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      Thermal biology of mosquito‐borne disease

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          Abstract

          Abstract Mosquito‐borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait‐based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species – including globally important diseases like malaria, dengue, and Zika – synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23–29ºC and declining to zero below 9–23ºC and above 32–38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25–26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.

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          Novel climates, no-analog communities, and ecological surprises

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            Thermal Adaptation

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

              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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                Author and article information

                Journal
                Ecology Letters
                Ecol Lett
                Wiley
                1461-023X
                1461-0248
                July 08 2019
                July 08 2019
                Affiliations
                [1 ]Department of Biology Stanford University 371 Serra Mall Stanford CA USA
                [2 ]Department of Entomology and Center for Infectious Disease Dynamics Penn State University University Park PA16802 USA
                [3 ]Department of Geography and Emerging Pathogens Institute University of Florida Gainesville FL USA
                [4 ]Department of Statistics Virginia Polytechnic and State University 250 Drillfield Drive Blacksburg VA USA
                [5 ]Center for Research on Health in Latin America (CISeAL) Pontificia Universidad Católica del Ecuador Quito Ecuador
                [6 ]Department of Biological Sciences Eck Institute of Global HealthEnvironmental Change Initiative University of Notre Dame, Notre Dame IN USA
                [7 ]School of Life Sciences University of KwaZulu‐Natal Durban South Africa
                [8 ]Department of Ecology and Evolutionary Biology and Department of Biomathematics University of California Los Angeles Los Angeles CA90095USA
                [9 ]Santa Fe Institute 1399 Hyde Park Rd, Santa Fe NM 87501USA
                [10 ]Institute for Global Health and Translational Sciences SUNY Upstate Medical University SyracuseNY13210 USA
                Article
                10.1111/ele.13335
                d73eec4d-f1c0-4a1e-82b3-99ee0db1a2dd
                © 2019

                http://doi.wiley.com/10.1002/tdm_license_1.1

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