Species Composition, Relative Abundance and Habitat Relationships of Small Mammals at the University of Dodoma

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The University of Dodoma University of Dodoma Institutional Repository http://repository.udom.ac.tz

Natural Sciences Master Dissertations

2014 Species composition, relative abundance and habitat relationships of small mammals at the University of Dodoma

France, Sophia

The University of Dodoma

France, S. (2014). Species composition, relative abundance and habitat relationships of small mammals at the University of Dodoma (Master's dissertation). The University of Dodoma, Dodoma. http://hdl.handle.net/20.500.12661/1685 Downloaded from UDOM Institutional Repository at The University of Dodoma, an open access institutional repository. SPECIES COMPOSITION, RELATIVE ABUNDANCE AND HABITAT

RELATIONSHIPS OF SMALL MAMMALS AT THE UNIVERSITY OF

DODOMA

Sophia France

Dissertation Submitted in Fulfilment of the Requirements for the Degree of Masters of Science in Natural Resources Management of the University of Dodoma

The University of Dodoma

October, 2014

CERTIFICATION

The Undersigned certify that he has read and hereby recommends for acceptance by the University of Dodoma a dissertation entitled Species composition, relative abundance and habitat relationships of small mammals at the University of

Dodoma in partial fulfilment of the requirements for the degree of Master of Science in Natural Resources Management of the University of Dodoma.

…………………………..

Dr. Chrispinus D. Rubanza

(SUPERVISOR)

Date……………………..

DECLARATION

AND

I Sophia France, declare that this dissertation is my own original work and that it has not been presented and will not be presented to any other University for a similar or any other degree award.

Signature………………..

No part of this dissertation may be reproduced, stored in any retrieval system, or transmitted in any form or by any means without prior written permission of the author or the University of Dodoma.

ACKNOWLEDGEMENTS

Glory is to almighty God. Special thanks to my lovely husband Kazawadi for his funding, advice and encouragement throughout the completion of this work.

I appreciate the greater contribution of Dr. Ratnayeke, in supervision, advice, comments and material support during the study. I also realize the contribution of Dr

Rubanza in report writing and supervision. My sincere thanks as well go to Mr

David Brandenburg and Gasper for their assistance in trap setting, and data collection.

I also extend my appreciation to my classmates for their support, encouragement, advice and useful comments during data collection and report writing.

Lastly, my sincere thanks to the University of Dodoma administration for allowing me to carry out a study of small mammals in the campus.

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ABSTRACT

A study of the species composition, relative abundance and habitat relationships of small mammals was carried out in Dodoma region. A total of 77 sites on the

University of Dodoma campus were surveyed for small mammals between

December 2013 and March 2014. Small mammals were captured using 4 x 4 x 12 inches Sherman live traps. Seven species were captured. Poisson regression was used to identify habitat variables associated with the capture frequency of small mammals. Logistic regression was used to identify habitat variables associated with the presence or absence of the most common species. Models were developed based on six different habitat variables. The community of small mammals at the

University of Dodoma campus was dominated by rodents, which formed 94% of all captures (N = 167). The distribution of the two common small mammal species

(Aethomys chrysophilus and Pelomys fallax) was similar (p>0.05). Small mammal abundance varied significantly among the three major habitat types (p<0.0001), with grasslands and sparse thickets supporting higher abundances of small mammals than forest. Aethomys chrysophilus was the most frequently captured species comprising

70.6% of all captures. The best Poisson regression model indicated that the proximity to grassland and low canopy cover were the best predictors of small mammal abundance in the study area (∆AICc weight = 0.905). Proximity to grassland was the best predictor of presence of both Aethomys chrysophilus (∆AICc weight= 0.3572), and Pelomys fallax (∆AICc weight = 0.2358). Management aimed at boosting small mammal populations to sustain populations of local carnivores may seek to maintain grassland openings and edges in forest habitats. Also agricultural pest management for rodents might be aided by reducing grassland habitat around crop fields.

TABLE OF CONTENTS

CERTIFICATION ...... i DECLARATION AND COPYRIGHT ...... ii ACKNOWLEDGEMENTS ...... iii ABSTRACT ...... iv TABLE OF CONTENTS ...... v LIST OF FIGURES ...... vii LIST OF TABLES ...... viii LIST OF APPENDICIES ...... ix ABBREVIATIONS ...... x

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Background information ...... 1 1.2 Statement of research problem ...... 3 1.3: Objectives...... 3 1.3.1: General objective ...... 3 1.3.2: Specific objectives ...... 4 1.4: Research questions ...... 4 1.5: Significance of the study ...... 4

CHAPTER TWO LITERATURE REVIEW ...... 6 2.1 Theoretical literature review ...... 6 2.1.1: Definition of key terms ...... 6 2.1.2 Empirical Literature review ...... 11 2.1.2.1 Role of biodiversity ...... 11 2.1.2.2 Role of small mammals ...... 11 2.2 Diversity of order Insectivora, Rodentia and Macroscelidea in Tanzania ...... 15 2.2.1 Order Rodentia ...... 15 2.2.2 Order Insectivora ...... 16 2.2.3 Order Macroscelidea ...... 17 2.3 Habitats of small mammals ...... 23 2.4 Status of small mammals in Tanzania ...... 24

2.5 Conceptual frame work ...... 25 2.5.1 Independent variables...... 25 2.5.2 Intermediate variables ...... 25

CHAPTER THREE: METHODOLOGY ...... 27 3.1. Study Area ...... 27 3.1.1 Location...... 27 3.1.2 Climate ...... 27 3.1.3 Flora and Fauna species ...... 27 3.2 Data Collection and Analyses ...... 29

CHAPTER FOUR : RESULTS AND DISCUSSION OF THE FINDINGS ...... 37 4.1 Results ...... 37 4.1.1 Species composition and relative abundance of small mammals in major habitat types at the University of Dodoma ...... 37 4.1.2 Habitat features associated with capture frequency of small mammals ...... 43 4.1.3 Habitat features associated with the presence/absence of individual species of small mammals ...... 47 4.2 Discussion of the findings ...... 51 4.2.1 Species composition and relative abundance of small mammals in major habitat types at the University of Dodoma...... 51 4.2.2 Habitat features associated with capture frequency of small mammals...... 53 4.2.3 Habitat features associated with the presence/absence of individual species of small mammals ...... 55

CHAPTER FIVE: CONCLUSION, RECOMMENDATIONS AND AREAS FOR FURTHER RESEARCH...... 58 5.1 Conclusion ...... 58 5.2 Recommendations ...... 60 5.3 Areas for further study ...... 61 REFERENCES ...... 62 APPENDIX ...... 73

LIST OF FIGURES

Figure 1: Images of some species of order Rodentia (a) Acomys spinosissimus (b) Aethomys chrysophilus...... 16 Figure 2: Hedgehog (Atelerix albiventris)...... 17 Figure 3: Rufous elephant shrew (Elephantalus rufescens)...... 18 Figure 4: Conceptual frame works ...... 26 Figure 5: Grassland habitat at the University of Dodoma 2014...... 28 Figure 6: Sparse thicket at the University of Dodoma 2014...... 29 Figure 7: Forest at the University of Dodoma 2014...... 29 Figure 8: Specimens in a zip bag ready for measurement of body mass...... 31 Figure 9: Percentage of captures showing different species of small mammal community at the University of Dodoma campus 2014...... 39 Figure 10: Cumulative number of small mammal species in study sites at the University of Dodoma Campus December 2013 to March 2014...... 40 Figure 11: Comparisons of relative abundance of small mammals in forest, grassland and sparse thicket at the University of Dodoma, Tanzania, 2014...... 43 Figure 12: Small mammal abundance (capture efficiency) among 77 study sites. .... 44

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LIST OF TABLES

Table 1: Families, genera and species of Order Rodentia , Insectivora and Macroscelidea found in Tanzania...... 19 Table 2: Small mammal species caught in Sherman‘s live trap at the University of Dodoma campus December 2013 to March 2014...... 38 Table 3: Contgency test for differences in distribution of two common small mammal species (Aethomys chrysophilus and Pelomys fallax) across different habitat types...... 41 Table 4: Frequency of capture of two small mammal species across different habitat types. Expected frequencies were based on the proportion of sites sampled in each habitat...... 42 Table 5: Comparison of means and variances of all variables used in Poisson regression analyses...... 45 Table 6: Poisson regression model selection results to assess habitat variable associated with abundance of small mammals at the University of Dodoma, Tanzania, 2013-2014...... 46 Table 7: Model averaged parameter estimates for Poisson regression models testing the association between habitat variables and small mammal‘s abundance.47 Table 8: Logistic regression model selection results to assess habitat variable associated with presence/ absence of Veldrat (Aethomys chrysophilus) at the University of Dodoma, Tanzania, 2013-2014...... 48 Table 9: Logistic regression model selection results to assess habitat variable associated with presence/absence of the creek-rat (Pelomys fallax) at the University of Dodoma, Tanzania, 2013-2014...... 49 Table 10: Parameter estimates for Logistic regression models relating habitat variables to presence/ absence of the veld-rat (Aethomys chrysophilus) and creek-rat (Pelomy fallax)...... 50

viii

LIST OF APPENDICIES

Appendix 1: Small Mammal Sampling Sheet ...... 73 Appendix 2: Sheet for recording habitat variables of the site...... 74 Appendix 3: Small mammal species trapped at University of Dodoma campus 2013 - 2014 m...... 75

ABBREVIATIONS

AIC Akaike‘s Information Criterion

CI Confidence Interval

GPS Global Positioning System

IUCN International Union for Conservation of Nature

NBS National Bureau of Statistics

SM Small Mammals

TNW Tanzania National Website

UDOM University of Dodoma

URT United Republic of Tanzania

CHAPTER ONE

INTRODUCTION

1.1 Background information

Biodiversity is the variability among living organisms on the earth, including the variability within genes, within and between species, and the variability within and among ecosystems that constitute life on the earth (Rands et al., 2010). The conservation of biodiversity is very important because all life, including that of humans depends on a variety of organisms, their habitats, their genes and the complex web of interactions that sustain them (Sayer and Campbell, 2004; Kareiva and Marvier, 2007). Biodiversity helps to increase the capacity of ecosystem to provide goods and service that satisfy human needs, directly or indirectly (McGrady-

Steed and Morin, 2000). Biodiversity provides important goods such as food, fuel, fibres, building materials and medicines. More importantly, the services of biodiversity include the regulation of local and global climate by forests, prevention of floods and diseases, purification of water, nutrient cycles, plant pollination, soil formation, primary production (photosynthesis) as well as fulfilment, leisure, and aesthetics (McNeely et al., 1990; De Groot et al., 2002; Kareiva and Marvier, 2007;

Butchart et al., 2010).

