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Estimating the distribution and abundance of Rhipicephalus appendiculatus in Africa
Preventive Veterinary Medicine, 11 ( 1991 ) 2 6 1 - 2 6 8
261
Elsevier Science Publishers B.V., A m s t e r d a m
Estimating the distribution and abundance of Rhipicephalus appendiculatusin Africa B.D. Perry a, R. Kruska b, P. Lessard c, R.A.I.
N o r v a l d a n d K. K u n d e r t b
"lnternational Laboratoo, for Research on Animal Diseases, P.O. Boa- 30709, Nairobi, Kenya bUnited Nations Environment Programme, P.O. Box 30552, Nairobi Kenya cVirginia-Maryland Regional College of Veterinao, Medicine, Blacksburg, VA 24061. USA dCollege of Veterinary Medicine, University of Florida, Gainesville, FL 32611-0633, USA
ABSTRACT Perry, B.D., Kruska, R., Lessard, P., Norval, R.A.I. and Kundert, K., 1991. Estimating the distribution and abundance ofRhipicephalus appendiculatus in Africa. Prey. Vet. Med., 11:261-268. The brown ear tick Rhipicephalus appendiculatus is responsible for transmitting the parasite Theileria parva in eastern, central and southern Africa, where it causes East Coast fever, Corridor disease and January disease in cattle. In an effort to assess the impact of these diseases and their control on livestock production in the region, studies are underway to model the factors controlling the distribution of the vector tick. Three recent studies have attempted to quantify and evaluate the variables influencing the distribution ofR. appendiculatus, and these are reviewed and discussed in this paper. The relationship between the distribution of an ecoclimatic index of suitability for the tick, calculated by the model CLIMEX on a 25 km 2 interpolated climate database for Africa, and of recorded observations of the tick itself, are evaluated. The sensitivity and specificity of the ecoclimatic index were calculated as 70.2-82.6% and 69.4-84.7%, respectively. In addition, satellite-derived mean maximum normalized difference vegetation index ( N D V I ) for 1987 was assessed as a predcitor of R. appendiculatus habitat in eastern Africa, by comparing the distribution of NDVI values in areas in which the presence of the tick has and has not been recorded. Some visual correlation was observed between mean maximum NDVI values of 0.15 or greater and known R. appendiculatus distribution in Kenya. In addition, this range of NDVI values corresponded closely to areas in Ethiopia where the tick does not occur but for which climatic suitability was predicted by CLIMEX. However, these NDVI values should not be considered universal predictors of habitat suitable for R. appendiculatus, and further study of the NDVI with ground truthing of habitat is considered necessary.
INTRODUCTION
The brown ear tick Rhipicephalus appendiculatus occurs in eastern, central and southern Africa, and is responsible for transmitting the protozoan parasite Theileria parva to cattle, causing East Coast fever, Corridor disease and January disease. The most important variables governing the distribution of the tick are considered to be climate and vegetation (Hoogstraal, 1956 ). Other important variables influencing tick distribution and abundance are the © 1991 Elsevier Science Publishers B.V. All rights reserved 0 1 6 7 - 5 8 7 7 / 9 1 / $ 0 3 . 5 0
262
B.D. PERRY ET AL.
