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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 season length and days of precipitation. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association with days of precipitation during the early period (DAY_PREC2–13) while the second column (b,d) shows the association with precipitation in the late period (DAY_PREC20–31).
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Figure 4: Association between season length and days of precipitation. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association with days of precipitation during the early period (DAY_PREC2–13) while the second column (b,d) shows the association with precipitation in the late period (DAY_PREC20–31).

Mentions: When considering only the early period, two models, out of 32 models produced, were selected to predict season length, explaining between 13 and 14% (R2 = 0.135, R2 = 0.141) of the variance, and differed in their inclusion/exclusion of temperature (Akaike weights were 0.21 and 0.77). From model averaging, the early period variables associated with earlier start of the season (ON, above) also predict increased season length, so higher NDWI and temperature predict a longer season (although note that following averaging procedures temperature is significant only at a 92% threshold, with p = 0.079), and more days of precipitation predict a shorter season. Again, distance to urban areas and rice fields were not significant predictors, for either of the two best models. For early period predictors only, an increase in NDWI10–21 from -0.1 to +0.06 predicts an increase of 14 days in season length (from 56 to 70 days), while an increase in days of precipitation from 7 to 30 days during the 12 week period (DAY_PREC2–13) predicts an eleven day decrease in season length (from 71 to 60 days) (Figure 4a). An increase of 5°C in LST8–19 (from 11 to 16°C) predicts an extension of 7 days in season length (from 65 to 72 days).


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 season length and days of precipitation. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association with days of precipitation during the early period (DAY_PREC2–13) while the second column (b,d) shows the association with precipitation in the late period (DAY_PREC20–31).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Association between season length and days of precipitation. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association with days of precipitation during the early period (DAY_PREC2–13) while the second column (b,d) shows the association with precipitation in the late period (DAY_PREC20–31).
Mentions: When considering only the early period, two models, out of 32 models produced, were selected to predict season length, explaining between 13 and 14% (R2 = 0.135, R2 = 0.141) of the variance, and differed in their inclusion/exclusion of temperature (Akaike weights were 0.21 and 0.77). From model averaging, the early period variables associated with earlier start of the season (ON, above) also predict increased season length, so higher NDWI and temperature predict a longer season (although note that following averaging procedures temperature is significant only at a 92% threshold, with p = 0.079), and more days of precipitation predict a shorter season. Again, distance to urban areas and rice fields were not significant predictors, for either of the two best models. For early period predictors only, an increase in NDWI10–21 from -0.1 to +0.06 predicts an increase of 14 days in season length (from 56 to 70 days), while an increase in days of precipitation from 7 to 30 days during the 12 week period (DAY_PREC2–13) predicts an eleven day decrease in season length (from 71 to 60 days) (Figure 4a). An increase of 5°C in LST8–19 (from 11 to 16°C) predicts an extension of 7 days in season length (from 65 to 72 days).

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