MODELLING SPATIAL DISTRIBUTION OF TSETSE (DIPTERA: GLOSSINIDAE)

IN MASOKA AREA, AN UNEXPLORED PART OF MBIRE DISTRICT,

ZIMBABWE

BY

GERALD CHIKOWORE

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Tropical Entomology

University of Zimbabwe Faculty of Science Department of Biological Sciences

May 2016

DECLARATION

I hereby declare that this thesis is my own original work and has not been submitted for a degree in any other university.

………………………………………… ……………………………… Gerald Chikowore Date

We as supervisors confirm that the work reported in this thesis was carried out by the candidate under our supervision. The thesis was examined and we approved it for final submission.

………………………………………... ………………………………. Dr. P. Chinwada Date

………………………………………… ……………………………… Dr. M. Zimba Date

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DEDICATION

This work is dedicated to my wife, Tinotenda and daughter Tinevimbo.

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ACKNOWLEDGEMENTS

I would like to extend my gratitude to my academic supervisors Drs P. Chinwada, M. Zimba, and L. Guerrini for their immense contribution and guidance in the conduct of this research. Their expertise and wisdom steered this work to its completion.

I am also indebted to the Division of Tsetse Control for allowing me to utilise their facilities during the study. Thanks are also extended to Tsetse Control Mashumbi field staff, Mr S. Katumba, Mr F. Hlekwayo, Mr S. Bangajena, Mr R. Joko, CAMPFIRE warden in Masoka, Mr M. Fakero and CIRAD member, Mr N. Chiweshe, for assisting in the collection of tsetse samples. Their endurance and hard work helped me cover as much of the Masoka area as possible in the simmering heat. Mr L. Nyakupinda patiently assisted in the analysis of data and I am grateful to him.

This work was conducted within the framework of the Research Platform “Production and Conservation in Partnership” (www.rp-pcp.org). The European Union and African Carribbean Pacific Group of states funded this research through the GeosAf project and I would like to acknowledge their contribution which made it possible to conduct all the fieldwork. The GeosAf project, implemented by CIRAD also imparted geomatic techniques which were essential in the spatial analysis of data in this project. Satellite images used in this research were provided by SPOT ISIS-CNES.

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ABBREVIATIONS AND ACRONYMS

ACP African Caribbean Pacific Group of States AUC Area Under the Curve AU-IBAR African Union Inter-African Bureau for Animal Resources AVHRR Advanced Very High Resolution Radiometer DLST Day Land Surface Temperature EU European Union FAO Food and Agriculture Organisation of the United Nations GeosAf Geomatic Tools Transferred to Animal Health Services in Southern Africa GIS Geographic Information Systems GPS Global Positioning System IAEA International Atomic Energy Agency ISIS-CNES Incitation à l'utilisation Scientifique des Images SPOT - Centre National d'études Spatiales MaxEnt Maximum Entropy MIR Mid Infra-Red MODIS Moderate-resolution Imaging Spectroradiometer NASA National Aeronautics and Space Administration NDVI Normalized Difference Vegetation Index NLST Night Land Surface Temperature NOAA National Oceanic and Atmospheric Administration PATTEC Pan-African Tsetse and Trypanosomiasis Eradication Campaign ROC Receiver Operated Characteristics RP-PCP Research Platform-Production and Conservation in Partnership SPOT Satellite Pour l’Observation de la Terre WHO World Health Organisation

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ABSTRACT

A study was conducted from March 2015 to December 2015 in order to model the distribution of two savannah species of tsetse (Glossina sensu stricto), Glossina morsitans morsitans and G. pallidipes in the Masoka area of the Mid-Zambezi valley in Zimbabwe. Two approaches were used. The first approach sought to model the probability of presence of both species in areas which were sampled but recorded zero tsetse catches using trap efficiency, sampling effort and suitable habitat cover. A probability threshold of 0.05 was used to distinguish areas which could be potentially infested from those that had low chances of tsetse occurrence. The resultant probability model pointed to an area of 104 km2 in size where G. m. morsitans could possibly be present (P > 0.05) whilst all areas which did not record G. pallidipes had a low probability of presence for the species (P < 0.05). This study showed that there was a high probability of tsetse presence in areas where the habitat was less degraded and low probability in settled areas where suitable tsetse habitat has been disturbed due to agricultural activities. The probability model therefore has the potential to optimize vector control strategies by streamlining areas of intervention. The second model was a predictive one built using tsetse presence-only data and climatic and environmental covariates. The model had an Area Under the Curve (AUC) of 0.80 for G. m. morsitans and 0.94 for G. pallidipes, indicating that the ability of the model to predict suitable tsetse habitat in the Masoka area was better than random (AUC = 0.5). Glossina morsitans morsitans occurrence was positively correlated to Normalised Difference Vegetation Index (NDVI), riverine forest and mopane woodlands whilst crop lands and temperature indices exhibited a strong negative correlation with its occurrence. Glossina pallidipes, on the other hand, had extremely specialised habitat requirements and was positively correlated to riverine forest. The species also had a positive correlation with NDVI but a negative correlation with mopane woodland.

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TABLE OF CONTENTS

DECLARATION ...... ii DEDICATION ...... iii ACKNOWLEDGEMENTS ...... iv ABBREVIATIONS AND ACRONYMS ...... v ABSTRACT ...... vi TABLE OF CONTENTS ...... vii LIST OF TABLES ...... ix LIST OF FIGURES ...... x CHAPTER 1 ...... 1 INTRODUCTION ...... 1 1.1 Overview ...... 1 1.2 Justification of the Study ...... 2 1.3 Objectives ...... 4 1.4 Hypotheses...... 4 CHAPTER 2 ...... 5 LITERATURE REVIEW ...... 5 2.1 Economic Importance of Tsetse and Trypanosomiasis ...... 5 2.2 Tsetse Taxonomy ...... 6 2.3 Tsetse Distribution in Zimbabwe ...... 7 2.4 Climatic and Environmental Requirements of Tsetse ...... 8 2.5 Host Preferences for Tsetse ...... 10 2.6 Tsetse Surveillance ...... 12 2. 7 Species Distribution Models ...... 14 2.8 Applications of Models in Tsetse and Trypanosomiasis Management ...... 17 CHAPTER 3 ...... 18 MATERIALS AND METHODS ...... 18 3.1 The Study Area ...... 18 CHAPTER 4 ...... 20 Probability of Tsetse Occurrence Despite a Sequence of Zero Catches ...... 20 4.1 Introduction ...... 20 4.2 Materials and Methods ...... 20

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4.3.1 Surveys ...... 24 4.3.2 Land cover mapping ...... 26 4.4 Discussion ...... 29 CHAPTER 5 ...... 32 Modelling the Probability Distribution of G. m. morsitans and G. pallidipes in Masoka Area Using Climatic, Environmental and Presence Only Data ...... 32 5.1 Introduction ...... 32 5.2 Materials and Methods ...... 32 5.3 Results ...... 37 5.4 Discussion ...... 44 CHAPTER 6 ...... 46 GENERAL DISCUSSION, CONCLUSION AND RECOMMENDATIONS...... 46 6.1 General Discussion ...... 46 6.2 Conclusion ...... 46 6.3 Recommendations ...... 47 REFERENCES ...... 48

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LIST OF TABLES

Table 1: Tsetse captured in the Masoka area between March 2015 and December 2015 ...... 25 Table 2: Apparent densities (mean ± SE) of tsetse in the Masoka area ...... 26 Table 3: Remotely-sensed data with original spatial and temporal resolution ...... 37 Table 4: Eigen values describing G. m. morsitans habitat on the ENFA ...... 39 Table 5: Eigen values describing G. pallidipes habitat on the ENFA ...... 42

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LIST OF FIGURES

Figure 1: Current tsetse infested area in Zimbabwe. Source: Tsetse Control Division Zimbabwe ...... 8 Figure 2: Definition of marginality in ENFA. It is the norm of the vector connecting µ to µ’. The available space is in light grey and the space used in dark grey...... 15 Figure 3: Definition of specialization which is the ratio of the variances between the two clouds, the available and the used one...... 16 Figure 4: Masoka study area ...... 19 Figure 5: Apparent densities of G .m. morsitans in Masoka area between March 2015 and December 2015 ...... 25 Figure 6: Apparent densities of G. pallidipes in Masoka area between March 2015 and December 2015 ...... 26 Figure 7: Land cover map of the study area ...... 27 Figure 8: Map showing probability of G. m. morsitans presence ...... 28 Figure 9: Map showing probability of G. pallidipes occurrence ...... 29 Figure 10: Relationship between the geographical space and the ecological niche (Source: Guerrini, 2016) ...... 33 Figure 11: Ecological Niche Factor Analysis for G. m. morsitans ...... 38 Figure 12: Predictive model for G. m. morsitans in Masoka area ...... 40 Figure 13: ROC curve and AUC for G. m. morsitans ...... 40 Figure 14: Ecological Niche Factor Analysis for G. pallidipes ...... 41 Figure 15: Predictive model for G. pallidipes ...... 43 Figure 16: ROC curve and AUC for the G. pallidipes model ...... 43

