MANYENYE 1322 Thesis FINAL DRAFT

IDENTIFICATION AND MAPPING RISK AREAS FOR ZEBRA POACHING: A CASE OF TARANGIRE NATIONAL PARK, TANZANIA

MANYENYE N.S. Hamisi February 2008

IDENTIFICATION AND MAPPING RISK AREAS FOR ZEBRA POACHING: A CASE OF TARANGIRE NATIONAL PARK, TANZANIA

MANYENYE N.S. Hamisi

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Biodiversity conservation

Thesis Assessment Board

Chairman: Prof. Dr. A.K. Skidmore, NRS Department, ITC External Examiner: Dr. S. van Wieren, Wageningen university Internal Examiner: Ms. Ir. Liza Groenendijk, PGM Department, ITC First supervisor: Drs. Henk Kloosterman, NRS Department, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Dedication

This thesis is dedicated to my late parents Samson Manyenye Kabou and Helena Kiziku Kulima and my bothers that always encouraged my pursuit of education. Abstract

Poaching is one of the major problems in wildlife conservation and management in the Tarangire ecosystem. Unfortunately it is not easy to identify poaching hotspots because poaching activities are dynamic and concealed in nature, thus there are no standardized methods to quantify them. This study used zebra poaching data as an indicator to human exploitation. The aim of this study was to identify risk areas for zebra poaching within and around Tarangire National Park, a core area of the Tarangire ecosystem. A GIS method was used to analyse the vulnerability of the park to illegal activities from the surrounding villages based on accessibility expressed in travel time. The accessibility attributes used were land cover types, slope of the terrain, road types and location of human settlements. Moreover, GIS was used to predict potential areas for zebra poaching and to explain the spatial distribution of poaching events of zebra within and around Tarangire National Park. In order to predict risk areas for zebra poaching, pseudo absence data were randomly generated in the study area to represent absence of poaching. Patrol data from park rangers representing presence of poaching were combined with pseudo absence data to get a presence absence dataset. The presence absence dataset and five predictor variables that are distance to roads, distance to villages, accessibility, land cover, and slope were tested for their significance in explaining poaching events of zebra within and around Tarangire National Park. A stepwise logistic regression method was used to select the explanatory variables. Among the five predictor variables only distance to roads and slope had significant relationships. These two predictor variables were used to generate a poaching risk map. Poaching events were observed to be spatially distributed around the park except in the south. The distribution of poaching events around the park could be linked with the seasonal migration of zebra as more events were recorded during the migration period. The poaching risk map indicates that areas near to roads and those with low slopes are potential areas for zebra poaching around the park. The accessibility map provided an insight on how vulnerable the park is to illegal activities including poaching for zebra with regard to travel time. The GIS based analysis presented in this study successfully identified and mapped risk areas for zebra poaching. Such maps are of practical and strategic use to wildlife managers in Tarangire National Park. They facilitate decision making on intervention programs and how best to direct law enforcement patrols within and around the park.

Keywords: Poaching, zebra, Seasonal migration, GIS, Mapping risk areas, Law enforcement, Tarangire ecosystem, Tarangire National Park.

i Acknowledgements

Many people and institutions have been in one way or the other very helpful in teaching, guiding and stimulating me during all the stages of writing this thesis. Through this I would like to express my thanks to all of them, as without their cooperation this thesis would not have been what it is now.

First of all I would like to thank the government of The Netherlands for sponsoring me through the Netherlands Fellowship Programme (NFP) to participate in the MSc programme at the International Institute for Geo-Information Science and Earth Observation (ITC).

It is my sincere pleasure to thank the Executive Director of Greener Environment Complex Limited (GEC Ltd) Mr. Lucas Kwezi for granting me a leave to attend the course.

I am very grateful to my first supervisor Drs. Henk Kloosterman for his tremendous support and inspiration and suggestions right from the commencement of this work. During this period I have known Henk as a sympathetic and principle centred person.

Thanks to Dr. Thomas Groen my second supervisor, Thomas thank you for your constructive comments, guidance and suggestions. His comments and suggestions made possible to shape the study in a more scientific way.

I further extend my appreciation to Dr. Michael Weir who has been of great help in various dimensions during the entire period of my study at ITC.

I thank the Director General of Tanzania National Parks Authority (TANAPA) Mr. Gerald Bigurube and the Manager of Ecological Monitoring Mr. Inyasi Lejora for granting permission to undertake my fieldwork in Tarangire National Park. Furthermore I thank the chief park warden Ms. Marietha Kibasa, ecologist Mr. John I. Gara, law enforcement warden Mr. Manendo Peter, GIS centre manager Mr. Langen R. Mathew and the management team of Tarangire National Park in general for providing me with necessary services, data, information and taking care of me in the wilderness of Tarangire.

I thank my fellow NRM students for 2006-2008, Kamal Thapaliya (Nepal), Daniel Tutu (Ghana), David Gwenzi (Zimbabwe), Eugene Kayijamahe and Elias Nyandwi (Rwanda) Wilber Wesonga Wejuli (Uganda), Stephen Maina Kiama (Kenya) and Ashenafi Erena (Ethiopia) for their cooperation and the time we spent sharing ideas.

I further extend my thanks to the fellow Tanzanians; Ladislaus Mkama Bigambo, Emil Karuranga, Miza Suleiman Khamis, Alphonce Mganga, Aloyce Temba, Dionis Rugai, Frank Makyao and Robin Wambura whom we used to interact and make jokes, these made us feel at home whilst we are abroad.

Many thanks go to the Manyenye family who have always supported and given me confidence and courage to pursue my dreams by planting the spirit of passionate.

Special thanks to my wife Neema, I say thank you for your love and prayers, and for holding the fort as a single parent for these many months whilst I was away. My son Kabou Derreck Manyenye thank you for enduring my absence for so long ‘ Poleni sana’ .

Finally my thanks to God my creator for many privileges He grants all my life. Without him I would not be alive to produce this thesis.

ii Table of contents

1. Introduction ...... 1 1.1. Background...... 1 1.1.1. Wildlife conservation and management in Tanzania ...... 2 1.1.2. The current wildlife conservation approach in Tanzania...... 3 1.1.3. Illegal hunting of wildlife in Tanzania...... 4 1.1.4. Illegal hunting in the Tarangire ecosystem ...... 5 1.2. Research problem ...... 6 1.3. Research objectives ...... 6 1.3.1. Main objective...... 6 1.3.2. Specific Objectives...... 6 1.4. Conceptual diagram ...... 6 1.5. Research questions...... 8 1.6. Hypothesis ...... 8 2. Description of the study area...... 9 2.1. The study area...... 9 2.1.1. Choice of the study area ...... 9 2.1.2. Geographical location...... 10 2.1.3. Vegetation ...... 11 2.1.4. Wildlife...... 11 2.1.5. Soil...... 12 2.1.6. Climate ...... 12 2.1.7. Land use and socio-economic activities...... 12 2.1.8. Human population ...... 12 2.2. The study animal: Plains zebra Equus burchelli (Gray 1824) ...... 13 2.2.1. Distribution of plains zebra ...... 14 2.2.2. Migration of plains zebra in the Tarangire ecosystem ...... 15 2.3. Geographic information systems and remote sensing in wildlife management ...... 17 3. Methods and materials ...... 18 3.1. Accessibility map...... 20 3.1.1. Land cover...... 20 3.1.2. Roads ...... 21 3.1.3. Land cover –roads travelling speed map...... 21 3.1.4. Slope...... 21 3.1.5. Settlements ...... 22 3.1.6. The accessibility (travel time) map ...... 22 3.2. Pre processing of poaching events data ...... 22 3.2.1. Illegal hunting data (presence data)...... 22 3.2.2. Pseudo absence data ...... 22 3.2.3. Presence absence data ...... 23 3.3. Logistic regression model...... 23 3.3.1. Presence absence data ...... 23 3.3.2. Distance from roads...... 23

iii 3.3.3. Distance from settlements ...... 23 3.3.4. Land cover, slope and accessibility maps...... 23 3.3.5. Selection of predictor variables...... 24 3.3.6. The poaching risk map ...... 24 4. Results ...... 25 4.1. Acessibility ...... 25 4.1.1. Land cover map ...... 25 4.1.2. Accuracy assessment ...... 26 4.1.3. Accessibility map ...... 27 4.1.4. Spatial distribution of poaching events ...... 28 4.2. Poaching risk...... 30 4.2.1. Logistic regression model...... 30 4.2.2. Poaching risk map...... 31 5. Discussion ...... 32 5.1. Relationship between poaching and the selected predictor variables...... 32 5.1.1. Roads ...... 32 5.1.2. Slope ...... 33 5.1.3. Seasonal distribution of poaching events ...... 33 5.1.4. Merits of the method used to identify risk areas for poaching...... 34 5.1.5. Data quantity and quality...... 34 6. Conclusion and recommendations...... 35 6.1. Conclusion ...... 35 6.2. Recommendation ...... 35 7. References ...... 37 8. Appendices ...... 42

