ASSESSMENT OF VERTEBRATE DIVERSITY IN ALABATA   NATURE RESERVE 
ABEOKUTA, SOUTH-WEST NIGERIA 
 
 
 
 
 
BY 
 
 
  
 
SHOTUYO, ABDUL LATEEF ADEREMI 
B.Sc. (Hons.), M.Sc. Wildlife Management (Ibadan) 
 
A thesis in the Department of Wildlife and Fisheries Management 
Submitted to the Faculty of Agriculture 
 in partial fulfillment of the requirements for the award of the degree of 
 
DOCTOR OF PHILOSOPHY 
 
 
of the 
 
 
UNIVERSITY OF IBADAN 
 
 
 
        NOVEMBER 2011 
 
i 
 
 
  
ABSTRACT 
 
 
The importance of wildlife, especially the vertebrates for game, tourism and medicinal use 
cannot be over emphasized. Nature reserves are also known to influence the ecosystem of its 
location. However, rapid increase in land use for agriculture and other physical developments 
are gradually reducing wildlife habitation including the Alabata area nature reserve of 
Abeokuta. Inventory of these resources in relation to their habitat parameters would inform 
their better management. Assessments of vertebrate and associated flora diversities were 
therefore carried out in Alabata Nature Reserve. 
2 2
The study covered 20 km  out of 97.3 km  area of Alabata Nature Reserve. Twenty sample 
plots each of 25m x 25m were laid randomly. Animals, vegetations, soil and level of human 
interference were assessed for 24 months in each plot cutting across wet and dry seasons. 
Animals were surveyed weekly using the King Census and Line Transect methods, by direct 
and indirect modes. Vegetation was surveyed using the Point Center Quarter method. Soil 
samples were collected randomly with auger at 0-15cm, 15-30cm and 30-45cm depth; air-
dried and analysed for pH, Organic Carbon (OC), nitrogen and Particle Size (PS) distribution 
using standard methods. Structured questionnaires were randomly administered to 20 
residents in the farm settlements adjoining the study site to assess the level of human 
interference. Data were analyzed using descriptive statistics, Dominance, Shannon Weiner, 
principal component as well as Simpson, Evenness and Equitability indices. 
 Forty species of wild vertebrate belonging to thirty-one families were encountered at the 
study site. Thryonomys swinderianus was the most abundant vertebrate species with a mean 
frequency of  319±40.8, followed by Xerus erythropus (143±2.9) and Arvicanthus niloticus 
(122±15.3) while Ploceus capensis (5±3.9) was the least abundant. Daniellia oliveri 
(1123±4.6) was the most abundant tree species, followed by Anona senegalensis (270±3.9) 
ii 
 
 
and Bridelia micrantha (179±3.5). Mean soil pH value was 5.4±0.2 and 6.6±0.3 during the 
wet and dry seasons respectively. The OC of the soil ranged from 13.2% to 66.8%, while 
nitrogen content was from 0.8% to 7.5% and mean PS was from 3.2±0.2 to 90.4±4.5. Hunting 
intensity was perceived to be low (20.0%), although burning due to stray fire was perceived 
to be high (46.0%) in the site. The animal species diversity indices were Shannon Weiner 
(0.6), Simpson Index (0.9), Evenness (0.4), Dominance (0.004) and Equitability (0.9) for the 
wet season. and Shannon Weiner (0.6), Simpson Index (0.9), Evenness (0.4), Dominance 
(0.005) and Equitability (0.9) for the dry season.The plant species diversity indices were 
Shannon Weiner  (0.6), Simpson Index (0.9), Evenness (0.5), Dominance (0.005) and 
Equitability (0.9) for wet season and Shannon Weiner (0.6), Simpson Index (0.9), Evenness 
(0.6), Dominance (0.9) and Equitability (0.9) for dry season. The principal component 
analysis and ordination showed that the studied ecosystem was not stable.  
Diversity of vertebrate species in Alabata Nature Reserve was high. Abundace of 
Thryonomys swinderianus and Xerus erythropus can be attributed to adequate food and cover 
provided by  trees. However, wildfire which is the greatest threat has to be controlled for the 
reserve to realize its full potentials. 
Keywords:  Wild vertebrate diversity, Alabata Nature Reserve, Wildfire, Wildlife habitat 
Word Count: 497 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
iii 
 
 
CERTIFICATION 
 
I certify that this work was carried out by Mr. Shotuyo, Abdul Lateef Aderemi in the 
Department of Wildlife and Fisheries Management, University of Ibadan, Ibadan 
 
 
------------------------------------------------------------------- 
 
Supervisor 
 
Professor I. A. Ayodele 
B.Sc. (Hons) Fisheries Management (Ibadan) 
M.Sc., Ph.D Wildlife and Range Management (Ibadan) 
Professor, 
Department of Wildlife and Fisheries Management 
University of Ibadan, Ibadan, Nigeria. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
iv 
 
 
DEDICATION 
 
 
 
 This work is dedicated to Almighty Allah, The Beneficent, The Merciful, for His 
support during the course of this work. 
 And to the memory of my parents Mr. D. O. Shotuyo and Mrs. W. T. Shotuyo for all 
their labour, may their souls rest in peace.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
v 
 
 
ACKNOWLEDGEMENTS 
 I give thanks to Allah for His assistance throughout the period of this programme. I 
will remain grateful for His timely interventions at various points of my academic and life 
pursuits. 
 I acknowledge with deepest regard and profound humility, my supervisor professor I. 
A. Ayodele for his constant encouragement, unrestricted accessibility and patient supervision 
at every stage of this work. It was through divine intervention using him, his constructive 
criticisms, valuable advice and useful suggestions that brought this study to a meaningful 
conclusion, and from whom wealth of experience and deep knowledge I have benefited 
immensely. I am sincerely indebted to him for his wonderful and brotherly care and this 
„opportunity „ at a very critical point in my academic carrier to work under a vast biodiversity 
expert like him. 
 My gratitude goes to the Authority of the University of Agriculture, Abeokuta, the 
Vice Chancellors, the Physical Planning Unit, Brother John Oyedepo of GPS Unit of 
RESDEC UNAAB. 
 I am grateful to the Deans of COLERM UNAAB for the opportunity to use the Four 
Wheel Drive of the College at every time I requested to use it. My heartfelt gratitude also 
extends to Dr. J. O. Shoaga, messers Bunmi Oladoyinbo, Segun Oladoye and M. O. O. 
Oyatogun who were sometimes my field assistant during the course of the field work. 
 I am grateful and highly indebted to all the Lecturers in the Department of Wildlife 
and Fisheries Management University of Ibadan, especially the Acting Head of Department 
Dr. T. S. Olaniran, Professors A. E. Falaye, F. O. Faturoti, I.A. Ayodele and Drs.  O. A. 
Oyelese, Akinyemi, ,O. A. Adetoro, G. A. Lameed, O. A. Omonona, O. A. Olukunle, Tola 
Jenyo Oni, B. T. Omitoyin, Y. E. Agbeja, E. K. Ajani, F. E. Akinwale, B. T. Olaifa, Fregene 
vi 
 
 
and S. O. Ojo for their invaluable contributions to my academic development. I am also 
grateful to all the non- academic staff of the Department. 
 This acknowledgement extends to the staff and my students in the Department of 
Forestry and Wildlife Management University of Agriculture, Abeokuta. In particular 
Professors B. A. Ola-Adams, S.A. Onadeko, S. A. Oluwalana, M. o. Adedire, A. M. 
Aduradola, Drs. A. C. Adetogun, M. F. Adekunle, Engnr. O. M. Aina, messers Akintunde, O. 
A., Omotola Jayeola, Yisau Steve, Mrs. Adedokun and Drs. O. F. Smith and E. I. Inah not 
forgetting Mrs. D. F. Abe and Easter Amodu (my word processing Consultants). 
 I must not fail to mention the encouragement, intervention and prayerful support of 
the following, Professors T. A. Arowolo, O. Bamgbose, W. O. Alegbeleye, S. O. Otubusin 
and Y. Akegbejo-Samson., B. A. Onilude of Department of Microbiology University of 
Ibadan, Drs. J. T. Bamgbose, B. O. Opeolu, F. O. A. George and many more well wishers too 
numerous to mention. 
 My thanks go to Hafsah Ajoke Shotuyo (My wife) the trio of Abdul Qahhar, 
Zafrullah, Muzaffar (My sons) and Zahhira (My little daughter) for their understanding and 
support. 
 Finally, the credit for this work belongs to Allah the Most High. 
 