Small mammals as part of biodiversity have great importance in the functioning of ecosystems (Leis et al., 2008). They are an integral part of food chain and serve as a major source of food for varieties of predators such as mammalian carnivores, birds of prey, and many species of snakes (Rosenzweig and Winakur, 1969; Greenwood,

1982; Mugatha, 2002). They also influence plant communities through seed predation, hence preventing the domination of few plant species in habitats

(Shenbrot et al., 1994; Leis et al., 2008). Some species of small mammals are pollinators while others are pioneers in new open habitats, following disturbances they become sources of seeds (Persson, 2007). Small mammal communities are highly sensitive to, and respond quickly to disturbance (Horváth et al., 2012). As small mammal communities are relatively easy to survey they have become important indicators of environmental health and faunal diversity (Barnett and

Dutton, 1995). They act as keystone species as well as pollinators (Persson, 2007), small mammals also facilitate the carbon cycle and energy flow and influence soil fertility (Mugatha, 2002). Small mammals moreover have particular educational and scientific values by enriching classroom experiences and providing model for field and laboratory research (Primack, 1993). On the other hand, some societies use small mammals as source of protein (Mugatha, 2002; Magige, 2012).

Most small mammals are not considered threatened species in IUCN Red list species

(IUCN June 2014). This may cause insufficiency in conservation of small mammals because smaller species receive little attention (Bill, 2000). Outside protected areas, human disturbance bring impacts to the population of many small mammal species resulting to local extinction (Horváth et al., 2012).

Construction activities taking place at the University of Dodoma campus could have caused dramatic changes to natural habitats, because construction always altars habitats (Geist and Lambin, 2000; Primack, 2006). Also unregulated exploitation of fuel wood from regenerating forest, burning of shrubs and young trees and clearing of natural forest around campus boundaries for corn cultivation progressively degrades remaining habitats. This trend have left most of habitats as islands surrounded by buildings, also amount of edges and their associated edge effects are

2 increasing. These changes may alter species composition in the small mammal community, where interior species that are sensitive to habitat alteration may decline while populations of species that do better in open or edge habitats may respond positively. For effective, coherent and serious conservation of small mammals, adequate and practicable information is needed.

1.2 Statement of research problem

There are numerous studies on small mammals in Tanzania. Most of these have been done in protected areas (Caro, 2002; Fitzherbert et al., 2007; Makundi et al., 2003;

Stanleyet al., 2007; Kiwia, 2009; Timbuka and Kabigumila, 2009; and Venance,

2010). Other studies have been done out of protected areas (Mulungu et al., 2008;

Carleton and Stanley, 2012; Byrom et al., 2014; Newmark et al., 2014).

Human activities such as construction, agriculture, and settlement affect community composition and population structure of small mammals (Geist and Lambin, 2000;

Primack, 2006; Kareiva and Marvier, 2007). Construction, agriculture and settlements have been taking place in and around the University of Dodoma campus.

The information on species composing small mammal community and their relative abundance in this altered environment were not vailable. The current study provides information on species of small mammals found in the campus irrespective of habitat fragmentation in the area. Also the study has offered information on habitat features associated with capture frequencies of small mammals in the area.

1.3: Objectives

1.3.1: General objective

This study was carried out based on general objective to identify species composition, relative abundance and habitat relationships of small mammals of 3

Order Rodentia, Insectivora and Macroscelidea at the University of Dodoma campus.

1.3.2: Specific objectives

The general objective was achieved by attaining the following specific objectives;

i. To identify the species composition and relative abundance of small

mammals in major habitat types at the University of Dodoma.

ii. To identify habitat features associated with capture frequency of small

mammals.

iii. To identify habitat features associated with the presence/absence of

individual species of small mammals.

1.4: Research questions

The following questions were answered to fulfil the above specific objectives.

1. What is the species composition and relative abundance of small mammals

found in the major habitat types at the University of Dodoma campus?

2. Are there associations between specific habitat characteristics and occurrence

of individual species of small mammals?

3. Are there associations between specific habitat characteristics and the overall

presence/absence of individual species of small mammals?

1.5: Significance of the study

Finding from this study documented species of small mammals found in the university of Dodoma campus. Correct identification and documentation of species and their habitat requirements is important for all types of management of individual species or populations and their habitats in any area (Sheil et al., 1999; Wells, 1999).

Under circumstance whereby small mammals act as pests or as disease vectors, one 4 can manage habitats to control small mammal abundance. Effective management of vectors including small mammals requires knowledge on biology, ecology and behavior of concern organisms (Flint and Dreistadt, 1998; Mulungu et al., 2014).

Therefore, inventory of small mammals at the University of Dodoma Campus provides a baseline data on small mammal population.

In a research environment, good research records are essential as foundation of future researches (Crowell et al., 2007). The findings of this study provide a foundation for further studies related to small mammals in semi-arid areas.

Furthermore, habitat configurations that support high small mammal abundance may also support larger populations of carnivorous species (Stevens, 2012; Prevedello et al., 2013). Therefore, results from the current study represent an important foundation on small carnivore in the areas which have been recorded to hold small mammals.

CHAPTER TWO

LITERATURE REVIEW

2.1 Theoretical literature review

2.1.1: Definition of key terms

2.1.1.1 Biodiversity

Biodiversity is the term used to describe the variety of life found on Earth and all of the natural processes; it includes ecosystem, genetic and cultural diversity, and the connections between these and all species (Rands et al., 2010). The different aspects of biodiversity all have a very strong influence on each other (Magurran and

Magurran, 1988). Species diversity is determined not only by the number of species within a biological community (species richness) but also by the relative abundance of individuals in that community.

2.1.1.2 Species diversity

Species diversity is a measure of the diversity within an ecological community that incorporates both species richness (the number of species in a community) and the evenness of species' abundances. Species diversity is one component of the concept of biodiversity (Guefack, 2007). Species diversity is influenced by species richness and the relative abundance of each individual species (Gotelli and Colwel, 2001). All else being equal, communities with more species are considered to be more diverse.

For example, a community containing ten species would be more diverse than a community with five species. General species diversity is explained by two factors; species richness and species evenness (Tokeshi, 1990).

2.1.1.3 Species richness

Species richness is the number of different species represented in an ecological community, landscape or region (Magurran, 1988). Species richness is simply a count of species and is expressed as an integer; it does not take into account the abundance of the species or their relative abundance or distribution. Normally the species richness of an area is affected not only by the number of individuals but also by the heterogeneity of the sampling site (Duffy et al., 2007). If individuals are drawn from different environmental conditions (or different habitats), the species richness of the resulting set can be expected to be higher than if all individuals are drawn from similar environments (Rands et al., 2010)

Species richness is often used as a criterion when assessing the relative conservation values of habitats or landscapes (Verberk, 2011). However, species richness is blind to the identity of the species. An area with many endemic or rare species is generally considered to have higher conservation value than another area where species richness is similar, but all the species are common and widespread (Michael and

Barry, 1996).

2.1.1.4 Species abundance

Species abundance is an ecological concept referring to the relative representation of a species in a particular ecosystem; it explains on how common a particular species is in a given community (Magurran, 1988). Species abundance correlates to incidence which is the frequency with which the species occurs at all in a sample.

Species abundance is applied to mammal species as well as birds, insects, and other creatures. It can even be applied to plants. Looking at species abundance and other

7 aspects of biodiversity help scientists to figure out what is going on within a particular ecological environment (Wright, 1991).

In practical terms, studies on species abundance might lead to a particular type of animal being labelled as an endangered species (Jonathan and Janneke 2009). If the population estimates are low enough, the species might be labelled a critically endangered species. This will generate some specific laws in many nations protecting the remaining population from hunting, poaching or even habitat encroachment. In general, species abundance research helps to ensure that some of the world‘s most interesting animals and creatures continue to exist (Gotelli and

Colwel, 2001).

2.1.1.5 Small mammals

Mammals are various warm-blooded vertebrate animals of the class mammalia whose young feed on milk that is produced by the mother's mammary glands (Rowe,

1988). Unlike other vertebrates, mammals have a diaphragm that separates the heart and lungs from the other internal organs, red blood cells that lack a nucleus, and usually hair or fur (Kemp, 2005). All mammals but the monotremes bear live young.

Mammals include rodents, cats, dogs, ungulates, cetaceans, and apes (Morris, 1987).

The term small mammal includes those species of class mammalia with average adult body weights less than 2 kg when adult (Kiwia, 2009). There are different species of small mammals some are rodents, insectivores, shrews, carnivores and primates (Nowak, 1999).

