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Present and future projections of habitat suitability of the Asian tiger mosquito, a vector of viral pathogens, from global climate simulation.

Proestos Y, Christophides GK, Ergüler K, Tanarhte M, Waldock J, Lelieveld J - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

Bottom Line: Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past.Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability.From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km(2) will potentially be exposed to Ae. albopictus.

View Article: PubMed Central - PubMed

Affiliation: Computation-based Science and Technology Research Center (CaSToRC), The Cyprus Institute, 20 Konstantinou Kavafi Street, 2121 Aglantzia, Nicosia, Cyprus y.proestos@cyi.ac.cy.

ABSTRACT
Climate change can influence the transmission of vector-borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian tiger mosquito (Aedes albopictus), which can transmit pathogens that cause chikungunya, dengue fever, yellow fever and various encephalitides. Using a general circulation model at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the twenty-first century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km(2) will potentially be exposed to Ae. albopictus. The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

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Comparing the ERA-Interim (EI) data with the model simulation over the period 2000–2009. (a) Mean near-surface temperature difference. (b) Average precipitation flux difference. Areas with hsi less than 10% are not considered. The same maps but with all areas included are shown in the electronic supplementary material, figures S12 and S13.
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RSTB20130554F2: Comparing the ERA-Interim (EI) data with the model simulation over the period 2000–2009. (a) Mean near-surface temperature difference. (b) Average precipitation flux difference. Areas with hsi less than 10% are not considered. The same maps but with all areas included are shown in the electronic supplementary material, figures S12 and S13.

Mentions: The scarcity of global gridded observational data for the meteorological fields, required for the vector distribution model, makes the evaluation of our high-resolution global datasets rather difficult. However, the EI dataset is considered the best alternative for comparison. The EI dataset is provided on a coarser spatial grid than our climate model, corresponding to a horizontal resolution of approximately 75 km (at the Equator), where data were stored with a frequency of 6 hours. First, we evaluate our results against the near-surface temperature field from the EI dataset for the relevant period of 2000–2009. This was done after our data had been (bi-linearly) interpolated in time and in grid space, respectively. Figure 2a displays the mean difference over the 10-year period (2000–2009). For the two results, we find a highly significant spatial (grid space) correlation, with Pearson's r-test correlation coefficient2, r = 0.99 [99.9%CI, p < 0.001] showing that differences are generally small. Monthly climatologies for relevant locations (green dots in figure 2) in all continents, for the decade 2000–2009, are shown in figure 3a. The comparison of the EMAC computed and EI assimilated seasonal cycles corroborates the good agreement, well within the 1σ variability, although in individual locations small biases can occur, which are, however, unlikely to have a significant effect on the results of the habitat suitability calculations.Figure 2.


Present and future projections of habitat suitability of the Asian tiger mosquito, a vector of viral pathogens, from global climate simulation.

Proestos Y, Christophides GK, Ergüler K, Tanarhte M, Waldock J, Lelieveld J - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

Comparing the ERA-Interim (EI) data with the model simulation over the period 2000–2009. (a) Mean near-surface temperature difference. (b) Average precipitation flux difference. Areas with hsi less than 10% are not considered. The same maps but with all areas included are shown in the electronic supplementary material, figures S12 and S13.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4342960&req=5

RSTB20130554F2: Comparing the ERA-Interim (EI) data with the model simulation over the period 2000–2009. (a) Mean near-surface temperature difference. (b) Average precipitation flux difference. Areas with hsi less than 10% are not considered. The same maps but with all areas included are shown in the electronic supplementary material, figures S12 and S13.
Mentions: The scarcity of global gridded observational data for the meteorological fields, required for the vector distribution model, makes the evaluation of our high-resolution global datasets rather difficult. However, the EI dataset is considered the best alternative for comparison. The EI dataset is provided on a coarser spatial grid than our climate model, corresponding to a horizontal resolution of approximately 75 km (at the Equator), where data were stored with a frequency of 6 hours. First, we evaluate our results against the near-surface temperature field from the EI dataset for the relevant period of 2000–2009. This was done after our data had been (bi-linearly) interpolated in time and in grid space, respectively. Figure 2a displays the mean difference over the 10-year period (2000–2009). For the two results, we find a highly significant spatial (grid space) correlation, with Pearson's r-test correlation coefficient2, r = 0.99 [99.9%CI, p < 0.001] showing that differences are generally small. Monthly climatologies for relevant locations (green dots in figure 2) in all continents, for the decade 2000–2009, are shown in figure 3a. The comparison of the EMAC computed and EI assimilated seasonal cycles corroborates the good agreement, well within the 1σ variability, although in individual locations small biases can occur, which are, however, unlikely to have a significant effect on the results of the habitat suitability calculations.Figure 2.

Bottom Line: Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past.Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability.From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km(2) will potentially be exposed to Ae. albopictus.

View Article: PubMed Central - PubMed

Affiliation: Computation-based Science and Technology Research Center (CaSToRC), The Cyprus Institute, 20 Konstantinou Kavafi Street, 2121 Aglantzia, Nicosia, Cyprus y.proestos@cyi.ac.cy.

ABSTRACT
Climate change can influence the transmission of vector-borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian tiger mosquito (Aedes albopictus), which can transmit pathogens that cause chikungunya, dengue fever, yellow fever and various encephalitides. Using a general circulation model at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the twenty-first century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that approximately 2.4 billion individuals in a land area of nearly 20 million km(2) will potentially be exposed to Ae. albopictus. The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

Show MeSH
Related in: MedlinePlus