Spatially explicit predictions of blood parasites in a widely distributed African rainforest bird

• Published in: Proceedings of the Royal Society. B, Biological sciences Vol. 278; no. 1708; pp. 1025 - 1033
• Main Authors: Sehgal, R. N. M., Buermann, W., Harrigan, R. J., Bonneaud, C., Loiseau, C., Chasar, A., Sepil, I., Valkiūnas, G., Iezhova, T., Saatchi, S., Smith, T. B.
• Format: Journal Article
• Language: English
• Published: England The Royal Society 07.04.2011
• Subjects: Africa, Central - epidemiology; Africa, Western - epidemiology; Animals; Aves; Avian Malaria; Bird Diseases - blood; Bird Diseases - epidemiology; Bird Diseases - parasitology; Birds; Blood; Disease models; Ecological modeling; Ecology; Environment; Forest ecology; Haematozoa; Malaria; Malaria, Avian - blood; Malaria, Avian - epidemiology; Maxent; Models, Biological; Olea; Parasites; Passeriformes - parasitology; Plasmodium; Plasmodium - isolation & purification; Predictive Maps; Prevalence; Protozoan Infections, Animal - blood; Protozoan Infections, Animal - epidemiology; Rain forests; Random Forest; Species; Species Specificity; Trypanosoma; Trypanosoma - isolation & purification; Trypanosomiasis - blood; Trypanosomiasis - epidemiology; Weather; Africa, Central; Africa, Western; Africa
• ISSN: 0962-8452; 1471-2945; 1471-2954
• DOI: 10.1098/rspb.2010.1720

Critical to the mitigation of parasitic vector-borne diseases is the development of accurate spatial predictions that integrate environmental conditions conducive to pathogen proliferation. Species of Plasmodium and Trypanosoma readily infect humans, and are also common in birds. Here, we develop predictive spatial models for the prevalence of these blood parasites in the olive sunbird (Cyanomitra olivacea). Since this species exhibits high natural parasite prevalence and occupies diverse habitats in tropical Africa, it represents a distinctive ecological model system for studying vector-borne pathogens. We used PCR and microscopy to screen for haematozoa from 28 sites in Central and West Africa. Species distribution models were constructed to associate ground-based and remotely sensed environmental variables with parasite presence. We then used machine-learning algorithm models to identify relationships between parasite prevalence and environmental predictors. Finally, predictive maps were generated by projecting model outputs to geographically unsampled areas. Results indicate that for Plasmodium spp., the maximum temperature of the warmest month was most important in predicting prevalence. For Trypanosoma spp., seasonal canopy moisture variability was the most important predictor. The models presented here visualize gradients of disease prevalence, identify pathogen hotspots and will be instrumental in studying the effects of ecological change on these and other pathogens.