2.1.1.6 Habitat

A word habitat has a Latin origin which means "the place of residence"

(Abercrombie et al., 1966). It refers to the natural environment of the animal/plant where they normally grow or live (Stephanie et al., 2006). Natural environment includes physical as well biological factors which supports life in that area (Shenbrot et al., 1994). Habitat can be classified as aquatic habitat and terrestrial habitat.

Terrestrial habitats are land habitats, like forests, grasslands, deserts, shorelines, and wetlands and also the man-made habitats, like farms, towns, and cities, and habitats that are under the earth, like caves and mine (Stephanie et al., 2006). Aquatic habitat includes oceans, rivers, lakes seas, dams and springs (Gotelli and Colwell, 2001). On the other hand a microhabitat is the small-scale physical requirements of a particular organism or population.

2.1.1.7 Grassland

Grassland is a large open area of country covered with grass, especially one used for grazing. The area is normally grassy, windy, and partly dries (Gibson, 2009). There are two types of grasslands: tropical grassland (savannah) a type of grassland which is hot all year with wet seasons that bring torrential rain .Temperate grasslands are those with hot summers and cold winters. The evaporation rate is high, so little rain makes it into the rich soil. It is located north of the tropic of cancer and south of the tropic of Capricorn (Wessells and Hopson, 1988). Deep rooted grasses dominate the flora in grassland; there are very few trees and shrubs in grassland, less than one tree per acre. Many animals, including countless small invertebrates and herds of large herbivores, are sustained by the high primary productivity of grasslands

(McNaughton, 1985).

2.1.1.8 Forest

Forest is a complex ecosystem in which trees are the dominant life form (Francis,

2010). Tree-dominated forests can occur wherever the temperatures rise above 10°C in the warmest months and the annual precipitation is more than 200 mm. They can develop under various conditions within these limits (Philip, 2003).

The kinds of soil, plant, and animal life differ according to the extremes of environmental influences (James, 2004). The forest is nature's most efficient ecosystem, with a high rate of photosynthesis affecting both plant and animal systems in complex org,anic relationships (Myers, 1996; Beer et al., 2003). The living parts of a forest include trees, shrubs, vines, grasses and other herbaceous

(non-woody) plants, mosses, algae, fungi, insects, mammals, birds, reptiles, amphibians, and microorganisms living on the plants and animals and in the soil.

These interact with one another and with the non-living part of the environment including the soil, water, and minerals, to make up what we know as a forest

(Mather, 1999).

2.1.1.9 Sparse thicket

Sparse thickets are areas with thinly scattered trees, bushes, and a ground cover of grasses and other herbaceous plants usually found in the many small openings created by the lack of canopy cover. In some places the area is made up by mature vegetation type and in other places it is the result of degradation of former forest or woodland by logging, overgrazing, or disturbance by fire (Bowman, 2003).

2.1.2 Empirical Literature review

2.1.2.1 Role of biodiversity

Biodiversity boosts ecosystem productivity where each species, no matter how small it is, has an important role to play. Biodiversity plays a role in atmospheric regulation (regulation of atmospheric chemical composition), climate regulation (at global and local levels), disturbance regulation (storm protection and flood control), erosion control and sediment retention, soil formation (weathering of rock and accumulation of organic material) and nutrient cycling (Gotelli and Colwell, 2001).

Biodiversity is also important in waste treatment (recovery and breakdown of toxics and nutrients), pollination (provisioning of pollinators for reproduction of plants) biological control, refugia (habitat for harvested species), food production , raw materials (production of timber, fuel and fodder), genetic resources (sources of unique biological materials for agriculture, medicine, and the like), recreation

(providing opportunities for recreational activities) providing of aesthetic beauty and support of diverse human cultures (McGrady and Morin, 2000; Kareiva and Marvier,

2007; Butchart et al., 2010).

2.1.2.2 Role of small mammals

2.1.2.2.1 Ecological role

Although small mammals live their lives unnoticed by most of people, they contribute to the overall variety or diversity of life on the earth, they are an important part of an ecosystem as primary consumers and as indicators of ecosystem stability

(Rosenzweig and Winakur, 1969). After disturbances such as fire, pioneering small mammals may be important seed dispersers for plant regeneration, also small mammals increase vegetation decomposition rates and they are more efficient than both ungulates and insects at mineralizing organic matter (Shenbrot et al., 1994). 11

Small mammals are also prey for many larger mammals, birds and reptiles. More broadly, niche separation of different species of small mammals on the forest floor may be an indicator of the number of available trophic pathways in a certain ecosystem (Ricklefs and Schluter, 1993).

Certain small mammals are plant pollinators. For example autumn crocus

(Colchicum autumnale) is a plant with dull-coloured floral parts that are typically pollinated by rodents. Autumn crocus is a medicinal plant. It contains the alkaloid colchicines which are used pharmaceutically to treat gout and familial Mediterranean fever. Pollination in this plant depends entirely on rodent species such as namaqua rock mouse (Aethomys namaquensis), pygmy mouse (Mus minutoides) hairy-footed gerbil (Gerbillurus paeba) and cape striped field mouse (Rhabdomys pumilio). There is evidence that, autumn crocus sets seeds only when cross-pollinated and that rodents are the only species to aid pollination (Persson, 2007).

2.1.2.2.2 Economic benefits of small mammals

Economically, small mammals may be useful to local human populations as sources of food during times of crop failure, or even when food conditions are good. For example, Mugatha ( 2002) documented the use of Cricetomys gambianus (giant rat) and cane rats (Thryonomys swinderianus) as important sources of protein in the East

African region including Kenya. In some places farmers do not use poison for reducing rodent problems because they sell or consume the rodents they catch

(Schiller et al., 1999). Thus small mammals serve as a source of food and income for trappers, and rural people are able to earn income through the sale of hunted small mammals like rabbits (Lepus victoriae), (Magige, 2012).

Negative effects of small mammals

2.1.2.3.1 Small mammals as pests

Damage by small mammals to crop plants is a common phenomenon in all parts of the world. Some crops, mainly cereals, legumes and cowpeas, are susceptible to attack during their entire life span in the field, while some roots and tubers are commonly attacked only at maturity (Schuller et al., 2007). Small mammals attack crop plants mainly to obtain food but in a few instances the roosting and nesting activities of some species may cause defoliation and serious mechanical damage resulting in the failure of the trees to produce fruits (Brown and Singleton, 1999).

The most important group of pestiferous mammals is the rodents, which include rats, mice, squirrels and dormice (Singleton and Petch, 1994). Rats and mice consume sown seeds and ripe fruits and also cut young and feeble stems of both crop plants and tree seedlings. More than 10 species of rats and mice are pests of 20 or more crop plants (Brown and Singleton, 1999). Most rat and mice pests belong to the genera Rattus, Mastomys, Arvicanthis, Dasymys, Lophuromys, Lemniscomys,

Praomys, Uranomys, Hybyomys and Mus. Gerbils and giant rats (Cricetidae) are able to burrow extensively and also cause losses similar to rats and mice (Schiller et al.,

1999).

The cane rat or ground-hog (Thryonomyidae) are the most important mammalian pest in the field, damaging over twelve varieties of crop plants by cutting the stems and consuming the soft inner tissues and fruits (Brown and Singleton, 1999).

The porcupine is a pest of roots and tubers. Squirrels strip the bark of trees but more importantly consume ripe fruits. The red-legged ground squirrel, Xerus erythropus,

13 causes damage similar to those caused by rats and mice. Dormice are pests of ripe cocoa pods and beans (Woods and Kilpatrick, 2005).

2.1.2.2.3 Small mammals as disease vectors

In relation to health effects to human beings and their livestock, small mammals are blamed as vectors of various bacteria and viruses that cause diseases to humans. For example, rodents such as mice and rats are vectors of the virus causing sindbis fever.

Also, boutonneuse fever caused by an obligate intracellular bacterium Rickettisia conorii affect dogs and other house pets; studies have shown that wild rodents and other mammals are reservoir of this pathogen (Rovery, 2008). Wild rodents are also reservoir of tick-born relapsing fever that affects livestocks (Schwan, 2002). Wild rodents such as Rhombomys opimus, Meriones erythrourus, M hurricanae, M. meridiamus are reservoirs of cutaneous leishmaniasis disease (Primack, 1993).

Plague is a deadly infectious disease which is caused by the enterobacteria Yersinia pesti that circulates in animal reservoirs, particularly rodents (Kilonzo et al., 2005).

A species of shrew (Sylvisorex ollula) has been identified as having Ebola viruses in their RNA and DNA (Morvan et al., 1999).

Rodents are major reservoirs of human pathogens such as Leptospira spp., the bacteria responsible for leptospirosis. People usually become infected through exposure to water contaminated by the urine of infected animals, mainly rats and mice. This disease occurs in most parts of the world (Kohn, 2010). Thus small mammals can pose a significant risk to human health.

2.2 Diversity of order Insectivora, Rodentia and Macroscelidea in Tanzania

A brief account of order Insectivora, Rodentia and Macroscelidea is presented below.

2.2.1 Order Rodentia

The order comprises small gnawing animals called rodents. These mammals are characterized by a single pair of continuously growing incisors in each of the upper and lower jaws that must be kept short by gnawing (Hutter et al., 2005). Common rodents include mice, rats, squirrels, porcupine, beavers, guinea pig and hamsters

(Horvath et al., 2012). The size of rodents varies greatly depending on species; in

Africa, rodent body mass can vary from 3 g (Mus haussa) to as much as 20 kg in some porcupines (Kingdon, 2013).