availability of wild and domestic hosts, and the nature and efficacy of tick control measures. The climatic (temperature and moisture) requirements influencing R. appendiculatus distribution have been quantified by Sutherst and Maywald ( 1985 ) on the basis of experimental data and field observations, and incorporated into the model CLIMEX which predicts climatic suitability for arthropod species. A growth index is calculated and moderated by four stress indices (heat, dry, wet and cold) to provide an ecoclimatic index (El) of suitability for tick survival and development. The vegetational requirements for the tick are known to include both grass and limited tree cover (Minshull and Norval, 1982). The tick thus occurs most commonly in savannah and woodland-savannah habitats, and tends to be absent from open plains and heavy forests. The role of the satellite-derived normalised difference vegetation index (NDVI) in studies of vector habitats has recently been reviewed (Hugh-Jones, 1989). This spectral vegetation index quantifies the level of photosynthetic activity of vegetation and is calculated from the advanced very high resolution radiometer (AVHRR) data provided by the National Oceanic and Atmospheric Administration's (NOAA) meteorological satellites. Three recent studies have attempted to quantify the variables influencing the distribution of R. appendiculatus in Africa. Lessard et al. (1990) incorporated eight variables into a geographical information system and developed an interpolated climatic database for Africa with which to run the model CLIMEX. Perry et al. (1990) correlated the distributions of EI values generated by CLIMEX and mean monthly maximum NDVI values for 1987 with the known distribution ofR. appendiculatus collections. Norval et al. ( 1990 ) used both the EI and NDVI values to assess the risk of spread ofR. appendiculatus into highland Ethiopia, where it currently does not occur. This paper reviews the results of these studies, discusses the validity of the techniques used and considers possible future developments. MATERIALS AND METHODS
An interpolated database for Africa of the climatic variables required to drive CLIMEX (mean monthly maximum and minimum temperature, rainfall and humidity), developed by Lessard et al. (1990) at a resolution of approximately 25 km 2, was used. The CLIMEX model was run for each of the 43 174 interpolated grid cells in the African continent on a Micro VAX 3600 mainframe computer (Digital Equipment Corporation, Boston, MA). The Fig. I. (A) The distribution of EI values in eastern Africa (modified from Lessard et al. (1990) ). (B) The distribution of R. appendiculatus collection sites in eastern Africa (modified from Lessard et al., 1990). (C) The distribution of mean monthly maximum NDVI values for 1987 in eastern Africa (modified from Lessard et ah, 1990).
DISTRIBUTION AND ABUNDANCE
263
O F R. APPENDICULATUS IN A F R I C A
A.
0 to
m
m m ~o m . m . mm m m
. o,-.o
m
B. ~udan
E|hiopla
C.
0 0.05 -0.10 -0.15 -0.20 ~0.25 -0.30
m , o~0
264
B.D. PERRY ETAL.
parameter values for R. appendiculatus used in the CLIMEX model were those described by Sutherst and Maywald (1985). Distribution maps of the EI values (on a scale of 1-100) for eastern Africa were produced and compared with known tick distribution. In order to calculate the sensitivity and specificity of the model under the circumstances of use described, all grid cells in the continent were surveyed for the recorded presence of R. appendiculatus and for the presence of EI values of one or greater. The same method was applied to assess the sensitivity and specificity of the model in Kenya and Zimbabwe to investigate the uniformity of these indices at different latitudes. In addition, the range of EI values was plotted for three latitude zones within the tick's distribution. A visual correlation of global area coverage (GAC) mean monthly maximum NDVI values for 1987 with the recorded distribution ofR. appendiculatus collections was made. In addition, the frequency distribution of NDVI values in Ethiopia and Kenya was compared. In the case of Kenya, the frequency of NDVI values in cells in which R. appendiculatus has been recorded was compared with the frequency in cells in which it has not. RESULTS
The relationship between EI values and the known distribution of collections ofR. appendiculatus for eastern Africa are shown in Fig. 1 (A) and 1 (B). Table 1 summarises the sensitivity and specificity analysis of the CLIMEX model, calculated for Africa as a whole, and for Kenya and Zimbabwe. Figure 2 (A) shows the range of El values in three latitude zones. The distributions of El values were significantly different between latitude zones. In eastern Africa (from 2 °N to 12 °S), values range from one to almost 90. In central Africa (12°S-23°S), no value above 40 is recorded. In southern Africa (below 23 °S), although almost half of the values are under 10, values up to 80 were recorded. The relationship between mean monthly maximum NDVI values for 1987 and the distribution of collections of R. appendiculatus for eastern Africa are shown in Fig. 1 (C). Figure 2 (B) displays the frequency distribution of NDVI values in the 25 km 2 cells in Kenya and Ethiopia. DISCUSSION
Rhipicephalus appendiculatus is dependent for its survival and development on the presence of a suitable environment, created for the most part by appropriate climate and vegetation. Clearly, the environment of the microhabitat will not necessarily directly equate with that described with the broad (and long-term) climatic parameters used to run CLIMEX. Thus, other models have been developed to simulate locality-specific population dynam-
DISTRIBUTION AND ABUNDANCE OF R. APPENDICULATUS IN AFRICA
265
TABLE 1 Sensitivity and specificity of the ecoclimatic index (El) in assessing the distribution of R. appendi-
culatus in Africa Location
Recorded presence ofR. appendiculatus (no. of grid cells) +
Presence of EI value of one orgreater (no. of grid cells)
Africa
Kenya
+ -
943 198
11740 30293
+
177
100
-
43
556
Zimbabwe + -
158
121
67
278
Sensitivity: Africa, 0.826; Kenya, 0.805; Zimbabwe, 0.702. Specificity: Africa, 0.721; Kenya, 0.847; Zimbabwe, 0.694.