iv List of figures

Figure 1-1: Conceptual diagram of the Tarangire National Park...... 7 Figure 2-1: Location of the study area in a map of Tanzania ...... 10 Figure 2-2: The savannah woodland in Tarangire National Park ...... 11 Figure 2-3: Wildebeest grazing in Tarangire national Park...... 11 Figure 2-4: A herd of plains zebra in the Tarangire National Park...... 13 Figure 2-5: Historical and current distribution of plains zebra in Africa Source: Moehlman (2002). ..14 Figure 2-6: Dispersal areas for migrating zebras in the Tarangire ecosystem...... 16 Figure 3-1: Flow chart of the methods...... 19 Figure 4-1: Classified land cover map of the areas within and round Tarangire National Park...... 25 Figure 4-2: Accessibility from villages surrounding Tarangire National Park...... 27 Figure 4-3: Spatial distribution of poaching events around Tarangire National Park ...... 28 Figure 4-4: Monthly records of poaching events from December 1998 - June 2007 ...... 29 Figure 4-5: Poaching risk areas...... 31

v List of tables

Table 3-1: Travelling speed per land cover type...... 20 Table 3-2: Slope steepness classes and slope correction factor...... 21 Table 4-1: Area covered by land cover types...... 26 Table 4-2: Accuracy assessment results...... 26 Table 4-3 Seasonal record of poaching events from December 1998 - June 2007...... 29 Table 4-4: Classification table for the null model...... 30 Table 4-5: Classification table for the full model ...... 30 Table 4-6: Variables in the equation ...... 30 Table 4-7: Model summary ...... 30

List of abbreviations

CBWM Community Based Wildlife Management GCA Game Controlled Area GIS Geographic Information Systems GPS Global Positioning System ICDP Integrated Community Development Projects MAFS Ministry of Agriculture and Food Security MNRT Ministry of Natural Resources and Tourism NBS National Bureau of Statistics Tanzania SPSS Statistical Package for Social Science TANAPA Tanzania National Parks Authority TCP Tarangire Conservation Project TNP Tarangire National Park TWCM Tanzania Wildlife Conservation Monitoring

vii

Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

1. Introduction

This research is about identification and mapping risk areas for zebra ( Equus burchelli (Gray)) poaching within and around Tarangire National Park (TNP). In this chapter the research background and problem are introduced followed by the specification of the research objectives, conceptual diagram, research questions and hypothesis.

1.1. Background Tropical ecosystems are under increasing pressure for bush meat supply to the surrounding local communities through traditional hunting and quota harvesting (Barve et al. 2005; Tambling and du Toit 2005). This is particularly observed in African National Parks and Game Reserves where the wildlife species are vulnerable to local extinction as a consequence of unsustainable hunting (Brashares et al. 2001).

A response to unsustainable level of bush meat hunting around national parks has been enforcement of wildlife laws (Jachmann and Billiouw 1997). However, enforcement of wildlife laws needs funding that is mostly limited in many conservation authorities, thus funding limits the efficiency of anti- poaching activities (Myers et al. 2000). One way to increase the efficiency of law enforcement activities would be to identify areas with high poaching risks. However, it is not easy to quantify poaching activities because they are dynamic and concealed in nature (Leader-Williams et al. 1990). Hofer et al. (2000) suggested identifying hotspot areas for illegal hunting using poaching data as an indicator to human exploitation.

Based on this study, park ranger’s patrol data on plains zebra poaching were used as an indicator for identifying and mapping risk areas for plains zebra poaching within and around Tarangire National Park, Tanzania. The choice of the plains zebra as an indicator species was influenced by the following factors:

i. Plains zebra is a migratory animal species in the Tarangire ecosystem, and migratory species are reported to have high risk to poaching, as illegal hunters use migration routes as target sites to maximize the chance of poaching (Hofer et al. 1996). ii. Plains zebra prefer open habitats that make it conspicuous and easily detectable by illegal hunters (Hofer et al. 1996; Kingdon 1997). iii. Plains zebra has ecological importance as a pioneer of the grazing succession within the grazers’ community in the grasslands. Its reduction in numbers would have negative consequences in the ecosystem (Bell 1971). iv. Plains zebra is one of the major tourist attractions in TNP thus it needs protection (TANAPA 2002).

1 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

1.1.1. Wildlife conservation and management in Tanzania Wildlife conservation in Tanzania dates back to 1891 when the laws controlling hunting were first enacted by the German rule. These laws aimed to regulate off take, the hunting methods and the trading of wildlife. The emphasis of laws was on big wild animals, which some of them were declared as endangered species, hence were fully protected (MNRT 1998). According to the Wildlife Division (1991) Game Reserves were first established under German colonial rule prior to World War I, and by 1914, there were 13 reserves. After 1918, the British government continued with this process and established Selous Game Reserve in 1922 and Serengeti Game Reserve in 1929. Game controlled areas were established in 1946 when tourist hunting was formalised. Serengeti National Park was the first to be gazetted as a national park in 1951, followed by Lake Manyara and Arusha national parks in 1960.

The current network of wildlife protected areas in Tanzania which cover more than 25% of its land include national parks, Ngorongoro conservation area, game reserves, game controlled areas and wildlife management areas (MNRT 1998). A primary role of the Tanzania’s national parks is conservation.

National parks are the highest level of conservation and protection in the country because of absence of direct utilisation of wildlife resources. The 16 national parks form the core of a much larger protected ecosystem set aside to preserve the country’s rich natural heritage. National parks provide secure breeding grounds for fauna and flora where they can thrive and be safeguarded from the conflicting interests of the growing human population. The existing national parks system protect a number of internationally recognised biodiversity and world heritage sites, thereby redressing the balance for those areas of the country affected by deforestation, agriculture and urbanisation (Mtoka 2007).

The national parks management system differs with other forms of protected areas mentioned above. In the Ngorongoro conservation area human settlement is allowed with small scale gardening but hunting is prohibited. In the game reserves and game controlled areas no human settlement is allowed, but licensed local and tourist hunting is allowed. Wildlife management areas are areas of communal land that contain wildlife where the local people have mandate to protect wildlife and natural resources and they are allowed to retain proportions of revenue accrued from hunting or tourism.

The creation of national parks in Tanzania during colonial time had several consequences to the surrounding communities. This conservation approach did not put any considerations on the local community adjacent to them. People were evicted from their lands for designation of the national parks that imposed rules and regulations to abide to, without any compromise. An example includes the eviction of the indigenous Maasai from the Serengeti plains and Ngorongoro crater in 1951 and 1975 respectively (Newmark et al. 1994). The colonial legislation did not take into account the intricate relationship between people and nature that were typical of the culture of the most African societies (Bell 1987). The emphasis was on the total restriction of wildlife resources use rather than developing strategies that would ensure long term utilisation, ecosystem renewal and consideration of adjacent community that depended on the wild resources for their livelihoods.

2 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

The exclusion of local communities from wildlife resources, alienation of land used for livestock grazing and other traditional use to be managed as protected areas discouraged local people to support wildlife conservation (Songorwa 1999). This situation naturally created antagonism and often resulted into conflicts between the surrounding communities and wildlife managers. The consequence of this conflicts led into extensive habitat destruction through wildfires and severe depletion of wildlife populations through poaching as well as negative eventualities on biological diversity in general (Songorwa 1999). This antagonism prompted conservationists to look for an alternative conservation approach that would harmonize the situation.

1.1.2. The current wildlife conservation approach in Tanzania The fences and fines approach 1 to wildlife conservation, which involved exclusion of local communities from wildlife management (see section 1.1.1), is now perceived by many conservationists to have failed in Africa (Songorwa 1999). An alternative approach has been sought for sustainable solution where Community Based Wildlife Management (CBWM) was introduced in the late 1980s. This conservation approach devotes rights in ownership and management responsibilities of the wildlife resources to rural communities (MNRT 1998). The CBWM is implemented through the Integrated Conservation and Development Projects (ICDP), which attempts to link conservation of biological diversity within protected areas with social and economic development outside protected areas (Ite 1996; Newmark 2000; TANAPA 1997).

In ICDPs, incentives are typically provided to local communities in the form of shared decision- making, employment, revenue sharing, limited harvesting of plant and animal species in the buffer zones, or provision of community facilities such as dispensaries, schools, bore holes, roads, and woodlots, in exchange for the community’s support for conservation (Newmark 2000). The ICDPs assumption is aimed at improving living conditions of people adjacent to protected areas to reduce pressure and dependence on biodiversity, leading to improved conservation (Ite 1996). However, local communities’ interest in protected areas conservation depends at least to some degree on how much they are still part of the ecosystem and how much their management of resources directly affects their own survival (Enters and Anderson 2000). In 1988 Tanzania National Parks Authority (TANAPA) started to implement ICDPs through the program known as Community Conservation Service (CCS) to work primarily with surrounding communities (Dembe and Bergin 1997).

