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Ecological Modeling of the Spatial Distribution of Wild Waterbirds to Identify the Main Areas Where Avian Influenza Viruses are Circulating in the Inner Niger Delta, Mali

EcoHealth volume 7, pages 283–293 (2010)Cite this article

Abstract

Predicting areas of disease emergence when no epidemiological data is available is essential for the implementation of efficient surveillance programs. The Inner Niger Delta (IND) in Mali is a major African wetland where >1 million Palearctic and African waterbirds congregate. Waterbirds are the main reservoir of Avian Influenza Viruses (AIV). Our objective was to model their spatial distribution in order to predict where these viruses would be more likely to circulate. We developed a generalized linear model (GLM) and a boosted regression trees (BRT) model based on total aerial bird counts taken in winter over 6 years. We used remotely sensed environmental variables with a high temporal resolution (10 days) to predict the spatial distribution of four waterbird groups. The predicted waterbird abundances were weighted with an epidemiological indicator based on the prevalence of low pathogenic AIV reported in the literature. The BRT model had the best predictive power and allowed prediction of the high variability of waterbird distribution. Years with low flood levels showed areas with a higher risk of circulation and had better spatial distribution predictions. Each year, the model identified a few areas with a higher risk of AIV circulation. This model can be applied every 10 days to evaluate the risk of AIV emergence in wild waterbirds. By taking into account the IND’s ecological variability, it allows better targeting of areas considered for surveillance. This could enhance the control of emerging diseases at a local and regional scale, especially when resources available for surveillance programs are scarce.

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Acknowledgments

We thank Bourama Niagate from the Direction Nationale des Eaux et Forêts du Mali, Jean-Marie Boutin and Jean Thal from the Office National de la Chasse et de la Faune Sauvage (ONCFS), and all the people that helped collect the data. We also thank Leo Zwartz and Jan Van der Kamp from Altenburg and Wymenga (A&W) for sharing their knowledge about the Inner Niger Delta, and for providing helpful advice on the spatial modeling. Finally, we thank Olivier Gimenez from the Centre National de Recherche Scientifique (CNRS) for advice during the early development of the models.

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Affiliations

  1. CIRAD ES, UR Animal et Gestion intégrée des risques (AGIR), TA C 22/E, Campus International de Baillarguet, 34398, Montpellier Cedex 5, France

    Julien Cappelle & Nicolas Gaidet

  2. ONCFS, Réserve de Chanteloup, 85340, l’Ile d’Olonne, France

    Olivier Girard

  3. Direction Nationale des Eaux et Forêts du Mali, BP 275, Bamako, Mali

    Bouba Fofana

  4. Wetlands International, BP 97, Mopti-Sevare, Mali

    Bouba Fofana

  5. Biological Control and Spatial Ecology, Université Libre de Bruxelles, av FD Roosevelt, 50, 1050, Brussels, Belgium

    Marius Gilbert

  6. Fonds National de la Recherche Scientifiques, rue d’Egmont 5, 1000, Brussels, Belgium

    Marius Gilbert

Authors
  1. Julien Cappelle
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  2. Olivier Girard
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  3. Bouba Fofana
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  4. Nicolas Gaidet
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  5. Marius Gilbert
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Corresponding author

Correspondence to Julien Cappelle.

Appendix

Appendix

Table 4 List of the 16 species used for the analysis classified in four systemic groups according to the role they may play in Avian Influenza Virus circulation
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Figure 5
figure 5

Residuals of the spatial distribution model of Ardeids and Palearctic Anatids. The residuals, i.e., the difference between the observed and predicted abundances, are displayed for two different years.

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Cappelle, J., Girard, O., Fofana, B. et al. Ecological Modeling of the Spatial Distribution of Wild Waterbirds to Identify the Main Areas Where Avian Influenza Viruses are Circulating in the Inner Niger Delta, Mali. EcoHealth 7, 283–293 (2010). https://doi.org/10.1007/s10393-010-0347-5

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Keywords

  • emerging infectious diseases
  • surveillance
  • wild birds
  • distribution models
  • AIV
  • boosted regression trees