Rodentia is the largest order of mammals, encompassing 2,277 of 55,422 living mammal species or approximately 42% of worldwide mammalian biodiversity

(Wilson and Reeder, 2005). Rodents are indigenous to every continent except

Antarctica and inhabit most small to large land bridge and oceanic islands. Of 15 families of the order rodentia 7 are endemic to Africa, they are also represented in

Tanzania (Wilson and Reeder, 2005).

Most rodents feed on plants and may be broadly categorized as herbivores

(vegetation eaters), granivores (those eating seeds and nuts) or frugivores (those eating fruits and flowers). Others are omnivores, feeding on plants and animals, usually arthropods. Very few feed exclusively on insects (Nowak, 1999).

Figure 1: Images of some species of order Rodentia (a) Acomys spinosissimus

(b) Aethomys chrysophilus.

Source: Field Data, 2014.

2.2.2 Order Insectivora

The name of the Order Insectivora comes from a latin language where insectum means "insect" and vorare means to eat (Hutter et al., 2005). They are named after their tendency to eat insects, but they also eat other invertebrates such as worms and even some vertebrates (fish, lizards) (Hutter et al., 2005). Organisms within this order are usually small and have long narrow snouts and five-clawed digits on each limb. Insectivorans occupy a variety of habitats and are widely distributed, globally: they may be terrestrial, fossorial or semi aquatic (Nowak, 1991). They can vary greatly in colouration, but typical wild specimens have brown or grey fur or spines with white or cream colored tips. Most insectivores lack a separate opening for the genitals and anus, and instead have a cloaca, which serves as the genital, urinary, and fecal system. Hence they are considered to be primitive mammals (Howard, 2001).

Insectivores have an excellent sense of smell and touch, but have poor sight and hearing. This order has 7 families with 65 genera and 390 species worldwide.

Figure 2: Hedgehog (Atelerix albiventris).

Source: Field Data, 2014.

2.2.3 Order Macroscelidea

Macroscelidea is an order which comprises the elephant shrews (Leis et al., 2008)

They are small mammals with brownish gray coats. They vary in size from about 10 to almost 30 cm, and from just under 50 g to over 500 g (Kingdon, 2013). All are four-legged with mouse-like tails, and rather long legs for their size which are used to hop and jump about like rabbits (Butler, 1995). Although the size of the trunk varies from one species to another, all are able to twist it about in search of food.

They can be found in almost any type of habitat, from the Namib Desert to boulder- strewn outcrops in South Africa to thick forest (Kohn, 2010). All elephant-shrews eat mainly insects, spiders, centipedes, millipedes, and earthworms. An elephant- shrew uses its nose to find prey and uses its tongue to flick small food into its mouth, much like an anteater. Currently, there are approximately 20 species of rat sized shrews (Douady et al., 2003).

Figure 3: Rufous elephant shrew (Elephantalus rufescens).

Source: Field Data, 2014.

Table 1: Families, genera and species of Order Rodentia , Insectivora and Macroscelidea found in Tanzania.

Order Family Genus Species Funisciurus F. carruthersi Heliosciurus H. gambianus, H. mutabilis, H. rufobrachium, H. undulatus Paraxerus P. boehmi, P. cepapi, P. lucifer,, P. palliatus,, P. vexillarius, Sciuridae P. flavovittis , P. ochraceus, Protoxerus P. stangeri Sciurus S. carolinensis Xerus X. rutilus Gliridae (Dormice) Graphiurus G. kellen, G. microtis, G. murinus Acomys C. spinosissimus,C. wilsoni Aethomys A. chrysophilus, A. hindeni, A. kaiseri RODENTIA Arvicanthis A. nairobae, A. niloticus Dasymys D. incomtus Muridae Gerbilliscus G. boehmi, G. inclusus, G. kempi, G. leucogaster, G. nigricaudus, G. robustus, G. validus Gerbillus G. harwood, G. pusillus

Table 1 continue: Families, genera and species of Order Rodentia , Insectivora and Macroscelidea found in Tanzania.

Order Family Genus Species Grammomys G. dolichurus, G. ibeanus, G. macmillani, G. kuru Hylomyscus G. denniae Lemniscomys L. macculus, L. rosalia, L. striatus, L. zebra Lophuromys L. flavopuntatus, L. sikapus Mastomys M. natalensis,M. pernanus Mus M. triton Mylomys M.dybowskii Myomyscus M. brockmani RODENTIA Muridae Oenomys O. hypoxanthus Otomys O. angoniensis, O. denti, O. lacustris, O. tropicalis, O. typus Praomys P. delectorum, P. jacksoni Rattus R. norvegicus, R. rattus Rhabdomys R. pumilio Taterillus T. emini

Table 1 continue: Families, genera and species of Order Rodentia , Insectivora and Macroscelidea found in Tanzania.

Order Family Genus Species Thallomys T. loring, T. paedulcus Muridae Uronomys U. ruddi RODENTIA Zelotomys Z. hildegardeae Anomaluridae Anomalurus A. derbianus Idiurus I. macrotis Bathyergidae Heliophobius H. argenteocinereus Hystricidae Hystrix H. africaeaustralis, H. cristata Thryonomyidae Thryonomys T. gregorianus, T. swinderianus INSECTIVORA Tenrecidae Potamagole P. velox Chrysochloridae Chrysochloris C. stuhlmanni C. allex, C. elgoniu, C. sansibarica, C. jacksoni, C. xantippe, Crocidura C. viaria, C. bloyeti, C. telfordi, C. monax, C. usambarae, C. Soricidae tansania, C. montis Suncus S. murinus, S. varilla Sylvisorex S. howelli, S. granti, S. johnstoni Myosorex M. geata, M. blarina

Table 1 continue: Families, genera and species of Order Rodentia , Insectivora and Macroscelidea found in Tanzania.

Order Family Genus Species Elephantalus E. rufescens MACROSCELIDAE Macroscelididae Rhynchocyon R. cirnei, R. petersi Petrodromus P. tetradactylus Source (Kingdon, 2013).

2.3 Habitats of small mammals

The habitats of small mammals are highly variable and numerous, from arid deserts to the arctic tundra (Morris, 1996). Small mammals live primarily on land, typically at ground level or beneath while others are arboreal. Some species are aquatic

(Kingdon, 2013). They can also survive in a wide range of habitats from tropical rainforests to temperate marshes, from thick forests to open fields, and from sea- level to mountainsides up to 14,760 feet (Primack, 1993). Some live close to humans in urban areas and even houses, while others make their home deep inside wetlands and rainforests. Small mammals can be found in almost every habitat (Saetnan and

Skarpe, 2006).

Habitat selection, which affects nearly all of an individual's subsequent choices concerning food, shelter, risk of predation, and mating opportunities, can be viewed as a hierarchical process in which organisms first choose a general place to live (a habitat) and then make subsequent decisions about the use of different patches or microhabitats (Orians and Wittenberger, 1991).

The spatial distribution of patches within a habitat affects the ability of different species to exist. A necessary requirement for existence is that each species possesses a habitat or patch in which it is the most efficient forager (Kotler and Brown, 1988).

Most natural assemblages comprise competing species that range from those with narrow habitat requirements (specialists) to others with wider habitat requirements

(generalists), with specialists competitively excluding less well-adapted species

(Morris, 1996). Brown (1996), demonstrated that widespread habitat generalists might coexist with competing habitat specialists if they exploit the shared environment at a larger spatial scale. Because habitat selection can occur anywhere

23 along a continuum of spatial scales it is often useful to investigate habitat selection at several scales, from microhabitat to macro habitat (Kotler and Brown, 1988).

2.4 Status of small mammals in Tanzania

Frequent studies, especially on inventories of small mammals have been conducted in different parts of Tanzania. A study of Stanley (1998) in South pares mountains documented 28 species of small mammals of which 15 species were rodents. The latter (2000) carried a study in Gonja forest reserve where nine species of small were recorded. Nevertheless Caro (2000) did a study on small mammals inside and outside Katavi national park; a study documented 9 species of small mammals representing two orders, Rodentia and Insectivora. A survey of small mammals done in Western Usambara Mountains (Makundi et al., 2003) recorded eleven species of small mammals. Stanley (2005) recorded 8 species of small mammals in Mikumi national park. The latter (2005) also recorded 11 species of small mammals in

Kwamgumi forest reserve while Fitzherbert et al., (2007) documented nineteen species of Rodentia, one species of Macroscelidea, and three species of Lipotyphla in Katavi ecosystem. Still. Stanley (2007) did an intensive inventory of the small mammals in Mikumi national park, the study documented two species of insectivores, one species of bat, and five species of rodents. Mulungu et al., (2008) surveyed small mammals in Kilimanjaro national park; the study recorded 16 species of rodents and four species of shrews. On the other hand a study of Timbuka and

Kabigumila (2009) in Serengeti kopjes documented 18 species of small mammala whereas a study of Kiwia (2009) recorded 11 small mammal species in Zaraninge forest in which rodents were accounting for 89.3% of the total catch and insectivores

10.7%.

2.5 Conceptual frame work

Habitats of small mammals are affected by different variables, which can be grouped into dependent, intermediate, and independent variables.

2.5.1 Independent variables

Anthropogenic activities such as agriculture, settlements (construction of buildings, water pipe lines, and roads) and tree harvesting are some of independent variables that directly alter the size and nature of any habitat (Gotelli and Colwell, 2001). Any change in natural habitat (habitat alteration) has impacts on demography, population structure, spatial range of species and community composition of organisms in an area (Cooper et al., 2008).