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LIST OF PLATES Plate 1: Epsilon trap for tsetse collection ...... 21 Plate 2: Sorghum and cotton fields in the Masoka area ...... 22 Plate 3: Mopane woodland in the Masoka area ...... 23 Plate 4: Riverine vegetation in the Masoka area ...... 23

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CHAPTER 1

INTRODUCTION

1.1 Overview

Trypanosomiasis is one of the major constraints to rural development in sub-Saharan Africa (Ducheyne et al., 2009). Tsetse (Glossina spp.), the primary vectors of animal and human trypanosomiasis, are found in the semi-arid, sub-humid and humid lowlands of 37 countries across the continent with a potential distribution range of some 8.7 million km2 (Rogers and Robinson, 2004). This disease places approximately 50 million cattle at risk with losses amounting to US$4.75 billion annually (Scoones, 2016).

The distribution of tsetse and their abundance play an important role in the epidemiology of trypanosomiasis and often form the basis for intervention programmes. Insect intervention and pre-intervention programmes require accurate and up–to–date information on the spatial and temporal distribution of target insects (Cox, 2007). However, according to Cecchi et al. (2015), it has been decades since the latest tsetse distribution maps at continental level were produced. Strategies to control or eventually eliminate the problem posed by trypanosomiasis must rely on tsetse ecology and suitable fly distribution data (Cecchi et al., 2008).

A number of studies have been carried out in order to understand tsetse population dynamics and these have resulted in an increased understanding of the link between the environment and the presence or abundance tsetse (Guerrini et al., 2009). It has also been established that tsetse are highly dependent on particular habitats for their survival, therefore ecological and land use change has a major impact on fly populations and the associated disease risks (Scoones, 2014). The distribution, prevalence and impact of vector-borne diseases are often affected by anthropogenic environmental changes that alter the interactions between the host, the parasite and the vector (Van den Bossche et al., 2010).

The distribution and abundance of organisms can be studied in two ways. The first method involving a biological approach in which demographic rates are measured and related to biotic and abiotic factors that influence them and the second based on a statistical analysis of

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the relationship between population data and environmental factors that are often measured for other purposes (Rogers and Williams, 1994).

Recent advances in geospatial technology have enabled the development of models in the study of diseases and parasites. According to Cecchi and Mattioli (2009), geo-referenced datasets and spatial analysis techniques have great potential to support the planning and implementation of interventions against human and animal diseases including African trypanosomiasis. Morris et al. (1994) also suggested that the trypanosomiasis-tsetse complex is an ideal application to which geographical computer simulation could be applied as part of the development of a decision support system for control of the disease. Distribution mapping based on Geographic Information Systems (GIS) can help identify areas of occurrence at the micro-level where species-specific and environmentally-friendly control measures can be strengthened (Ozdenerol et al., 2008).

1.2 Justification of the Study

Human settlements in Southern Africa have been expanding rapidly in recent years and have gradually encroached onto large wilderness areas that have abundant populations of game and savannah species of tsetse (Van den Bossche, 2001). These expansions, particularly in the Zambezi valley, have been driven by cotton farming rather than tsetse control as previously thought (Baudron et al., 2011). However, settlement in tsetse-infested areas exposes humans and their livestock to trypanosomiasis.

Tsetse distribution and abundance information is critical in analysing the trypanosomiasis risk on a spatial dimension. This information can be obtained through surveillance programmes which aim at data collection and analysis to produce predictions either over time and/or space to inform the health sector over disease occurrence. However, surveillance systems frequently face problems of underfunding, organisation and continuity, leading to partial or complete failure of vector-borne disease control programmes (Gorla, 2006).

In recent years, tsetse and trypanosomiasis distribution models have been developed at different scales of study. These distribution models have been produced at a continental scale from low spatial resolution data, using the Advanced Very High Resolution Radiometer (AVHRR) data from the NOAA (www.noaa.gov) satellite that present a spatial resolution of

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28 km (Rogers and Randolph, 1991). This level of resolution does not allow the identification of suitable habitats for tsetse flies that are found diluted in the surrounding pixels. Results from a study exploring the potential usefulness of a selection of global GIS datasets to map the distribution and densities of tsetse flies show that datasets with global or regional scope are not suitable for use at a local scale (Guerrini et al, 2009). Locally, studies by Matawa et al. (2013) have shown great potential in modelling tsetse distribution. However, the spatial resolution of existing tsetse distribution models is not sufficient to guide the eradication of tsetse and trypanosomiasis (Dicko et al., 2014).

In Zimbabwe, existing information on tsetse distribution and abundance is not up-to-date, thus there remains much uncertainty about the distribution, impact and importance of human and animal trypanosomiasis (Scoones, 2014). As a result, resources are unnecessarily spread over wide areas due to limited information. Modelling tsetse distribution using high resolution, remotely-sensed data on a local scale has the potential to provide the much-needed information.

Climatic factors, in particular rainfall, temperature and relative humidity have been shown to be important parameters in determining tsetse distribution and population dynamics. The relationship between these factors and tsetse population dynamics is therefore a potential area for modelling and further development of existing models (Leak, 1994).

This study therefore seeks to update the existing knowledge and generate high-resolution tsetse distribution information in the process. Models generated will guide tsetse intervention programmes, leading to a reduction in intervention costs as resources are directed towards needy areas.

Questions could be asked regarding the possibility of delineating the biological (fundamental niche) distribution of tsetse in the Masoka area of Mbire District based on remotely-sensed environmental and climatic data as well as the need to examine areas infested with tsetse flies in the same locality for intervention purposes. This will help to better understand the dynamics of the trypanosomiasis vector for effective management of the disease.

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1.3 Objectives

1.3.1 Main objective

The main objective of this study was to develop models for delineating tsetse-infested areas in the Masoka area, Mbire District of Zimbabwe and define priority areas for intervention programmes.

1.3.2 Specific objectives

1) To model the probability of Glossina morsitans morsitans and G. pallidipes occurrence in the Masoka area. 2) To model the spatial distribution of G. m. morsitans and G. pallidipes in Masoka area.

1.4 Hypotheses

1) Glossina morsitans morsitans and G. pallidipes are present in sampled areas of the Masoka area despite zero catches. 2) The spatial distribution of G. m. morsitans and G. pallidipes in Masoka area is determined by vegetation cover, day and night land surface temperature and air temperature.

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CHAPTER 2

LITERATURE REVIEW

2.1 Economic Importance of Tsetse and Trypanosomiasis

Tsetse flies are obligate blood feeders, which make them insects of medical and veterinary importance (Service, 2004). The economic impacts of tsetse are diverse and complex with direct effects on animal production and human health, as well as indirect effects on settlement patterns, land use, draught power, animal husbandry and farming (Bourn et al., 2005). Their function as transmitters of trypanosomes to both humans and animals causes great suffering to humans and impacts on agricultural production (Krinsky, 2002).

African animal trypanosomiasis has had a profound impact on the ability of parts of the continent to sustain a highly productive livestock population on the African plains (Molyneux and Ashford, 1983). Tsetse flies are a major cause of rural poverty in sub-Saharan Africa because they prevent mixed farming (Krinsky, 2002). Tsetse-infested land is often cultivated by people using hoes rather than more efficient draught animals because trypanosomiasis weakens and often kills these animals. Those cattle that survive produce little milk, gravid cows often abort their calves, and manure is not available to fertilize the worn-out soils (Molyneux and Ashford, 1983; Krinsky, 2002).

The importance of animal trypanosomiasis in endemic areas is hinged on the effect of the disease on production indices and the cost of controlling the disease to allow profitable animal production (AU-IBAR, 2013). According to Kristjanson et al. (1999), direct aggregate losses due to animal trypanosomiasis in the estimated 47 million cattle living in the tsetse regions may exceed US$1,3 billion annually, whilst the overall annual direct lost potential in livestock and crop production was estimated at US$ 4.5 billion (Budd, 1999; DFID, 2001). The domestic animal disease therefore continues to inhibit agricultural productivity and economic development (Krinsky, 2002).