 
 
 
 
 
 
 
vii 
 
 
TABLE OF CONTENT 
           
 Page 
TITLE PAGE           i 
ABSTRACT           ii 
CERTIFICATION          iv 
DEDICATION          v 
ACKNOWLEDGEMENTS         vi 
TABLE OF CONTENT         viii 
LIST OF TABLES          xii 
LIST OF FIGURES          xiii 
CHAPTER ONE          1 
1.0   INTRODUCTION         1 
1.1   Background          1 
1.2  Statement of Problem         4 
1.3   Justification of the Study        5 
1.4   Objectives          5 
CHAPTER TWO          6 
2.0   LITERATURE REVIEW        6 
2.1   Biodiversity Concept And Assessments      6 
2.1.1 Values of Biodiversity        9 
2.1.2 Direct use values         16 
2.1.3 Timber values         16 
2.1.4 Fuelwood and charcoal        18 
2.1.5 Non-timber forest products       18 
viii 
 
 
2.1.6 Indirect use values         18 
2.1.6.1 Watershed protection        20 
2.1.6.2 Carbon storage and sequestration loss rates for tropical forests.   20 
2.1.6.3 Option and existence values       21 
2.1.7 Tourism and recreation values       22 
2.1.8 Forests biodiversity        23 
2.1.9 Distribution of World‟s Forest       25 
2.1.10 Status of Biodiversity in Forest Biomes      26 
2.1.11 Boreal forests         27 
2.1.12 Temperate forests         30 
2.1.13 Tropical forests         35 
2.2 Causes of Forest Biological Diversity Loss     42 
2.2.1 Threats to biological diversity       42 
2.2.2 Lack of capacity, technical and financial resources    44 
2.2.3 Lack of secure land tenure and land rights and uneven distribution of ownership 44 
2.2.4 Lack of good governance        48 
2.2.5 Ill-defined regulatory mechanism and lack of law enforcement   48 
2.2.6 Illegal logging          49 
2.2.7 Lack of scientific knowledge and inadequate use of local knowledge  51 
2.2.8 Under –valuation of forest biological diversity goods and services   52 
2.2.9 Lack of cultural identity and spiritual values      53 
2.2.10 Deficiencies in the flow of information in decision makers and to local communities  
54 
2.2.11 Lack of Environmental Impact Assessments or Strategic Environmental Assessments 
54 
ix 
 
 
2.2.12 Perverse incentives and subsidies and ill-defined developmental programmes     
57 
2.2.13 Poverty             59 
2.2.14 Population Change            59 
2.2.15 Globilisation             60 
2.2.16 Unsustainable production and consumption patters        62 
2.2.17 Political unrest and war           64 
2.2.18 Conversion of forests to agricultural land         65 
2.2.19 Dismantling of agro-forestry system          65 
2.2.20 Overgrazing             66 
2.2.21 Natural Hazards and Forest Fires          66 
2.2.22 Actions and priorities for conservation and sustainable use of biodiversity    68 
2.2.23 Assessment and monitoring                68 
2.2.24 Conservation and sustainable use          69 
2.2.25 Institutional and socio-economic enabling environment       69 
CHAPTER THREE             71                                
3.0 MATERIALS AND METHODS          71 
3.1     The Study Area             71 
3.1.1   Land use history                 74 
3.1.2 Vegetation              74 
3.1.3 Climate             75 
3.2 Materials             75 
3.3 Sampling procedures            75 
3.3.1 Data Collection            76 
3.3.2 Vegetation Survey             76 
x 
 
 
3.4 Animal (Vertebrates) Survey          77             
CHAPTER FOUR           80 
4.0       RESULTS           80 
4.1      Plant frequency distribution and relative abundance      80 
4.2 Animal frequency distribution and relative abundance     102 
 
4.3 Soil analysis           126 
4.4 Diversity indices, analysis of variance and correlation            128 
CHAPTER FIVE        137 
5.0 DISCUSSION         137 
5.1 Discussion         137 
5.11 Species Diversity, Correspondence Analysis     137 
5.12 Soil structure, texture and chemical composition    142 
 CHAPTER SIX        144 
6.0 CONCLUSION AND RECOMMENDATION    144 
6.1 Conclusion         144 
6.2 Recommendation        145 
REFERENCES        146 
 
 
 
 
 
 
 
 
xi 
 
 
LIST OF TABLES 
          Page 
Table 1: Scientific Names and Codes of Plants in the  Study Area    84 
Table 2: Average Frequency of Plants in the Study Area     94 
Table 3: Scientific names and Codes of Animals in the Study Area    106 
Table 4 : Average Frequency of Animals in the Study Area     110 
Table 5: Mode of Animal Identification       112 
Table 6: Crosstabs of Animal Abundance and Distance     113 
Table 7: Monthly Abundance of Animals       114 
Table 8: Crosstab of Distance and Order       115 
Table 9: Regression Analysis of Distance of Sighting and Season    116 
Table 10: Land Use Changes in the Study Area (1984 - 2008)    117 
Table 11: Soil characteristics parameter of the study Area     130 
Table 12: Animal Diversity Indices of the Study Area     132 
Table 13: Plant Diversity Indices of the Study Area      133 
Table 14: Problems Confronting the Study Area      134 
Table 15: Means of meteorological Observations of the Study Area (2005 -2008)   135 
 
 
  
   
       
   
 
 
 
 
xii 
 
 
LIST OF FIGURES 
Fig. 1: Map Of the University of Agriculture showing the Study Area   75 
Fig. 2: Map of Study Area         76 
Fig. 3: Percentage Average Relative Abundance of Plant Species in the Study Area  100 
Fig. 4: Average Raining Season Plant Species Frequency of Abundance   101 
Fig. 5 : Average Dry Season Plant Species Frequency of Abundance   102 
Fig. 6:  Rainy Season Mean Number of Plants per plot in the Study Area   103 
Fig. 7: Dry Season Mean Number of Plants per plot in the Study Area   104 
Fig. 8: Percentage Average Abundance of Animals in the Study Area   118 
Fig.9 : Average Frequency of Animals Sighted in the Study Area    119 
Fig. 10: Order of Animals Sighted in the Study Area     120 
Fig. 11: Percentage Average Monthly Animal Abundance in the Study Area  121 
Fig. 12: Animal Sighting Indicator of the Study Area     122 
Fig. 13: Average Animals Sighted in the Rainy Season in the Study Area   123 
Fig. 14: Average Animals Sighted in the Dry Season in the Study Area   124 
Fig. 15: Average Frequency of Animals along Transects in the Study Area   125 
Fig. 16  Average Rainy Season Abundance of Animals in the Study Area   126 
Fig. 17: Average Dry Season Abundance of Animals in the Study Area   127 
Fig. 18: Modes of Animal Identification in the Study Area     128 
Fig.19: Principal Correspondence Analysis of Animals in the Study Area   136 
Fig.20: Ordination Diagram of Animals in the Study Area     137 
Fig.21: Sighting of Animals According to Distance from Transects in the Wet Season 138 
Fig.22: Sighting of Animals According to Distance from Transects in the Dry Season 139 
 
 
xiii 
 
 
CHAPTER ONE 
 
 
 1.0           INTRODUCTION 
 
1.1 Background 
 
The moist tropical forest of Central and West Africa, with the multitude of 
plants and animal species found within them, are one of the world‟s greatest 
biological treasures, and represents one of the most valuable assets for many countries 
in equatorial Africa. Rain forests are valuable because they serve so many life-
sustaining functions. They provide food such as fruits, nuts and meat to people who 
live near them. They provide building materials and medicines for local uses, as well 
as timber for export. Intact rain forests stabilize soils, reducing erosion and hence 
providing clean water to drink, and play a key role in the regulation of climate, both 
locally and globally. The beauty, diversity and rarity of rain forest species attract 
tourists and scientists from all over the world, as well as inspiring unique and lasting 
cultural traditions among the people of the forested African countries.  
The African rain forest still covers a vast area, stretching from Guinea in the 
west across to the coast of East Africa, but it faces a wide range of threats. The rain 
forests of east and West Africa have already been reduced by human activities in the 
last century or so, and today little forest vegetation survives outside protected forest 
reserves, wildlife sanctuaries and national Parks. The central African forest block 
remains largely intact, but even the most remote areas are likely to be affected in the 
near future by combined forces of deforestation and exploitation. As human 
population increases steadily, and more and more land is needed for agriculture, and 
as technology advances, exploitation for timber, meat and other forest products 
becomes more intensive and damaging. 
1 
 
 
Under this scenario protected areas and their management staff have a crucial 
role to play if biological diversity is to be conserved. However, just as there are a 
wide variety of habitats and vegetation types within the forest, protected areas are 
designed to fulfill many different roles and face a wide variety of threats. Many 
protected areas have been established throughout forested Africa, with reserve 
boundaries, hunting restrictions and certain management goals, among other things, 
certainly described in legal documents. However, these protected areas do not 
function as they intended to protect the natural resources contained within them.         
o ! o
The Nigerian rain forest zone occurs between latitude 4 51  And 7 N and longitudes 
3o 30! And 3o 37!E. It covers an area of about 95,560Km which represents about 
10% of the Nigerian land area. 
The vegetational structure of the Nigerian rainforest region is being altered at 
a fast rate, transforming to vegetational types such as derived savannah in most of 
Oyo, Ogun and Anambra states, and also to dry semi-deciduous rainforest types in 
parts of Oyo, Ondo and Ogun States. The trend in the rapid depletion of the natural 
rainforest has been due to population pressure, slow growth rate in agriculture 
production and sufficiency and threat to rural livelihood income security. At the same 
time human pressure on land is eating at the delicate environmental equilibrium that 
has evolved over centuries. Forest cover is shrinking and biodiversity is getting lower. 
The threat therefore, to the remaining pockets of rainforest becomes greater (IITA, 
1996). It has been observed that the future of the Nigerian rainforest is bleak and the 
whole of Nigerian rainforest may disappear in this century if this trend is allowed to 
continue. Certainly this millennium is headed for a surprise. 
Biodiversity is the total richness of biological variation. Usually the scope of 
biodiversity is considered to range from the genetic variation of individual organisms 
2 
 