2.5.2 Intermediate variables

These are variables that explain a relation between other variables (Nature and size of habitat are two intermediate variables that links independent variables

(anthropogenic activities) to dependent variable (availability of resources in the habitat). Alteration of nature and size of habitats (habitat fragmentation, edge effect, habitat loss, accumulation of wastes, pathogens and disease) affects resource availability (food, shelter, hiding environment and mating opportunities) in habitats

(Orians and Wittenberger, 1991).

2.5.3 Dependent variable

These are variables which are affected by independent variables; they are sometimes called status of the effect because they can be changed (Cooper et al.,

2008).Availability of resources (amount of food, space for shelter and reproduction, cover and hiding environment) for organisms in habitat are determined by the extent of human activities in such habitat. In most cases anthropogenic activities alters the

25 nature and size of habitat, this alteration directly reduces amount of resources (space, food and cover) in the habitat.

Independent Variable Intermediate Dependent variables

 Agriculture variables  Food availability

 Settlements  Nature of habitat  Availability of Space for

 Tree harvesting  Size of habitats mate and settlement.

 Construction  Availability of cover and hiding .environment

Figure 4: Conceptual frame works

CHAPTER THREE

METHODOLOGY

3.1. Study Area

This study was carried out at the University of Dodoma campus which is 7 km south of Dodoma town. The campus covers an area of about 600 hectare (UDOM, 2007).

Vegetation of an area can be grouped into three major types of vegetation: grasslands, forest, and sparse thicket.

3.1.1 Location

Dodoma region is 41,310 km2 and forms about 5% of the Tanzania mainland. It lies at 4 to 7° S and 35 to 37°E. It is situated on a plateau at an average elevation of approximately 1000 m (NBS, 2007). The region has a population of 2,083,588 inhabitants (URT, 2012).

3.1.2 Climate

The average rainfall is about 570 mm, and approximately 85% of this rain falls in the months between December and April. Temperature in the area varies according to altitude but generally the average maximum and minimum is 31and 18˚C, respectively (NBS, 2007). Temperature is always high during the day time and low at night time in most of the months in a year.

3.1.3 Flora and Fauna species

UDOM campus is dominated by natural vegetation and in areas close to buildings there are exotic plants, which are grown as ornamentals. Most of the vegetation of the study area represents the vegetation of central Tanzania. The area is composed of a mixed association of grass species and herbs. The common grass species include;

Urochloa trichopus, Dactyloctenium spp, more than five species of Aristida , 27

Eragrostis spp. and Chloris spp. Perennial grass species found in isolated areas are

Cynodon spp., Cenchrus ciliaris, Hyparrhenia spp., Panicum spp, and Digitaria setivala. Annual herbs included Astripomoea spp, Crotalaria spp., Commelina spp.,

Cleome spp., and Ipomea spp. The common perennial weeds include Solanum pandoroeforme, Sida grewiodes, and Tephrosia incana while some of common tree species are Cassia spp., Croton spp., Acacia spp. (Kitalyi and Kabatange, 1986). The area also support a number of wild animals that are able to survive well in dry regions with less vegetation, inadequate water, high population of people moving every time and fragmented habitats.

Figure 5: Grassland habitat at the University of Dodoma 2014.

Source: Field Data, 2014.

Figure 6: Sparse thicket at the University of Dodoma 2014.

Source: Field Data, 2014.

Figure 7: Forest at the University of Dodoma 2014.

Source: Field Data, 2014.

3.2 Data Collection and Analyses

3.2.1 To identify the species composition and abundance of small mammals in

major habitat types at the University of Dodoma campus.

A total of 77 sites were sampled for small mammals over a four months period from

December 2013 to March 2014. Survey sites were selected randomly in the field with the aid of a GPS. The sites were located in three major habitat types (forest, sparse thicket and grassland).

Small mammals at each site were trapped using 5 locally made Sherman-style live traps (4 x 4 x 12 inches) baited with a mixture of ground roasted maize and peanut butter mixed with sunflower oil and roasted ground small fish (Stanley et al., 2007;

Timbuka and Kabigumila, 2009). A combination of baits was used because different species get attracted to different types of baits (Bond et al., 1980).

Traps were randomly placed at each site and spaced at an interval of 3 to 5 m from one another. The criteria for placement depended on trails of small mammals and for safety of traps at a site. A minimum distance of 100 m between adjacent sites as described in (Gibson et al., 2004) was used. This was because most small mammals have small home ranges ranging from75 to 100 m (Abramson et al., 2006). Traps were set for 5 nights at a site and were checked twice a day, early in the morning immediately following sunrise and in the late afternoon (Fitzherbert et al., 2007).

Photos were taken of each new species together with collection of voucher specimens. Photos, physical characteristics, including measurements of body mass, head body length, tail length, and pelage characteristics, were used to identify individuals to species level with the aid of reference guides (Nowak, 1999; Kingdon,

2013). Voucher specimens of each species were euthanized to verify identification in the laboratory.

Figure 8: Specimens in a zip bag ready for measurement of body mass.

Source: Field Data, 2014.

Row x column contingency test was used (Sokal and Rohlf, 1995) to assess whether the proportions of different small mammal species captured (recaptures excluded) were independent of habitat type (grassland, forest, sparse thicket).

Derivation of expected frequencies followed the model;

...... (1)

th th Where Eij was the expectation for the i row and j column of the contingency table,

th th Ri = i row total, Cj = j column total, and N = total captures. Calculations were performed in Microsoft Excel (version 2007).

Pearson χ2 test of association was used to assess the distribution of the most common small mammal species by habitat type. Expected frequencies were adjusted according to the proportion of sites sampled in each habitat. The null hypothesis was

31 that frequency of captures of any one species was similar among the different habitat types.

Kruskall Wallis test (Sokal and Rohlf, 1995) was used to assess variation in small mammal abundance among habitat types and test the null hypothesis that small mammal abundance was similar in all three habitats.

...... (2)

Where: ni= the number of observations in group i rij= the rank (among all observations) of observation j from group i

N = the total number of observations across all groups

...... (3)

...... (4)

r is the average of all the ri j

If the test indicated significant variation in small mammal abundance among habitat types, pair- wise tests between habitats were conducted using a Mann-Whitney U test (Sokal and Rohlf, 1995).

……………… (5)

n1 is the sample size for sample 1, and

R1 is the sum of the ranks in sample 1.

Calculations were performed using Program R (version 2.14.1; R Development Core

Team 2007) statistical software.

3.2.2 To identify habitat features associated with capture frequency of small

mammals

Capture success, (the dependent variable), was used as an index of abundance.

Selection of habitat variables for measurement considered aspects of the environment that consideration on possible determinants of small mammal abundance and habitat requirements.

Measurements of habitat variables such as distance of a sample site to a grass land edge, creek, building, or a large (> 16 m2) rock outcrop, elevation and canopy cover were made either directly in the field or a combination of direct field measurements and satellite imagery. Distance measurements from a study site to a nearby creek, building, rock and grassland were obtained directly by entering the coordinates of study sites into a current satellite photo of UDOM on Google Earth Maps and measuring Euclidean distance using the ruler function. Elevation of the site was obtained by using a GPS.

For canopy cover, a photo of the canopy above each trap was taken by placing a digital camera 6 inches above the trap and taking a 1920 x 1080 pixel image of the area directly above the trap. The percentage canopy cover above each trap was obtained by overlaying a 6 x 4 grid over the photo and counting the cells that were at least 50% covered by canopy. Percentage canopy cover was obtained by dividing this number by the total (24) and multiplying by 100 (Lauri et. al., 2006). Average

33 canopy cover per site was obtained from the mean obtained from the 5 traps at each site.

The habitat variables (distance of the site to grassland, to creek, to buildings, rocky outcrops, elevation above sea level, and canopy cover were selected. The parameters were considered to be important sources of food, cover, and reproduction sites for small mammal species in an area.

Large rock outcrop (> 16 m2) was selected because it may provide cover from predators, or nest/den sites for small mammals. Several studies (Prevedello et al.

2013), revealed the presence of unique fauna species which prefer to live in elevated habitats and near the rock out crops. Distance of the site to the edge of grassland was also selected as small mammal‘s abundance predictor because grassland is rich in food, provides cover, and nesting sites, and therefore associated with abundance of small mammals.

Environmental variables (distance of the site to creek, grassland, buildings, rocky outcrop) were converted to ordinal scale (1, 2, 3, 4, or 5) except elevation. Canopy cover was broken into an ordinal scale where 1 – 20% = 1, 21 – 40% = 2, 41 – 60%

= 3, 61 – 80% = 4, and 81 – 100% = 5. Distance of the site to a near building, grassland, creek and > 16 m2 rock outcrop was also broken into an ordinal scale where 1 - 25 m = 1, 26 - 50 m = 2, 51-75 = 3,76 – 100 = 4 and > 100 m = 5

(Fitzherbert, 2007).

Poisson regression (Jones et al., 2002) was used to evaluate the effect of one or more habitat variables (independent variables) on the abundance of small mammals (the dependent variable) at a site.

Analysis of 10 models using different combinations of the habitat variables was done and Akaike‘s information criterion adjusted for small sample size (AICc) was used to examine the evidence for competing models (Burnham and Anderson, 2002).

Program R (version 2.14.1 Development Core Team 2007) statistical software was used to perform Poisson regression. A given Akaike weight (AICc weight) was used as the evidence in favour of the best model out of a set of models under consideration. If the highest ranking models differed by a ∆AICc of less than 2, 95% confidence intervals of model-averaged parameter estimates were used to identify useful variables (Eric-Jan and Simon, 2004).

