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Using habitat models to map diversity: pan-African species richness of ticks (Acari: Ixodida)

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Abstract Aims

To show how logistic regression models for individual species can be used to produce improved estimates of species richness at a continental scale; to present these data for African ticks (Acari: Ixodida); and to address the question of whether there is a latitudinal gradient in tick species richness. Location

Africa. Methods

A database of 34,060 collection records for African ticks is used to produce a pan-African map of known tick species richness at 0.25 × 0.25-degree resolution. The likely distributions of seventy-three species are then estimated from environmental factors using logistic regression, and localities where there is a suitably high probability of occurrence for a given species are added to the original data for that species. These augmented data are combined to produce a map of the predicted pan-African distribution of tick species richness. The relationship of species richness to latitude is considered along a transect placed across some of the more extensively collected areas. Results

Maps of known and predicted pan-African tick species richness are presented, and deficiencies in the available data are highlighted. Correlations using both known and predicted estimates of tick species richness suggest that ticks follow similar species richness patterns to those described for African mammals and birds, with a latitudinal gradient and highest species richness in east equatorial Africa. Tick species ranges are log-normally distributed. Main conclusions

Carefully constructed probability surfaces offer a more powerful approach to mapping species ranges than simple presence-absence maps. Such models are a useful extension to current biogeographical methods and have a wide range of potential applications in ecology, epidemiology and conservation. Tick species richness at a continental scale follows similar trends to those reported for mammals and birds.

Keywords: Africa; Ixodidae; Ticks; biogeography; diversity; logistic regression; richness

Document Type: Research Article


Publication date: 2000-03-01

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