However, despite the introduction of the integrated conservation approach in Tanzania, still there are problems facing wildlife conservation sector such as escalating illegal hunting of wildlife and low budgetary allocation to carry out wildlife conservation activities (MNRT 1998). Illegal hunting is a big challenge to sustainability of wildlife because its effect leads to population decline and eventually extinction of some wildlife species .

1 The American National Parks Model

3 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

1.1.3. Illegal hunting of wildlife in Tanzania Illegal hunting is defined as the unlawful hunting, capture or killing of wildlife species for either subsistence or commercial purposes without permit from mandated authorities (MNRT 1974). There are two forms of poaching namely subsistence and commercial. Subsistence poaching is described as killing of wildlife for domestic uses such as meat and blood (Loishooki and Tesha 2006). Commercial poaching involves hunting and capturing of live animals and sometimes involves extraction of derivatives and meat for trade. Poaching is done for several reasons, among others are people’s believes that wild meat is better than livestock meat and has medicinal value while others do it as part of their culture as they have been hunting since time immemorial (Loishooki and Tesha 2006).

For centuries until the restrictions on commercial hunting were introduced in early 1900s, the territory of Tanganyika that was later became Tanzania was an important source of ivory and rhino horns supplying the markets in the Middle and Far East countries (Baldus and Siege 2001). During the colonial rule, the government introduced hunting licences, fees and regulations for firearms while hunting. These new rules were difficult for local people to comply with and therefore local people were excluded from wildlife management. This means that local communities were not allowed into these areas and were seen as potential poachers (Crush 1980). Thus the conflict between locals and conservationists was exacerbated.

Due to the fences and fines approach the wildlife inside the parks had certainty of protection but outside the parks the situation quickly became critical (Baldus and Siege 2001). Wildlife was being killed at unsustainable levels. Poaching began to intensify and crossed the park boundaries in the late in the 1970s that eventually affected the population of elephants and rhinos. In this time the Tanzania’s economy went into rapid decline, thus park budgets and resources collapsed and it is acknowledged that poaching increased markedly (Hilborn et al. 2006). Poaching was mainly for trophy of ivory and horns from elephants ( Loxodonta africana (Blumenbach)) and rhinoceros (Diceros biconis (L)) respectively. The increase of poaching of elephants and rhinos was probably stimulated by high prices of ivory and rhino horns in the 1970s. In the 1980s declines of rhinos and elephants were recorded in some of the major protected areas including Ruaha and Serengeti National Parks. As a result the rhinoceros numbers declined for about 95% across their range (Metzger et al. 2007).

In view of that the government of Tanzania launched a nationwide special anti-poaching operation called “Operation uhai” 2 in 1989 to halt poaching (Songorwa 1999). This anti-poaching operation that took about two years for a while succeeded to reduce the problem (Songorwa 1999). However, due to availability of market for wildlife products and the need of locals for livelihoods, poaching continued to be a problem until now.

2 Uhai is Kiswahili term meaning life

4 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Although the prohibition of illegal use of bush meat is well defined in the wildlife legislation and policy, the increasing poverty levels and unemployment within the country, coupled with inadequate enforcement capacity resulted to increase of poaching for subsistence and for trade (Wato et al. 2006). Campbell and Borner (1995) and Hofer et al. (1996) reported that the population of zebra in the Serengeti National Park, that remained stable for about 30 years, appeared to be decreasing due to poaching and human encroachment.

In Africa illegal hunting of zebra has depleted the population through out its range (see figure 2-5). The cause of wildlife decline in these areas are the recent civil wars in Rwanda, Somalia, Sudan, Ethiopia and Uganda that resulted into dramatic decline in all wildlife population including the plains zebra. The abundance of weaponry and destruction of infrastructure for wildlife protection in these countries led to severe over hunting of zebra and other large mammals for meat (Mohlen 2004). Legal hunting may also be responsible for plains zebra decline in some areas since its control is difficult, especially when professional hunters are not involved. Population decline of zebra has been also reported by TWCM (2000) to occur in the Tarangire ecosystem of the northern Tanzania .

1.1.4. Illegal hunting in the Tarangire ecosystem The Tarangire ecosystem is composed of Tarangire National Park, Lake Manyara National Park, Mkungunero game reserve, Lolkisale, Mto wa Mbu and Simanjiro game controlled areas (see figure 2-6 in chapter 2). This ecosystem is one of the East African savannahs that support high concentrations and diversity of large mammals (Lamprey 1964). The ecosystem is a site of global biodiversity significance, and has the second highest abundance of migratory zebra and wildebeest after that of the Serengeti- Maasai Mara ecosystem (Gereta et al. 2003).

Despite the ecological importance of the ecosystem, particularly the Tarangire National Park, the park is progressively been transformed into environment hostile to wildlife (Borner 1985; Newmark 1996). Among the hostile environments are illegal hunting and habitat fragmentation. TANAPA (2002) and Sachedina (2006) reported that illegal hunting is taking place around the park, and poachers come from the villages surrounding the park. This may be influenced by presence of all weather roads from Arusha to Makuyuni to Dodoma and that from Arusha, Simanjiro to Babati. These roads make the area accessible through out the year. Roads influence the increase of human population and subsequent increase in illegal hunting. Thus poachers walk from the villages towards the park via roads and natural trails.

Recent surveys in the Tarangire ecosystem indicated steeply declining wildlife populations, with notable declines occurring in the zebra and wildebeest populations that dominate the ungulate community. Recorded zebra numbers in the ecosystem declined by approximately 60%, from around 35,000-40,000 animals in 1988-1990 to only 10,000-15,000 a decade later (TWCM 2000). Driving transect counts conducted in TNP in 1998 further indicated that zebra and wildebeest populations have declined by approximately 40% and 60% respectively (Kideghesho 2000). This indicates that plains zebras are suffering from human threats including illegal hunting in areas around TNP.

5 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

1.2. Research problem Based on the aerial surveys conducted by the Tanzania Wildlife Conservation Monitoring (see section 1.1.4), it is evident that the population of plains zebra has declined. Being a pioneer species in the grassland within the grazers community, its reduction in numbers carries an important implication that would affect the grazing succession and feeding efficiency of other herbivores and the ecosystem function in general (Bell 1971).

Despite the ecological significance of this species in the Tarangire ecosystem, the risk areas for zebra poaching in relation to accessibility factors around Tarangire National Park are not yet identified. In order to identify areas with high poaching risks, it is necessary to investigate the relationship between accessibility factors and poaching events of zebra within and around the park.

1.3. Research objectives

1.3.1. Main objective The main objective of this study is to identify accessibility factors explaining occurrence of poaching around Tarangire National Park in order to come up with a poaching risk map. The poaching risk map would facilitate the park management on planning of anti-poaching activities.

1.3.2. Specific Objectives The specific objectives of this study are:

i. To determine the spatial and temporal patterns of poaching events of zebra within and around Tarangire National Park. ii. To investigate the relationship between accessibility factors and poaching events of zebra within and around the park. iii. To develop a poaching risk map

1.4. Conceptual diagram Figure 1-1 is the conceptual diagram of the Tarangire National Park showing factors attributing to poaching, with probable consequences of population decline of plains zebra. Dark blue circles indicate state variables, light blue circles indicate process and arrows indicate the perceived cause and effect relations. In this study only the accessibility sub system (demarcated with dashed lines) was considered for investigating the relationship with poaching of events of plains zebra. Since presence of zebra is the pre-requisite for poaching, poaching risk is determined by accessibility (see figure 1-1).

6 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Immigration Roads Travel speed per road type

Distance from Human settlement Unemploym population Poverty ent Settlements increase Travel speed per landcover type Land cover Land use Livelihood type change alternative Poaching Presence of risk poacher Accessibility Habitat fragmentati on Landform slope

Zebra population Poaching decline Slope steepness

Low Presence of enforcemen zebra t of wildlife Inadequate laws fund

Conservatio Migration n approach

Water and phosphorus Season KEY availablity

State variables

Process

The perceived cause and effect relation

Boundary of the analysed sub system

Figure 1-1: Conceptual diagram of the Tarangire National Park

7 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

1.5. Research questions Based on the study objectives and the conceptual diagram, the following questions were formulated.

i. Where does poaching of zebra’s mostly occur within and around Tarangire National Park? ii. What is the relationship between poaching events of zebra and the accessibility factors? iii. Where are the potential poaching areas for zebra?

1.6. Hypothesis

Ho: There is no relationship between poaching events of zebra and the accessibility factors (distance to roads, human settlements, land form and land cover).

Ha: There is a relationship between poaching events of zebra and the accessibility factors (distance to roads, human settlements, land form and land cover).