ics, such as T3Host (Floyd et al., 1987). CLIMEX is, therefore, a valuable tool at the continental and national levels of resolution to aid decisions by donor agencies and governments on vector-borne disease control. As climatic databases improve, it will become valuable at higher levels of resolution. The CLIMEX model, used under the conditions described, predicts widespread suitability for R. appendiculatus through most of sub-Saharan Africa. The model appears to be a fairly sensitive and specific test for R. appendiculatus when evaluated against the recorded presence of the tick. However, as surveys for tick presence have not been carried out in many areas of its possible distribution range, this method probably underestimates both sensitivity and specificity. Although the sensitivity and specificity of the El are lower in Zimbabwe than Kenya or Africa as a whole, this difference is not statistically significant, indicating uniformity of these indices throughout the continent. However, the ranges of El values are lower in southern latitudes than more northerly latitudes, suggesting lower tick abundance in southern Africa. The validity of this deserves some scrutiny. The EI is an annual value, based in this case on monthly climatic data. In some high-rainfall areas of the distribution of R. appendiculatus in Kenya (such as the Lake Victoria basin), all instars of the tick can be found throughout the year. In contrast, the tick is strictly seasonal in Zimbabwe and adult ticks are found only during the months of January-March, coinciding with the single rainy season in that country. Thus, the lower E1 values in central Africa may reflect lower overall annual abundance of the tick, resulting from extended periods during which adult ticks are not active. Unfed adult ticks
266
B.D. P E R R Y E T A L .
Proportion of cells 60 .
.
.
.
.
.
.
50403020-
1o| .
0 1-10
.
.
.
.
.
.
.
11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
Ecoclimatic index value m E Zone 1 (2N-12S)
~
Zone 2 (12S-238)
[~
Zone 3 ( 2 3 8 - )
No. G r i d Cells 400 -
300 -
200
100
-
0 -.05-0
0-.05
.05-.10
.10-.15
.15-.20
.20-.25
.25-.30
.30-.35
.35-.40
NDVI V a l u e Ethiopia (all cells) ->~" Kenya (cells where R. appendic, recorded)
~
Kenya (all cells)
-~"
Kenya (cells
where
R. appendic, not recorded)
Fig. 2. (A) The frequency distribution of EI values by latitude zone (from Perry et al., 1990). (B) The frequency distribution of mean monthly maximum NDVI values for 1987 in grid cells of 25 km 2 in Kenya and Ethiopia. (from Norval et al., 1991; first published in Preventive VeterinaryMedicine, 10(3): 163-172.)