 
within and among populations of a species to different species occurring together in 
ecological communities. Some definitions of biodiversity also include the spatial 
patterns and temporal dynamics of populations and communities on the landscape. 
The geographical scales at which biodiversity can be considered ranged from local to 
regional, state or provincial, national, continental, and ultimately to global. (Ayodele 
and Lameed, 1999). 
Biodiversity at all scales is severely threatened by human activities, making it 
one of the most important aspects of the global environmental crisis. Humans have 
already caused permanent losses of biodiversity through the extinction of many 
species and the loss of distinctive, natural communities. Ecologist predict that unless 
there are substantial changes in the way human affect ecosystems, there will be much 
larger losses of biodiversity in the near future.(Dawson et.al 2011) 
Human activities such as overgrazing, deforestation, bush fires, mining, 
urbanization and cultivation are the principle causes of habitat destruction. These 
activities are expanding in line with human population growth and poverty increase. 
Maintaining the high quality habitats and ensuring the long-term ecological integrity 
is therefore increasingly becoming an important management challenge. 
Establishment of wildlife PAs has been adopted as the most feasible strategy to this 
end. Currently some 104,791 PAs covering a total area of about 20 million km2 or 
12.7% of the earth‟s surface have been created. This is a dramatic increase compared 
to only 8,500 PAs covering some 7.7 km2 (equivalent to 5.2% of the earth‟s sur face) 
existed in the last decade (IUCN1990). 
In Africa loss of wildlife habitats is a widespread phenomenon. The current 
loss is estimated at 60%. Human population pressure is cited as the main contributor 
to this loss, mainly through deforestation prompted by increased demand for arable 
3 
 
 
land, settlements and fuelwood. The majority of sub-Saharan Africa's population is 
dependent on fuelwood: 82% of all Nigerians, 70% -Kenyans, 80% -Malagasies, 74% 
Ghanaians, 93 - Ethiopians, 90% - Somalians and 81% - Sudanese. 
Biodiversity can be protected in ecological reserves. These are protected areas 
established for the conservation of natural values, usually the known habitat of 
endangered species, threatened ecosystem, or representative examples of widespread 
communities. The World Conservation Union, World Resources Institute, and United 
Nations Environment Program are three important agencies whose mandates center on 
the conservation of world‟s biodiversity. 
Human activities especially agriculture have a significant implication for wild species 
of flora and fauna. Species capable of adapting to the agricultural landscape may be 
limited directly by the disturbance regimes of grazing, planting and harvesting, and 
directly by the abundance of plants and insect foods available. Some management 
techniques, such as drainage, create such fundamental habitat changes that there are 
significant shifts in species composition (McLanghlin and Mineau 1995). 
 
1.2 Statement of Problem 
  Rapid development in form of physical structures and several farms are 
gradually reducing habitats for wildlife in the Alabata Area of Abeokuta. Human 
activities such as overgrazing, deforestation, bush fires, mining, urbanization and 
cultivation are the principle causes of habitat destruction. These activities are 
expanding in line with human population growth in the Alabata Area. Maintaining the 
high quality habitats and ensuring the long-term ecological integrity is therefore 
increasingly becoming an important management challenge. Biodiversity at all scales 
is severely threatened by human activities, making it one of the most important 
4 
 
 
aspects of the global environmental crisis. Human activities especially agriculture 
have a significant implication for wild species of flora and fauna. Humans have 
already caused permanent losses of biodiversity through the extinction of many 
species and the loss of distinctive, natural communities. It is thus expedient to create a 
corridor for wildlife to thrive undisturbed, hence establishment of the Alabata Nature 
Reserve. Fauna species loss is imminent when human activities are uncontrolled in 
natural ecosystems. The management of these resources therefore requires a 
comprehensive inventory; hence the assessment of Alabata Nature Reserve. 
 
1.3 Justification  
Rapid development in form of physical structures and several farms are 
gradually reducing habitats for wildlife in the Alabata Area of Abeokuta. Fauna 
species loss is imminent when human activities are uncontrolled in natural 
ecosystems. 
Biodiversity can be protected in strict nature reserve, ecological reserves, etc. 
These are protected areas established for the conservation of natural values, usually 
the known habitat of endangered species, threatened ecosystem, or representative 
examples of widespread communities. 
 
No comprehensive scientific information is yet available on the biodiversity of 
the University of Agriculture, Abeokuta almost ten thousand (10,000) hectares of 
land. It has become almost increasingly difficult to utilize in a sustainable manner any 
one particular resource in the absence of a comprehensive inventory of the natural 
resources for a holistic sustainable planning, utilization and management. The need 
for an appropriate management strategy becomes expedient. 
5 
 
 
 
1.4 Objectives 
1. Evaluate the flora and fauna species diversity in the Nature Reserve 
2. Determine the species status present in the Nature Reserve 
3. Evaluate the soil status of the Nature Reserve 
4. Assess the impact of human activities on the Nature Reserve 
 
 
 
 
 
 
6 
 
 
CHAPTER TWO 
 
2.0      LITERATURE REVIEW 
 
2.1    BIODIVERSITY CONCEPT AND ASSESSMENTS 
 In the past one decade, the complex problems surrounding biological diversity 
or biodiversity arise when it was recognised that there were many more species on 
earth that scientist had yet described, and that the rate of extinction of species far 
exceeds the rate of their preservation. The need to conserve them as a foundation for 
sustainable development becomes very important. As the worldwide loss of 
biodiversity has been accelerated in recent decades, awareness has grown of the 
potentially disastrous consequences of this trend for the earth‟s ecological functions 
and fulfillment of basic human development needs. (Pereira et al 2010). 
 In the developing countries, particularly in Africa, biodiversity is a matter of 
survival. The livelihoods of great majority depend on free and open access to great 
variety of biological resources for food, fuel, medicines, housing materials and 
economic security BSP, 1993). Based on all these, protection of biodiversity becomes 
necessary for the maintenance of the biological resource base. Likewise, in Nigeria 
the rising concern for biodiversity conservation and protection stems from our 
dependence on the biological resources and rapid reduction in biodiversity of few 
pristine and natural areas, which remain. ( Perrigs et al, 2011) 
 Biological diversity is a broad scientific issue, involving aspects of species 
richness, species composition, habitat structure, landscape pattern, ecological process, 
and biological conservation. The convention on biological diversity which came into 
force at the end of 1993 defined: Biodiversity as „The variability among living 
organism from all sources including terrestrial, marine and other aquatic ecosystems 
7 
 
 
and ecological complexes of which they are part. This includes diversity within 
species, between species and ecosystems (UNEP, 1992, Wikipedia, 2009). McNeely 
et al., (1990) sees biodiversity as an umbrella term for the degree of nature‟s variety. 
It encompasses all species of plants, animals and microorganisms, the ecological 
processes of which they are part. 
 The simplest definition of biodiversity is the number of species found in an 
area called species richness,(Dolev and Carmel,2009).  For practical reasons, one has 
to confine the count to those species with which one is familiar, leaving out all the 
others because many taxa are still unknown, even taxonomically, let alone 
ecologically (Hengeveld, 1996). For this and other reasons, species richness is still 
commonly used in the context or biological conservation. 
 Biodiversity is usually recognised as the concept of three distinct levels 
namely: (a) Genetic diversity; (b) Species diversity; and (c) Ecosystem diversity 
(UNCBD, 1992, Ayodele and Lameed 1999). To consider all the ramifications of 
biodiversity at the genetic, species and ecosystem levels in a landscape is not a simple 
task. As a result, species diversity is usually viewed the key when evaluating 
biodiversity. 
 Species – based approach entail the review of taxa with the aim of identifying 
species considered to be high priority for conservation. Species diversity is the variety 
of different species found in an area. In this case, the number is often used as a 
measure. In some cases taxonomic diversity is used, as it considers the relationships 
of species to each other. Genetic diversity is the variety of genes within species i.e. 
biochemical units of hereditary information passed on by parents that determine the 
physical and biochemical characteristics of their offspring. This form of diversity, 
according to McNeely et al., (1990) can be between populations of the same species 
8 
 
 
or within distinct populations. Ecosystem diversity can be at the national or sub-
national levels. It can also be referred to as the diversity of habitats and processes 
occurring within the ecosystem. However, ecosystems are not closed systems. It is 
difficult to define them, but the assessment of biodiversity at this level is certainly 
very important especially in determining priorities for conservation. (Hawksworth et 
al, 2011). 
 However, it is perfectly feasible to maintain species independent of the 
ecosystems or habitats in which they normally occur. At whatever level the problem is 
looked at, it is axiomatic that the maintenance of species diversity and in particular 
the prevention of species extinction is pivotal to the conservation of biodiversity. 
 Biodiversity can be quantitatively expressed from different perspectives 
depending on the aspects (or functions) of biodiversity under study. On the spatial and 
temporal scales, numerous proposals for measuring biodiversity is in itself proof of 
the complexity of the problem and of the difficulties in designing strategies that can 
be carried out in some reasonable amount of time and with sensible investment in 
resources (Hawksworth, 1995 and 2007). Since diversity is the variety of living 
systems, at a number of different levels of resolution, it will be difficult to summarize 
using one measure. 
 The concept of diversity which takes species abundance into account is also 
known as within habitat diversity (Alpha diversity) (Linsenmair, 1997) while Beta 
diversity is a measure of the replacement of habitats. As such, it corresponds to the 
spatial contiguity of different communities or habitats (Cody, 1993). Although, beta 
diversity differs from alpha diversity, it does not add a new type of variation, its 
difference depending on the spatial scale initially chosen. 
9 
 
 
 Finally, Gamma diversity is understood to mean the diversity of a large area. 
Linsemair, 1997 also defines Gamma or total diversity of a landscape or geographic 
area, as a product of alpha diversity of its communities and the degree of beta 
differentiation among them. Also, in working with species, that is with the “original 
diversity.” Haper and Hawksworth (1995 and 2007) focus on the approaching 
complex problem of measuring biodiversity which depends on the location of the 
study area on two scales: (1) That is structured in terms of space and (2) the other in 
terms of time. So, studies carried out from an ecological perspective are done within 
limited areas. 
 