Tsetse flies also transmit a disease to humans, called Human African Trypanosomiasis (HAT) or sleeping sickness. According to Simarro et al. (2012), 70 million people in 20 countries are at different levels of risk with only 3-4 million of them covered by active surveillance. Since

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the disease tends to affect economically active adults, the total cost to a family with a patient is about 25% of a year’s income (Shaw, 2004).

2.2 Tsetse Taxonomy

Tsetse flies are Brachycera Cyclorrhapha Diptera, Schizophora and Calyptratae, close to Muscidae. The Glossinidae family was created for these insects, comprising a single genus, Glossina (WHO, 2013). The flies are long, robust, and brown-black to brown whose length, without the proboscis, is between 6 and 16 mm (Service, 2004). Males are generally smaller than females. At rest, tsetse wings, which are hyaline or slightly smoked in colour, are crossed above the abdomen (like scissors), and their posterior end exceeds its extremity (Warnes, 1997). The mouth parts are long and sharp, located at the base of the head, they are directed forward at rest and are protected by maxillary palpi of the same length (WHO, 2013).

The genus Glossina has got some 29 to 31 species and sub-species, which are all likely to transmit the trypanosomiasis. These have been placed into three species groups, which are sometimes given sub-generic status (Haeselbarth et al, 1966). These are the fusca group (subgenus Austenina), palpalis group (subgenus Nemorhina), and morsitans group (subgenus Glossina). These groupings are based primarily on morphological features of the adult genitalia. Jordan (1993) also noted that these groups also reflect differences in distribution, habitat and behaviour.

2.2.1 Austenina (fusca group)

This group consists of large species (11–16 mm) with an abdomen of a more or less uniform brown. The tarsus of the posterior legs is brown-black, sometimes only the last two segments being black (Service, 2004). Species of the fusca group typically occur in lowland rain forests of West and Central Africa with the exception of G. longipennis and G. brevipalpis, which occur in the drier regions of eastern Africa. Species of the fusca group are most often found in forested habitats, such as rain, swamp and mangrove forests (Krinsky, 2002). Increasing human activity in forests is tending to make them disappear and some are excellent vectors of animal trypanosomiasis, but there are few cattle in the areas in which they occur (WHO, 2013).

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2.2.2 Nemorhina (palpalis group)

These are medium sized (8–10 mm) to small (6–8 mm) tsetse with a black-brown abdomen which sometimes has dark spots on a clear greyish layer (Service, 2004). All the segments of the tarsus of the posterior legs have dark brown or black bristles (WHO, 2013). The palpalis group is usually associated with riverine vegetation, primarily along watercourses in Western and Central Africa, but some species also extend into savannah regions between river systems (Krinsky, 2002).

2.2.3 Glossina sensu stricto (morsitans group)

These species are of medium size (8–11 mm), and the abdomen is generally coloured with dark spots on a clear yellowish layer (Service, 2004). The morsitans group is primarily associated with drier savannahs and is most often found in dry thickets, scrub vegetation, and areas of savannah woodland with a primarily central and south-eastern distribution (Krinsky, 2002; Kuzoe and Schofield, 2004). Tsetse species found in Zimbabwe belong to the morsitans group. The main species are Glossina morsitans morsitans Westwood 1850 and Glossina pallidipes Austen 1903. However, Glossina austeni Newstead 1912 and Glossina brevipalpis Newstead 1910 have also been recorded in small areas of Zimbabwe, particularly along the eastern border of Zimbabwe and Mozambique (Warnes, 1997).

2.3 Tsetse Distribution in Zimbabwe

Tsetse flies occur in the tropical and subtropical regions of sub-Saharan Africa, approximately 15° N to 26° S. In Zimbabwe, the flies occupied most of the area lying below the 1,050 m contour in two distinct belts before 1896. The northern belt included the basins of the Zambezi River and its major tributaries and the south-eastern belt occupied the basins of Save, Runde and Limpopo Rivers (Cockbill, 1975). Currently the fly occupies approximately 28,000 km2 in North-Western and Northern Zimbabwe as a result of various intervention measures (Figure 1) (Tsetse Control Division, 2015).

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Figure 1: Current tsetse infested area in Zimbabwe. Source: Tsetse Control Division Zimbabwe

2.4 Climatic and Environmental Requirements of Tsetse

2.4.1 Temperature

Tsetse need particular temperatures and humidity to survive and these can only be encountered in tropical areas (WHO, 2013). The ecological limit of tsetse observed in Zimbabwe before the rinderpest epizootic of 1896 coincided with the 20°C mean annual isotherm indicating that temperatures lower than these were detrimental to their presence (Warnes, 1997). According the World Health Organisation (WHO) (2013), the thermal optimum for all tsetse species is about 25°C with temperatures above 36°C causing suffering to both pupae and adults and death of adults when temperatures exceed 38–40°C. Hargrove et al. (2012) noted that changes in temperature are of importance as they affect population dynamics of tsetse than any other meteorological variable.

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Temperature has a significant influence on the reproductive behaviour and feeding habits of tsetse with studies carried out in the Luangwa valley of Zambia showing that the age structure of tsetse populations is largely dependent on temperature and relative humidity (Munang'andu et al., 2012). Adults are paralysed by the cold, and pupae cannot develop normally in temperatures lower than 16°C (WHO, 2013). Tsetse birth rate is generally low in the cold season when the pupal and inter-larval periods are at their maximum (Hargrove et al., 2012; Munang'andu et al., 2012).

Mean temperatures are important since they affect the general metabolic rate and thus influence factors such as the rate at which tsetse need to feed, the frequency with which they deposit their larvae, and the time spent in the pupal phase. Field observations by Hargrove et al. (2012) also revealed that tsetse are very sensitive to high temperatures such that maximum temperatures may have an additional effect on mortality, independent of the effects of mean temperature.

2.4.2 Altitude

The ecological limit of tsetse in Zimbabwe coincides with the 1,050 m contour (Warnes, 1997; Cockbill, 1975). De Deken, (2013) noted that mountains exceeding 1,600 m appear to be barriers to tsetse although in Ethiopia and Cameroon, tsetse flies have been found at altitudes above 2,000 m. Therefore, altitude as a factor does not seem to have an effect on the distribution of tsetse rather it is its effect on temperature that affects tsetse. This could be supported by the coincidence of the 1,050 m contour, the 20oC isotherm and the observation of tsetse presence (Warnes, 1997).

According to De Deken (2013), the discovery of tsetse at altitudes above 2,000 m could be an indicator of a rise in temperatures, thus further indicating that altitude on its own does not seem to play a major role in the distribution of the fly. Matawa et al. (2013) further supported this assertion when they observed that predictions for tsetse were low at altitudes above 1,100 m in Western Zimbabwe and attributed this to the effect of altitude on the tsetse microclimate, especially temperature. In the Luangwa valley of Zambia, Munang'andu et al. (2012) also observed that high altitude areas had long periods of low temperatures thus the majority of the flies failed to emerge from puparia and a large number of those that did were malformed and quickly died.

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Besides climatic factors, environmental changes have an essential impact on vector distribution and disease; they are complex and result primarily from human activities (Sutherst, 2004). Hartemink et al. (2015) also indicated that landscape characteristics affect the spatial and temporal dynamics of several vector-borne diseases through their effects on habitat suitability for vectors and hosts within a suitable climate envelope.

2.4.3 Vegetation

Tsetse flies pass most of their adult time at rest in shaded places in forested areas. The preferred sites are the lower woody parts of vegetation whilst many tsetse flies hide in holes in the trunks of trees and between roots. All forms of woodland, from savannah to rain forest, with the exception of grasslands, can provide a suitable habitat for some tsetse species, but no single vegetation type is suitable for all species (De Deken, 2013).

The nature of vegetation cover across sub-Saharan Africa dictates species distributions in tsetse flies (Syed and Guerin, 2004). In the wetter areas, the flies are observed to roam more widely over the woodland, but in drier areas their movements are restricted to mesophytic vegetation of the watercourses (Jordan, 1986). In Eastern and Southern Africa, the miombo woodlands dominated by Brachystegia and Julbernardia that extend from Mozambique to Tanzania, as well as the mopane woodlands (Colophospermum mopane) in Zambia and Zimbabwe are the typical G. morsitans morsitans habitats (WHO, 2013).

Glossina pallidipes occurs over a relatively wide range of climatic and vegetation conditions. These savannah tsetse flies tend to spread out during the rainy season in arid areas, while during the dry season they concentrate in denser vegetation along drainage lines or in better watered woodlands (De Deken, 2013). The distribution of G. pallidipes is patchy, from Ethiopia to Mozambique and it is commonly sympatric with G. m. morsitans, especially in the mopane woodlands of Zambia and Zimbabwe (Torr et al., 2012).

