Abiotic and biotic determinants of the seasonal dynamics of the tick Rhipicephalus appendiculatus in South Africa |
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Authors: | SARAH E. RANDOLPH |
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Affiliation: | Department of Zoology, University of Oxford, U.K. |
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Abstract: | Abstract. The creation of a generic population model for the tick Rhipicephalus appendiculatus requires a detailed, quantified understanding of the interactions of these ticks with their biotic and abiotic environment in the different parts of their range, from the tropical regions of East Africa to the temperate regions of South Africa. The much greater seasonal variation in climatic conditions, particularly temperature, further from the equator introduces variable development rates and diapause into the life cycle. Estimates of natural temperature-dependent interstadial development periods, derived from a combination of published laboratory and field data, were applied to published data on the seasonal abundance of R.appendiculatus on three farms in the Eastern Cape Province of South Africa. This enabled an assessment of which ticks of one stage give rise to which ticks of the next stage, from which (a) the onset and duration of diapause in unfed adults, and (b) seasonal interstadial mortality indices in the form of fc-values, could be estimated. The contribution of biotic (tick density) and abiotic (climatic) factors as predictors of mortality at each life stage was investigated by step-wise multiple regression. Density-independent mortality at the female-to-larval stage is correlated with geographically variable climatic factors, minimum temperature at two farms and minimum relative humidity at the third. The other two stages are governed by density-dependent mortality, which, it is argued, may be caused largely by the hosts' acquired resistance to ticks. As expected on theoretical grounds, this density dependence is weaker nearer to the edge of the tick's range and for the more vulnerable immature stages. |
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Keywords: | Rhipicephalus appendiculatus ticks diapause development rates mortality population models seasonality |
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