Simulating tick distributions over sub‐Saharan Africa: the use of observed and simulated climate surfaces

Abstract

Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or highresolution model-derived climate data [Division of Atmospheric Research limited-area model (DARLAM)] for predictive species modelling, using tick distributions from sub-Saharan Africa.Location The analysis is restricted to sub-Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 · 60 km.Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. ResultsThe predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels.Main conclusions The use of data derived from high-resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km 2 for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.

Identification of hard ticks (Acari: Ixodidae) and seroprevalence to Theileria parva in cattle raised in North Kivu Province, Democratic Republic of Congo

“…The species was not found at higher altitude which confirms previous studies carried out in Rwanda at altitudes comprised between 1,000 and 1,550 m (Bazarusanga et al 2007a) and in Uganda between 1,100 and 1,350 m (RubaireAkiiki et al 2004). It is well known that tick distribution and abundance are largely determined by different bio-climatic factors (Estrada-Peña 2003; Moorling et al 2004) including the availability of appropriate hosts (Cummings 2002;Olwoch et al 2003). During the present work, rainfalls were low at high altitude.…”

The Known Distribution and Ecological Preferences of the Tick Subgenus Boophilus (Acari: Ixodidae) in Africa and Latin America

“…Modelling strategies for predicting potential distributions of ticks have focused on the characterization of the bioclimate envelope (Cumming 2002;Olwoch et al 2003;Estrada-Pen˜a 2003). Some recent studies have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions (Davies et al 1998;Lawton, 2000).…”

Climate Change Effects on Plant Disease: Genomes to Ecosystems

“…In many cases, temperature increases are predicted to lead to the geographic expansion of pathogen and vector distributions, bringing pathogens into contact with more potential hosts (10,103) and providing new opportunities for pathogen hybridization (22,23). Increased transportation and human movement may act synergistically with temperature changes (4,48).…”