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Modelling the Abundances of Two Major Culicoides (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal.

Diarra M, Fall M, Lancelot R, Diop A, Fall AG, Dicko A, Seck MT, Garros C, Allène X, Rakotoarivony I, Bakhoum MT, Bouyer J, Guis H - PLoS ONE (2015)

Bottom Line: Despite modelling large Culicoides counts (over 220,000 Culicoides captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models.The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels.Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models.

View Article: PubMed Central - PubMed

Affiliation: Institut Sénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal; Université Gaston Berger, Laboratoire d'Etudes et de Recherches en Statistiques et Développement, Saint-Louis, Sénégal.

ABSTRACT
In Senegal, considerable mortality in the equine population and hence major economic losses were caused by the African horse sickness (AHS) epizootic in 2007. Culicoides oxystoma and Culicoides imicola, known or suspected of being vectors of bluetongue and AHS viruses are two predominant species in the vicinity of horses and are present all year-round in Niayes area, Senegal. The aim of this study was to better understand the environmental and climatic drivers of the dynamics of these two species. Culicoides collections were obtained using OVI (Onderstepoort Veterinary Institute) light traps at each of the 5 sites for three nights of consecutive collection per month over one year. Cross Correlation Map analysis was performed to determine the time-lags for which environmental variables and abundance data were the most correlated. C. oxystoma and C. imicola count data were highly variable and overdispersed. Despite modelling large Culicoides counts (over 220,000 Culicoides captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models. The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels. According to this model, meteorological variables that contribute to explaining the dynamics of C. oxystoma and C. imicola abundances were: mean temperature and relative humidity of the capture day, mean humidity between 21 and 19 days prior a capture event, density of ruminants, percentage cover of water bodies within a 2 km radius and interaction between temperature and humidity for C. oxystoma; mean rainfall and NDVI of the capture day and percentage cover of water bodies for C. imicola. Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models. Further work should also assess whether other trapping methods such as host-baited traps help reduce overdispersion.

No MeSH data available.


Related in: MedlinePlus

Observed vs Predicted monthly Culicoides counts.Monthly C. oxystoma (A) and C. imicola (B) counts observed vs. predicted by Poisson, NB, PRME-S, PRME-SD, in each site (in column). Months’ quotation: 1 = January to 12 = December.
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pone.0131021.g003: Observed vs Predicted monthly Culicoides counts.Monthly C. oxystoma (A) and C. imicola (B) counts observed vs. predicted by Poisson, NB, PRME-S, PRME-SD, in each site (in column). Months’ quotation: 1 = January to 12 = December.

Mentions: Results from modelling the female C. oxystoma and C. imicola monthly population dynamics for 4 models are shown in Fig 3. Visual inspection of these results shows that the PRME-SD better estimates the observed data. Further support is provided by the root mean square error (RMSE). PRME-SD models had the lowest mean prediction errors both in the training dataset and in the test dataset: for C. oxystoma RMSE = 0.72 in the training dataset (respectively RMSE = 0.76 in the test dataset); for C. imicola RMSE = 0.87 in the training dataset (respectively RMSE = 0.89 in the test dataset) (Table 1).


Modelling the Abundances of Two Major Culicoides (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal.

Diarra M, Fall M, Lancelot R, Diop A, Fall AG, Dicko A, Seck MT, Garros C, Allène X, Rakotoarivony I, Bakhoum MT, Bouyer J, Guis H - PLoS ONE (2015)

Observed vs Predicted monthly Culicoides counts.Monthly C. oxystoma (A) and C. imicola (B) counts observed vs. predicted by Poisson, NB, PRME-S, PRME-SD, in each site (in column). Months’ quotation: 1 = January to 12 = December.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487250&req=5

pone.0131021.g003: Observed vs Predicted monthly Culicoides counts.Monthly C. oxystoma (A) and C. imicola (B) counts observed vs. predicted by Poisson, NB, PRME-S, PRME-SD, in each site (in column). Months’ quotation: 1 = January to 12 = December.
Mentions: Results from modelling the female C. oxystoma and C. imicola monthly population dynamics for 4 models are shown in Fig 3. Visual inspection of these results shows that the PRME-SD better estimates the observed data. Further support is provided by the root mean square error (RMSE). PRME-SD models had the lowest mean prediction errors both in the training dataset and in the test dataset: for C. oxystoma RMSE = 0.72 in the training dataset (respectively RMSE = 0.76 in the test dataset); for C. imicola RMSE = 0.87 in the training dataset (respectively RMSE = 0.89 in the test dataset) (Table 1).

Bottom Line: Despite modelling large Culicoides counts (over 220,000 Culicoides captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models.The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels.Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models.

View Article: PubMed Central - PubMed

Affiliation: Institut Sénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal; Université Gaston Berger, Laboratoire d'Etudes et de Recherches en Statistiques et Développement, Saint-Louis, Sénégal.

ABSTRACT
In Senegal, considerable mortality in the equine population and hence major economic losses were caused by the African horse sickness (AHS) epizootic in 2007. Culicoides oxystoma and Culicoides imicola, known or suspected of being vectors of bluetongue and AHS viruses are two predominant species in the vicinity of horses and are present all year-round in Niayes area, Senegal. The aim of this study was to better understand the environmental and climatic drivers of the dynamics of these two species. Culicoides collections were obtained using OVI (Onderstepoort Veterinary Institute) light traps at each of the 5 sites for three nights of consecutive collection per month over one year. Cross Correlation Map analysis was performed to determine the time-lags for which environmental variables and abundance data were the most correlated. C. oxystoma and C. imicola count data were highly variable and overdispersed. Despite modelling large Culicoides counts (over 220,000 Culicoides captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models. The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels. According to this model, meteorological variables that contribute to explaining the dynamics of C. oxystoma and C. imicola abundances were: mean temperature and relative humidity of the capture day, mean humidity between 21 and 19 days prior a capture event, density of ruminants, percentage cover of water bodies within a 2 km radius and interaction between temperature and humidity for C. oxystoma; mean rainfall and NDVI of the capture day and percentage cover of water bodies for C. imicola. Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models. Further work should also assess whether other trapping methods such as host-baited traps help reduce overdispersion.

No MeSH data available.


Related in: MedlinePlus