2.5 Host Preferences for Tsetse

Tsetse flies are obligate haematophagous insects hence their distribution and densities are largely affected by the distribution of hosts which are essential for their survival. The choice of hosts by tsetse is influenced to a certain extent by the distribution and abundance of the hosts. Some host species are widely distributed with lesser restrictive habitat, while some 10

species are highly restricted in their habitat requirements (Munang'andu et al., 2012). The feeding preferences of tsetse flies appear to rely not only on the nutritional value of the blood of their hosts, as some savannah species (G. m. morsitans, G. m. centralis, G. m. submorsitans) have been reported to feed preferentially on suidae (warthog and bush pig), whereas others (G. pallidipes, G. longipalpis) sometimes feed exclusively on bovidae (buffalo, bushbuck and other antelope) or hippopotamuses in some areas (WHO, 2013). Seasonal variations in the movement of wild hosts have been reported to significantly influence the ecological behaviour of Glossina species with a wide dispersal during the rainy season as water supplies are widely distributed and assuming a concentration along riverine as water sources dry up (Manang’andu et al., 2012). This trend of seasonal movement has a significant influence on the distribution of tsetse flies rendering the riverine area to be the most densely populated with tsetse populations.

Some wild animals such as zebra, wildebeest and oryx that are common in tsetse habitats are rarely bitten by the flies, possibly because their colour is less attractive or because their skin contains repellent substances, as shown recently for waterbuck (Saini and Hassanali, 2007). Humans are bitten by tsetse flies and they contract sleeping sickness; however, it has long been known that human odour repels morsitans group tsetse flies (WHO, 2013).

Cattle form an important host for tsetse especially in areas adjoining tsetse-infested areas where there is high agricultural activity and high cattle densities. Studies by Van den Bossche and Staak (1997) in Zambia revealed that a large proportion of tsetse blood meals were from cattle. This was attributed to human encroachment into wildlife areas and the resultant decline in wildlife numbers. At times, tsetse choose to feed on low-reactive individuals as adult cattle (Torr and Hargrove, 1998; Schofield and Torr, 2002). According to WHO (2013), when warthogs and antelopes disappear from an area that has no cattle, savannah tsetse flies become scarce. The flies of the morsitans group are usually very sensitive to agricultural development, degradation of natural habitats and reduced wildlife density (especially G. swynnertoni, G. pallidipes and G. longipalpis), although they will feed readily on domestic livestock when available.

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2.6 Tsetse Surveillance

Entomological surveillance is used to determine changes in the geographical distribution and density of the vector, evaluate control programmes, obtain relative measurements of the vector population over time and facilitate appropriate and timely decisions regarding interventions. It may also serve to identify areas of high-density infestation or periods of population increase (WHO, 1997).

Sampling of tsetse is often used to assess the apparent density, a parameter related to true density. True density is often described as the number of tsetse flies per surface unit and usually depends on the renewal of the population, the mortality rate and the rates of immigration and emigration (De Deken, 2013). Tsetse behaviour with respect to method of capture also affects the assessment of tsetse density. Each capture method gives a more or less significant bias while capturing only among certain parts of the population of tsetse flies (Warnes, 1997). Tsetse activity also influences estimation of density as it varies according to the moment the density is monitored as influenced by climatic conditions, place of sampling, sex, age and some cyclic phenomena such as hunger or pregnancy (De Deken, 2013).

Two types of surveys can be conducted on tsetse depending on the objective of the survey (Warnes, 1997). Longitudinal surveys aim at assessing population dynamics and behaviour of tsetse with respect to the spatial and seasonal variations of the environment, whilst cross- sectional sampling of adult tsetse is carried out to study their distribution, determine their trypanosome infection rate or the effectiveness of control measures (De Deken, 2013).

Host-seeking behaviour studies have led to the development of a number of surveillance methods to capture tsetse in proportion to their density (Lawyer and Perkins, 2004). These studies revealed that tsetse utilize both vision and odour detection to locate hosts and these visual and olfactory stimuli attract tsetse flies respectively at short (less than 15 m) and long range (less than 100 m for the odour of a single ox) (De Deken, 2013). The different sampling methods and gadgets designed for tsetse make use of these stimuli (Warnes, 1997). However, recently developed traps bear little resemblance to actual hosts (Lawyer and Perkins, 2004).

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If the tsetse population would be in equilibrium and no immigration or emigration would occur, an estimate of the real population density may be obtained using mark-release- recapture experiments (De Deken, 2013). In this technique, a random sample of the population is marked, released and recaptured at a later date. The population density (푋) can then be calculated from the formula:

푋 푅 = 푌 푀

Where: 푌 = the number of all recaptured flies (marked and unmarked), 푀 = number of all marked flies, 푅 = number of marked flies recaptured.

It is assumed that all individuals have the same probability of being captured in the second sample, regardless of whether or not they were previously captured in the first sample (Krebs, 1999).

2.6.1 Stationary sampling devices

Several traps have been designed to catch different tsetse species and are normally used in conjunction with a variety of odour attractants to improve efficiency (Leak et al., 2008). Traps have been used for tsetse since the 1920s (Warnes, 1997) with the biconic trap of Challier Laveissière often being considered as the golden standard (De Deken, 2013). One of the most important characteristics of tsetse traps are the blue and black colours, where the blue attracts tsetse flies while black encourages them to land. Besides traps, there are other stationary sampling devices such as electric grids and artificial refuges (Warnes, 1997).

Savannah species of tsetse are most efficiently captured by the Epsilon trap (Hargrove and Langely, 1990). This trap was developed as an alternative to the F3 trap developed by Flint in 1985 (Leak et al., 2008). The Epsilon trap looks like an equilateral triangle, with the lower half of front folded back into the trap to form a shelf. A piece of black cloth measuring 0.5 × 1 m is sewn into the rear inside of the trap whilst all other surfaces are blue. The cone is recessed, with its apex level with the top. The trap is supported internally by poles held by guy ropes (Warnes, 1997; Leak et al., 2008).

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2.6.1 Mobile sampling devices

Techniques in this category exploit the propensity of tsetse to be attracted to moving objects in the form of game animals, domestic animals, people and vehicles among others (Warnes, 1997). The widely operated mobile survey technique is the fly-round. This technique has been the mainstay of tsetse surveys in Zambia and Zimbabwe before the advent of traps, partly because it was realised that human odour repels tsetse (Leak, et al., 2008).

When conducting a fly-round, two fly catchers carry a black-coloured flag, baited with a sachet containing a tsetse fly attractant (butanone or octenol), on a tour which is representative of the tsetse habitat, stopping at well-defined points and capturing during a fixed period the tsetse which are attracted (Warnes, 1997). Sometimes an ox is led instead of the black flag to increase the catch. According to De Deken (2013), this method does not give an idea of the true fly density but makes it possible to observe fluctuations in the density of the flies over time and more males than females are captured.

2. 7 Species Distribution Models

An important step in understanding the ecology of vectors for the purposes of intervention is to determine the environmental causes of the spatial and temporal variation in their numbers (Nakato and Ayanlande, 2009). This can be achieved through the use of statistical and computational methods and techniques as well as digital processing of satellite images. These techniques expand the prospects for research on the spatial distribution of diseases and the possibility of creating risk maps based on multivariate and hierarchical models.

The principle of species distribution models is to relate known locations of a species with the environmental characteristics of these locations in order to estimate the response function and contribution of environmental variables (Austin et al., 2006). This function will then be used to predict the potential geographical range of a species (Elith and Leathwick, 2009). These models estimate the fundamental ecological niche in the environmental space, which is the response of the species to abiotic environmental factors (Soberón and Peterson, 2005). The estimated response of the species is then projected onto the geographical space to derive the probability of presence for any given area (Fourcade et al., 2014).

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2.7.1 The Ecological Niche Factor Analysis

The Ecological Niche Factor Analysis (ENFA) is a method based on a comparison between the environmental niche of the species and the environmental characteristics of the entire study area, also termed background data (Hirzel et al., 2002). The ENFA only needs a set of presence data (no absences are required) and a set of background predictors and is a variant of the principal component analysis (PCA) (Engler et al., 2004). However, whereas in PCA the successive axes are simply selected to match the direction of maximum variance in the multidimensional eco-geographical space, ENFA’s principal components all possess a true ecological meaning for the modelled species. The construction of these axes is based on two important concepts: marginality and specialization.

Marginality is used to measure niche central position. It captures the dimension in the ecological space in which the average conditions where the species lives differs from the global conditions. It measures the square of the distance between the available average space, and the average space used by the species. From a geometrical point of view, it is the norm between the origin of the ecological space and the centre of gravity of the space used by the species (Figure 2). The available habitat (light grey area in Figure 2) is described by a set of p environmental variables. Each variable is associated with as many raster maps of the entire area which are each constituted of n pixels.