8 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2. Description of the study area

2.1. The study area In 1970 the status of the Tarangire Game Reserve was changed to national park. This area is of great importance from national, regional and international point of view, this is because of its hydrological regime that ensures a permanent water source for the wildlife species. Seven conservation significances influenced changes of the status of the area (TANAPA 2002):

i. To protect a critical dry season watering place that serves both the resident and migratory wildlife species of the entire Tarangire ecosystem. ii. To protect and conserve species and genetic diversity, non living resources and ecology critical to the entire Tarangire ecosystem. iii. To protect the most important Tanzania’s wetlands and hydrological regimes that supports a variety and abundance of both flora and fauna. iv. To protect unique association and mosaic of habitats that gives the park the character unique in the national parks system. v. To protect wildlife corridors and dispersal areas, to promote sustainability of existing resource use and to restore resources threatened by extinction. vi. To protect scenic quality and aesthetic attributes of the park and encourage low impact tourism. vii. To protect the variety of habitats for a wide range of species including endangered, threatened and rare species of exceptional value.

2.1.1. Choice of the study area The study area was chosen based on the following criteria: i. Tarangire National Park (TNP) represents one of the areas in Tanzania with critical human wildlife conflicts (livestock and crop raiding, illegal hunting and encroachment). ii. In the dry season TNP is the only source of water for both resident and migratory wildlife species in the entire Tarangire ecosystem. iii. Identification of risk areas for zebras poaching within and around TNP is of paramount importance because most of the law enforcement activities are ad hoc and not cost effective (TANAPA 2002).

Based on the above characteristics of the TNP, it is apparent that a study in this area can contribute significantly in enhancing the conservation of zebra and other wildlife species in the ecosystem. Since poaching events were essential data in this study the final demarcation of the study area was determined by areas covered by TNP patrol teams. Figure 2-1 indicates the location of the study area.

9 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.1.2. Geographical location Tarangire National Park (figure 2-1) is located between 3 °40 ′S and 4 °35 ′S latitude and 35 °50 ′E and 36 °20 ′E longitude at an elevation of between 1200 and 1600 meters above sea level. Tarangire National Park occupies an area of 2642km², making it the fifth largest park in Tanzania. It lies 118 Km southwest of Arusha within the administrative districts of Babati Simanjiro and Kiteto in Manyara region and Monduli district in Arusha region and Kondoa district in Dodoma region (TANAPA 2002) .

Figure 2-1: Location of the study area in a map of Tanzania

10 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.1.3. Vegetation The Tarangire National Park is situated in the wooded steppe in an arid Acacia savannah belt that is dominated by Acacia and Commiphora species (figure 2-2) (TANAPA 2002). The most important vegetation types comprise a combination of riparian woodland, Acacia tortilis , Acacia-Comphora woodland, riverine grassland, Combretum-Dalbergia woodland, Acacia drepanolobium woodland, deep gully vegetation and grassland with scattered baobab trees

Figure 2-2: The savannah woodland in Tarangire National Park

2.1.4. Wildlife Tarangire National Park provides habitat for a large diversity of fauna. Important wildlife found in the park are elephant, zebra, wildebeest, lion, cheetah, leopard, lesser and greater kudu, oryx, hartebeest, buffalo, giraffe, impala, gerenuk reedbuck, bushbuck, warthog etc. There are about 550 bird species that include ostrich, parrots, eagles, pelicans, lovebirds, hornbills, weavers, kori bustard etc. and multitude of herpeto fauna (TANAPA 2002). Figure 2-3 shows one of the key wildlife species in Tarangire National Park.

Figure 2-3: Wildebeest grazing in Tarangire national Park

11 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.1.5. Soil The soils on well drained areas consist of dark red sandy clay loam. In flood plains and in depressions there are black clay soils commonly known as black cotton soils. These black cotton soils are sticky and expand in the wet season and are poorly drained (TANAPA 2002).

2.1.6. Climate TNP lies within the ecological zone IV characterized by semi arid climate (Pratt and Gwynne 1977). The climate of TNP has high variability and periodic drought. The rainfall pattern consists of the short rains between October and December followed by a dry spell in January, with long rains between February and May. The average annual rainfall is 660mm. Temperatures are highest from December to February and the months with lowest temperatures are June and July The average maximum and minimum temperatures are 27ºC and 16ºC respectively (TANAPA 2002).

2.1.7. Land use and socio-economic activities Livestock husbandry is the main livelihood in the rangelands of Arusha and Manyara regions (MAFS 2006; Sachedina 2006). The Maasai which is the major ethnic group in the Tarangire ecosystem traditionally are pastoralists; they have been immigrating in the Simanjiro district in the search of pasture for their livestock (TCP 1998). However, there is increasingly shift from pastorals to agricultural farming due to immigration of other ethnic groups into this area, hence the pastoral nature of the Maasai is changing slowly over time. The community in the area is no longer of pure pastoralists; it is a mixture of agriculturalist, agro pastorals, commercial businessmen and nomadic pastoralists (Kimolo 2001). Agriculture farming is gaining importance in this area, because of poor performance of livestock due to recurrent droughts and the increase of human population around Simanjiro and Mto wa Mbu (TCP 1998). The main crops produced are both cash and food crops that include sorghum, millet, maize, banana, beans, cowpeas, pigeon peas, green peas, sweet potatoes and cotton. Apart from livestock husbandry and agriculture other economic activities undertaken in the communities’ adjacent Tarangire National Park are charcoal burning, mining, sport hunting, commercial hunting, local artisan, small business, food vendoring, sales of handcraft in curio shops for tourists and working in tourist industry as local tour guides, cooks and porters (TANAPA 2002).

2.1.8. Human population The human population around TNP is increasing. Between 1988 and 2002 the population around TNP grew from 183,344 to 251,490 (Bureau of Statistics 1989; NBS 2003). The increasing human population raises the demand for basic resources such as food, and one way is through poaching and expansion of crop land. The expansion of cropland threatens the integrity of the wildlife dispersal areas and increases the isolation where animals find it difficult to adapt to human dominated habitats (Mwalyosi 1991). As a result their migratory routes are blocked, their habitats fragmented and wildlife is being confined into small diminishing patches surrounded by human settlement (Newmark 1996)

12 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.2. The study animal: Plains zebra Equus burchelli (Gray 1824 ) The Plains zebra Equus burchelli is within Order; Perisodactyla , Family; Equidae , Genus; Equus (Kingdon 1997). The habitat of the plains zebra is the savannah ecosystem (figure 2-4) that range from grassland to open woodlands (Kingdon 1997; Moehlman 2002). They prefer firm grounds and move off water logged areas in the wet season and during flooding. The plains zebra has a social system based on a family of 1 to 6 females led by a male. The home range of this species is 80- 200km², with seasonal migration and daily change of locations to sleep, graze, roll in dust, rubbing against trees and drinking (Haltenorth and Diller 1996). This species is a water dependent, non ruminant herbivore, non selective and roughage grazer which feeds on both short young shoot and long flowering grasses (Arsenault and Owen-Smith 2002; Bell 1969; Owaga 1975).

Ecologically zebra is a pioneer in the grassland community where wildebeest and Thomson gazelles follow once the zebras have broken down and opened up the dense stands of grassy stems by grazing and trampling. This activity makes the vegetation to be shorter and remove the older growth layer of lignified stems and seed heads to stimulate tender and nutritious new growth for selective grazers (Beekman and Prins 1989; Bell 1969; Bell 1971). Thus plains zebra plays a great role in assisting the latter members of the succession within grazer community and enrich the variety and numbers of herbivores that could be sustained in the grassland ecosystem (Bell 1971; Hansen et al. 1985; Owaga 1975). Plains zebra spent most of the day time grazing especially in the long dry season when food is scarce (Beekman and Prins 1989). In the dry season approximately 65% of the time in 24 hours is spent for grazing (Gakahu 1984; Grogan 1978). During this period zebra use more time to graze to compensate food quality by higher intake and passage rate. This physiological adaptation to feeding behaviour is referred to as throughput rate (Beekman and Prins 1989) .

Figure 2-4: A herd of plains zebra in the Tarangire National Park

13 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.2.1. Distribution of plains zebra

Historically plains zebra ranged throughout the eastern, southern and south-western regions of Africa (figure 2-5). Currently the global population of plains zebra resides only in Kenya, South Africa, Tanzania, Zambia and Zimbabwe. This species has gone extinct in some countries of its historical range due to various factors that include illegal hunting, uncontrolled legal hunting and habitat loss by human activities (Borner 1985; Campbell and Borner 1995; Madhusudan and Karanth 2002; Moehlman 2002).

Figure 2-5: Historical and current distribution of plains zebra in Africa Source: Moehlman (2002).