enter a daylength-induced diapause for a period of 3-6 months (Short and Norval, 1981 a,b; Rechav, 1982), thus avoiding the effects of heat stress during the hottest period of the year. Diapause appears to occur south of approximately 12°S, but not in the tropical areas of the tick's distribution. For a better understanding of the epidemiology of the diseases transmitted by R. appendiculatus, it is also important to know the abundance of each instar during its period (s) of activity, and this may not be uniformly measured in Africa by CLIMEX owing to the influence exerted by seasonal effects in south-
DISTRIBUTION AND ABUNDANCE OF R. APPENDICULATUS IN AFRICA
267
ern Africa. It is difficult to make direct comparisons of tick abundance on cattle in Kenya and Zimbabwe owing to confounding factors such as differences in the innate resistance of cattle to ticks and different acaricide use. However, extremely high levels of abundance ofR. appendiculatus have been reported on wildlife in game parks in the highveld of Zimbabwe (Lightfoot and Norval, 1981; Norval and Lightfoot, 1982), where ticks are not controlled by acaricide usage. This provides evidence of the seasonal suitability of the higher rainfall areas of central Africa for this species; comparable levels of abundance of R. appendiculatus in game parks in eastern Africa have not been reported. Although there is a remarkable visual correlation with mean maximum NDVI values for 1987 of 0.15 or greater with known R. appendiculatus distribution, this should be interpreted with extreme caution. There may be some value in comparing the distribution of values within the distribution range of the tick to those in an area into which the tick may potentially spread, such as that carried out between Kenya and Ethiopia (Norval et al., 1991, Fig. 2 (B)), but it is not possible at this stage to correlate mean monthly NDVI values with R. appendiculatus distribution. Kruska and Perry ( 1991 ) have shown the dynamic nature of these values in Zimbabwe, and suggested that GAC data are unlikely to be of value in predicting R. appendiculatus habitat in the communal lands of Zimbabwe owing to the varieties of habitat existing within each pixel sampled. In future studies, it is proposed to evaluate interpolated climate databases of higher resolution and to explore the use of other models, such as BIOCLIM (CSIRO, Canberra, N.S.W. ), to project the likely distribution of tick vectors and the impact of their control in Africa. REFERENCES Floyd, R.B., Maywald, G.F. and Sutherst, R.W., 1987. Ecological Models. 1. A population model of Rhipicephalus appendiculatus. In: R.W. Sutherst (Editor), Ticks and Tick-borne Diseases. Proceedings International Workshop Ecology of Ticks and Epidemiology of Tick-borne Diseases, 17-21 February 1986, Nyanga, Zimbabwe. ACIAR Proceedings Series, Australian Centre for International Research, ACIAR, Canberra, No. 17, pp. 72-75. Hoogstraal, H., 1985. African Ixodoides. I: Ticks of the Sudan. Bureau of Medicine and Surgery, Department of the Navy, Washington, DC, Res. Rep. NM 005 050.29.07, 1101 pp. Hugh-Jones, M.E., 1989. Applications of remote sensing to the identification of the habitats of parasites and disease vectors. Parasitol. Today, 5:244-251. Kruska, R.L. and Perry, B.D., 1991. Evaluation of grazing lands of Zimbabwe using the AVHRR normalized difference vegetation index. Prev. Vet. Med., 11: 363-365. Lessard, P., L'Eplattenier, R., Norval, R.A.I., Kundert, K., Dolan, T.T., Croze, H., Walker, J.B., lrvin, A.D. and Perry, B.D., 1990. Geographical information systems for studying the epidemiology of cattle diseases caused by Theileria parva. Vet. Rec., 126:255-262. Lightfoot, C.J. and Norval, R.A.I., 1981. Tick problems in wildlife in Zimbabwe. 1: The effects of tick parasitism on wild ungulates. S. Afr. J. Wildl. Res., 11: 41-45.
268
B.D. PERRYET AL
Minshull, J.I. and Norval, R.A.I., 1982. Factors influencing the spatial distribution of Rhipicephalus appendiculatus in Kyle Recreational Park, Zimbabwe. S. Aft. J. Wildl. Res., 12: I 18123. Norval, R.A.I. and Lightfoot, C.J., 1982. Tick problems in wildlife in Zimbabwe. Factors influencing the occurrence and abundance of Rhipicephalus appendiculatus. Zimbabwe Vet. J., 13: I 1-20. Norval, R.A.I., Perry, B.D., Gebreab, F. and Lessard, P., 1991. East Coast Fever: a problem of the future for the Horn of Africa? Prev. Vet. Med., 10:163-172. Perry, B.D., Lessard, P., Norval, R.A.I., Kundert, K. and Kruska, R., 1990. The effect of climate vegetation on the distribution of the tick Rhipicephalus appendiculatus in Africa. Parasitol. Today, 6: 100-104. Rechav, Y., 1982. Dynamics of tick populations (Acari: Ixodoidea) in the Eastern Cape Province of South Africa. J. Med. Entomol., 19: 679-700. Short, N.J. and Norval, R.A.I., 198 l a. The seasonal activity of Rhipicephalus appendiculatus Neumann 1901 (Acarina: Ixodidae) in the highveld of Zimbabwe Rhodesia. J. Parasitol., 67: 77-78. Short, N.J. and Norval, R.A.I., 198 lb. Regulation of seasonal occurrence in the tick Rhipicephalus appendiculatus Neumann 190 I. Trop. Anim. Health Prod., 13:19-26. Sutherst, R.W. and Maywald, G.F., 1985. A computerised system for matching climates in ecology. Agric. Ecosystems Environ., 13: 281-299.