Figure 2: Definition of marginality in ENFA. It is the norm of the vector connecting µ to µ’. The available space is in light grey and the space used in dark grey.

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A large marginality value implies that the conditions where the species is found are “far” from the global environmental conditions. In contrast, specialization measures the spread and usage of the ecological space along dimensions of niche use (Figure 3). The higher this value, the narrower the space used by the species. Consequently, the species niche can be summarized by an index for marginality and another one for specialization, represented on a factor map within the biplot framework (Gower and Hand, 1996).

Figure 3: Definition of specialization which is the ratio of the variances between the two clouds, the available and the used one.

2.7.2 The Maximum Entropy (MaxEnt) Species Distribution Model

Maximum Entropy (MaxEnt) is one of most widely used species distribution models. It is based on a machine-learning method based on the information theory concept of maximum entropy. The idea of MaxEnt is to estimate a target probability distribution by finding the probability distribution of maximum entropy (closest to uniform), subject to a set of constraints that represent our incomplete information about the target distribution (Phillips et al., 2006). The modelling process involves, gathering relevant data, assessing the accuracy and comprehensiveness of the species data and deciding the relevance and completeness of the predictors (Elith and Leathwick, 2009).

When applied to presence-only species distribution modelling, MaxEnt uses the pixels of the study area as the space on which the probability distribution is defined. Pixels with known species occurrence records constitute the sample points whilst climatic variables, elevation, 16

soil category, vegetation type or other environmental variables are the features (Phillips et al., 2006). The logistic output from this method is a suitability index that ranges between 0 (less suitable habitat) and 1 (highly suitable habitat).

2.8 Applications of Models in Tsetse and Trypanosomiasis Management

Mathematical and computer-based simulation models have been extensively used to try to understand how best to manage integrated tsetse and trypanosomiasis control efforts (Peck and Bouyer, 2012). In ecology, such models have been helpful in giving broad generalizations about population dynamics and control. According to Robinson et al. (2002), modelling has the advantage that it allows multiple objective decision-making, which may be helpful in deciding which control methods should be recommended for intervention. Dicko et al. (2015) advocated for the upscaling of their spatio-temporal model of sleeping sickness risk in Africa in order to develop an early warning system for the disease.

In Senegal, models of tsetse distribution have been used to optimize tsetse eradication under the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC) initiative of the African Union (AU) (Dicko et al., 2013). Model results in this instance informed about the particular ecology of tsetse in the target area. MaxEnt predictions allowed optimizing efficiency and cost within the Senegalese project. However, models have in many ways inadequately addressed key aspects of the fly's biology and ecology, particularly the spatio- temporal variability of its habitats which are critical in any control efforts (Peck and Bouyer, 2012). Mathematical models have inherent limitations that must be considered in their use for control programs. Management programs are often vulnerable to naively using these models inappropriately.

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CHAPTER 3

MATERIALS AND METHODS

3.1 The Study Area

The study was conducted in the Masoka area, Mbire District (16.00° to 16.28°S and 30.1° to 30.28°E) (Figure 4) between March 2015 and December 2015. This area falls under Natural Farming Region IV of Zimbabwe. This region receives between 650 and 800 mm of rainfall annually and is suitable for livestock and drought-resistant crop production. During the dry season, most of the vegetation sheds off its leaves and annual grasses and shrubs dry off. This leaves a concentration of leafy vegetation along water courses, although most of these are temporary. The area is part of the Community Areas Management Programme for Indigenous Resources (CAMPFIRE) scheme, which advocates for the conservation of natural resources, including wildlife. The area thus has a variety of wild animals, the most common ones being buffalo (Syncerus caffer), elephants (Loxodonta africana), warthog (Phacochoerus africanus), among other important tsetse hosts. The distribution of these wild hosts in the dry season is mainly influenced by water availability, with a general increase in densities towards Chewore Safari Area, a protected Parks and Wildlife Authority of Zimbabwe Estate.

According to the 2012 National Census (Zimstat, 2012), the community has an estimated population of 1,632 inhabitants distributed among 300 households. Agriculture is one of the major activities, with production centred on cattle and goat rearing, cotton and small grains production. Cattle form an important source of blood meal for tsetse, especially in areas with low wild host densities. The Masoka community has a herd of 180 cattle (Division of Veterinary Services Nov 2015 Census). The tsetse population has least been affected by intervention programmes instituted by the Division of Tsetse Control.

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Figure 4: Masoka study area

3.2 General Methods

Two models, tsetse probability model and the habitat suitability (predictive model) were built using entomological data collected from an extensive survey conducted in the Masoka area between March 2015 and December 2015.

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CHAPTER 4

Probability of Tsetse Occurrence Despite a Sequence of Zero Catches

4.1 Introduction

Any vector management programme requires data on species present, the relative abundance, distribution, and temporal and spatial dynamics of the population. These data enable program managers to select appropriate control tactics and develop plans of how and where to deploy these as adapted to the characteristics of the target zone/population (Bouyer et al., 2010). In order to judiciously manage limited resources, there is need to develop mechanisms to delimit target areas with a higher degree of precision. The calculated probability of presence using the Barclay and Hargrove model (Barclay and Hargrove, 2005) provides a method to improve the cost effectiveness of the sampling process (Bouyer et al., 2010).

This study therefore aimed to determine the probability of both G. pallidipes and G. m. morsitans occurring in areas which were sampled but no tsetse were captured. This model is essential in the declaration of a pest-free status and can also be utilized to optimize vector management strategies.

4.2 Materials and Methods

4.2.1 Entomological data

Tsetse data were obtained using methods outlined in the Food and Agriculture Organisation (FAO)/International Atomic Energy Agency (IAEA) entomological baseline data collection manual of 2008 (Leak et al., 2008). Epsilon traps (Plate 1) baited with sachets of 3-n-propyl- phenol, octenol and 4-methyl-phenol were placed at a density of 1 trap/ 4 km2 and allowed to stand for seven days before checking them. Traps were set facing the direction of the prevailing winds (usually the West) in an open space within the natural tsetse habitat. Each sampling site was geo-referenced using a hand-held GPS receiver.

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Plate 1: Epsilon trap for tsetse collection

Samples were collected from 105 sites in seventy-three 2 km × 2 km grids between March 2015 and December 2015. A high-resolution remotely-sensed satellite image acquired on the 9th of November 2014 by the Satellite Pour l’Observation de la Terre 5 (SPOT 5 with a spatial resolution of 2.5 m) was used to identify the different land cover classes. Captured flies were identified morphologically using identification keys developed by Buxton (1955) and Mulligan (1970) and specimens were preserved in 90% alcohol.

4.2.2 Land classification

Tsetse habitat was obtained through land cover classification of satellite imagery. The resultant classification was used in the generation of the probability model (as area of suitable habitat within each grid). The classification was derived from a SPOT 5 satellite image acquired on the 9th of November 2014 with a spatial resolution of 5 m (pansharpened to a resolution of 2.5 m using the panchromatic band). Seven major land cover classes were defined based on field observations and these were riverine vegetation, brush land, burnt areas, mopane/combretum woodlands, bare areas (including dry riverbeds), water and fields (Plates 2-5). Regions of interest (ROI) were defined on the image in ENVI 4.5 (www.exelisvis.co.uk) based on the seven classes identified and these were used to train the 21

software. The image was then classified using the maximum likelihood technique and accuracy of the classification was assessed by the Kappa Coefficient from the confusion matrix.

Plate 2: Sorghum and cotton fields in the Masoka area

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Plate 3: Mopane woodland in the Masoka area

Plate 4: Riverine vegetation in the Masoka area

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4.2.3 Tsetse probability modelling

The absence of tsetse catches in a given locality does not imply that there are no tsetse flies (Bouyer et al., 2010). The Barclay and Hargrove model of 2005 (Barclay and Hargrove, 2005) was therefore used to evaluate the probability that tsetse were actually present in a grid cell when sampled through a given sampling effort (number of traps/day). The model was applied to all 2 km × 2 km grid cells where sampling was done but no tsetse were caught. The model gives the probability of observing a sequence of zero catches when in fact there are insects in the sampled area using the following formula:

푃 = exp⁡(−푆 × 푡 × σ ×λ⁡)

where: S = the number of traps deployed in the total area, t = number of days for which each trap is operated, σ = trap efficiency, and λ = population density (number of insects/area of suitable habitat).