2.1.1 Values of Biodiversity 
 A variety of reasons have been advanced for valuing biodiversity. BSP (1993) 
noted that people value biological resources in different ways: spiritually, 
economically, aesthetically, culturally, and scientifically. Biodiversity values also 
differ at the local, national and international levels. Boyd (1992) noted that biological 
diversity is perceived from many angles ethical and religious, aesthetic and emotive, 
economic, utilitarian, legal and mandatory, scientific and technological. 
 Biodiversity values can be categorized as: (1) human utilitarian; (2) ecological 
utilitarian; (3) human non-consumptive and (4) ethical or intrinsic. On the whole, it 
has been suggested that biodiversity could be valued for the sake of its own existence 
since all creations have a right to exist (Naess, 1986; Norton, 1987; McNeely et al., 
1990). We share the earth with at least five million other species all of which have a 
right to survival. 
 Unfortunately, biodiversity is under threat due to the extinction of species 
which is now taking place at an unprecedented rate, possibly 100 times greater than 
10 
 
 
the background or natural rate and these losses are almost all human induced. It is 
desirable that we find ways to live in greater harmony with nature because the 
consequences of failing to take action will be unpleasant. Therefore, it has to be 
valued because its conservation would leave options open for use in future. 
 IUCN (1990) also reported that more than anywhere else on earth, human 
well-being in Africa depends on the continued productivity of biological resources. 
Africa rely on access to these resources to meet their daily subsistence needs, to 
generate employment and cash, and in many cases to form the basis of their natural 
economics, and as Africa is, and will continue to be, dependent on its biological 
resources for food, shelter, and income. Africa needs, therefore; to maintain its 
healthy productive ecosystems to meet the challenges of coming decades. 
 Likewise, Nigeria‟s predominantly rural populations live in over 100,000 
villages and hamlets (FEPA, 1992). The majority of the rural populations depend on 
wild sources of protein supply including fish, snails, rodents, insects and available 
resources at their disposal with little or no regard for perpetuity. These resources cater 
for the shelter, food and domesticated livestock for the rural populace (FEPA, 1992). 
The Gulf of Guinea coastal zone is the economic and political nerve centre of the 
countries within this zone. For instance, oil found within the coastal zone in Nigeria 
forms the backbone of the Nigerian economy and almost of its fishery resources 
found within the coastal zone. In addition the coastal zone is also the food basket of 
the sub-region (Awosika and Ibe, 1998). 
 
Forests world-wide generate a wide range of goods and services that benefit 
humankind. From an economic perspective these values can be conveniently 
classified as:  
11 
 
 
(a) Direct use values: values arising from consumptive and non-consumptive 
uses of the forest, e.g. timber, fuel, bush meat, food and medicinal plants, 
extraction of genetic material and tourism. 
(b) Indirect use values: values arising from various forest services such as 
protection of watersheds and the storage of carbon. 
(c) Option values: values reflecting a willingness to pay to conserve the option 
of making use of the forest even though no current use is made of it. 
(d) Non-use values (also known as existence or passive-use values): these 
values reflect a willingness to pay for the forest in a conserved or 
sustainable use state, but the willingness pay is unrelated to current or 
planned use of the forest. 
There are other notions of values, for example, moral or ethical value, spiritual 
and religious value and cultural value. Moral and ethical values tend to relate to 
„intrinsic‟ qualities of the forest and are generally not subject to quantification. The 
same is true of spiritual and religious values whereby forests embody characteristics 
venerated by individuals and communities. There are, however, links between these 
notions of value and economic value. In particular, non-use values are known to 
reflect many different motivations, motivations that include the individual‟s concern 
for intrinsic values. But notions of values based on intrinsic qualities are different to 
economic values in that the latter are always „relational‟ i.e. they derive from human 
concerns and preferences and are therefore, values conferred by human beings. 
Stakeholder analysis analyses the individuals, groups and institutions with an 
interest („stake‟) in forests, assesses the nature of that interest, the impacts that such 
stakeholders have on forest integrity and ways in which those interest can be served in 
12 
 
 
a sustainable manner. Table below sets out the classification of forest values and 
interests that various stakeholders have in those values. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
13 
 
 
Forest values and stakeholder interests 
 
Direct use value Main stakeholders and their Impact on forest integrity 
interest 
Timber Logging companies (profit) Often unsustainable 
Government (royalties) Usually low tax-take 
Fuelwood Local communities (high value) Usually sustainable  
NTFPs Local communities (high value) Usually sustainable 
Genetic information Plant breeding companies (profit) Sustainable 
   -Agriculture Drugs companies (profit) Sustainable 
   -Pharmaceutical Local communities (medicines) Sustainable 
Recreation Tourism (revenue leakage issue) Usually sustainable 
Nearby urban dwellers Sustainable 
Research/education Local and international universities Sustainable 
Cultural religious Local communities Sustainable 
 
Indirect use values Main stakeholders and their Impact on forest integrity 
interest 
Watershed functions   
Soil conservation Local and regional communities Usually unappropriated 
Water supply Local and regional communities Usually unappropriated 
Water quality Local and regional communities Usually unappropriated 
Flood protection Local and regional communities Usually unappropriated 
Global climate   
Carbon storage Global community (Climate Favours conservation 
protection) 
Carbon fixing Local community (carbon trades) Favours conservation 
Global community (climate Favours conservation 
protection) 
 
 
 
 
 
14 
 
 
 Forest values and stakeholder interests contd. 
 
Biodiversity Local communities Favours conservation 
Amenity (local) Nearby residents Unappropriated benefit 
Forest value Main stakeholders and their Impact on forest integrity 
interest 
Option and existence   
value Global community Appropriable 
debt for nature swaps, donations,  
forest funds, GEF etc)  
local and regional communities Not usually appropriated 
 
   
Land conservation Main stakeholders and their 
values interest 
Crops Agriculturists Inconsistent with forest 
conservation 
Pasture  Ranchers: Inconsistent with forest 
Local communities conservation  
Private business 
Logging Logging companies Generally unsustainable 
Governments 
Agro-forestry Local communities Potentially sustainable 
Agri-business Private companies Inconsistent with forest 
conservation 
Aquaculture Private companies Usually inconsistent with 
(mangrove) Local communities mangrove conservation 
 
Source: CBD (2002) 
 
 
 
 
15 
 
 
  
 An important feature of the table is that forest conservation values can accrue to local 
communities (e.g. shifting agriculture) but that such practices are increasingly unsustainable 
as less open access forest is available. The effect of the „diminishing frontier‟ is that fallow 
plots are revisited before regeneration has fully occurred, so that second and third round crop 
production takes place on increasingly „mined‟ soils. Indigenous peoples and local 
communities may benefit at least in the short term from other conversion activities, e.g. 
employment from logging operations. Often, however, the converted land use involves 
ownership by other agencies, e.g. national or regional government and larger corporations, 
with the effect of displacing local communities. For indigenous peoples this can also create 
and trigger far-reaching social and cultural disruption, without opportunities for earning 
money. 
 The table also illustrates that local communities benefit substantially from forest 
goods and services. In particular, fuelwood and other NTFPs can account for a major fraction 
of local community income. Communities could benefit further from the monetization of 
carbon storage and sequestration flows through private carbon trades and/or trades as 
envisaged in the flexibility mechanisms of the Kyoto protocol. The same is true of market 
creation in watershed protection benefits, as shown in Costa Rica‟s Forest Law of 1996, and 
in the formalization of intellectual property rights in genetic information under the 
Convention on Biological Diversity. Local communities might therefore be beneficiaries of 
processes designed to appropriate the benefits from forest non-market values. The inverse of 
this proposition is also true – they are likely to be the major losers from processes that 
continue to convert forest land.  
  However, there are many potential negative impacts with these flexibility 
mechanisms, such as displacement of indigenous peoples and local communities from their 
16 
 
 
lands, forest destruction, denial of land and land use rights, commercialization and 
monetization without corresponding development opportunities. 
 
2.1.2  Direct use values 
 The value of forests is most commonly associated with the production of timber 
and fuelwood. These are major products for many countries, providing building materials, 
energy, pulp and paper, industrial raw materials and valuable foreign exchange. Estimates by 
3
FAO (2001), show that global production of roundwood reached 3335 million m  in 1999, a 
little more than half of which is used for fuelwood and the remainder for industrial 
roundwood. 
 
2.1.3 Timber values 
 Two types of timber use to be distinguished: commercial and non-commercial. 
Local uses may be commercial or can relate to subsistence, e.g. building poles. World 
industrial roundwood production expands substantially between 1960 and 1990 from some 
3 3
1.0 billion m  to 1.6 billion m  but has since fallen back to some 1.5 billion m3 in the late 
1990s (FAO, 2001). Tropical wood production in 19999 represented a relatively small 
proportion of overall global production of the various commodities: about 15% of the world‟s 
industrial roundwood production, 14% of sawnwood, 15% of wood-based panels and 9% of 
paper and paperboard (FAO, 2001). Industrial roundwood production in 1999 was dominated 
by developed countries, which together accounted for 79% of total global production. 
Industrial roundwood production varied from year to year during the 1990s, but the overall 
trend was relatively flat. This was a significant change from the rapid growth that occurred 
prior to 1990. Wood-based panel and paper/paperboard production show a steadily rising 
demand, which is partially offset by reductions in the demand for sawnwood. 
17 
 
 
 Fibre production has risen nearly 50% since 1960 to 1.5 billion m3 annually. In 
most industrial countries, net annual tree growth exceeds harvest rates; in many other regions, 
however, more trees are removed from production forests than are replaced by natural 
growth. Fibre scarcities are not expected in the foreseeable future. The potential for forest 
plantations to partially meet demand for wood and fibre for industrial use is increasing. 
Although accounting for only 5% of global forest cover, forest plantations were estimated, in 
the year 2000, to supply about 35% of global roundwood, with an anticipated increase to 44% 
by 2020. In some countries, forest plantation production already contributes the majority of 
industrial wood supply (Carle et al., 2001) 
 In a comprehensive survey of sustainable forestry practice, Pearce et al., (2001) 
found that sustainable forest management is less profitable than non-sustainable forestry, 
although problems of definition abound. Profit here refers only to the returns of logging 
regime and do not include the other values of the forest. Sustainable timber management can 
be profitable, but conventional (unsustainable) logging is more profitable. This result is 
largely due to the role that discount rates play in determining the profitability of forestry. The 
higher the discount rate the less market value is attached now to yields in the future. If 
logging can take place in natural forests with maximum harvest now, this will generate more 
near-term revenues than sustainable timber practice. Similarly, sustainable timber 
management involves higher costs, e.g. in avoiding damage to standing but non-commercial 
trees. The non-timber benefits, including ecological and other services, from sustainable 
forests must exceed the general loss of profit relative to conventional logging for the market 
to favour sustainable forestry. The conclusion was also supported by a study of tropical 
forests in Peru, by Rice et al., (1997). 
 