261
Elsevier Science Publishers B.V., A m s t e r d a m
Estimating the distribution and abundance of Rhipicephalus appendiculatusin Africa B.D. Perry a, R. Kruska b, P. Lessard c, R.A.I.
N o r v a l d a n d K. K u n d e r t b
"lnternational Laboratoo, for Research on Animal Diseases, P.O. Boa- 30709, Nairobi, Kenya bUnited Nations Environment Programme, P.O. Box 30552, Nairobi Kenya cVirginia-Maryland Regional College of Veterinao, Medicine, Blacksburg, VA 24061. USA dCollege of Veterinary Medicine, University of Florida, Gainesville, FL 32611-0633, USA
ABSTRACT Perry, B.D., Kruska, R., Lessard, P., Norval, R.A.I. and Kundert, K., 1991. Estimating the distribution and abundance ofRhipicephalus appendiculatus in Africa. Prey. Vet. Med., 11:261-268. The brown ear tick Rhipicephalus appendiculatus is responsible for transmitting the parasite Theileria parva in eastern, central and southern Africa, where it causes East Coast fever, Corridor disease and January disease in cattle. In an effort to assess the impact of these diseases and their control on livestock production in the region, studies are underway to model the factors controlling the distribution of the vector tick. Three recent studies have attempted to quantify and evaluate the variables influencing the distribution ofR. appendiculatus, and these are reviewed and discussed in this paper. The relationship between the distribution of an ecoclimatic index of suitability for the tick, calculated by the model CLIMEX on a 25 km 2 interpolated climate database for Africa, and of recorded observations of the tick itself, are evaluated. The sensitivity and specificity of the ecoclimatic index were calculated as 70.2-82.6% and 69.4-84.7%, respectively. In addition, satellite-derived mean maximum normalized difference vegetation index ( N D V I ) for 1987 was assessed as a predcitor of R. appendiculatus habitat in eastern Africa, by comparing the distribution of NDVI values in areas in which the presence of the tick has and has not been recorded. Some visual correlation was observed between mean maximum NDVI values of 0.15 or greater and known R. appendiculatus distribution in Kenya. In addition, this range of NDVI values corresponded closely to areas in Ethiopia where the tick does not occur but for which climatic suitability was predicted by CLIMEX. However, these NDVI values should not be considered universal predictors of habitat suitable for R. appendiculatus, and further study of the NDVI with ground truthing of habitat is considered necessary.
INTRODUCTION
The brown ear tick Rhipicephalus appendiculatus occurs in eastern, central and southern Africa, and is responsible for transmitting the protozoan parasite Theileria parva to cattle, causing East Coast fever, Corridor disease and January disease. The most important variables governing the distribution of the tick are considered to be climate and vegetation (Hoogstraal, 1956 ). Other important variables influencing tick distribution and abundance are the © 1991 Elsevier Science Publishers B.V. All rights reserved 0 1 6 7 - 5 8 7 7 / 9 1 / $ 0 3 . 5 0
262
B.D. PERRY ET AL.