14 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.2.2. Migration of plains zebra in the Tarangire ecosystem Zebra and other herbivores in the Tarangire ecosystem exhibit seasonal migration from the park to different dispersal areas in the dry season (Kahurananga and Silkiluwasha 1997; Lamprey 1963; Lamprey 1964). In the dry season (June-November) the migration starts in early July and zebras concentrate within the national park because of water availability in the Tarangire River. During the rain season (December-May) most of them leave the park in mid December and disperse into a wide range of the Tarangire ecosystem (TCP 1998). The dispersal areas of zebra and wildebeest in Tarangire ecosystem were first mapped by Lamprey (1964). The main migration routes of zebra and wildebeest in Tarangire ecosystem are shown in figure 2-6.

There are several reasons for migration behaviour of large herbivores proposed by different authors. Fryxell and Sinclair 1988 suggested that the migratory behaviour of ungulates is evolved to enhance access to shifting patterns of food abundance and access to patches with high quality food. However, in the Tarangire ecosystem which is similar to the Serengeti ecosystem, migrant animals including zebra move away from areas with the highest resource abundance during wet season and return during the dry season (Kahurananga and Silkiluwasha 1997). Voeten (1999) reported that phosphorus is an important factor driving the migration of zebra and wildebeest in the Tarangire ecosystem. The level of phosphorus is low in TNP during wet season to sustain the lactating females, thus animals are forced to migrate seasonally to Simanjiro plains in search of pastures rich in phosphorus mineral (TCP 1998). Kreulen (1975) also found that timing of wildebeest migration in the Serengeti coincided with high calcium level in grasses selected by wildebeest. McNaughton (1990) revealed widespread mineral deficiencies (Cu, Zn, Ca, N, Na, Mg and P) in the dry season forage of the Serengeti and suggested that the migratory herds of the Serengeti move along a forage mineral gradient.

To study the migratory animal species several techniques have been used. These techniques include the conventional and the current techniques of Geographic Information Systems and Remote Sensing. The GIS and RS tools have gained importance on studying the ecology of wide ranging animals. This is because GIS and RS tools enable to identify threats of the dispersal areas and the distribution of the animal species. Section 2.3 gives the background of GIS and RS application in wildlife management.

15 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Figure 2-6: Dispersal areas for migrating zebras in the Tarangire ecosystem

16 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

2.3. Geographic information systems and remote sensing in wildlife management Geographic Information Systems are software packages used for the capture, manipulation, analysis and display of spatial referenced data (Wilkie and Finn 1996). GIS and remote sensing tools enable the quantification and understanding of spatial and temporal processes that accompany with the spatial dynamics as well as the presentation of how extensive the phenomenon is on a spatial scale. GIS analyze questions of a spatial nature for example location of an organism relative to other organisms or the environment that influences its functioning (Johnston 1998).

Nowadays many protected areas are confronted with intensity of land use at their periphery, thus species with large home range may be at risk when an individual cross the protected area boundary (de Leeuw et al. 2002).

GIS and remote sensing (RS) tools were first introduced in wildlife management in the second half of 1980s (de Leeuw et al. 2002). During this time these tools were used to produce habitat suitability maps for wildlife that integrated various explanatory variables. The introduction of GIS and RS in wildlife management improved the traditional methods of displaying maps because it facilitates updating of information, hence monitoring of resources relevant to wildlife becomes relatively easy as GIS allows combining and comparison of different layers (Toxopeus 1998). Therefore with these advantages GIS and remote sensing tools form basic sources of information that may contribute to sustainable wildlife management and conservation.

Practical examples of GIS application in wildlife management have been demonstrated by several authors. To mention a few are: Metzger et al. (2007) produced a poaching risk map for rhinoceros based on distance travelled by hunters from settlements to the park, the frequency and location of arrested illegal hunters and the rate of animal off take within the park. Barve et al. (2005) mapped threats to a wildlife sanctuary in Southern India. They established threat indices resulting from human settlement, major and minor roads and slope steepness of the terrain. Other authors that commended the importance of GIS and RS in wildlife management and monitoring include (Foley 2002; Kundi and Ngereja 2003; Oindo 2001; Ottichilo 1999; Ottichilo 2000; Ottichilo et al. 2000a; Ottichilo et al. 2000b; TCP 1998; Toxopeus 1996; Toxopeus and Bronsveld 1995).

17 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3. Methods and materials

This chapter describes the methods and materials used in the study. The steps and processes that were carried out in this research are shown in figure 3-1. As can be seen in the conceptual diagram (figure 1-1), a poaching event is determined by the presence of poachers and zebra. Presence of poachers is determined by poaching risk. Poaching risk is a function of accessibility, distance from human settlements, distance from roads, land cover and slope of the terrain. Accessibility is expressed in travel time from human settlements. Travel time is a result of travel speed which is a function of road type, distance from settlement, land cover types and slope steepness.

In order to establish relationships between poaching events and explanatory variables, a stepwise regression method was executed where a poaching event was the dependent variable and distance to roads, distance to villages, land cover, accessibility and slope were predictor variables.

The methods used to develop the accessibility map expressed in travel time are explained in section 3.1. Preparation of poaching events data for logistic regression analysis is explained in section 3.2 and the logistic regression model is explained in section 3.3.

18 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Aster image Roads polyline DEM raster Settlements August 2006 map map

classification Road types Calculate

Classified image Rasterize Slope % raster map Accuracy assessment Road raster map Reclassify Landcover map Apply travel speed Slope% Apply travel speed reclassified Travel map speed per roads type Travel speed Study area per land cover Apply slope type reduction factor Merge

land cover- Slope TNP Pseudo roads travel speed poaching absence speed map reduction data data map Merge by multiplication Illegal Land cover- hunting Merge road-slope data travel speed map Apply inverse Presence values absence data Landcover- roads-slope Cost weighted travelling distance condition map Calculate distances Distance map (in meters)

Distance map/ Roads Settlement 6000 (m/hr)

Accessibilit Presence Land cover Slope class y map Distance to Distance to absence map % map (travel time roads settlement data km/hr)

Extract value to points using presence absence points

Predictor variables with raster values

Logistic regression

KEY Selected Hypothesis predictor tested Data variables

Process Map calculation using logistic regression equation Direct data Decision Poaching risk map Document

Same data

Figure 3-1: Flow chart of the methods

19 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3.1. Accessibility map

The accessibility map was developed by combining the land cover travel speed map, road travel speed map, slope speed map, then cost weighted with settlements (see figure 1-1). The details of each step are explained in sections 3.1.1, 3.1.2, 3.1.3 and 3.1.4., 3.1.5. Section 3.1.6 describes the final step to achieve the accessibility map expressed in travel time (km/hour).

3.1.1. Land cover Prior to the field work, the Aster satellites imagery of August 2006 (bands 1, 2 and 3 with a resolution of 15m) was processed and combined to create RGB 321 False Colour Composite (FCC) of the study area. The FCC imagery was used for unsupervised classification using maximum likelihood classifier to get an impression of possible variation in land cover types. Unsupervised classification produces classes of land cover types based on their spectral characteristics of the whole scene to be mapped. The obtained classes were used as a basis for field work planning as described in Schowengerdt (1983). During the field survey, simple random technique was applied according to Thomson (2002). 120 random points were generated using Hawths tool in ArcGIS 9.2. The random points were evaluated if they were evenly distributed over the classes produced by unsupervised classification. A Garmin GPS 12XL unit was used for field navigation to locate the sample points. In the field the observed samples of land cover types were classified as woodland (canopy cover >20%,<50% height 20m), shrub land (cover >20%, height 6m), water and bare land (land with non woody plants) following the classification system developed by Pratt and Gwynne (1977). 40 ground truth points were used to re classify the image using the maximum likelihood classifier (supervised classification) in Erdas software version 9.1. Then an accuracy assessment was performed using the remaining 80 sample points to validate the classified map. The final land cover map was an input data for accessibility analysis (see figure 3-1).

In the process of creating the accessibility map the land cover types of the land cover map were assigned a corresponding travelling speed according to Toxopeus (1996) (see table 3-1) to get a travelling speed per land cover type map. Land cover types were used in the accessibility analysis because not all areas within and around Tarangire National Park (TNP) have roads, thus land cover types have influence on travelling speed. Different locations within TNP have different land cover types that do affect the walking speed of people.

Table 3-1: Travelling speed per land cover type Land cover Travelling speed (km/hr) Shrub land 0.7 Woodland 1 Bare soil 3 Water 0

20 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3.1.2. Roads Road is one of the factors influencing accessibility. In generating the accessibility map, first the road layer was clipped to fit in the study area and then rasterized to allow map calculations (figure 3-1). A travelling speed (by walking) of 6 kilometres per hour suggested by Toxopeus (1996) was assigned to the road type (viz. all weather roads) to get a roads travelling speed map.

3.1.3. Land cover –roads travelling speed map Land cover–roads travelling speed map was obtained by merging the travelling speed per land cover type map with the roads travelling speed map (see figure 3-1) in ArcGIS version 9.2.

