Modelling the spatial-temporal distribution of tsetse (Glossina pallidipes) as a function of topography and vegetation greenness in the Zambezi Valley of Zimbabwe (original) (raw)
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For eradicating vector borne diseases, earth observation and geoinformation sciences are adding the crucial component of spatial extrapolation from ground observations. For monitoring tsetse flies (Glossina spp.) prevalence in the environment the maximum entropy (MAXENT) technique, presence-only method, was used to map the distribution of their suitable habitat based on remotely sensed vegetation cover and elevation, as well as temperature and rainfall data, derived from WorldClim datasets. In particular, the habitats of Glossina morsitans and Glossina pallidipes were modelled. The main aim was to model the distribution of suitable Glossina spp. habitat in relation to environmental factors such as vegetation cover, elevation, rainfall and temperature. Georeferenced tsetse observation data was collected by the Tsetse Control Division of the Ministry of Agriculture, Mechanisation and Irrigation Development of Zimbabwe. The data was collected spanning the wet and dry seasons in the whole study area. The results of the jackknife of variable importance show which factor contributed the most in defining the outcome of the G. morsitans as well as the G. pallidipes model. The models for both G. morsitans and G. pallidipes have an AUC greater than 0.9. Therefore, it can be concluded that the distribution of suitable Glossina spp. habitat can be modelled based on presence observations only and with remotely sensed vegetation cover and elevation as well as climate data.
Medical and Veterinary Entomology, 1997
Tsetse are vectors of trypanosomes that cause diseases both in humans and livestock. Traditional tsetse surveys, using sampling methods such as Epsilon traps and black screen fly rounds, are often logistically difficult, costly and time-consuming. The distribution of tsetse, as revealed by such survey methods, is strongly influenced by environmental conditions, such as climate and vegetation cover, which may be readily mapped using satellite data. These data may be used to make predictions of the probable distribution of tsetse in unsurveyed areas by determining the environmental characteristics of areas of tsetse presence and absence in surveyed areas. The same methods may also be used to characterize differences between tsetse species and subspecies. In this paper we analyse the distribution of Glossina morsitans centralis, Glossina morsitaps morsitans and Glossina pallidipes in southern Africa with respect to single environmental variables. For G.m,centralis the best predictions were made using the average NDVI (75% correct predictions; range >0.37) and the average of the maximum temperature (70% correct predictions; 27.0-29.2"C). For G.m.morsitans the best prediction was given by the maximum of the minimum temperature (84% correct predictions; range >18.8"C), and for G.pallidipes, also by the maximum of the minimum temperature (86% correct predictions; range >19.6"C). The following paper compares a range of multivariate techniques for making predictions about the distribution of these species in the same region.
2016
For eradicating vector borne diseases, earth observation and geo-information sciences are adding the crucial component of spatial extrapolation from ground observations. For monitoring tsetse flies (Glossina spp.) prevalence in the environment the maximum entropy (MAXENT) technique, presence-only method, was used to map the distribution of their suitable habitat based on remotely sensed vegetation cover and elevation, as well as temperature and rainfall data, derived from WorldClim datasets. In particular, the habitats of Glossina morsitans and Glossina pallidipes were modelled. The main aim was to model the distribution of suitable Glossina spp. habitat in relation to environmental factors such as vegetation cover, elevation, rainfall and temperature. Georeferenced tsetse observation data was collected by the Tsetse Control Division of the Ministry of Agriculture, Mechanisation and
A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya
PLoS ONE, 2010
Background: Trypanosoma spp, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools.
The Journal of applied ecology, 2018
Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local-scale (10s km), while interventions often extend across 100s km. Geostatistical models have been used to interpolate between points where data are available, but this still requires costly sampling across the entire area of interest. Instead, we used geostatistical models to predict local-scale spatial variation in the abundance of tsetse-vectors of human and animal African trypanosomes-beyond the spatial extent of data to which models were fitted, in Serengeti, Tanzania.We sampled and >10 km inside the Serengeti National Park (SNP) and along four transects extending into areas where humans and livestock live. We fitted geostatistical models to data >10 km inside the SNP to produce maps of abundance for the entire region, including unprotected areas.Inside the SNP, ...
The impact of habitat fragmentation on tsetse abundance on the plateau of eastern Zambia
Preventive veterinary medicine, 2009
Tsetse-transmitted human or livestock trypanosomiasis is one of the major constraints to rural development in sub-Saharan Africa. The epidemiology of the disease is determined largely by tsetse fly density. A major factor, contributing to tsetse population density is the availability of suitable habitat. In large parts of Africa, encroachment of people and their livestock resulted in a destruction and fragmentation of such suitable habitat. To determine the effect of habitat change on tsetse density a study was initiated in a tsetse-infested zone of eastern Zambia. The study area represents a gradient of habitat change, starting from a zone with high levels of habitat destruction and ending in an area where livestock and people are almost absent. To determine the distribution and density of the fly, tsetse surveys were conducted throughout the study area in the dry and in the rainy season. Landsat ETM+ imagery covering the study area were classified into four land cover classes (mun...
Geocarto International, 2019
Tsetse flies transmit trypanosomes that cause Human African Trypanosomiasis (HAT) in humans and African Animal Trypanosomiasis (AAT) in animals. Understanding historical trends in the spatial distribution of tsetse fly habitat is necessary for planning vector control measures. The objectives of this study were (i) to test for evidence of any trends in suitable tsetse fly habitat and (ii) to test whether there is an association between trypanosomiasis detected from livestock sampled in dip tanks and local tsetse habitat in the project area. Results indicate a significant decreasing trend in the amount of suitable habitat. There is no significant correlation between trypanosomiasis prevalence rates in cattle and distance from patches of suitable tsetse habitat. The observed low trypanosomiasis prevalence and the lack of dependence on suitable tsetse fly habitat can be explained by the observed decreases in suitable tsetse habitat, which themselves are due to expansion of settlement and agriculture in North Western Zimbabwe.
Background: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover.
Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya
International Journal of Health Geographics, 2009
Background: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover.