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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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Related in: MedlinePlus

Association between season length, total abundance and late season temperatures. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association between late season temperature (LST16–27) and season length; the second column (b,d) shows the association between late season temperature (LST21–32) and mosquito abundance. Note that plots in the second column show transformed data on the y axis (i.e. x0.2); back transformed values are presented in the text to assist interpretation.
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Figure 5: Association between season length, total abundance and late season temperatures. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association between late season temperature (LST16–27) and season length; the second column (b,d) shows the association between late season temperature (LST21–32) and mosquito abundance. Note that plots in the second column show transformed data on the y axis (i.e. x0.2); back transformed values are presented in the text to assist interpretation.

Mentions: When incorporating late period variables in addition to early period, 128 full models were produced and six of them were selected as best, with R2 between 0.147 and 0.160 and Akaike weights between 0.28 and 0.06. Improvement to the model fit from inclusion of late period variables was therefore minimal, when compared to early period predictors alone (see above). Comparison of the model terms suggests, however, that precipitation during the late period (DAY_PREC20–31) has the opposite effect of precipitation during the early period (DAY_PREC2–13) (Figure 4b). More days of precipitation during the late period predict a longer season, such that an increase from 12 to 39 days of precipitation (DAY_PREC20–31) predicts a seven day increase in season length, whereas in the early period only model, more days of precipitation delay the season start and so shorten season length (as described above). The association with late period precipitation is stronger than that of early period precipitation, so that when both terms are included in the same model, early period precipitation becomes non-significant with a predictor weight of only 0.4, as compared to a high significance of p= 0.004 and a weight of 0.79 for late period precipitation (Table 2). Late period temperatures (LST16–27) have a marked impact on season length such that a shift of 6°C (from 19 to 25°C) predicts a lengthening of the season by 22 days (Figure 5a). As for precipitation, the addition of late period temperature renders early period temperature non-significant, with predictor weight of only 0.53, as compared to late period temperature which is both highly significant (p = 0.003) and has a high predictor weight (0.98) (Table 2). The most important model term in terms of predictor weight was, however, NDWI measured during the early period (NDWI10–21), which is positively associated with season length, and retains the same high predictor weight (1) in both groups of models (early only, early + late) (Table 2). An increase in NDWI10–21 from -0.1 to +0.06 predicts an increase in season length of 14 or 17 days (the greater increase being predicted by the early + late models).


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, total abundance and late season temperatures. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association between late season temperature (LST16–27) and season length; the second column (b,d) shows the association between late season temperature (LST21–32) and mosquito abundance. Note that plots in the second column show transformed data on the y axis (i.e. x0.2); 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 5: Association between season length, total abundance and late season temperatures. Panels a-b show model predictions; panels c-d show partial residuals. The first column (a,c) shows the association between late season temperature (LST16–27) and season length; the second column (b,d) shows the association between late season temperature (LST21–32) and mosquito abundance. Note that plots in the second column show transformed data on the y axis (i.e. x0.2); back transformed values are presented in the text to assist interpretation.
Mentions: When incorporating late period variables in addition to early period, 128 full models were produced and six of them were selected as best, with R2 between 0.147 and 0.160 and Akaike weights between 0.28 and 0.06. Improvement to the model fit from inclusion of late period variables was therefore minimal, when compared to early period predictors alone (see above). Comparison of the model terms suggests, however, that precipitation during the late period (DAY_PREC20–31) has the opposite effect of precipitation during the early period (DAY_PREC2–13) (Figure 4b). More days of precipitation during the late period predict a longer season, such that an increase from 12 to 39 days of precipitation (DAY_PREC20–31) predicts a seven day increase in season length, whereas in the early period only model, more days of precipitation delay the season start and so shorten season length (as described above). The association with late period precipitation is stronger than that of early period precipitation, so that when both terms are included in the same model, early period precipitation becomes non-significant with a predictor weight of only 0.4, as compared to a high significance of p= 0.004 and a weight of 0.79 for late period precipitation (Table 2). Late period temperatures (LST16–27) have a marked impact on season length such that a shift of 6°C (from 19 to 25°C) predicts a lengthening of the season by 22 days (Figure 5a). As for precipitation, the addition of late period temperature renders early period temperature non-significant, with predictor weight of only 0.53, as compared to late period temperature which is both highly significant (p = 0.003) and has a high predictor weight (0.98) (Table 2). The most important model term in terms of predictor weight was, however, NDWI measured during the early period (NDWI10–21), which is positively associated with season length, and retains the same high predictor weight (1) in both groups of models (early only, early + late) (Table 2). An increase in NDWI10–21 from -0.1 to +0.06 predicts an increase in season length of 14 or 17 days (the greater increase being predicted by the early + late models).

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