3.1.4. Slope According to Kahurananga and Silkiluwasha (1997) the topography of TNP is varied, thus variation in slope steepness of the topography in TNP affects the travelling speed. The slope steepness expressed in percentage was calculated from digital elevation model using a spatial analyst tool included in ArcGIS version 9.2 and reclassified into in six classes (figure 3-1). Each slope class was assigned a travel speed correction factor. The output of this process was a slope travelling speed map. The slope correction factors were adapted from Toxopeus (1996) as indicated in table 3-2. These slope correction factors represent the reduction in travelling speed depending on the slope steepness (Mannathoko et al. 1990). This means the steeper the slope the more it will reduce the walking speed of people.

In the next step the slope travelling speed map was multiplied with the land cover-roads travelling speed map to get a land cover-roads-slope speed map. After that an inverse values operation was applied using a raster calculator to obtain a land cover-roads-slope travelling condition map. Table 3-2: Slope steepness classes and slope correction factor

Slope % Slope correction factor 0-5 1 5-10 0.9 10-20 0.8 20-30 0.7 30-40 0.6 40 and higher 0.5

21 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3.1.5. Settlements

The location of human settlements influences accessibility and may estimate the vulnerability of an area. In the accessibility analysis distance from settlements (villages) to different destinations were calculated (see figure 3-1). A cost weight distance function in ArcGIS version 9.2 was used to calculate the accumulated cost of travel (resistance against travel). In this process the source points were the villages and the weight map was the land cover-roads-slope travelling condition. After a cost weighted distance calculation was done a distance map was generated (with a measurement unit in meters).

3.1.6. The accessibility (travel time) map The distance map generated in section 3.1.4 (see figure 3-1) was divided by 6000 meters (an estimated walking speed with no resistance, see section 3.1.2) using raster calculator in ArcGIS to get an accessibility map expressed in travel time (km/hour).

3.2. Pre processing of poaching events data Poaching data were processed for logistic regression analysis. Poaching data (in this case was regarded as presence data) were combined with pseudo absence data to get presence absence dataset. The presence absence dataset was used as a dependent variable in the logistic regression analysis (section 3.3).

3.2.1. Illegal hunting data (presence data) Illegal hunting data (50 poaching events) were recorded by park rangers during patrols in Tarangire National Park (TNP). TNP use the conventional law enforcement in the form of foot and car patrols. Patrols are done in every month in an organized and ambush bases. During patrols, park rangers record the GPS location and name of the area patrolled, number of illegal hunters apprehended, number of poachers escaped and number of animals killed on each incidence.

3.2.2. Pseudo absence data Pseudo absence data are point data that assume to represent true absence of poaching in a certain landscape. These point data are generated randomly from background areas from which poaching data are missing (figure 2-1 in section 2.1.1). In this study pseudo absence data are important to be generated because only the presence of poaching data is available; therefore data with zero values are necessary in order to carry out a logistic regression to assess if there is a relationship between poaching events and the predictor variables. Peace and Boyce (2006) stated that pseudo absence data when combined with presence only data of poaching they are used in regression based models to predict the relative likelihood of occurrence of poaching. In this study 80 pseudo absence points were randomly generated using Hawths tool in ArcGIS version 9.2.

22 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3.2.3. Presence absence data The presence absence dataset was obtained by merging presence data and pseudo absence data (see figure 3-1). In the presence absence dataset, presence data were labelled 1 and the pseudo absence data were labelled 0. The merging process was done in ArcGIS version 9.2.

3.3. Logistic regression model Logistic regression is part of the generalized linear model. The logistic regression differs with other statistical methods because it is not affected by the assumption of variance inequalities across the groups, and is applicable whenever the dependent variable is binary (Hosmer and Lemeshow 2000). In this study the dependent variable (poaching) was binary characterized by absence and presence of illegal kills labelled 0 and 1 respectively. The usefulness of logistic regression is that it provides knowledge of the relationships and strengths among variables. Thus with these merits it was used in this research to investigate the relationship between poaching events and the predictor variables.

In the logistic regression model presence absence data and five predictor variables were used to explain the relationship with poaching. The predictor variables used were distance to roads, distance to villages, accessibility, and land cover and slope (figure 3-1). 3.3.1. Presence absence data In the logistic regression analysis the presence absence dataset of poaching was used as a dependent variable.

3.3.2. Distance from roads Distances from roads as explanatory variable were calculated from presence absence dataset using the near distance function in ArcGIS version 9.2 (figure 3-1). The presence absence dataset was an input feature from which distances were calculated and the roads were near features .

3.3.3. Distance from settlements Distances from settlements (villages) were computed from presence absence dataset using the near distance function in ArcGIS version 9.2 (figure 3-1). The presence absence dataset was an input feature from which distances were calculated and villages were near features.

3.3.4. Land cover, slope and accessibility maps The presence absence dataset was used to extract value to points in the land cover, slope and accessibility maps. The extracted raster values from each map were predictor variables used in logistic regression analysis (see figure 3-1).

23 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

3.3.5. Selection of predictor variables A stepwise logistic regression method was used for selection of the predictor variables that could explain the relationship with poaching events. A stepwise regression method was chosen for the analysis because it begins with all predictor variables in the model and variables are eliminated from the model in an iterative process until when no more variables can be eliminated from the model (Hosmer and Lemeshow 2000). The results of logistic regression analysis were used to test the hypothesis and for generation of the poaching risk map. Logistic regression analysis was done using the Statistical Package for Social Science (SPSS) software version 15.

3.3.6. The poaching risk map Risk areas for zebra poaching within and around Tarangire National Park were predicted using the selected variables in the logistic regression model. The predictor variables used were distance to roads and slope. First the road layer was converted into raster using spatial analyst tool in ArcGIS 9.2. In the next step the road raster map was combined with the slope raster map in raster calculator using the following logistic regression equation:

exp( β0+ β1x1 +β2x2) p=

1+ exp( β0+ β1x1+β2x2))

Where: β0= Constant =1.096

β1= Road intercept =-0.241 x1= Road raster map

β2= Slope intercept =-0.100 x2 Slope raster map

After this calculation the output was the poaching risk map. The figures used in this calculation are indicated in table 4-6.

24 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

4. Results

4.1. Acessibility

4.1.1. Land cover map Figure 4-1 shows the classification of the Aster image of August 2006 (section 3.1.1). Four classes were distinguished namely shrub land, woodland, water and bare land. The developed land cover map shows that most of the area is covered by shrub land (52.88%) and woodland (46.73%) vegetation types as indicated in table 4-1.

Figure 4-1: Classified land cover map of the areas within and round Tarangire National Park

25 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Table 4-1: Area covered by land cover types Class name Area covered (ha) Percentage Descriptions Woodland 24,302 46.73 Dominant speces: Combrteum mole, Adansonia digitata, Acacia senegalensis, Acacia tortilis, Combretum sp.

Shrub land 27,501 52.88 Dominant species: Balanite aegyptica, Dalbergia melanoxylon, Acacia mellifera, Commiphora sp, Grewia bicolar, Acacia drepanolobium.

Water 85 0.17 Lake, river and ponds

Bare land 112 0.22 Covered by non woody plants

Total 52,000 100

4.1.2. Accuracy assessment Eighty field observation points were used to validate the classified Aster image of August 2006. The result of the classification accuracy assessment is shown in table 4-2. The overall classification accuracy obtained was 71.25%.

Table 4-2: Accuracy assessment results Class Name Reference Classified Number Producers Users Accuracy % Totals Totals Correct Accuracy % Woodland 35 39 26 74.29 67.67 Shrub land 27 23 18 66.67 78.26 Water 8 10 6 75.0 60.0 Bare land 10 8 7 70.0 87.50 Total 80 80 57 Overall classification accuracy 71.25%

26 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

4.1.3. Accessibility map Figure 4-2 shows the accessibility of the area translated in travel time in hours (by walking) from the villages neighbouring Tarangire National Park (TNP). The travel time was the criterion used to represent the vulnerability of TNP to illegal activities including poaching. A short travel time means that the areas are easy to access and hence prone to illegal activities. A long travel time means the areas are difficult to access. The general interpretation is that the shorter the travel time the more vulnerable the area is to poaching. This map is a result of cost weighting of villages with land cover types, roads and slope with the associated travelling speeds described in the methods chapter 3.

Figure 4-2: Accessibility from villages surrounding Tarangire National Park

27 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

4.1.4. Spatial distribution of poaching events The spatial distribution of poaching events is illustrated in figure 4-3. The results show that 60% of the events occurred outside the park in the north, north east and eastern parts in the wet season and 40% on the western parts in the dry season. Table 4-3 shows the seasonal occurrence of poaching events from December 1998 - June 2007.