3.2.3 To identify habitat features associated with the presence/absence of

individual species of small mammals

Because not all of the individuals were marked during the trapping sessions, and to avoid potential bias due to trap happy animals, a presence/absence approach was selected for modelling individual species, rather than the use of abundance data alone. In this case, six predictor variables (distance of a sample site to grassland, to creek, to building, to rock, percentage canopy cover and elevation) were modelled using logistic regression, with presence or absence of each individual species as the dependent variable.

Assessment of those species detected in at least five sites was done: the veld rat

(Aethomys chrysophilus) and creek rat (Pelomys fallax) were considered. Other species (Paraxerus cepapi, Acomys spinosissimus, Lemniscomys zebra, Atelerix albiventris and Elephantulus rufescence) were caught in few sites and were therefore omitted from this analysis. A question was whether presence of Veld rat or a Creek

35 rat (dependent variable) in an area can be predicted by environmental variables (site distance to grass land, distance to creek, distance to building, distance to rock outcrop, canopy cover and elevation of the site above sea level). Therefore 10 models were developed using logistic regression to identify habitat predictors of the presence/absence of veld rats, and creek rats in the study area.

Evaluation on each model using AICc was done. In the first stage of model selection, the model with the lowest AICc value was selected as the best model because it was considered to be a good predictor of dependent variables (Burnham and Anderson,

2002). When the highest ranking models differed by a ∆AICc of less than 2, 95% confidence intervals (CI) of model-averaged parameter estimates were used to identify useful variables. Analyses were performed using program R (version 2.14.1;

R Development Core Team 2007) statistical software.

CHAPTER FOUR

RESULTS AND DISCUSSION OF THE FINDINGS

4.1 Results

4.1.1 Species composition and relative abundance of small mammals in major

habitat types at the University of Dodoma

Results on species composition and relative abundance are indicated in Table 2. A total of 77 sites were sampled for small mammals around the University of Dodoma campus from December 2013 to March 2014. A sum of 167 individual of small mammals from Orders Rodentia, Insectivora and Macroscelidea were caught in 385 trap nights. Seven species were obtained (five rodents, 1 Insectivore and 1 species of

Macroscelidea). Aethomys chrysophilus dominated the community of small mammals in the campus, followed by Pelomys fallax. Other species were represented in few numbers. Figure 9 indicates percentage captures of species.

Table 2: Small mammal species caught in Sherman’s live trap at the University of Dodoma campus December 2013 to March 2014.

Habitat Order Family Scientific name Common name Forest Grass land Sparse Thicket Total Muridae Aethomys chrysophilus Red Veld rat 13 67 38 118 Muridae Pelomys fallax Creek groove- toothed swamp rat 1 27 11 39 Rodentia Sciuridae Paraxerus cepapi Smith‘s Bush squirrel 1 0 0 1 Muridae Acomys spinosissimus Least Spiny Mouse 4 0 0 4 Muridae Lemniscomys zebra Heuglin's striped grass mouse 0 1 0 0 Insectivora Erinaceidae Atelerix albiventris, Four toed hedgehog 2 0 0 2 Macroscelidida Macroscelididae Elephantulus rufescens East Africa shrew 3 0 0 3 Source: Field Data, 2014.

Figure 9: Percentage of captures showing different species of small mammal

community at the University of Dodoma campus 2014.

Source: Field Data, 2014.

Figure 10 shows the cumulative number of small mammals with number of sites.

The number of small mammal species caught initially increased as the number of study sites increased from one to 10, four species (Paraxerus cepapi, Atelerix

39 albiventris, Elephantulus rufescence and Aethomys chrysophilus) falling in three different orders were caught. At 11th site a new species (Pelomys fallax) was caught making a total of 5 species. The number of small mammal species caught remained constant (5 species) up to site number 36 where a new species (Lemniscomys zebra) was caught increasing number of species to 6.There was no new catch up to site number 50 where Acomys spinosissimus was caught and this made total of 7 species caught among 77 study sites.

Figure 10: Cumulative number of small mammal species in study sites at the

University of Dodoma Campus December 2013 to March 2014.

Source: Field Data, 2014.

Table 3 shows the distribution of the two common small mammal species (Aethomys chrysophilus and Pelomys fallax) across different habitat types. The presented results suggested that, proportions were similar in each habitat type (grassland, sparse thicket and forest).

Table 3: Contgency test for differences in distribution of two common small

mammal species (Aethomys chrysophilus and Pelomys fallax) across

different habitat types.

Species Inference Habitat types Forest Grassland Sparse thickets Total Observed 13 67 38 118 Aethomys Expected 11 71 37 119 chrysophilus χ2 0.58 0.19 0.04 0.81 Observed 1 27 11 39 Pelomys fallax Expected 3 23 12 38 χ2 1.77 0.57 0.11 2.45 Total 30.35 188.76 98.15 317.26 Source: Field Data, 2014.

Aethomys chrysophilus was associated much more than expected with grassland habitats, slightly more than expected in sparse thickets, and less than expected in forest as indicated in 4 (χ2 = 62.10, df = 2, p <0.0001). Pelomys fallax had a similar distribution: captures were fewer than expected in forest, slightly less than expected in sparse thicket and much more than expected in grassland (χ2 = 36.21, df = 2, p <

0.0001).

Table 4: Frequency of capture of two small mammal species across different

habitat types. Expected frequencies were based on the proportion of

sites sampled in each habitat.

Species Inference Habitat types Total Forest Grassland Sparse thickets Aethomys crysophilus Observed 13 67 38 118 Expected 53.63 35.25 29.12 118 χ2 30.79 28.61 2.71 62.10 Pelomys fallax Observed 1 27 11 39 Expected 17.73 11.65 9.62 39 χ2 15.78 20.23 0.2 36.21 Source: Field Data, 2014.

There was variation in small mammal abundance among habitat types as shown in

Figure 11 (Kruskal-Wallis p < 0.0001). Pair-wise tests between habitats (forest, grassland, and sparse thicket) indicated a significant difference in small mammal abundance between forest and grassland (U = 709.5, n1 = 35, n2 = 23, p < 0.0001), between forest and sparse thicket (U=505, n1=23, n2=19, p < 0.002) but not between grassland and sparse thicket (U=275.5, n1=23, n2=19, p≥0.175)

Figure 11: Comparisons of relative abundance of small mammals in forest,

grassland and sparse thicket at the University of Dodoma,

Tanzania, 2014.

Source: Field Data, 2014.

Error bars represent standard errors of the mean. Bars with different letters are significantly different (p<0.002).

4.1.2 Habitat features associated with capture frequency of small mammals

Most sites had a capture frequency ranging from 0 or 1. 27 sites had a capture frequency ranging from 2 to 5 individuals. Few sites had capture frequencies greater than 6 individuals and one site (located in grassland) had maximum capture frequency of 14 individuals as indicated Figure 12.

Figure 12: Small mammal abundance (capture efficiency) among 77 study sites.

Source: Field Data, 2014.

For all variables, variances were approximately equal to or did not greatly exceed the mean as indicated in Table 5.

Table 5: Comparison of means and variances of all variables used in Poisson

regression analyses.

Variables Mean Variance Variance/mean Abundance 2.19 7.19 3.32 Average canopy cover 3.35 3.02 0.90 Distance to building 4.62 0.90 0.19 Distance to grassland 2.62 3.19 1.21 Distance to creek 3.79 2.59 0.68 Distance to rock 3.77 3.34 0.89 Elevation 1269.26 2780.04 2.19

Source: Field Data, 2014.

The best Poisson regression models for the influence of habitat variables on the abundance of small mammals indicated that the proximity to grassland and low canopy cover were the best predictors of overall small mammal abundance (Table

6). Model 5 was the highest ranking model with the lowest AICc value, and differed from the next best model by a ∆AICc of 4.6 suggesting that this single model had the best fit with an AICc weight > 90%.

Table 6: Poisson regression model selection results to assess habitat variable

associated with abundance of small mammals at the University of

Dodoma, Tanzania, 2013-2014.

a b Model AICc ∆AICc ∆AICc df

weight Distance to grassland, canopy cover 293.91 0.00 0.905 3 Elevation, canopy cover 298.50 4.59 0.091 3 Distance to grassland, distance to creek 304.74 10.82 0.004 3 Distance to grassland, 318.57 24.66 0.000 2 Distance to grassland, distance to building 320.35 26.43 0.000 3 Distance to grassland, elevation 320.53 26.62 0.000 3 Distance to creek 328.83 34.92 0.000 2 Distance to rock 340.85 46.94 0.000 2 Distance to rock, elevation 341.93 48.01 0.000 3 Distance to building 374.38 80.46 0.000 2

AICc Akaike‘s Information Criterion with finite sample size aAkaike‘s Information Criterion (AIC) adjusted for small n,

b Difference in AICc compared with lowest AICc model. cNumber of model parameters

Source: Field Data, 2014.

Model-averaged parameter estimates indicated that the two habitat variables included in the highest ranking model were indeed significant predictors of small mammal abundance as shown in Table 7. Sites situated within or close to grassland with little to no canopy cover had a higher probability of capturing an abundance of small mammals.

Table 7: Model averaged parameter estimates for Poisson regression models

testing the association between habitat variables and small

mammal’s abundance.

Model-averaged Standard 95% lower 95% upper Variable parameter estimate error CI CI Intercept 2.1062a 0.4184 1.2860 2.9263 Distance to grassland -0.1526a 0.0724 -0.2945 -0.0108 Canopy cover -0.3203a 0.0670 -0.4517 -0.1889 Distance to rock 0.0000 0.0000 0.0000 0.0000 Elevation -0.0001 0.0002 0.0005 0.0003 Distance to creek 0.0008 0.0008 -0.0024 0.0008 Distance to building 0.0000 0.0000 0.0000 0.0000 a Confidence interval excludes zero.