This probability was calculated for each grid cell using the specific number of traps, duration of trapping, and the total surface area of suitable habitat in each grid cell. In the absence of any control effort, the minimum number of flies in the sample area was set at 10, considering that this is an underestimation for any resident tsetse population in the absence of control effort. The goal of this exercise was to detect resident tsetse populations and was not related to assessing dispersal. The trap efficiency, defined as the probability that a trap catches a fly in an area of 1 km2/day, was defined as 0.1 for G. m. morsitans and 1 for G. pallidipes. These figures were calculated by Hargrove and Barclay (2005) at Antelope Island in Kariba.

4.3 Results

4.3.1 Surveys

The extensive survey conducted in the Masoka area indicated the presence of two tsetse species, G. morsitans and G. pallidipes in the area. A total of 105 sites were surveyed with 190 G. m. morsitans being captured from 40 sites (38%) while 34 G. pallidipes were captured from 15 sites (14%) (Table 1). Seven sites recorded both species. Catches were mainly

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recorded to the north-west of the study area which is a section bordering Chewore Safari Area, a protected wildlife estate (Figure 5).

Table 1: Tsetse captured in the Masoka area between March 2015 and December 2015

Tsetse Species Number of tsetse Number of positive sites Percentage of captured positive sites G. m. morsitans 190 40 38 G. pallidipes 34 15 14

Figure 5: Apparent densities of G .m. morsitans in Masoka area between March 2015 and December 2015

Although the mean apparent density of G. m. morsitans was 0.28 flies/trap/day, actual catches fluctuated between 0 and 3.7 flies/trap/day (Table 2). Glossina pallidipes catches were mainly recorded along the major drainage lines in the study area. The mean apparent density of G. pallidipes was 0.05 flies/trap/day while actual catches fluctuated between 0 and 0.86 flies/trap/day (Table 2).

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Table 2: Apparent densities (mean ± SE) of tsetse in the Masoka area

Tsetse Species Apparent density (flies/trap/day)

G. m. morsitans 0.28 ± 0.05 G. pallidipes 0.05 ± 0.02

Figure 6: Apparent densities of G. pallidipes in Masoka area between March 2015 and December 2015

4.3.2 Land cover mapping

Of the seven landcover types identified in the study area, only two were suitable for both G. m. morsitans and G. pallidipes. These were the riverine vegetation, which is mainly preferred by G. pallidipes and woodlands of predominantly mopane (Colophospermum mopane) interspaced with combretums. Mopane vegetation mainly harbours G. m. morsitans. Suitable tsetse habitat covered 366 km2 of the study area, which means 83% of the 440 km2 surveyed was found to be suitable for tsetse. The classification had an overall accuracy of 97.38%

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(8636/8868) and a Kappa Coefficient of 0.9617, which are measures of confusion between the classes (Figure 7).

Figure 7: Land cover map of the study area

4.3.3 Probability of G. morsitans morsitans occurrence

Of the 73 grid cells where traps were deployed, surveys had already demonstrated the presence of G. m. morsitans in 32 cells. The probability model was therefore applied to the remaining 41 grid cells where no G. m. morsitans were trapped. The analysis indicated a probability of tsetse presence below 0.05 (the level of risk accepted) in 15 grid cells where no tsetse were captured whilst the remaining 26 grid cells had a probability greater than 0.05. (Figure 8). The area infested with G. m. morsitans was therefore to be 128 km2 (28%) and 104 km2 (24%) could possibly be infested despite recording no tsetse catches. An area of size 60 km2 (14%) had a low probability of tsetse presence whilst the remaining 148 km2 (34%) was not sampled.

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Figure 8: Map showing probability of G. m. morsitans presence

4.3.4 Probability of G. pallidipes occurence

Of the 73 grid cells where traps were deployed, surveys had already demonstrated the presence of G. pallidipes in 11 cells. The probability model was therefore applied to the remaining 62 grid cells where no G. pallidipes were trapped. The analysis indicated a probability of tsetse presence below 0.05 (the level of risk accepted) in the entire 62 grid cells where no tsetse were captured (Figure 9). The area infested with G. pallidipes was observed to be 44 km2 (10%), of which the area with a low probability of tsetse presence was 248 km2 (56%) whilst the remaining 148 km2 (34%) was not sampled.

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Figure 9: Map showing probability of G. pallidipes occurrence

4.4 Discussion

The present study gave indications of mixed G. m. morsitans and G. pallidipes populations in Masoka area. The predominant species was observed to be G. m. morsitans with a north- westerly distribution. The total area confirmed to be infested with the species was estimated at 128 km2 on a spatial resolution of 2 km⁡×⁡2 km. Glossina pallidipes was observed to be less extensively distributed with an apparent density of 0.05 flies/trap/day.

Application of the Barclay and Hargrove model to the G. m. morsitans results obtained in the present study revealed that the species could potentially be present in a 104 km2 (2 km × 2 km spatial resolution) area which was sampled but recorded zero catches. According to Barclay and Hargrove (2005), traps are capable of capturing only 0.1% of the total G. m. morsitans population in a given area. This figure indicates that for any fly that enters a trap, 99.9% would remain uncaptured. Glossina morsitans morsitans is known to prefer moving objects than stationary ones hence a reduction in trap efficiency (Warnes, 1997).

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Land cover classification of the study area resulted in the delineation of vegetation cover which is dominated by mopane woodlands. These are dry woodlands occurring on heavier textured soils (Warnes, 1997). This habitat is mainly suitable for G. m. morsitans (FAO, 1996). The probability of G. m. morsitans presence was therefore influenced by the extent of the mopane/combretum woodlands. Grids which had more than 60% suitable habitat cover had higher probabilities of G. m. morsitans occurrence despite recording zero catches in the survey. Vegetation thus is a critical determinant of tsetse distribution, an observation supported by Jordan (1993). During peak dry season, leafless miombo and mopane woodlands support self-sustaining populations of G. m. morsitans which prosper more than in leafy vegetation (Leak, 1999). According to Cecchi et al. (2008), the important role of savannah habitats for the morsitans group is clearly apparent with deciduous woodlands and deciduous shrublands with sparse trees accounting for over 50% of total distribution.

According to De Deken (2013), the morsitans (savannah) group of tsetse is sensitive to agricultural development. This was consistent with the probability model which also produced low probabilities in inhabited areas of Masoka where suitable habitat has been fragmented due to agricultural activities. This observation is consistent with findings by McDermott et al. (2001) in Kenya where they noted that increasing human populations lead to a decline in tsetse population through habitat loss, particularly in the Lake Victoria basin region. In Masoka, most of the cleared areas had less than 60% of suitable vegetation cover.

The model further predicted probabilities of capture despite a sequence of no catches below 0.05 for G. pallidipes. This prediction can be supported by entomological results obtained in the same area where the catch composition had very few G. pallidipes in comparison with G. m. morsitans. Traps are capable of capturing 1% of G. pallidipes in 1 km2 in a day, making them more efficient for the species hence had there been more of G. pallidipes in the area, more of the species would have been captured. Therefore, given this efficiency and sampling effort applied to Masoka area, it was observed that the chances of G. pallidipes occurring in areas which did not record catches were very low.

Habitat conditions in Masoka area could be the major cause of low G. pallidipes as the area is dominated by dry savannah as compared to riverine thickets which are preferred by the species. According to FAO (1996), G. pallidipes requires thickets and does not occur above 2,000 m. However, under mild climatic conditions, this species can spread through areas of 30

scattered thickets, woodland and even open country. This study was conducted during the dry season hence the species could not be detected in the woodlands.

Although the probability model provides a good measure of tsetse-infested areas, it has some inherent weaknesses. Bouyer et al. (2010) noted that one of the parameters used to calculate the probability, the population density, could be higher than the assumed 10 flies in the absence of control efforts. Resultantly, the probability could be overestimated. The model is based on trapping effort, trap efficiency and area of suitable habitat (Barclay and Hargrove, 2005). However, it does not take into account some important determinants of tsetse distribution. These include the distribution of hosts and some climatic and environmental characteristics of the habitat. Barclay and Hargrove (2005) also pointed out that an added complication arises if detectability itself is density-dependent or if the area of attraction depends on weather and other factors. The model also depends on sampled locations for calculations to be possible, an exercise which has got a cost bearing. This leaves unsurveyed areas without information critical for decision making.