Figure 4-3: Spatial distribution of poaching events around Tarangire National Park

28 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Table 4-3 Seasonal record of poaching events from December 1998 - June 2007

Season poaching events Percentage

Wet 30 60

Dry 20 40

Total 50 100

Figure 4-4 is the graphical presentation of temporal pattern of poaching events indicated in figure 4-1 and in table 4-1. These are monthly records of poaching events from December 1998 to June 2007. Poaching events in areas surrounding TNP show an increasing trend from December with a high peak in February, and a decrease from March to May. High numbers of incidences are also noted in June and decrease from July to November. High numbers of incidences in February and June are results of migration of zebras to and from the park as described in chapter 2 section 2.2.2

5 Incidences 4

r y h e y er er ar b April May Jun Jul gust Marc anuary ebru Au tem cembe J F p Octob e Se November D Month

Figure 4-4: Monthly records of poaching events from December 1998 - June 2007

29 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

4.2. Poaching risk

4.2.1. Logistic regression model In the logistic regression model 61 non-poaching events were correctly predicted out of 80 sample points. 32 poaching events were correctly predicted out of 50 poaching events (see table 4-5). The overall accuracy of the full model was 71.5%; this means that the model explained 10% more than the null model (61.5% for null model, Table 4-4).

Five predictor variables were tested for their significance in explaining poaching of zebra within and around TNP using stepwise logistic regression. The predictor variables used were distance to roads, distance to villages, land cover, accessibility and slope of the terrain. Among the five predictor variables only distance to roads (p=0.000) and slope (p=0.015) remained in the model with significant negative relationships with poaching events (p<0.05). The model summary (table 4-7) shows 27.7% proportion of variance explained by the model with two remained variables. This result suggests that poaching events can not only be explained with distance to roads and low slopes. Possibly there are other factors which could contribute indirectly to poaching in combination with these factors. In nature different factors work together. Table 4-4: Classification table for the null model

Observed Predicted Poaching Percentage 0 1 Poaching 0 80 0 100.0 1 50 0 0.0 Overall percentage 61.5

Table 4-5: Classification table for the full model Observed Predicted Poaching Percentage 0 19 Poaching 0 61 0 76.0 1 18 32 64.0 Overall percentage 71.50

Table 4-6: Variables in the equation B Wald df Significance Exp(B) Distance to roads -0.241 16.735 1 0.000 0.786 Slope -0.100 2.490 1 0.015 0.905 Constant 1.096 8.875 1 0.003 2.992

Table 4-7: Model summary -2Loglikelihood Cox and Snell R Square Nagelkerke R Square 143.547 0.204 0.277

30 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

4.2.2. Poaching risk map Figure 4-5 shows the probability of occurrence of zebra poaching within and around Tarangire National park. Potential areas for zebra poaching are those close to roads with red to yellowish tones. This map is a result multiplying the roads and slope raster maps as described in chapter 3 section 3.3.6. The probability of poaching ranges from 0-1 as indicated on the legend in figure 4-5.

Figure 4-5: Poaching risk areas

31 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

5. Discussion

5.1. Relationship between poaching and the selected predictor variables In the logistic regression analysis results revealed that distance to roads and slope had significant relationships with poaching events.

5.1.1. Roads In the context of natural resources conservation, roads facilitate people movements to areas previously inaccessible. This entails that people may move from areas with scarce resources to areas with resource abundance. Toxopeus (1996) stated that the easier the area to access the sooner people may tend to go to poach. This means that poaching within and around national parks is largely a function of accessibility of the area by illegal hunters.

Based on the research results (appendix 8-4), most of the illegal hunting events occurred in areas within one to seven kilometres from roads. This suggests that illegal hunters avoid long distance travelling near or inside the park boundary. This could be linked with reducing the risk of encountering law enforcement patrols. Hofer et al. (2000) stated that extended travelling distances for hunting in a protected area increases the chances of poachers to be arrested and increases the costs of hiring porters to carry the meat out of the park. In addition to that, illegal hunters will have to carry snares to the sites and their catch back home. Since illegal hunters walk, it is a cost for them to travel far for poaching (Fitzgibbon et al. 1995).

In the analysis (appendix 8-4), it was found that few poaching events were recorded within one kilometre from roads. This phenomenon could be linked with the fact that animals avoid these areas because of traffic disturbances such as road kills, noises and fumes from vehicles. Forman and Alex (1998) reported that large mammal species tend to have low population densities within 100 to 200 meters from roads due to avoidance of traffic effects and predators moving along roads. Furthermore, Wato et al. (2006) observed in Tsavo National Park that snare abundances, which are signs for poaching, are low closer to roads but their abundance increased at a distance of four kilometres away from roads. This observation entails that poachers requires regular checking of their snares so that the ensnared animals do not break and escape, or taken by other predators, or rot, and hence become unsuitable for human consumption.

From the discussion above it is clearly seen that presence of roads contributes to poaching activities within or near national parks. Apart from facilitating zebra poaching in TNP, roads have negative effects to other organisms in the ecosystem such as barrier effects and habitat fragmentation (Forman and Alexander 1998; Lee et al. 2006).

32 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

5.1.2. Slope Slope showed a significant negative relationship with poaching. This means that poaching events occurred in relatively flat areas. Mannathoko et al. (1990) commented that slope steepness of an area influences the walking speed of people. He suggested that areas with low slopes are easy to walk and those with steep slopes are difficult to walk. Therefore, poachers might avoid areas with steep slopes.

5.1.3. Seasonal distribution of poaching events The research findings revealed that there is a seasonal difference in occurrence of poaching events of zebra within and around Tarangire National Park (TNP). About 60% (out of 50 events) of poaching events occurred in the wet season (table 4-3). This is the season where zebras are found outside the park dispersing in a wide range of different dispersal areas in which protection is minimal. These dispersal areas are Lolkisale, Mto wa Mbu and Simanjiro Game Controlled Areas (GCAs) and Mkungunero Game Reserve (figure 2-6). In these GCAs tourist hunting takes place between June and November (MNRT 1974). During the hunting season poachers are temporarily hampered to access the GCAs by tourist hunters and the game officers. Caro et al. (1998) contended that once the hunting season is over, poachers move into these areas to poach. This implies that poachers take advantage of lower enforcement while the target animal species are still available. The observation of more poaching events (figure 4-4) during the migration seasons in TNP is similar to the findings reported by (Hofer et al. 1996; Loibooki et al. 2002) in the Serengeti National Park where large herds of wildebeest and zebra migrate to the north and north western parts of the park. The migrant zebras and wildebeest move from Serengeti National Park to Maasai Mara from June to October and return to Serengeti during the onset of the rain season in December (Fryxell and Sinclair 1988), where as in the Tarangire ecosystem the zebra start migrating from the park to dispersal areas in December and return into the park in the beginning of the dry season in June (Lamprey 1964; TCP 1998).

According to the law enforcement reports (unpublished and personal communication) the high number of poaching events in the wet season (figure 4-4) from December to February could be attributed to food shortage in the villages surrounding TNP. Most of the arrestees participated in zebra poaching reported to do so for food and livelihood.

The results revealed that most of the illegal hunting events occurred on the western part of TNP during the dry season. This pattern is explained by water availability in TNP where migrating animals come back to TNP for watering. The north-west part of TNP is used by zebras to move to and from the eastern shore of Lake Manyara National Park (LMNP) and TNP. This area between TNP and LMNP has pools that hold water during the dry season, hence used by regularly drinking animals, including the zebras (TCP 1998). This gives poachers an opportunity to detect the zebras.

33 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

5.1.4. Merits of the method used to identify risk areas for poaching The methodology used in this study was considered to be appropriate and effective. One of the credits achieved with this method is the successful identification of potential areas for zebra poaching (figure 4-5). This is supported with the research results illustrated in figures 4-3 and 4-4 that show explicitly the spatial and temporal patterns of poaching events for about ten years. This time period is relatively adequate to give an indication of how the phenomenon is recurrent over time and space. The variation in occurrence of poaching events in different areas and seasons within a year may help to inform the park management which time of the year poaching is at high and low levels. Thus a seasonal patrol calendar can be developed based on these observations. In addition to spatial and temporal aspects, the accessibility map (figure 4-2) that was translated into travel time in hours would help to identify villages that are threats to the park. The threat villages toward the park can be identified first by looking on the incidences of illegal activities committed by inhabitants of a respective village, and then the accessibility map can be used to locate how far the villages are from the park. With these insights of spatial and temporal patterns, the out put maps (figures 4-2 and 4-3) are of practical and strategic use to wildlife managers in TNP. They depict vulnerable areas to poaching. The poaching risk map (figure 4-5) is useful to park rangers because it depicts potential areas for poaching. Thus the three output maps would be helpful in decision making especially on how best to direct law enforcement patrols and implementation of intervention programs in villages neighbouring the park.

5.1.5. Data quantity and quality This study used poaching data to estimate the vulnerability of TNP to zebra poaching. It was assumed that the relationship of patrols and encounter with illegal hunters to be reliable and being true accounts of the events. However, Jachmann (2008) commented that poaching data faces the problem of omission attributed to high vegetation density and rainfall in the wet season that may limit visibility and accessibility of the patrolling teams. These results to no or low encounters with poaching events as some of the areas within and around the park are not patrolled in certain periods of the year.