 
18 
 
 
2.1.4 Fuelwood and charcoal 
3
 FAO (2001) statistics suggest that, in 1999, some 1.75 billion m  of wood was 
extracted from forests for fuelwood and conversion to charcoal. Of this total, roughly one-
half came from Asia, 26% from Africa, 10% from South America, 8% from North and 
Central America, and 5% from Europe. The International Energy Agency (1998) estimates 
that 11% of the world‟s energy consumption comes from biomass, mainly fuelwood. IEA 
(1998) estimates that 19% of China‟s primary energy consumption comes from biomass, the 
figure for India is 42%, and the figure for developing countries is generally about 35% 
(UNDP 2000). 
 All sources agree that fuelwood is of major importance for poorer countries and for 
the poor within those countries. While fuelwood may be taken from major forests, much of it 
comes from woodlots and other less concentrated sources. Extraction rates may or may not be 
sustainable, depending on geographic region. Almost no fuelwood and charcoal is traded 
internationally.  
 As with other non-timber products local values of fuelwood and charcoal can be 
highly significant in terms of the local economy. Shyamsundar and Kramer (1997) show that 
the value of fuelwood per household per annum for villages surrounding Mantadia National 
Park in Madagascar is $39. This can be compared with an estimated mean annual income of 
$279, i.e. collected fuelwood from the forest accounts for 14% of household income. 
Houghton and Mendelsohn (1996) found that the value of fuelwood constitutes from 39-67% 
of local household income from fodder, fuel and timber in the Middle Hills of Nepal. 
 
2.1.5 Non-timber forest products 
 NTFP extraction may be sustainable or non-sustainable and few studies make 
observations as to which is the case. One example of sustainable use is Sinharaja Forest 
19 
 
 
Reserve in Sri Lanka, where the most popularly collected NTFPs (Galamus species/rattans, 
Caryota urens/kithul palm used for jiggery production, wild cardamom and a medicinal herb, 
Costcinium fenestratum) all performed better in undisturbed forest, where they were either 
absent or showed growth (Gunatilleke et al., 1995). 
 Extractive uses include: taking mammals, fish, crustaceans and birds for local or 
international trade or for subsistence use, taking plants products such as latex, wild cocoa, 
honey, gums, nuts, fruits, flowers/seeds, berries, fungi and spices, also plant material for local 
medicines, rattan and fodder for animals. Detailed analysis of the available studies suggests 
that economic values of NTFP (net values, i.e. net of costs) cluster from a few dollars per 
hectare per annum up to around US$100/ha/yr. Lampietti and Dixon (1993) suggested a 
„default value of around US$70 per hectare, and Pearce (1998) has suggested US$50. 
However, these values cannot be extrapolated to all forest. Typically, the higher values relate 
to readily accessible forests, values for non-accessible forests would be close to zero in net 
terms due to the costs of access and extraction. 
 The benefits of NTFPs accrue mainly to local communities. The size of the 
population base making use of the forests may be comparatively small and the implied value 
per hectare may therefore also be small due to the unit values being multiplied by a 
comparatively small number of households. It is important to discern, as far as possible, what 
the value of NTPFs is a percentage of household incomes. Available studies suggest NTFPs 
may account for 30-60% of local community household income and in some cases the 
amount exceeds 100% of other income. This perspective demonstrates the critical importance 
of NTFPs as a means of income support. Indeed, it underlines (a) the need to ensure that 
measurements of household income include the non-marketed products taken „from the wild‟ 
and (b) the role that NTFPs play in poverty alleviation. 
 
20 
 
 
2.1.6 Indirect use values 
2.1.6.1 Watershed protection 
 Watershed protection functions include: soil conservation and hence control of 
siltation and sedimentation; water flow regulation, including flood and storm protection; 
water supply and quality regulation, including nutrient outflow. The effects of forest cover 
removal can be dramatic if non-sustainable timber extraction occurs, but care needs to be 
taken not to exaggerate the effects of logging and shifting agriculture (Hamilton and King, 
1983) or permanent conversion to agriculture. Available studies suggests that watershed 
protection values appear to be small when expressed per hectare, but it is important to bear in 
mind that watershed areas may be large, so that a small unit value is being aggregated across 
a large area. Secondly, such protective functions have a „public good‟ characteristic since the 
benefit accruing to any one household or farmer also accrue to all others in the protected area. 
Third, the few studies available tend to focus on single attributes of the protective function - 
nutrient loss or flood prevention etc. The aggregate of different protective function is the 
relevant value. Fourth, the Hodgson and Dixon study (1988) for the Philippines suggests that 
fisheries protection values could be substantial in locations where there is a significant in-
shore fisheries industry. Comprehensive estimates have still to be researched.  
 
2.1.6.2 Carbon storage and sequestration loss rates for tropical forests.  
 An average closed primary forest has some 280 tonnes/ha of carbon and if 
converted to shifting agriculture would release about 200 tonnes of this, and a little more if 
converted to pasture or permanent agriculture. Open forest would begin with around 115 tC 
and would lose between a quarter and third of this on conversion. Using such estimates as 
benchmarks, the issue is what the economic value of such carbon stock is. A significant 
literature exists on the economic value of global warming damage and the translation of these 
21 
 
 
estimates into economic value of a marginal tonne of carbon. A recent review of the literature 
by Clarkson (2001) suggested a consensus value of US$34/tC. Tol et al., (2000) also review 
the studies and suggest that it is difficult to produce estimates of marginal, damage above 
US$50/tC. Taking US$34-50/tC as the range produces very high estimates for the value of 
forests as carbon stores. In practical terms, however, a better guide to the value of carbon is 
the price at which it is likely to be traded in a „carbon market‟. Carbon markets have existed 
since 1989 and refer to the sums of monies that corporations and governments have been 
willing to invest in order to sequester carbon or prevent its emission. More sophisticated 
markets will emerge as emissions trading schemes develop under Kyoto Protocol. Zhang 
(2000) suggests that, if there are no limitations placed on worldwide carbon trading, carbon 
credits will exchange at just under US$10 per tC. At this carbon „price‟ tropical forest carbon 
storage would be worth anything from US$500 per hectare to US$2000/hectare, confirming 
the view of a number of commentators that carbon values could easily dominate the 
economic values of tropical forests. Carbon regimes in temperate countries have also been 
extensively studied and afforestation carbon values probably range from about US$100 to 
$300/ha. These sums are „one off‟ and therefore need to be compared to the price that is paid 
for forest for conversion to agriculture or logging. In most cases, carbon storage is more than 
competitive with conversion values. These values relate to forests that are (a) under threat of 
conversion and (b) capable of being the subject of deforestation avoidance agreements. 
 
2.1.6.3 Option and existence values 
 There are three contexts in which option and existence values might arise: (a) 
someone may express a willingness to pay to conserve the forest in order that they may make 
some use of it in the future, e.g. for recreation. This is known as an option value, (b) someone 
may express a willingness to pay to conserve a forest even though they make no use of it, nor 
22 
 
 
intend to. Their motive may be that they wish their children or future generations to be able to 
use it. This is a form of option value for others‟ benefit, sometimes called a bequest value, (c) 
someone may express willingness to pay to conserve a forest even though they make no use 
of it, nor intend to, nor intend it for others‟ use. They simply wish the forest to exist. 
Motivations may vary, from some feeling about the intrinsic value of the forest through to 
notions of stewardship, religious or spiritual value, the rights of other living things, etc. This 
is known as existence value. 
 There are few studies of the non-use values of forests. The available evidence 
suggests that (a) existence values can be substantial in contexts where the forests in question 
are themselves unique in some sense, or contain some form of highly prized biodiversity – 
the very high values for sported owl (Strix occidentalis) habitats illustrates this; and (b) 
aggregated across households, and across forests generally, existence values are modest when 
expressed per hectare of forest. 
 
2.1.7 Tourism and recreation values 
 Ecotourism is a growing activity and constitutes a potentially valuable non-
extractive use of tropical forests. Caveats to this statement are (a) that it is the net gains to the 
forest dwellers and/or forest users that matter; (b) tourism expenditures often result in profits 
for tour organizers who do not reside in or near the forest area, and may even be non-
nationals; (c) the tourism itself must be „sustainable‟ honouring the ecological carrying 
capacity of the area for tourists. In principle, tourism values are relevant for any area that is 
accessible by road or river. Some forest ecotourist sites attract enormous numbers of visitors 
and consequently have very high per hectare values. Values clearly vary with location and the 
nature of the attractions and none of the studies available estimates the extent to which 
expenditures remain in the region of the forest. For tropical forests, values range from a few 
23 
 
 
dollars per hectare to several hundred dollars. A substantial number of studies exist for the 
tourism and recreational value of temperate forests. Indicative values for European and North 
American forests suggest per person willingness to pay of around $1-3 per visit. The resulting 
aggregate values for forests could therefore be substantial. Elasser (1999) suggests that forest 
recreation in Germany is worth some $2.2 billion per annum for day-users alone and a further 
$0.2 billion for holiday visitors. 
 
2.1.8 Forests biodiversity 
 Besides supplying timber and other forest products, forests have a vital effect on 
processes of great significance for people. They influence local and regional climates, 
generally by making them milder, and they help to ensure a continuous of clean water. Some 
forests, notably tropical cloud forests, even increase the availability of water by intercepting 
moisture from clouds. Watershed forests are particularly important because they protect soil 
cover on site and protect areas downstream from excessive floods and other harmful 
fluctuations in stream flow. By thus reducing the silt load of rivers, watershed forests also 
helps prevent the clogging of reservoirs, irrigation systems, canals and docks, and the 
smothering by sediments of coral reefs. 
 Yet watershed forests are being widely devastated by clearance for agriculture, by 
logging and cutting for fuel, by grazing, and by badly managed road building. The results can 
be extremely expensive. It costs Argentina $ 10 million a year to dredge silt from the estuary 
of the River Plate and keep Buenos Aires open to shipping. Eighty percent of the 100 million 
tones of sediment that every year threatens the harbor come from only Four percent of the 
drainage basin, the heavily overgrazed catchment area of Bermejo River 1,800 Km upstream. 
(Pereira, (1973). In India the annual cost of damage by floods ranges from $140 million to 
$750 million. 
24 
 
 
 Sedimentation as a result of careless use watershed can cut drastically the life of 
reservoirs, hydroelectric facilities and irrigation systems. The capacity of India‟s Nizam-sagar 
reservoir has been halved  (from almost 900 million m3 to fewer than 340 million m3) and 
there is now not enough water to irrigate the 1,100 Km2 of sugarcane and rice for which it 
was intended and hence not enough sugarcane to supply local sugar factories.  Deforestation 
in northern Luzon in the Philippines has silted up the reservoir of the Ambuklao Dam so fast 
that its useful life has been reduced from 60 to 32 years (USAID, 1979). Such problems are 
not confined to developing countries, for example, it has estimated that more than 1,000 
million m3 of sediment are deposited every year in the major reservoirs of the USA 
(Holeman, (1968). Although they have not been calculated (indeed, probably cannot be), the 
global costs of sediment removal, river dredging, reconstruction of irrigation systems and loss 
of investment in expensive structures like dams must be huge. Only 10% of the world‟s 
populations live in mountainous areas, but another 40% live in the adjacent plains (FAO, 
1978); so the lives and livelihoods of half the world directly depends on the way in which 
watershed ecosystems are managed. 
 In areas under shifting cultivation forests also act to restore soil fertility. More than 
200 million people occupying about 30 million Km2 of tropical forests live by practicing 
shifting cultivation. The fallow period lasts from 8-12 years in tropical rain forests to 20-30 
years in drier areas, and during this time the forest cover enables the soil to regenerate. This 
is a stable, productive practice if the population itself is stable; but if populations are 
growing, which nowadays they usually are, the pressure on land increases, fallow periods 
shorten, the soil has no chance to regenerate, and wider and wider tracts of otherwise 
productive forest land are destroyed. Almost two-thirds of land under shifting cultivation is 
upland forest, much of it on steep slopes, and the resulting erosion is severe (FAO, 1978). In 
the Ivory Coast, shifting cultivation reduced the forest cover by 30% between 1956 and 1966 
25 
 
 
and now only 50,000 km2 remain out of the 150,000 Km2 that is believed to have existed at 
the beginning of this century (FAO, 1978). Similarly, shifting cultivators clear about 3,500 
Km2 a year in the Philippines, in Mindanao alone they cleared 10,000 Km2 between 1960 
and 1971( FAO, 1971). 
 
2.1.9 Distribution of World’s Forest 
 The area of the world‟s forest, including natural forest and forest plantations, was 
estimated to be 3869 million ha in 2000, equivalent to almost 30% of the ice-free land area of 
the earth (FAO, 2001). The three major forest biomes are boreal, temperate and tropical. In 
terms of area, the forests are roughly equally divided between tropical/sub-tropical forests 
and temperate/boreal forests. The remaining closed forests amount to 21.4% of the Earth‟s 
land area and occur predominantly in boreal forests (1000 million ha) and tropical areas (680 
million ha); other remaining forests (1820 million ha) are fragmented (UNEP 2001). 
 The majority of the forested area consists of natural forest (95%), with commercial 
plantations comprising 3% and other forest plantations making up the remaining 2% (Carle et 
al., 2001; FAO 2001). Under the FAO definition, natural forest include all forest “composed 
of indigenous trees, not planted by man or in other words, forests excluding plantations”, 
while plantations include “forest stands established by planting or/and seeding in the process 
of afforestation or reforestation. They are either introduced species (all planted stands) or 
intensively managed stands of indigenous species, which meet all the following criteria: one 
or two species at plantation, even age class, regular spacing”. A little over half (55%) of the 
world‟s forest are located in developing countries. Two-thirds are found in only ten 
developing countries: Brazil has 544 million ha, Indonesia 105 million, Democratic Republic 
of Congo 135 million ha, Peru 65 million ha, India 64 million ha, Mexico 55 million ha, 
Bolivia 53 million ha, Colombia 50 million ha, Venezuela 50 million ha and Sudan 42 
26 
 
 
million ha. More than three quarters of the temperate and boreal forests are situated in just 
four countries: Russian Federation 851 million ha, Canada 245 million ha, USA 226 million 
ha and China 163 million ha. At the global level about 30,350 protected areas have been 
established, covering 8.8% of land area (IUCN, 1998). Green and Paine (1997) have 
endeavoured to estimate the extent to which major biomes, including various categories of 
forest, are represented in the global protected areas network. In this analysis, tropical forest 
types are better represented in protected areas than temperate forest types, mainly due to 
more extensive deforestation over a longer period in temperate regions of Eurasia. The 
overall figures for tropical forests appear satisfactory, approximating the 10% target 
established at the IV World Parks Congress (IUCN, 1993), but in reality overestimate the 
extent to which forest ecosystems are being properly conserved in protected areas.  
 A survey of 10 developing countries with major forest resources found that only 
10% of forest protected areas are secure in the long-term, with 60% currently secure but with 
threats likely in the near future and more than 20% are suffering from degradation, (Dudley 
and Stolton, 1999). 
 
2.1.10 Status of Biodiversity in Forest Biomes 
 Forest biological diversity can be quantified at several scales, these include: 
assessing the genetic components within species, counting the number of species per unit area 
(local, regional, national, continental, global), determining numbers and arrangement of 
forest types and their age, classifying types of forest ecosystems, determining communities of 
species associated with forest ecosystem and describing landscape structure (UNEP, 1995). 
 
 
 
27 
 
 
2.1.11 Boreal forests 
 Boreal Forests, including tundra woodlands, extend over about 1270 million 
hectares, or about one third of the world‟s forest cover. The boreal forest is the second largest 
terrestrial biome after tropical forests. This northern circumpolar biome is strongly 
characterized by coniferous ecosystems with low tree species richness, extensive and fairly 
uniform stands and relatively short-lived species (<200 years), which are under fire, wind and 
insect disturbance regimes. Extreme oceanic types with broad-leaved deciduous tress are 
found in northwestern Europe, where the tree limit is formed by Betula pubescens subsp. 
czerepanovii. Similar ecological conditions prevail in northern Asia, Alaska, and northern 
Canada, with stunted Picea, Larix, Pinus pumila and Betula nana at the treeline.  
 Boreal landscapes are composed of a complex of plant communities that, aside 
from vast tracts of forest stands, include various wooded and open mires of bogs, numerous 
water bodies of varying size, rivers, rock outcroppings and natural grasslands and ferns 
(Walter, 1979; Barbour and Christensen, 1993). 
 The Wisconsin glacial events, 10,000- 14,000 years ago, forced plant and animal 
life south, followed by northward migration, in recurrent cycles.  
 The boreal forest biome is distributed across areas formerly covered by continental 
glaciers and, consequently, the land has supported forest cover for only 3,000 to 7,000 years 
(Ritchie 1987). The number of tree species that characterise these forests is therefore low, 
especially in the Euro-Siberian area, where major watercourses and mountain ranges run at 
right angles to the direction in which the species migrated northwards. As a result of the post-
glacial history of the biota, many boreal and subarctic tundra species have wide distributions. 
There are relatively few endemics at the species level; most of these occur these occur in the 
extreme eastern and western parts of the continents, close to ancient refuges. Due to wide 
distributions and varying environmental conditions, evolution at the level of ecotypes and 
28 
 
 
subspecies is common and some genera, such as Carex and Betula, show wide-scale 
hybridization (Jonsell, 2000). 
 Boreal forest stands normally contain no more than a few species, primarily of the 
genera Picea, Pinus, Abies, Larix, Thuja, Betula, Prunus, Alnus and Populus, and they often 
form monocultures, particularly in the case of Picea, pinus and Larix. These genera are 
panboreal and members of the four deciduous genera (Betula, Prunus, Alnus and Populus ) 
grow more rapidly than conifers and tend to occupy sites    immediately following stand 
disturbance. Tree richness in North American forests is greater than in the Euro-Siberia 
region. In North America, four of the six principle boreal forest species extend across the 
continent, though no single tree species is panboareal. Picea mariana grows on poor soils and 
forms the northern treeline continent-wide. Where fire is uncommon, Abies spp. often 
predominates in the eastern and continental North American boreal zone. In Eurasia, this 
genus is ecologically largely replaced by two species of Larix. Larch forest, mostly consisting 
2
of Larix gmelinii, covers 2.5 million km  in continental Siberia where much of the terrain has 
deep permafrost. Larix sibirica often forms monotypic stands following disturbance by fire 
(Schulze et al., 1996). While in North America, Larix laricina is rarely a dominant species, 
and is found mainly in cold, wet and poorly drained sites such as in sphagnum bogs and 
muskeg. In Europe, only Picea abies and Pinus sylvestri  are true dominants of the boreal 
zone, and are often mixed in successional phases with broad-leaved deciduous tree species 
such as Betula pendula, B. pubecens, Populus termula and Alnus glutinosa and A. incana. In 
more eastern European regions, Picea abies is replaced by the closely related Picea obovata, 
with Abies sibirica, Larix sibirica and Pinus cembra subsp. sibirica. There is a broad belt of 
hybrids, Picea abies x P. obovata, between their natural regions. In Eurasia, the proportion of 
Picea gradually decreases eastward while that of Larix increases correspondingly. In northern 
Japan, the number of coniferous species increases again. 
29 
 