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CHAPTER 5

Modelling the Probability Distribution of G. m. morsitans and G. pallidipes in Masoka Area Using Climatic, Environmental and Presence Only Data

5.1 Introduction

In pest species management, survey data is rarely available to describe its presence at every location on the landscape. Models are therefore used to interpolate, or extrapolate beyond the locations where species presence is known, by relating its presence to environmental variables (Pearce and Boyce, 2006). Remotely-sensed data allow assessment of the distribution of resources over large and inaccessible areas and GIS-based analysis can be used to bring together a wide range of information sources including remote sensing, land use, topographic , historical and vector data together (Cox, 2007). Modelling can thus help identify areas of high risk at national or regional level.

The aim of this study was to produce predictive models for G. m. morsitans and G. pallidipes using climatic and environmental covariates. Models of this nature are useful in determining the distribution of the two species in Masoka area thus guiding the drafting of intervention plans.

5.2 Materials and Methods

Two main steps were followed in the prediction of suitable tsetse habitat and coming up with a probability distribution model. The first was an exploratory analysis of the niches of both species through the Ecological Niche Factor Analysis and secondly the predictive modelling of each species distribution. To better understand these models, two areas must be distinguished, the ecological space and the geographic space. The ecological space is linked to the concept of niche and depends on the choice of environmental variables that characterize it, whereas the geographical area is a projection of the ecological space in a particular area. This space is made up of cells or pixels which cover this region. Conventionally, this is done by modelling initially the niche in the ecological space, and then the final result is projected on the geographical space (Figure 10).

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Figure 10: Relationship between the geographical space and the ecological niche (Source: Guerrini, 2016)

5.2.1 Ecological niche factor analysis (ENFA)

A multivariate statistical method, the Ecological Niche Factor Analysis (ENFA) described by Hirzel et al. (2002) was used to characterize the habitat of the two species in the study area and the R package adehabitatHS (Calenge, 2006) was used. The analysis was done using presence data only and environmental covariates. The environmental space actually used by the species was compared with the available environmental space using two indicators, marginality and specialization. A large marginality value implies that the conditions where the species is found are far from the global environmental conditions whilst specialization 33

measures the spread and use of the ecological space along dimensions of niche use. The higher this value, the narrower the space used by the species (Hirzel et al., 2002). The species niche was summarized by an index for marginality and another for specialization, represented on a factor map within the biplot framework.

5.2.2 Maximum Entropy (MaxEnt)

In the second and final step, a statistical model was used to predict both species’ realized niches. Maximum Entropy (MaxEnt) (www.cs.princeton.edu/~schapire/maxent), which is a species distribution model with good performance, was used. The MAXENT software, a machine learning algorithm that applies the principle of maximum entropy to predict the potential distribution of species from presence-only data and environmental variables, was used. An approximation that satisfies any known constraints on the unknown distribution, and that subject to those constraints, the distribution should have maximum entropy (Jaynes, 1957). Entropy can be defined by the formula:

퐻(휋̂) = − ∑푥∈푋 휋̂ (푥) ln 휋̂(x)

where: 휋̂ = an estimation of the unknown probability distribution over a finite set of X points (pixels in the study area).

The distribution 휋̂ assigns a non-negative probability π(x) to each point X, and these probabilities sum to 1. The entropy is non-negative and is at most the natural log of the number of elements X (Phillips et al., 2006). The model used 70% of the tsetse presence as the training data whilst the remaining 30% was used to validate the model. Climatic and environmental data were resampled to a spatial resolution of 1 km and used as the known features in determining the probability distribution of tsetse at unknown locations in the study area. The model was constructed using the statistical package R. Each tsetse species, i.e. G. morsitans and G. pallidipes, was modelled separately.

Model performance was assessed using various matrices generated by the MaxEnt software. These were threshold-dependent quantitative evaluations. Threshold independent evaluations included model validation based on data composed of presences and absences using existing evaluation statistics for presence–absence data, which are the area under the receiver operator

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characteristic (ROC) curve, Area Under the Curve (AUC), as spelt out by Phillips et al. (2006).

5.2.3 Entomological data

Entomological data were obtained from an extensive survey conducted in the Masoka area between March 2015 and December 2015. The data were used to define the presence locations, which are the pixels where tsetse flies were observed whilst MaxEnt generated pseudo-absences from the background data. The sites where tsetse flies were observed are shown in Figures 5 and 6.

5.2.4 Remotely-sensed data

5.2.4.1 Moderate Resolution Imaging Spectroradiometer (MODIS) data

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands (http://modis.gsfc.nasa.gov). MODIS products were acquired from NASA Earth Observing System data server.

Among the various sensors and types of satellite images used, MODIS images are considered to have a good compromise between spatial and temporal resolution and this is one reason why they are widely used in epidemiological studies (Hay et al., 1997). Satellite images spanning 10 years (January 1, 2003 - January 31, 2013) from the Terra and the Aqua satellites were downloaded and cleaned using the statistical package R. The sensors of these satellites provide free MODIS images at high and medium spatial resolution (250 m to 1 km). All the time series of satellite images were used to calculate statistics (mean and quartiles) which quantify environment and climate dynamics.

5.2.4.1.1 Temperature indices

Temperature is a parameter that plays an important role in the tsetse life cycle and Land Surface Temperature is among the commonly used temperature indicators. Land Surface 35

Temperature (LST) is calculated from the measurement of radiation emitted by the earth surface and it is highly correlated with the air temperature indicator (Vancutsem et al., 2010). According to Neteler (2010), LST is used in many studies of species distribution and spatial epidemiology. In this study, two indicators for measuring the air temperature were used. These were the LST day and LST night. The first is an average approximation of the day temperature and the second measures the average thermal activity during the night.

5.2.4.1.2 Vegetation indices

Among the indices commonly used in epidemiological studies is the NDVI (Normalized Difference Vegetation Index), which is a measurement of chlorophyll activity. This index allows the differentiation of bare ground from the vegetation. In addition to the NDVI, other vegetation indices such as EVI (Enhanced Vegetation Index) can be used but according to Hay et al. (1996), NDVI is particularly useful in areas where vegetation is sparse in forest areas. Regarding the Masoka area, the NDVI was the most appropriate and was used to capture the effect of woody vegetation on tsetse habitat. It is also important to note that this is an indicator that has already been used several times to predict the flies’ density (Rogers et al., 1996; Guerrini, 2009) in West Africa.

5.2.4.1.3 Reflectance indices

Mid Infra-Red (MIR) is used to measure the reflectance of bare ground. This index is correlated to the Land surface temperature. Luxuriant vegetation is characterized by a low MIR. With the NDVI, this index allows one to characterize the vegetation well as soil temperature.

5.2.4.2 Satellite Pour l’Observation de la Terre (SPOT 5) data

An image acquired on the 9th of November 2014 by the SPOT 5 satellite was classified to produce a land cover map (see Figure 7). Mopane, riverine forest, crop field, shrub lands (bush land) were incorporated into the model as categorical variables. Table 3 summarises remotely-sensed products used in this study.

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Table 3: Remotely-sensed data with original spatial and temporal resolution Name Product Type Spatial resolution (m) Temporal resolution (days) NDVI MOD13A2/MYD13A2 Vegetation 1,000 × 1,000 16 MIR MOD13A2/MYD13A2 Thermal 1000 × 1,000 16 DLST MOD11A2/MYD11A2 Thermal 1,000 × 1,000 8 NLST MOD11A2/MYD11A2 Thermal 1,000 × 1,000 8 Landcover SPOT 5 Vegetation 5 × 5 -

5.3 Results

5.3.1 Predictive model for G. m. morsitans

The occurrence of G. m. morsitans was highly correlated to vegetation indices (Forest, bushland, mopane and NDVI). Temperature-related indices LST and MIR and crop lands, however, exhibited a negative correlation to the species. High temperature indices also corresponded to low vegetative cover. The Ecological Niche Factor Analysis also pointed to a wider selection of habitat by G. m. morsitans as evidenced by a wider variance of the ecological space occupied on the factor analysis (the dark grey polygon) (Figure 11). The analysis also showed that the species can thrive in areas with low vegetation cover and high temperature indices as evidenced by the overlap of the dark grey polygon into the negative region of the biplot. The species was not shown to occur randomly in the study area as all marginality values were lower and specialization values were greater than 0 (Table 4).

The predictive model for G. m. morsitans (Figure 12) had an Area Under the Curve (AUC) of 0.8, which was higher than the random prediction of 0.5 (Figure 13). The diagonal line represents random predictions with an AUC of 0.5. An area of 124 km2 on the predictive model had a probability of G. m. morsitans occurrence of 0.5 or greater.