availability of wild and domestic hosts, and the nature and efficacy of tick control measures. The climatic (temperature and moisture) requirements influencing R. appendiculatus distribution have been quantified by Sutherst and Maywald ( 1985 ) on the basis of experimental data and field observations, and incorporated into the model CLIMEX which predicts climatic suitability for arthropod species. A growth index is calculated and moderated by four stress indices (heat, dry, wet and cold) to provide an ecoclimatic index (El) of suitability for tick survival and development. The vegetational requirements for the tick are known to include both grass and limited tree cover (Minshull and Norval, 1982). The tick thus occurs most commonly in savannah and woodland-savannah habitats, and tends to be absent from open plains and heavy forests. The role of the satellite-derived normalised difference vegetation index (NDVI) in studies of vector habitats has recently been reviewed (Hugh-Jones, 1989). This spectral vegetation index quantifies the level of photosynthetic activity of vegetation and is calculated from the advanced very high resolution radiometer (AVHRR) data provided by the National Oceanic and Atmospheric Administration's (NOAA) meteorological satellites. Three recent studies have attempted to quantify the variables influencing the distribution of R. appendiculatus in Africa. Lessard et al. (1990) incorporated eight variables into a geographical information system and developed an interpolated climatic database for Africa with which to run the model CLIMEX. Perry et al. (1990) correlated the distributions of EI values generated by CLIMEX and mean monthly maximum NDVI values for 1987 with the known distribution ofR. appendiculatus collections. Norval et al. ( 1990 ) used both the EI and NDVI values to assess the risk of spread ofR. appendiculatus into highland Ethiopia, where it currently does not occur. This paper reviews the results of these studies, discusses the validity of the techniques used and considers possible future developments. MATERIALS AND METHODS
An interpolated database for Africa of the climatic variables required to drive CLIMEX (mean monthly maximum and minimum temperature, rainfall and humidity), developed by Lessard et al. (1990) at a resolution of approximately 25 km 2, was used. The CLIMEX model was run for each of the 43 174 interpolated grid cells in the African continent on a Micro VAX 3600 mainframe computer (Digital Equipment Corporation, Boston, MA). The Fig. I. (A) The distribution of EI values in eastern Africa (modified from Lessard et al. (1990) ). (B) The distribution of R. appendiculatus collection sites in eastern Africa (modified from Lessard et al., 1990). (C) The distribution of mean monthly maximum NDVI values for 1987 in eastern Africa (modified from Lessard et ah, 1990).
DISTRIBUTION AND ABUNDANCE
263
O F R. APPENDICULATUS IN A F R I C A
A.
0 to
m
m m ~o m . m . mm m m
. o,-.o
m
B. ~udan
E|hiopla
C.
0 0.05 -0.10 -0.15 -0.20 ~0.25 -0.30
m , o~0
264
B.D. PERRY ETAL.
parameter values for R. appendiculatus used in the CLIMEX model were those described by Sutherst and Maywald (1985). Distribution maps of the EI values (on a scale of 1-100) for eastern Africa were produced and compared with known tick distribution. In order to calculate the sensitivity and specificity of the model under the circumstances of use described, all grid cells in the continent were surveyed for the recorded presence of R. appendiculatus and for the presence of EI values of one or greater. The same method was applied to assess the sensitivity and specificity of the model in Kenya and Zimbabwe to investigate the uniformity of these indices at different latitudes. In addition, the range of EI values was plotted for three latitude zones within the tick's distribution. A visual correlation of global area coverage (GAC) mean monthly maximum NDVI values for 1987 with the recorded distribution ofR. appendiculatus collections was made. In addition, the frequency distribution of NDVI values in Ethiopia and Kenya was compared. In the case of Kenya, the frequency of NDVI values in cells in which R. appendiculatus has been recorded was compared with the frequency in cells in which it has not. RESULTS
The relationship between EI values and the known distribution of collections ofR. appendiculatus for eastern Africa are shown in Fig. 1 (A) and 1 (B). Table 1 summarises the sensitivity and specificity analysis of the CLIMEX model, calculated for Africa as a whole, and for Kenya and Zimbabwe. Figure 2 (A) shows the range of El values in three latitude zones. The distributions of El values were significantly different between latitude zones. In eastern Africa (from 2 °N to 12 °S), values range from one to almost 90. In central Africa (12°S-23°S), no value above 40 is recorded. In southern Africa (below 23 °S), although almost half of the values are under 10, values up to 80 were recorded. The relationship between mean monthly maximum NDVI values for 1987 and the distribution of collections of R. appendiculatus for eastern Africa are shown in Fig. 1 (C). Figure 2 (B) displays the frequency distribution of NDVI values in the 25 km 2 cells in Kenya and Ethiopia. DISCUSSION
Rhipicephalus appendiculatus is dependent for its survival and development on the presence of a suitable environment, created for the most part by appropriate climate and vegetation. Clearly, the environment of the microhabitat will not necessarily directly equate with that described with the broad (and long-term) climatic parameters used to run CLIMEX. Thus, other models have been developed to simulate locality-specific population dynam-
DISTRIBUTION AND ABUNDANCE OF R. APPENDICULATUS IN AFRICA
265
TABLE 1 Sensitivity and specificity of the ecoclimatic index (El) in assessing the distribution of R. appendi-
culatus in Africa Location
Recorded presence ofR. appendiculatus (no. of grid cells) +
Presence of EI value of one orgreater (no. of grid cells)
Africa
Kenya
+ -
943 198
11740 30293
+
177
100
-
43
556
Zimbabwe + -
158
121
67
278
Sensitivity: Africa, 0.826; Kenya, 0.805; Zimbabwe, 0.702. Specificity: Africa, 0.721; Kenya, 0.847; Zimbabwe, 0.694.