 
 Conifers comprise the bulk of the biomass in these boreal ecosystem, although 
most forests also include a variety of deciduous trees and shrub species, dwarf-shrubs 
(notably member sof the Ericacea), grasses, sedges and herbs. In general, species diversity in 
taiga communities increases with length of the growing season, increasing soil fertility and 
favourable drainage. A comparatively moderate richness of bryophytes, lichens and fungi 
occur in many boreal forest types, they are especially common in older forest with their 
volume of decaying wood. 
 Animal species richness generally declines with increasing latitude, and boreal 
forests maintain fewer species than do temperate or tropical forests. Studies have shown a 
longitudinal gradient in the species richness of herbivores, with the region near the Bering 
Sea being particularly species poor (Danell et al., 1996). The fact that this region supports the 
woody species most chemically defended against browsing suggests that such gradients of 
plant chemical defence in boreal forests may be also partly responsible for gradients of 
mammalian species richness (Pastor et al., 1996). An important and characteristic component 
of boreal fauna is migratory birds which breed in summer in the boreal forest and winter in 
more southern areas. In many cases, these tropical and neo-tropical migrants travel thousands 
of kilometers between their winter and summer ranges. Species which must over-winter in 
northern forests have developed a range of adaptations to cold climates including hibernation, 
thick fur, denning beneath the snow, and the ability to maintain life with reduced availability 
and quality of forage, such as by storing fat in the fall and then losing weight over-winter. 
Caribou or reindeer (Rangifer tarandus) can make use of lichens, a group of species not fed 
upon by other boreal animals. Large predators still remain common in Canada, Alaska USA, 
(bears Ursus Americana, Ursus arctos, wolf Canis lupus) and Russian boreal forests (wolf 
and tiger Panthera tigris altaica), but are absent from Scandinavia, although wolves have 
been recorded over the past decade. The large ungulates species are panboreal, including 
30 
 
 
moose (Alces alces) and caribou. Food webs are not complicated and are a few common 
herbivores dominate the deits of all predators, avian and mammalian. Small herbivores (and 
their predators) in boreal systems are well-known for their periodicity, or even cycling (e.g., 
Krebs et al. 1995, Stenseth et al. 1998), which appears related to both food availability and 
predation rate. The dominant cycle length for a wide variety of mammals and birds in North 
America appears to be about ten years, while in Fennoscandia its length is usually four years 
(Keith, 1963; Finerty, 1980; Erlien and Tester, 1984). Such fluctuations represent a 
temporally dynamic aspect of biodiversity. Cycles of herbivores may result in differential 
survival of their preferred food species, such as fir, aspen and birch, as well as their predators, 
such as warbles that prey on budworm (Choristoneura fumiferana), or Canada lynx (Lynx 
Canadensis) that prey on small mammals (Keith, 1963; Hansson, 1979; Haukioja et al., 1983; 
Bryant and Chapin, 1986; McInnes et al., 1992; Stenseth et al. 1998; Thomas et al, 2007).  
 There appears to be relatively few vertebrate animal species with highly restricted 
habitats or niches in boreal forests, although several species relying on dead wood or cavities 
to nest or breed find old forest to be optimal habitat (Thompson and Angelstam 1999). 
Relatively few boreal species are listed by IUCN (2000) as threatened, however, several of 
the large carnivores such as Siberian tiger and brown bear are threatened. 
2.1.12 Temperate forests 
 The temperate forest biome, located in the mid-latitudes, occupies a climatic zone 
with pronounced variations in seasonal temperatures, characterized by distinct winter and 
o
summer seasons, but with a daily mean temperature over 10 C for more than 120 days 
(Walter, 1979). This biome occurs primarily in the northern hemisphere, while in the 
southern hemisphere, it is limited to the southern part of the Andes in Chile and in portions of 
New Zealand, South Africa and southern Australia. Temperate forests are dominated by 
31 
 
 
deciduous tree species and, to a lesser extent, evergreen broad-leaf and needle-leaf species 
(Melilo et al, 1993). More than 50% of the original temperate forest cover has been converted 
to agriculture (Matthews, 1983). Unfortunately, most forest statistics do not distinguish 
between natural forest, secondary forest and plantations. Occurrence of temperate forests is 
highly concentrated in the Russian Federation alone holding over 41% of the world‟s 
temperate forests. However, from an ecological perspective, some of smaller temperate 
forests are critical sources of biological diversity, including for example, those in parts of 
Europe, Australia, South Africa and geographically isolated and highly endemic natural 
forests of New Zealand. 
 In Europe, temperate forests extend over some 160 million ha, which represents 
slightly less than half of the original forest cover. In Western Europe, it is estimated that the 
extent of remaining old growth and semi-natural forest is only 0.8% of the original forest 
cover (Mathews, 1983). Eastern Europe has more old growth forest than in the west 
(Ryzkowski et al., 1999). In the United states, less than 2% of the original temperate forests 
remain, although proportions vary regionally. For example, the states of Washington and 
Oregon have 13% old growth temperate forests remaining. In British Columbia, Canada, 
almost 40% of the original natural forests remain, although some of these are subject to 
intensive forest management (Canadian Council of Forest Ministers, 2000). New Zealand 
retains less than 24% of its native forests (Clout and Gaze, 1984) and in Australia, the amount 
of the original temperate forest varies from 5-20%. In some temperate areas of developing 
countries, there is a net loss of forest cover, Chile, for example, loses about 20,000 ha/year 
(FAO, 2001). 
 The annual productivity of natural northern temperate forests is about900 to 1000 
2 2
g/m  but 1000 to 1400 g/m  in old southern temperate forests of North America (Lieth and 
32 
 
 
Whitaker, 1975). However, there is obviously a large variation associated with these figures 
depending on site, elevation, type and age of forest. Mediterranean forests constitute a 
distinct sub-zone of temperate biome and occur between 30 and 40 degree latitude on the 
west and south-west coasts of the continents. Their climate is characterized by hot, dry 
summer and mild, moist winters. The Mediterranean sub-zone in the Americas occupies 
coastal California in the United States and the coastal region of Chile. In Africa, similar 
forests extend around the Cape of Good Hope; and also occur in the southern part of 
Australia. However, the largest Mediterranean sub-zone is located around the Mediterranean 
Sea and includes the southern part of Europe, the south-west part of Asia and north coast of 
Africa. In Europe, the Mediterranean sub-zone has been the cradle of several civilizations, 
one replacing another over centuries, and this has resulted in a long history of extensive 
environmental change as a result of economic, cultural and social activities. The area 
surrounding the Mediterranean Sea was originally covered with forest of Cedrus libani, 
Quercus ilex, Quercus cerris, Arbutus unendo, Pinus halepensis, Pinus nigra, but the 
Mediterranean hillsides were transformed hundreds of years ago into terraces of fruit 
orchards, gardens, olive tree and fig tree plantations, as well as human settlements. Areas that 
have escaped cultivation are covered with shrubs and bushes, resulting in Maccia (maquis), a 
woody secondary vegetation cover (Ovington, 1983). Few areas of original forest remain, and 
in particular the formerly important forest areas of Turkey, Greece, Lebanon, Israel Iraq and 
Syria have been decimated by many centuries of human exploitation. 
 More than 1200 tree species are represented in temperate biome (Ovington, 1983; 
Schulze et al., 1996). Globally, temperate deciduous forests maintain a large variation in 
species richness, resulting largely from climates and differences in geological history. During 
the Tertiary period (3 million years+ ago), the three deciduous forest regions of the northern 
hemisphere are thought to have had a fairly uniform tree flora. Europe and North America 
33 
 
 
were still closely related floristically and there were also many common species in Europe 
and Asia (Walter and Straka, 1970). However, during the Pleistocene glaciations, the east to 
west orientation of mountain systems, such as the Alps, the Caucasus and the Himalayas, 
apparently formed a barrier, resulting in the Euro-Siberian flora being reduced as many 
species could not survive the cold in various refugia. However, in North America, the 
mountain chains are oriented north to south, enabling easy migration, so most species 
survived the glacial periods in southern locations (Ritchie, 1987). The highest temperature 
species post-glacial survival, and hence current diversity, is in Asia (Ohsawa, 1995), with 
four times the number of tree species there than in North America (Huntely, 1993).  
 East Asia‟s forests are very rich in woody plant species, with almost 900 trees and 
shrubs. That is almost six times greater than in North America, where the second most 
diverse temperate forests occur. The temperate forests of Europe are more impoverished, with 
just 106 tree species and significantly fewer families and genera than in North America. The 
southern hemisphere generally has even fewer species than Europe (except for Australia with 
its high diversity of Eucalyptus and Acacia species), but there is high endemism with most 
species belonging to different families from those found in the northern hemisphere, 
suggesting major differences in evolutionary history. Transition zones between tropical and 
temperate forest biomes, are comparatively species rich. These occur, for example in Japan 
and southern United States, where temperate lowland forests merge with subtropical 
evergreen broad-leaf forests. In southern Canada, the maximum tree species richness in 
temperate forests is approximat ely 60 species, but by mid-latitudes in eastern United States, 
the same biome contains over 100 species, illustrating the general latitudinal relationship of 
species diversity, i.e. diversity increasing towards the equator (Stevens, 1989). 
34 
 
