emily organization: Geography Department, University of California, CA, 93106, Santa Barbara, USA – sequence: 7 givenname: Amy surname: McNally fullname: McNally, Amy organization: Geography Department, University of California, CA, 93106, Santa Barbara, USA – sequence: 8 givenname: Samraat surname: Pawar fullname: Pawar, Samraat organization: Department of Biomathematics, David Geffen School of Medicine, University of California, CA, 90095-1766, Los Angeles, USA – sequence: 9 givenname: Sadie J. surname: Ryan fullname: Ryan, Sadie J. organization: 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, NY, 13210, Syracuse, USA – sequence: 10 givenname: Thomas C. surname: Smith fullname: Smith, Thomas C. organization: Ecology, Evolution, and Marine Biology Department, University of California, CA, 93106, Santa Barbara, USA – sequence: 11 givenname: Kevin D. surname: Lafferty fullname: Lafferty, Kevin D. organization: Ecology, Evolution, and Marine Biology Department, University of California, 93106, Santa Barbara, CA, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26853511$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23050931$$D View this record in MEDLINE/PubMed |
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| Title | Optimal temperature for malaria transmission is dramatically lower than previously predicted |
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