In the analysis part as described in chapter 3 section 3.2.2, pseudo absence data were used with the assumption that they represent true absences of poaching events. This however, might not be the case because these points were not physically recorded in the field. Therefore it may contain unknown numbers of poaching presences resulting to a contaminated sample of poaching absences (Graham et al. 2004). This means that poaching of zebra might have occurred in a particular pseudo point but not witnessed by the patrol teams or poachers may have killed the animal and carried it without leaving any evidence of kill. The consequence of using pseudo absence data is that it only allows prediction of relative probabilities of occurrence of an event (Pearce and Boyce 2006).

The use of poaching data only is not adequate to identify areas prone to poaching. Other data like long term population censuses and monitoring of distribution of species have to be considered. With these concepts in mind, it is therefore acknowledged that the data used in this study may have some deficiencies in quantity and quality.

34 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

6. Conclusion and recommendations

6.1. Conclusion Based on the research questions and the hypothesis, it can be concluded from the study as follows: Research question 1: Where does poaching of zebra’s mostly occur within and around Tarangire National Park?

The research results showed that poaching events occurred around Tarangire National Park except in the southern part of the park.

Research question 2: What is the relationship between poaching events of zebras and the accessibility factors?

To investigate the relationship between poaching and accessibility, a total of five predictor variables were used to test for their significance in explaining poaching of zebra within and around Tarangire National Park. Among the five predictor variables, distance to roads and slope had statistically significant negative relationships. Thus the null hypothesis was rejected.

Research question 3: Where are the potential poaching areas for zebra?

The potential areas for zebra poaching are those with low slopes and near to roads .

The general conclusion of the study is that the GIS based analysis presented in this research successfully identified and mapped risk areas for zebra poaching within and round Tarangire National Park.

6.2. Recommendation It is evident that there is a demand for meat or other products of zebra by people living adjacent to TNP because poaching is continuing. Therefore the study recommends the following:

Law enforcement efforts have to be concentrated in the north, northeast and the eastern parts of TNP during the wet season and more efforts to the western and south west parts in the dry season. However, it will be worthwhile to divide the patrol teams into groups and enhance them with communication tools such as radio calls (walk talkies), because poachers may adapt the seasonal patrol routine and change the hunting habit and areas.

35 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Fortunately, plains zebra are killed primarily as a source of food and the meat is mostly consumed in the nearby communities making it a locally, rather than an internationally driven phenomenon. This makes the solution of the problem simpler, for example the priorities should be to develop alternatives to livelihoods through revenue sharing programs between the park and the surrounding communities in order for the communities to derive direct benefit from wildlife. The livelihood alternatives may include introduction of income generating activities such as beekeeping projects, community campsites, revolving funds through community conservation banks, or community ownership of wildlife through wildlife management areas. Some of these alternatives to livelihoods worked in the communities adjacent to Udzungwa Mountains National Park in Tanzania. Poaching events are relatively reduced and villagers showed willingness to provide information on poaching to the park authority (Harrison 2006; Mtoka 2007).

Therefore, it is suggested that both the community conservation education and extension services implemented by TANAPA to be continued and focus on capacity building on establishment and implementation of alternatives to livelihoods. However, these initiatives need collaboration and commitment from different stakeholders including the central government, district councils, conservation agencies, private sectors, environmental activists and the communities surrounding TNP. Collaboration among stakeholders is necessary because it would allow sharing of resources such as expertise and avoid duplication of projects within the same area.

It is therefore urged that alternatives to livelihoods in the community surrounding TNP should be a priority if conservation and utilisation of wildlife are to be kept within sustainable limits.

36 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

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41 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

8. Appendices

Appendix 8-1: Output of the logistic regression model Tables a-d show the backward stepwise conditional method analysis in the logistic regression performed in SPSS software.

a. Classification table for the null model

Predicted

Poaching Percentage Observed Correct 0 1 Step 0 Poaching 0 80 0 100.0 1 50 0 .0 Overall Percentage 61.5

b. Classification table for the full model

Predicted

Poaching Percentage Observed 0 1 Correct

Step 1 Poaching 0 61 19 76.2

1 17 33 66.0

Overall Percentage 72.3

Step 2 Poaching 0 62 18 77.5

1 17 33 66.0

Overall Percentage 73.1

Step 3 Poaching 0 61 19 76.2

1 18 32 64.0

Overall Percentage 71.5

Step 4 Poaching 0 61 19 76.2

1 18 32 64.0

Overall Percentage 71.5

42 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

c. Variables in the equation

B S.E. Wald df Sig. Exp(B) Step 1 Dist_villages -.104 .065 2.517 1 .113 .901 Dist_roads -.243 .072 11.424 1 .001 .784 Landcover -.007 .051 .021 1 .885 .993 Slope -.131 .072 3.365 1 .067 .877 Accessibility .092 .088 1.094 1 .296 1.097 Constant 1.612 .528 9.318 1 .002 5.012 Step 2 a Dist_villages -.105 .065 2.555 1 .110 .901 Dist_roads -.245 .071 11.778 1 .001 .783 Slope -.132 .072 3.347 1 .067 .877 Accessibility .093 .088 1.123 1 .289 1.098 Constant 1.583 .489 10.489 1 .001 4.870 Step 3 a Dist_villages -.047 .035 1.835 1 .176 .954 Dist_roads -.214 .063 11.552 1 .001 .808 Slope -.106 .063 2.814 1 .093 .899 Constant 1.477 .469 9.912 1 .002 4.381 Step 4 a Dist_roads -.241 .059 16.735 1 .000 .786 Slope -.100 .063 2.490 1 .015 .905 Constant 1.096 .368 8.875 1 .003 2.992

d. Model summary

-2 Log Cox & Snell R Nagelkerke R Step likelihood Square Square 1 140.526 .222 .302 2 140.547 .222 .302 3 141.661 .216 .293 4 143.547 .204 .277

43 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-2: Roads distance map used for generating a poaching risk map

44 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-3: Slope map used for generating a poaching risk map

45 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-4: Distance of poaching events from roads in ascending order

Poaching event Distance of poaching events from roads (meters) 1 55.42382371810 2 438.59490923600 3 540.96388768300 4 587.72304549900 5 663.27621046200 6 864.69890260800 7 1098.78025341000 8 1193.91596384000 9 1238.11201406000 10 1285.49059942000 11 1321.71487269000 12 1635.82523977000 13 1641.65918240000 14 1717.35578333000 15 1778.42636963000 16 1879.00075655000 17 1900.35595416000 18 1906.59287270000 19 1977.15526147000 20 2029.57779834000 21 2172.33045440000 22 2330.19075720000 23 2668.22533088000 24 2694.25481497000 25 2794.31327799000 26 2897.26843697000 27 2913.61890198000 28 2954.97663857000 29 3160.46945244000 30 3233.71825561000 31 3409.50921593000 32 3557.83015960000 33 3589.92568130000 34 4075.99923425000 35 4147.57000197000 36 4383.99047434000 37 4776.35178297000 38 4783.29659880000 39 4943.59077571000 40 5153.99538943000 41 6016.97790650000 42 6028.59274473000 43 6631.94970564000 44 7048.46686491000 45 7179.55272990000 46 7396.61262180000 47 7475.75049466000 48 10565.72168850000 49 10705.65480450000 50 17229.69527860000

46 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-5: Data sheet for vegetation cover

Sample number: Date GPS X Observer name: Y Vegetation Dominant species % Cover Other observations cover type

47 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-6: Data types used in the study

Several data types from different sources were used in this research. GIS layers were obtained from Tarangire National Park (TNP), land cover data were collected during the field work in TNP. The satellite imagery and the digital elevation model were acquired from ITC. All the datasets used were projected to the same geographic coordinate system of the study area (Projected Coordinate System: WGS_1984_UTM_Zone_37S, Projection: Universal Transverse Mercator GCS_WGS_1984, Datum: D_WGS_1984 Prime Meridian: Greenwich, Unit: Meters) to allow map compatibility for overlay and map calculations

Data Data type Source Aster imagery of August 2006 (15 m Raster ITC resolution) Digital elevation model (90x 90m) Raster ITC Roads layer Poly line - Tarangire National Park vector Park boundary Polygon- Tarangire National Park vector Settlement Point-vector Tarangire National Park Poaching events Pont-vector Tarangire National Park Pseudo absence points Point- vector Randomly generated using ArcGIS 9.2 in the study area Land cover data Point, with Field work Tarangire National description of Park the cover type

48 Identification and mapping risk areas for zebra poaching: a case of Tarangire National Park, Tanzania

Appendix 8-7: Sample of the patrol form used by park rangers in Tarangire national Park

Date Name of Signs of No. of No. of Species of No. of GPS location the area poaching poachers poachers animal animals patrolled arrested escaped killed killed

Remarks: