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. 2015 May 1:8:258.
doi: 10.1186/s13071-015-0865-7.

Modelling the spatial distribution of the nuisance mosquito species Anopheles plumbeus (Diptera: Culicidae) in the Netherlands

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Modelling the spatial distribution of the nuisance mosquito species Anopheles plumbeus (Diptera: Culicidae) in the Netherlands

Adolfo Ibañez-Justicia et al. Parasit Vectors. .

Abstract

Background: Landscape modifications, urbanization or changes of use of rural-agricultural areas can create more favourable conditions for certain mosquito species and therefore indirectly cause nuisance problems for humans. This could potentially result in mosquito-borne disease outbreaks when the nuisance is caused by mosquito species that can transmit pathogens. Anopheles plumbeus is a nuisance mosquito species and a potential malaria vector. It is one of the most frequently observed species in the Netherlands. Information on the distribution of this species is essential for risk assessments. The purpose of the study was to investigate the potential spatial distribution of An. plumbeus in the Netherlands.

Methods: Random forest models were used to link the occurrence and the abundance of An. plumbeus with environmental features and to produce distribution maps in the Netherlands. Mosquito data were collected using a cross-sectional study design in the Netherlands, from April to October 2010-2013. The environmental data were obtained from satellite imagery and weather stations. Statistical measures (accuracy for the occurrence model and mean squared error for the abundance model) were used to evaluate the models performance. The models were externally validated.

Results: The maps show that forested areas (centre of the Netherlands) and the east of the country were predicted as suitable for An. plumbeus. In particular high suitability and high abundance was predicted in the south-eastern provinces Limburg and North Brabant. Elevation, precipitation, day and night temperature and vegetation indices were important predictors for calculating the probability of occurrence for An. plumbeus. The probability of occurrence, vegetation indices and precipitation were important for predicting its abundance. The AUC value was 0.73 and the error in the validation was 0.29; the mean squared error value was 0.12.

Conclusions: The areas identified by the model as suitable and with high abundance of An. plumbeus, are consistent with the areas from which nuisance was reported. Our results can be helpful in the assessment of vector-borne disease risk.

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Figures

Figure 1
Figure 1
Anopheles plumbeus female (source: A. Ibañez-Justicia).
Figure 2
Figure 2
Observed presence and absence points and map of the estimated environmental suitability for An. plumbeus. A- Presence and absence observed during the National Mosquito Survey program carried out from April to October 2010–2013. B- Environmental suitability map of An. plumbeus produced using classification random forest. Environmental suitability is depicted using a gradient fill: blue indicates low environmental suitability, red indicates high suitability. Locations where other surveys took place are also shown on the map (black squares).
Figure 3
Figure 3
Percentage of positive sites of An. plumbeus per week in 2010–2013.
Figure 4
Figure 4
Observed and estimated abundance of An. plumbeus. A – Observed abundance represented as log 10 (abundance + 1). B – Map of the estimated abundance produced using a regression random forest. A darker colour indicates higher abundance.

References

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