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Figure 11: Ecological Niche Factor Analysis for G. m. morsitans

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Table 4: Eigen values describing G. m. morsitans habitat on the ENFA Variable Marginality Specialization LST_iqr 0.01718 0.049319 LST_mean -0.58236 0.034245 LST_q1 -0.44477 0.010832 LST_q3 -0.34778 0.064691 MIR_mean 0.028828 0.254855 MIR_q1 -0.08752 0.142298 MIR_q3 0.195484 0.074436 NDVI_iqr 0.146343 0.058183 NDVI_mean 0.128171 0.334345 NDVI_q1 0.14797 0.560084 NDVI_q3 0.168505 0.222805 Bushland 0.265744 0.02927 Crop -0.12315 0.646539 Mopane 0.053878 0.062832 Riverine.forest 0.343895 0.005189

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Figure 12: Predictive model for G. m. morsitans in Masoka area

Figure 13: ROC curve and AUC for G. m. morsitans

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5.3.2 Predictive model for G. pallidipes

The occurrence of G. pallidipes was mainly influenced by riverine forest and was also positively correlated to other vegetation variables such as NDVI and bushland. All NDVI statistics (1st and 2nd quartiles and inter-quartile range) were positively correlated to G. pallidipes. Temperature indices, with the exception of the inter-quartile range of the land surface temperature, were negatively correlated with G. pallidipes. The species occupied a very narrow space in Masoka area (narrow dark grey polygon) (Figure 14), indicating highly specialised habitat requirements. Mopane woodlands had a negative correlation with G. pallidipes occurrence although they had minimal inertia whilst mean soil temperature had the greatest inertia. The species were not randomly distributed as marginality values were very small and specialization values were large (Table 5).

Figure 14: Ecological Niche Factor Analysis for G. pallidipes

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Table 5: Eigen values describing G. pallidipes habitat on the ENFA Variable Marginality Specialization LST_iqr 0.143644 0.060765 LST_mean -0.50715 0.138042 LST_q1 -0.49445 0.00778 LST_q3 -0.24584 0.075109 MIR_mean -0.10004 0.638509 MIR_q1 -0.1686 0.233036 MIR_q3 0.03891 0.382619 NDVI_iqr 0.247805 0.381518 NDVI_mean 0.225144 0.423478 NDVI_q1 0.17172 0.147367 Bushland 0.163875 0.060472 Mopane -0.01532 0.108129 Riverine.forest 0.45675 0.03461

The predictive model for G. pallidipes (Figure 15) had an Area Under the Curve (AUC) of 0.94, which was higher than the random prediction of 0.5 (Figure 16). The diagonal line represents random predictions with an AUC of 0.5. An area of 56 km2 on the predictive model had a probability of G. pallidipes occurrence of 0.5 or greater.

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Figure 15: Predictive model for G. pallidipes

Figure 16: ROC curve and AUC for the G. pallidipes model

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5.4 Discussion

The present study highlighted a wide distribution of suitable G. m. morsitans habitat and a more restricted G. pallidipes habitat in the Masoka area. Glossina morsitans morsitans was found to occur over a wider range of habitat conditions than G. pallidipes. The Masoka area is mainly dominated by the savannah type of vegetation which is dominated by the mopane species, a habitat type mainly suitable for G. m. morsitans hence its wider distribution. Mopane trees shed their leaves during the dry season but G. m. morsitans can survive by resting in the shade created by the bole of the trees.

In their studies in Kenya, De Visser et al. (2010) noted that the habitat of tsetse is influenced by availability of moisture, temperature and vegetation cover and consistent with this observation, the habitat of G. m. morsitans and G. pallidipes in this study was found to be strongly correlated with vegetation cover. Interestingly, in this study, the negative influence of crop fields on the distribution of G. m. morsitans was strong as they had the largest variance. According to WHO (2013), savannah species are very sensitive to agricultural development and have in some cases disappeared where agriculture has intensified.

This study also showed that the ecological niche of G. pallidipes in the Masoka area correspond to riverine vegetation with a tree density sufficient to provide adequate shade and buffer temperature and relative hygrometry variations in comparison with macroclimatic conditions occurring in the surrounding open environments. Studies by Dicko et al. (2013) on Glossina palpalis gambiensis, a riverine species, showed that air temperature in dense tree vegetation in riverine forests can be 4°C lower compared with the surroundings, and relative humidity 15% higher, thus providing resting sites for the species in contrast to the more open habitat into which they may disperse for short periods (some hours) in search of a blood meal, namely their hunting sites. This was similar to conditions where G. pallidipes was found in the Masoka area.

Although Torr et al. (2012) state that G. m. morsitans and G. pallidipes occur sympatrically in mopane woodlands, this study showed that there tends to be niche differentiation at the microclimatic level with G. m. morsitans occupying the open savannah and G. pallidipes occupying the thicker riverine forest. This is supported by FAO (1996) who noted that G. pallidipes tends to occupy thickets than open woodlands.

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The distributions of both species were negatively affected by temperature. Several authors have articulated the importance of temperature to tsetse survival. (e.g. WHO, 2013; Hargrove et al., 2012; De Visser et al., 2010; Dicko et al., 2013). Tsetse flies survive within a narrow optimal temperature range, with extremes having a debilitating effect on their survival.

Although the MaxEnt model had high predictive power, it is limited to the spatial dimension and as such does not capture the dynamics of tsetse habitat on a temporal scale. Tsetse, especially G. pallidipes, tend to concentrate in leafy vegetation along watercourses (De Deken, 2013) during the dry season as predicted by the model but tend to disperse during the rainy season as the environment would now be more homogenous. The model also leaves out another important variable which determines the distribution of tsetse, the spatial distribution of preferred hosts. It has been noted that the distribution of hosts influence tsetse distribution (Munang'andu et al., (2012). Inclusion of such an important parameter would have improved the understanding of habitat selection by the different tsetse species. The MaxEnt model is a presence-only modelling technique which generates pseudo-absences from background data. In this study, although absence data was available but not utilized, the resultant model was a good fit even for the absence data.

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CHAPTER 6

GENERAL DISCUSSION, CONCLUSION AND RECOMMENDATIONS

6.1 General Discussion

This study showed that modelling is an essential process in the analysis of tsetse distribution data. Analysis of tsetse occurrence data in relation to the environment within the area under consideration can help in the generation of new information which would not have been possible had the catch data been considered in isolation.

Probability of occurrence modelling in areas where sampling was conducted but no catches were recorded proved to be an essential methodology in the analysis of tsetse survey data and subsequently in the blocking of the area into priority areas for intervention purposes. When applied in conjunction with predictive modelling, this technique has the potential to reduce areas under focus resulting in the optimal utilization of resources. According to De Visser et al. (2010), efforts to control trypanosomiasis are hampered by a lack of information and costs associated with the identification of tsetse-infested areas hence modelling proves to be an essential tool to bridge the gap between costs and obtaining relevant tsetse distribution data. Matawa et al. (2013) also highlighted the importance of modelling in optimizing costs of intervention.

This study also proved that tsetse can be present despite the absence of catches and a number of factors can be attributed to this. These range from the very low densities, trap efficiencies and even the site of deployment. Cecchi et al. (2015) also pointed to the survey period, type of trap and strategy of trap deployment as some of the factors affecting tsetse catches. The study further proved that tsetse distribution can be determined by climatic and environmental variables with vegetation being closely associated with tsetse occurrence. This link has been confirmed in many studies (Guerrini et al., 2009; Bouyer et al., 2010).

6.2 Conclusion

This study revealed that the absence of tsetse catches within traps in the Masoka area did not imply the absence of the flies from the area. The probability model assists in deciding with some degree of certainty whether to declare an area pest-free or consider it as infested. This

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process is essential at all stages of pest management, that is, the planning stage, monitoring progress and declaration of success of a programme.

The study showed that it is possible to determine suitable habitats for G. m. morsitans and G. pallidipes in the Masoka area by using climatic and environmental variables. Predictive modelling thus proves to be an effective way of obtaining tsetse distribution data which is essential in the decision making process. Inclusion of the modelling in pest management programmes can help streamline areas of focus thus managing costs. Remotely-sensed data is now readily available online for free, a development which can greatly assist in habitat analysis given minimal insect occurrence data. Resultantly, confirmatory ground surveys and intervention programmes can then be directed to areas of suitable habitat rather than entire areas.

6.3 Recommendations

From this study, it is clear that more variables need to be incorporated in the models so as to find the best one(s) to explain the selection of habitats by tsetse flies. One of the most important determinants of tsetse distribution which should be included is the distribution of preferred tsetse hosts.

This study was conducted at spatial resolutions of 2 km and 1 km for the probability and the predictive models, respectively. There is scope to improve the resolution, especially for the predictive model as MODIS data can be obtained at a higher resolution of 250 m. It is therefore necessary to model the distribution at a higher resolution and see if there would be an improvement in the predictive powers of the models.

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