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Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy.

Rosà R, Marini G, Bolzoni L, Neteler M, Metz M, Delucchi L, Chadwick EA, Balbo L, Mosca A, Giacobini M, Bertolotti L, Rizzoli A - Parasit Vectors (2014)

Bottom Line: Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance.Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology.Proximity to urban areas was not a significant predictor in any of our models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Biodiversità ed Ecologia Molecolare, Centro Ricerca e Innovazione, Fondazione Edmund Mach, San Michele all'Adige, TN, Italia. roberto.rosa@fmach.it.

ABSTRACT

Background: West Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk.

Methods: We analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields.

Results: Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C).

Conclusions: Climate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

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Association between the start of the mosquito season and environmental variables. Panels a-c show model predictions; panels d-f show partial residuals. The first column (a,d) shows the association between the start of the season and temperature (LST8–19), the second (b,e) shows the association with NDWI10–21 and the third (c,f) shows the association with precipitation (DAY_PREC6–17). Note that all plots show transformed data on the y axis (i.e. x1.3); back transformed values are presented in the text to assist interpretation.
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Figure 3: Association between the start of the mosquito season and environmental variables. Panels a-c show model predictions; panels d-f show partial residuals. The first column (a,d) shows the association between the start of the season and temperature (LST8–19), the second (b,e) shows the association with NDWI10–21 and the third (c,f) shows the association with precipitation (DAY_PREC6–17). Note that all plots show transformed data on the y axis (i.e. x1.3); back transformed values are presented in the text to assist interpretation.

Mentions: For the start of the season (ON) 32 full models were produced and a single best model was selected, explaining 26% (R2 = 0.258, Akaike weight = 0.96) of the variance; remaining models had ΔAIC > 4 and were disregarded (see section C of Additional file 1 for more details). Model outputs (Table 1) are therefore based on a single model, rather than averages from multiple models as elsewhere. Within the measured range of environmental data, temperature had the greatest effect on the start of the season. Higher spring temperatures were associated with an earlier start to the season, such that an increase of 5°C in LST8–19 (from 11 to 16°C) predicts the start of the season some 14 days earlier (a shift in the average ON from day 187 to 173) (Figure 3a). Increasing NDWI also predicts an earlier start to the season, such that a shift in NDWI10–21 from -0.1 to +0.06 led to a start of the season 10 days earlier (Figure 3b), while more days of precipitation delayed the start of the season such that an increase in DAY_PREC6–17 from 14 to 37 days of precipitation during the 12 week period led to a delay in the start of the season of 10 days (Figure 3c). All terms selected in the best models (LST8–19, NDWI10–21 and DAY_PREC6–17) were highly important with a predictor weight equal to or very close to 1 (Table 1). Neither distance to urban area or rice fields were significant predictors.


Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy.

Rosà R, Marini G, Bolzoni L, Neteler M, Metz M, Delucchi L, Chadwick EA, Balbo L, Mosca A, Giacobini M, Bertolotti L, Rizzoli A - Parasit Vectors (2014)

Association between the start of the mosquito season and environmental variables. Panels a-c show model predictions; panels d-f show partial residuals. The first column (a,d) shows the association between the start of the season and temperature (LST8–19), the second (b,e) shows the association with NDWI10–21 and the third (c,f) shows the association with precipitation (DAY_PREC6–17). Note that all plots show transformed data on the y axis (i.e. x1.3); back transformed values are presented in the text to assist interpretation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4061321&req=5

Figure 3: Association between the start of the mosquito season and environmental variables. Panels a-c show model predictions; panels d-f show partial residuals. The first column (a,d) shows the association between the start of the season and temperature (LST8–19), the second (b,e) shows the association with NDWI10–21 and the third (c,f) shows the association with precipitation (DAY_PREC6–17). Note that all plots show transformed data on the y axis (i.e. x1.3); back transformed values are presented in the text to assist interpretation.
Mentions: For the start of the season (ON) 32 full models were produced and a single best model was selected, explaining 26% (R2 = 0.258, Akaike weight = 0.96) of the variance; remaining models had ΔAIC > 4 and were disregarded (see section C of Additional file 1 for more details). Model outputs (Table 1) are therefore based on a single model, rather than averages from multiple models as elsewhere. Within the measured range of environmental data, temperature had the greatest effect on the start of the season. Higher spring temperatures were associated with an earlier start to the season, such that an increase of 5°C in LST8–19 (from 11 to 16°C) predicts the start of the season some 14 days earlier (a shift in the average ON from day 187 to 173) (Figure 3a). Increasing NDWI also predicts an earlier start to the season, such that a shift in NDWI10–21 from -0.1 to +0.06 led to a start of the season 10 days earlier (Figure 3b), while more days of precipitation delayed the start of the season such that an increase in DAY_PREC6–17 from 14 to 37 days of precipitation during the 12 week period led to a delay in the start of the season of 10 days (Figure 3c). All terms selected in the best models (LST8–19, NDWI10–21 and DAY_PREC6–17) were highly important with a predictor weight equal to or very close to 1 (Table 1). Neither distance to urban area or rice fields were significant predictors.

Bottom Line: Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance.Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology.Proximity to urban areas was not a significant predictor in any of our models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Biodiversità ed Ecologia Molecolare, Centro Ricerca e Innovazione, Fondazione Edmund Mach, San Michele all'Adige, TN, Italia. roberto.rosa@fmach.it.

ABSTRACT

Background: West Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk.

Methods: We analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields.

Results: Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C).

Conclusions: Climate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

Show MeSH
Related in: MedlinePlus