Source: Field Data, 2014.

4.1.3 Habitat features associated with the presence/absence of individual species

of small mammals

Logistic regression models were evaluated to identify important habitat variables associated with the presence/absence of Aethomys chrysophilus and Pelomys fallax.

For A. chrysophilus, ∆AICc of the first three models differed by < 2 suggesting that these models had equivalent support as indicated in Table 8. Distance to grassland, canopy cover and distance to buildings were variables included in these models.

Table 8: Logistic regression model selection results to assess habitat variable

associated with presence/ absence of Veldrat (Aethomys chrysophilus)

at the University of Dodoma, Tanzania, 2013-2014.

a b Model AICc ∆AICc ∆AICc weight df Distance to grassland 86.0505 0.0000 0.3572 2 Distance to grassland, canopy cover 87.1356 1.0851 0.2076 3 Distance to grassland, distance to building 87.3350 1.2845 0.1879 3 Distance to grassland, elevation 88.2080 2.1575 0.1215 3 Distance to grassland, distance to creek 88.2162 2.1657 0.1210 3 Elevation, canopy cover 95.4488 9.3984 0.0033 3 Distance to rock 97.8097 11.7593 0.0010 2 Distance to rock, elevation 99.4403 13.3899 0.0004 3 Distance to creek 104.9166 18.866 0.0000 2 Distance to building 106.3676 20.3172 0.0000 2 aAkaike‘s Information Criterion (AIC) adjusted for small n,

b Difference in AICc compared with lowest AICc model,

cNumber of model parameters

Source: Field Data, 2014.

For P. fallax, four models had the smallest AICc values with ∆AICc < 2 as shown in

Table 9. Thus 4 models had equivalent support.

Table 9: Logistic regression model selection results to assess habitat variable

associated with presence/absence of the creek-rat (Pelomys fallax) at

the University of Dodoma, Tanzania, 2013-2014.

a b Model AICc ∆AICc ∆AICc weight df Distance to grassland 67.8562 0.0000 0.2358 2 Distance to grassland, distance to creek 67.8726 0.0164 0.2339 3 Distance to grassland, elevation 68.6568 0.8006 0.1580 3 Distance to grassland, canopy cover 69.2411 1.3849 0.1180 3 Distance to grassland, distance to building 70.0222 2.1659 0.0798 3 Distance to creek 69.9588 2.1026 0.0824 2 Distance to rock 71.4270 3.5707 0.0396 2 Elevation ,canopy cover 71.9435 4.0873 0.0305 3 Distance to rock, elevation 72.9021 5.0458 0.0189 3 Distance to building 76.5261 8.6698 0.0031 2 a b Akaike‘s Information Criterion (AIC) adjusted for small n, Difference in AICc

c compared with lowest AICc model, Number of model parameters

Source: Field Data, 2014.

A model-averaging approach was used with reference based on the entire set of models. Parameter estimates indicated that one habitat variable (distance to grassland) was a significant negative predictor for the presence of both species

Aethomys chrsophilus and Pelomys fallax (Table10). There was a negative association between presence of both Creek rat (Pelomys fallax) and Veld rat

(Aethomys chrsophilus) with the increase of distance of the site from grassland; that is, the two species were more likely to be present in sites located in grassland or close to grassland. The probability of getting them was low as the distance of a site from grassland increased.

Table 10: Parameter estimates for Logistic regression models relating habitat

variables to presence/ absence of the veld-rat (Aethomys

chrysophilus) and creek-rat (Pelomy fallax).

Variable Model-averaged Standard 95% lower 95% parameter estimate error CI upper CI Veldrat (Aethomys chrysophilus) Intercept 2.3415 1.9036 -1.3896 6.0725 Distance to grassland -0.6883a 0.1871 -1.0550 -0.3217 Distance to rock 0.0007 0.0007 -0.0007 0.0020 Canopy -0.0455 0.0559 -0.5015 0.0640 Elevation -0.0001 0.0009 -0.0018 0.0016 Distance to creek -0.0007 0.0229 -0.0456 0.0448 Distance to building -0.1300 0.1103 -0.3462 0.0861 Creek rat (Pelomys fallax) Intercept -1.8674 6.2544 -14.1260 10.3912 Distance to grassland -0.6419a 0.2820 -1.1945 -0.0893 Distance to rock 0.0008 0.0383 -0.0742 0.0758 Canopy -0.0432 0.0480 -0.1373 0.0509 Elevation 0.0015 0.0030 -0.0044 0.0074 Distance to creek -0.03530 0.1245 -0.2794 0.2088 Distance to building -0.1251 0.2852 -0.6841 0.4340 a Confidence interval excludes zero

Source: Field Data, 2014.

4.2 Discussion of the findings

4.2.1 Species composition and relative abundance of small mammals in major

habitat types at the University of Dodoma.

Species recorded in the current study have been recorded in other parts of Tanzania in different surveys. For example, Acomys spinosissimus has been recorded in many parts of Tanzania such as Udzungwa National park (Stanley et al., 2005; Katavi ecosystem (Fitzherbert, 2007), Estern parts of Tanzania (Massawe et al., 2011; Peter et al., 2012), Kilimanjaro National Park (Mulungu et al., 2008). Other species recorded by former surveys include Aethomys chrysophilus in Estern parts of

Tanzania (Massawe et al., 2011; Peter et al., 2012), Lemniscomys zebra in Estern parts of Tanzania (Massawe et al., 2011), Elephantalus rufescence was recorded in

Serengeti national park (Timbuka and Kabigumila, 2009), Pelomys fallax and

Paraxerus cepapi in most parts of Tanzania (Kingdon, 2013). The current study recorded one species of Order Insectivora (Atelerix albiventris), although many studies in Tanzania have documented different species of genus Crocidura as the most dominant insectivores (Hutterer, 1993; Stanley et al., 1998b; Stanley et al.,

2005; Fitzherbert, 2007; Stanley, 2007; Mulungu et al., 2008; Timbuka and

Kabigumila, 2009). Rattus rattus has been similarly recorded at the University of

Dodoma campus (Nyondo, 2014). There was no any record on the presence of the multimammate rat, Mastomys natalensis, although former studies indicate the dominance of this species in many different ecosystems of Tanzania (Stanley et al.,2000; Caro, 2002; Fitzherbert, 2007; Massawe et al., 2011; Mulungu et al.,

2014; Makundi et al., 2014). The noted variability in identified species of small mammals could be associated to area coverage at site specific characteristics.

Reproduction and population density fluctuations of Mastomys natalensis are linked to the duration and amount of rainfall in an area. In areas where rainfall is high or where the wet season is sufficiently long, population densities of this species can exceed 100 animals per hectare; in areas with low rainfall or short wet seasons, the population density of M. natalensis declines exponentially (Makundi et al., 2014).

Mastomys natalensis is reportedly a species that prefers farmland or fallow mosaic habitats (Kingdon, 2013). The extreme aridity in Dodoma region and the lack of farm land or fallow mosaic in the study area may both contribute to the absence of

Mastomys natalensis.

Rodent species dominated the small mammal community in this study. The domination of rodents in small mammal communities is possibly a result of the morphological and ecological diversity of this large order. Ecologically they are incredibly diverse, some species spend their entire lives above the ground, in the tree canopy of trees, and others infrequently emerge from beneath ground. Some species are highly aquatic while others are equally specialized for life in the desert. Because plants are the most abundant food source for terrestrial mammals, most members of

Order Rodentia are herbivores. They exploit abroad spectrum of foods, some of them exhibiting much generalized food habits including omnivory, and others demonstrating highly specialized diets, for example, feeding on just a few species of invertebrates or fungi (Caro, 2001; Coppeto et al., 2006).

The findings of this study correspond with previous studies, which exhibit large numbers of rodent species that comprise communities of small mammals. For example, a study done by Carleton (1984), Savage and Long (1986), Wilson and

Reeder (2005) recorded large number of rodent species in small mammal

52 communities. Also some surveys of small mammals done in different parts of

Tanzania such as Udzungwa National park (Stanley, 2005) Katavi ecosystem

(Fitzherbert, 2007), Serengeti national park (Timbuka and Kabigumila, 2009), Most parts of Tanzania (Kingdon, 2013), point out the dominance of rodent species in small mammal communities.

Possible biases of these findings may be due to the trapping protocol. The habitat of small mammals is vertically stratified, and arboreal, fossorial and semi-fossorial species may have not been adequately sampled because traps were not placed in trees or in underground cavities. Also bait acceptability varies seasonally, varies among different species and is a function of the availability of food in the habitat; the bait used in this study may have been more attractive to certain speciesof.

Moreover, because of trap theft by humans, and bait theft by mongoose, some capture sites may not have been adequately sampled and may have caused failure to detect some species of small mammals. For example, the least spiny mouse (Acomys spinosissimus) may be under represented in this study results because traps placed near to rock outcrops were frequently raided by dwarf mongoose.

4.2.2 Habitat features associated with capture frequency of small mammals.

The noted findings in the current study concur with the findings reported previously in Serengeti national park kopjes (Timbuka and Kabigumila, 2009), where capture frequency of small mammals was greater in grasslands of Maasai Kopjes and

Wogakuria Kopjes than in other areas of Serengeti national park.

The proportions of different small mammal species differed among capture sites because of difference in microhabitat characteristics of a site. About 58% of

53 captured individuals were in sites with low canopy caver, which were located close or in grassland while 29% were in sparse thickets and 13% were caught in forest.