ics, such as T3Host (Floyd et al., 1987). CLIMEX is, therefore, a valuable tool at the continental and national levels of resolution to aid decisions by donor agencies and governments on vector-borne disease control. As climatic databases improve, it will become valuable at higher levels of resolution. The CLIMEX model, used under the conditions described, predicts widespread suitability for R. appendiculatus through most of sub-Saharan Africa. The model appears to be a fairly sensitive and specific test for R. appendiculatus when evaluated against the recorded presence of the tick. However, as surveys for tick presence have not been carried out in many areas of its possible distribution range, this method probably underestimates both sensitivity and specificity. Although the sensitivity and specificity of the El are lower in Zimbabwe than Kenya or Africa as a whole, this difference is not statistically significant, indicating uniformity of these indices throughout the continent. However, the ranges of El values are lower in southern latitudes than more northerly latitudes, suggesting lower tick abundance in southern Africa. The validity of this deserves some scrutiny. The EI is an annual value, based in this case on monthly climatic data. In some high-rainfall areas of the distribution of R. appendiculatus in Kenya (such as the Lake Victoria basin), all instars of the tick can be found throughout the year. In contrast, the tick is strictly seasonal in Zimbabwe and adult ticks are found only during the months of January-March, coinciding with the single rainy season in that country. Thus, the lower E1 values in central Africa may reflect lower overall annual abundance of the tick, resulting from extended periods during which adult ticks are not active. Unfed adult ticks
266
B.D. P E R R Y E T A L .
Proportion of cells 60 .
.
.
.
.
.
.
50403020-
1o| .
0 1-10
.
.
.
.
.
.
.
11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
Ecoclimatic index value m E Zone 1 (2N-12S)
~
Zone 2 (12S-238)
[~
Zone 3 ( 2 3 8 - )
No. G r i d Cells 400 -
300 -
200
100
-
0 -.05-0
0-.05
.05-.10
.10-.15
.15-.20
.20-.25
.25-.30
.30-.35
.35-.40
NDVI V a l u e Ethiopia (all cells) ->~" Kenya (cells where R. appendic, recorded)
~
Kenya (all cells)
-~"
Kenya (cells
where
R. appendic, not recorded)
Fig. 2. (A) The frequency distribution of EI values by latitude zone (from Perry et al., 1990). (B) The frequency distribution of mean monthly maximum NDVI values for 1987 in grid cells of 25 km 2 in Kenya and Ethiopia. (from Norval et al., 1991; first published in Preventive VeterinaryMedicine, 10(3): 163-172.)
enter a daylength-induced diapause for a period of 3-6 months (Short and Norval, 1981 a,b; Rechav, 1982), thus avoiding the effects of heat stress during the hottest period of the year. Diapause appears to occur south of approximately 12°S, but not in the tropical areas of the tick's distribution. For a better understanding of the epidemiology of the diseases transmitted by R. appendiculatus, it is also important to know the abundance of each instar during its period (s) of activity, and this may not be uniformly measured in Africa by CLIMEX owing to the influence exerted by seasonal effects in south-
DISTRIBUTION AND ABUNDANCE OF R. APPENDICULATUS IN AFRICA
267
ern Africa. It is difficult to make direct comparisons of tick abundance on cattle in Kenya and Zimbabwe owing to confounding factors such as differences in the innate resistance of cattle to ticks and different acaricide use. However, extremely high levels of abundance ofR. appendiculatus have been reported on wildlife in game parks in the highveld of Zimbabwe (Lightfoot and Norval, 1981; Norval and Lightfoot, 1982), where ticks are not controlled by acaricide usage. This provides evidence of the seasonal suitability of the higher rainfall areas of central Africa for this species; comparable levels of abundance of R. appendiculatus in game parks in eastern Africa have not been reported. Although there is a remarkable visual correlation with mean maximum NDVI values for 1987 of 0.15 or greater with known R. appendiculatus distribution, this should be interpreted with extreme caution. There may be some value in comparing the distribution of values within the distribution range of the tick to those in an area into which the tick may potentially spread, such as that carried out between Kenya and Ethiopia (Norval et al., 1991, Fig. 2 (B)), but it is not possible at this stage to correlate mean monthly NDVI values with R. appendiculatus distribution. Kruska and Perry ( 1991 ) have shown the dynamic nature of these values in Zimbabwe, and suggested that GAC data are unlikely to be of value in predicting R. appendiculatus habitat in the communal lands of Zimbabwe owing to the varieties of habitat existing within each pixel sampled. In future studies, it is proposed to evaluate interpolated climate databases of higher resolution and to explore the use of other models, such as BIOCLIM (CSIRO, Canberra, N.S.W. ), to project the likely distribution of tick vectors and the impact of their control in Africa. REFERENCES Floyd, R.B., Maywald, G.F. and Sutherst, R.W., 1987. Ecological Models. 1. A population model of Rhipicephalus appendiculatus. In: R.W. Sutherst (Editor), Ticks and Tick-borne Diseases. Proceedings International Workshop Ecology of Ticks and Epidemiology of Tick-borne Diseases, 17-21 February 1986, Nyanga, Zimbabwe. ACIAR Proceedings Series, Australian Centre for International Research, ACIAR, Canberra, No. 17, pp. 72-75. Hoogstraal, H., 1985. African Ixodoides. I: Ticks of the Sudan. Bureau of Medicine and Surgery, Department of the Navy, Washington, DC, Res. Rep. NM 005 050.29.07, 1101 pp. Hugh-Jones, M.E., 1989. Applications of remote sensing to the identification of the habitats of parasites and disease vectors. Parasitol. Today, 5:244-251. Kruska, R.L. and Perry, B.D., 1991. Evaluation of grazing lands of Zimbabwe using the AVHRR normalized difference vegetation index. Prev. Vet. Med., 11: 363-365. Lessard, P., L'Eplattenier, R., Norval, R.A.I., Kundert, K., Dolan, T.T., Croze, H., Walker, J.B., lrvin, A.D. and Perry, B.D., 1990. Geographical information systems for studying the epidemiology of cattle diseases caused by Theileria parva. Vet. Rec., 126:255-262. Lightfoot, C.J. and Norval, R.A.I., 1981. Tick problems in wildlife in Zimbabwe. 1: The effects of tick parasitism on wild ungulates. S. Afr. J. Wildl. Res., 11: 41-45.
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Minshull, J.I. and Norval, R.A.I., 1982. Factors influencing the spatial distribution of Rhipicephalus appendiculatus in Kyle Recreational Park, Zimbabwe. S. Aft. J. Wildl. Res., 12: I 18123. Norval, R.A.I. and Lightfoot, C.J., 1982. Tick problems in wildlife in Zimbabwe. Factors influencing the occurrence and abundance of Rhipicephalus appendiculatus. Zimbabwe Vet. J., 13: I 1-20. Norval, R.A.I., Perry, B.D., Gebreab, F. and Lessard, P., 1991. East Coast Fever: a problem of the future for the Horn of Africa? Prev. Vet. Med., 10:163-172. Perry, B.D., Lessard, P., Norval, R.A.I., Kundert, K. and Kruska, R., 1990. The effect of climate vegetation on the distribution of the tick Rhipicephalus appendiculatus in Africa. Parasitol. Today, 6: 100-104. Rechav, Y., 1982. Dynamics of tick populations (Acari: Ixodoidea) in the Eastern Cape Province of South Africa. J. Med. Entomol., 19: 679-700. Short, N.J. and Norval, R.A.I., 198 l a. The seasonal activity of Rhipicephalus appendiculatus Neumann 1901 (Acarina: Ixodidae) in the highveld of Zimbabwe Rhodesia. J. Parasitol., 67: 77-78. Short, N.J. and Norval, R.A.I., 198 lb. Regulation of seasonal occurrence in the tick Rhipicephalus appendiculatus Neumann 190 I. Trop. Anim. Health Prod., 13:19-26. Sutherst, R.W. and Maywald, G.F., 1985. A computerised system for matching climates in ecology. Agric. Ecosystems Environ., 13: 281-299.