The influence of habitat on species distribution has been reported in the work of

Rosenzweig and Winakur (1969) where several habitat variables, including foliage height diversity, vegetation density and soil structure, significantly influenced species distributions both between and within habitats. The latter authers also observed several cases in which the microhabitats of 2 species were complementary

(variables that limited one species complemented those that limited another). In addition Rosenzweig and Winakur (1969) interpreted this complementarity as evidence of interspecific competition. Also a study done by Laurance (1994) recorded variation in distribution of small mammal species with respect to habitat variables such as vegetation type, elevation and amount of rainfall received at the area. The latter recorded different species of small mammals whose distribution was determined by similar habitat variables. These observations can be related with the findings of the current study where habitat characteristics (grassland and low canopy cover) that supported high abundance of Aethomys chrysophilus also supported high abundance of Pelomys fallax indicating utilization of similar resources (similar habitat requirements) and interspecific competition among them.

In additional, a study done by Brown (Brown and Lieberman, 1973) examined the influence of habitat variables on rodent species composition and diversity in desert sand dune habitats in eastern California, Nevada and western Utah. Although these variables did not explain variation in species diversity among isolated dune systems.

Brown and Lieberman (1973) did find significant differences among species in the

54 distribution of horizontal foraging activities in relation to perennial vegetation within a dune.

Similar results to the current study have been reported elsewhere, M'Closkey (1975) investigated ecological separation between locally sympatric populations of

Peromyscus leucopus and Microtus pennsylvanicus in a wet prairie habitat in southern Ontario. The latter authors found significant microhabitat segregation based on a combination of foliage height diversity, tree density and depth of the mat of perennial grass.

Presented results in the current study have demonstrated that the local distributions and abundances of small mammal species within a habitat are directly related to the availability of preferred microhabitats. Results in the current study show that grassland seemed to be a variable attracting high abundance of small mammals. The noted lack of clear relationship between small mammal abundance and distance of the site to creeks, to buildings, to rock outcrops, or elevation of a site above sea level may suggest that, these variables currently have no effects on abundance of small mammals.

4.2.3 Habitat features associated with the presence/absence of individual species

of small mammals

Veld rat (Aethomys chrysophilus) was present in all three major habitat types of the

University of Dodoma campus (grassland, sparse thickets and forest). This species have been reported as habitat generalist inhabiting mopane woodland, rocky terrain, bush and savannah (Delany, 1971; Wilson and Reeder, 2005; Kingdon, 2013).

Pelomys fallax was present in grassland habitat and in sparse thickets which were

55 close to creeks, and no individual of this species was found in forest. Presented findings may be related with the study done by Musser and Carleton (2005) that

Pelomys fallax is found in savannah habitats with permanent cover of grass or bushes. In drier regions it is confined to permanent damp areas. It is also found in cultivated areas (Musser and Carleton, 2005).

Paraxerus cepapi is a savanna woodland species, occurring particularly in mopane woodland, Acacia woodland and mixed associations such as Acacia/Terminalia,

Acacia/Combretum and others. The species is less common in

Brachystegia/Julbernardia and Baikiaea woodland probably because tree species in these habitats do not provide tree holes necessary for resting and breeding sites

(Kingdon, 2013). The species is arboreal and terrestrial, and living mainly in groups

(Grubb, 2008). To a large extent the latter explains the absence of this species in sparse thickets and grassland.

A study done by Schlitter (Schlitter, 2008) reported the presence of Acomys spinossisimus in forests characterized with rocky outcrop and in woodland, which is in line with the findings in the current study. The latter is further exhibited by the fact that during the current study four individuals were caught and three of them were in forest with rocky while one individual was caught in woodland habitats.

Delay (1971) and Timbuka and Kabigumila (2009) recorded Lemniscomys zebra as living near streams and living around thick grassy tussocks and in grassland. The findings of the current study concur with reported results in previous studies as was shown by revealed presence of Lemniscomys zebra in grassland habitats.

Earlier literatures designate that Elephantalus rufescens are found in a variety of habitats including open plains arid low lands, savannas, deserts, thorn bush and tropical forests (FitzGibbon, 1995). Majority of E. rufescens are forest dwellers that often live in burrows, ground depressions, rock crevices, termite mound crevices or under logs. Some construct nest on the forest in which they sleep in when not active

(Schlitter, 2008). In the current study three caught individuals of this species were present in forest. On the other hand Atelerix albiventris lives in the forests and deserts of Africa which is further exhibited by the fact that in the current study this species was recorded in forest area.

CHAPTER FIVE

CONCLUSION, RECOMMENDATIONS AND AREAS FOR FURTHER

RESEARCH

5.1 Conclusion

The study on species composition, relative abundance and habitat relationship of small mammals was carried out at the University of Dodoma campus. Relevant literature on the ecology of small mammals was reviewed. Data were collected from

77 sites falling in three major habitat types: grassland, sparse thicket and forest. A total of 167 individuals‘ comprising of seven species of small mammals were caught. The community of small mammals was dominated by a rodent species

Aethomys chrysophilus which constituted 70.6% of all captures.

Abundance of small mammals varied among three major habitat types at the

University of Dodoma (grassland sparse thicket and forest). Grasslands had the highest abundance composing 58% of all captures, followed by sparse thicket (29%), and lastly forest (13%).

Small mammal capture frequencies was negatively associated with distance of the capture site to grassland and percentage canopy cover of the site; that is, sites with low percentage canopy cover which were located in grass land or a short distance from grassland had a higher abundance of small mammals. The abundance of small mammals declined as the distance of site to grassland and amount of canopy cover on a site increased.

Aethomys chrysophilus was present in all three major habitat types. Pelomys fallax was found in grassland and sparse thickets; while Lemniscomys zebra was found in

58 grassland habitats only. Other species (Paraxerus cepapi, Elephantulus rufescence,

Atelerix albiventris and Acomys spinnosissimus were recorded in forest, but their capture frequencies were low.

Small mammal species diversity in the study area was much lower than documented by former studies done in protected areas of Tanzania. For example; Katavi National park (Caro, 2002; Fitzherbert et al., 2007),Western Usambara Mountains (Makundi et al.,2003), Mikumi National park (Stanleyet al., 2007; Venance, 2010), Tarangire

National Park (Stanley et al., 2007), Zaraninge Coastal Forest (Kiwia,2009), and

Serengeti National Park Kopjes (Timbuka and Kabigumila, 2009). Low species diversity may be the consequence of habitat degradation and fragmentation together with human disturbances. The current study has identified small mammal‘s species that have been able to adjust to human altered environment.

Species diversity differed among habitat types, most of species caught were in forest, though their relative abundance was low. Sparse thickets and grass land had few species but in high abundance.

Most of small mammals captured (Aethomys chrysophilus, Acomys spinosissimus,

Elephantalus rufescens and Atelerix albiventris) during the study are currently reported as not threatened or of least concern (IUCN June 2014). By being in this conservation status, they may face a problem of local extinction due to increasing rate of habitat loss and fragmentation in the campus. New conservation strategies for wild fauna and their habitats may serve these species exposed to local extinction due to habitat loss and fragmentation.

5.2 Recommendations

Local communities currently exploit natural habitats by harvesting trees for charcoal and firewood. Furthermore, construction activities on the campus take place at the expense of vegetation cover and numerous plant species which could support many wild animals in the area. The conversion of forest and thicket to open areas and grassland actually may stimulate population growth of some species such as

Aethomys chrysophilus which is regarded as a pest species (Monadjem et al., 2011).

Also forest exploitation will reduce species richness in the small mammal community in an area due to habitat loss to some species which are forest dwellers such as Acomys spinosissimus, Atelerix albiventris, Paraxerus cepapi and

Elephantalus rufescens. Conservation of natural habitats is important to maintain species richness in an area and reduce the risk of population growth of pest species

(Aethomys chrysophilus).

Capture frequencies of creek rat were substantially fewer than those of veld rats.

Thus, although proximity to creeks was a variable that appeared in the top models predicting creek rat presence, model-averaged results indicated no significant association with creeks, probably owing to sample size constraints. Most of the creek rats (Pelomys fallax) in this study were captured in grassland close to dry creek beds.

Although creeks are source of water to different small mammals, creek rats seem to use these habitats even when they are dry. It could be speculated that certain vegetation characteristics along creek beds may be important for Pelomys fallax. The specific microhabitat features that contribute to the association of P. fallax with creeks are uncertain and warrant closer study.

5.3 Areas for further study

More studies on small mammals in the campus need to be done to fill the information gap. Future research should focus on;

i. Assessment of population dynamics of small mammal species in the university

of Dodoma campus in different seasons of the year.

ii. Influence of plant species richness on the Small mammal species richness in

the University of Dodoma campus.

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APPENDIX

Appendix 1: Small Mammal Sampling Sheet

Site No……………………… Location…………………….

Date Small mammals Head-body Tail length Weight Other ID caught at the site length (cm) (cm) (gm) features

Appendix 2: Sheet for recording habitat variables of the site.

Site Distance of the site(m) to a near Canopy Elevation of No Creek Building Grassland > 16 m2 rock cover (%) site above outcrop sea level

Appendix 3: Small mammal species trapped at University of Dodoma campus

2013 - 2014 m.

Aethomys chrysophlus Acomys spinossimus

Source: Field Data, 2014.

Pelomys falax Lemniscomys zebra

Source: Field Data, 2014.

Elephantalus rufescens

Source: Field Data, 2014.

Paraxerus cepapi Atelerix albiventris Source: Field Data, 2014.