 
 Temperate forests tend to support their largest variety of species on nutrient-rich 
soils, and species richness also seems to be greater on alkaline and neutral soils than on acid 
soils (SCOPE, 1996). Local species richness in many of these forests is highly variable, 
ranging from monocultures to multi-species forests. In many areas of the temperate biome, 
large stands of deciduous forests may be composed of a single tree species. For instance, 
Fagus sylvatica dominates deciduous forests in Europe; F. orientalis forms nearly pure 
stands in the wetter regions of Japan. In Europe, on calcareous soils with high water tables, 
Quercus and Carpinus become dominant rather than Fagus. In North America, Fagus rarely 
dominates forests, but pure stands of Betula and Populus are common, as is the case in 
Siberia and northern Japan. Nothofagus occurs in monocultures in New Zealand and South 
America. Quercus and Pinus are global species found in most northern hemisphere temperate 
forests. In Australia, forests are dominated by extremely diverse genus Eucalyptus with more 
than 70 species in 16 forest types (Ovington and Pryor, 1983) whereas Quercus is absent. 
Although alpha-diversity (patch-scale or within-site diversity) may be low, beta-diversity 
(regional or among-site diversity) in the temperate biome forests can be quite high. 
 In North America, an important temperate coniferous forest belt occurs along most 
of the west coast from Alaska southwards to northern California. The forests lie on the 
windward side of the coastal mountain chain, which runs the length of the continent. The 
forests, collectively referred to as temperate rainforests, exhibit a high level of biological 
diversity with a large number of endemic plants and animals (Ruggiero et al., 1991; Castellon 
and Siering, 2007). They are characterized by several long-lived tree species (>100 year) and 
contain the tallest trees in the world (to 95m), including: Sequoia sempervirens, Sequoia 
gigantean, Pseudotsuga menziesii, Pica sitchensis, Tsuga  heterophyla, Thuja plicata and 
Chamaecyparis nootkatensis (Maser, 1990). The management of the temperate rainforests 
forests has generated more controversy than any of the other North American forest types 
35 
 
 
because of their species diversity, complex functioning and the particularly majestic 
characteristics of the old-growth trees, which can exist for many centuries in a gap-phase 
dynamic condition (Maser, 1990). 
 As with boreal forests, the fauna of temperate forests, especially the birds and 
mammals, can have a wide distribution and even extend to other biomes. For example, 
Neotropical migrants‟ birds of North America, numbering about 250 species, make the 
annual trip from the tropics to the temperate regions, and changes in the extent and condition 
of either forest biome can affect the populations of these birds in both continents. Survival of 
these birds is important because smaller numbers may allow defoliating insects to reach 
epidemic proportions more frequently and this further endangers the survival of some species 
(UNEP, 1995). Not all temperate forests host fauna with such a wide distribution. In the 
forest of southern South America, Southeast Asia, Australia and New Zealand, there are 
many endemic species of mammals and birds that are highly localized. 
 More animal species have become extinct in the past 100 years, or have their range 
and population substantially reduced, in the temperate forest biome than in the other biomes 
(Hilton-Taylor, 2000). Falling particularly into this category are the large ungulates including 
extinct aurochs (Bos Taurus) and tarpan (Equus gmelini silvaticus), endangered bison (Bison 
bonasus) and declining fallow-deer (Cervus dama) and moufflon (Ovis musimon) in Eastern 
Europe. The general reduction of forest cover, combined with hunting and/or trapping, has 
caused the reductions of many large carnivores such as the brown bear (Urus arctos), lynx 
(Felis spp.), cougar (Puma spp.) glutton or wolverine (Gulo gulo) and wolf (Canis spp.) 
(Hilton-Taylor, 2000; Pimm et al.,1995). Within the past 20 years in North America, the 
passenger pigeon (Ectopistes migratorius), Carolina parakeet (Cornuropsis carolinensis), 
ivory-billed woodpecker (Campephilus principalis), Bachman‟s warbler (Vermivora 
bachmanii) and the eastern cougar (Puma concolor) have become extinct (Pimm et al., 1995). 
36 
 
 
The USA has the highest number of threatened species as listed by IUCN (2000) at 997 
species, with most of these occurring in temperate ecosystems. 
2.1.13 Tropical forests 
 In the tropical forest biome, three major regions are recognized: American, African 
and Indo-Malaysian (Whitmore, 1984,1990). Tropical forests may be  broadly classified as 
moist or dry, and further subdivided into rainforest (some 66% of the tropical moist forest), 
cloud forest, evergreen season forest, semi-evergreen tropical forest, moist deciduous forest 
(monsoon forest), dry deciduous forest, and mangrove Rainforests occur in Central and South 
America, Africa, the Indo-Malaysian region and in Queesland, Australia. Where several dry 
months (60 mm rainfall or less) occur regularly in the tropics, monsoon or season forests 
(closed forests) have together been termed “tropical moist forest” (. Cloud forests situated at 
middle to high altitudes derive a significant part of their water supply from cloud and fog, 
and hence these support a rich abundance of vascular and nonvascular epiphytes. The 
evergreen seasonal forest are found in regions where every month is wet (100 mm rainfall or 
more) and in areas with only short dry periods (Whitmore, 1990; Sahney et al, 2010). 
 Dry tropical rainforest were originally described as “evergreen, hygrophilous in 
character, at least 30 m high in thick-stemmed Ilianas, and in woody as well as herbaceous 
epiphytes.” Mangroves are the characteristic littoral formations of tropical and subtropical 
sheltered coastlines, they have been variously describe as “coastal woodland,” “tidal forest” 
and “mangrove forest.” Basing his work on previous classifications, Whitmore (1990) has, 
for convenience, grouped the formations within tropical rainforest according to the main 
physical characteristics of their habitats, noting that the naming of vegetation types is always 
problematic. In this arbitrary arrangement, the first division is between climates with a dry 
season and those that are perhumid (for moist forest), the second division (for rain forest), is a 
crude measurement of soil water availability and distinguishes swamp from drier land forests.  
37 
 
 
 The third division is based on soils and, within dryland forests, distinguishes those 
on parent materials with atypical properties – peat, quartz sand, limestone, and ultrabasic 
rocks – from the widespread “zonal” soils mainly ultisols and oxisols. Finally there is a 
division of the forests on zonal soils by altitude. In the Indo-Malaysian region the tropical 
rainforest lies as a belt of evergreen vegetation extending through the Malay Archipelago 
from Sumatra in the west to New Guinea in the east (Whitmore, 1984). This is the non-
seasonal humid zone of the Southeast Asian dipterocarp forests. Parches of rainforest, or 
outliers, are found in southern Thailand, in Sri Lanka, India, northern Queensland in 
Australia and on the Melanesian islands of the Pacific.  
 Where seasonality of rainforest occurs, it produces a strong temporal effect on 
primary production (Orians et al, 1996). Productivity varies considerably among the primary 
tropical forest types; Lieth and Whitaker (1975) and Murphy (1975) provide the following 
2 2
data: tropical rainforest: 1800-3210 g/m ; cloud forest: 2400 g/m ; dry deciduous and mixed 
2 2
tropical forests: 1040-1230 g/m ; for seasonal forest, a single estimate of 1340 g/m  from 
2 2
west Africa, and for mangrove: 930 g/m  from the Caribbean and 1000 g/m  at 10 to 25 years 
of age at Matang, in Peninsular Malaysia. The rainforest data show a primary productivity 2-
4 times greater than that recorded in boreal forests and correlate broadly to a general 
latitudinal reduction in diversity of plants and animals north from the tropical forest biome. 
 Tropical forests are the most species rich and diverse forests on earth, estimated to 
contain at least 50% of all plant and animal species (Myers1986). This is especially true for 
wet tropical forests, where, for example, some 700 tree species have been recorded in 10 
selected 1-hectare plots in Borneo (UNEP, 1975). Estimated number of tree species in the 
tropics ranges from 17,000 in Africa (Hamilton, 1989) to more than 30,000 in central 
America (Prance, 1989). However, within tropical moist forests, species richness varies 
greatly by region and some tropical moist forests actually have relatively low tree species 
38 
 
 
diversity. In the Amazon Basin, for example, less than 90 tree species per hectare have been 
recorded in the eastern portions compared with nearly 300 species/ha in the western areas 
(WCMC 1992).  
 Mangrove forest have relatively low terrestrial species richness, with counts in 
some river deltas of only about 30 species (IUCN, 2000), although the aquatic life they 
support is diverse and abundant. African rainforest have fewer plant species than other 
tropical regions (by about 20%), with several pantropical genera and families (e.g., 
Lauraceae, Myrtaceae and Palmae) being either absent or poorly represented (Jacobs, 1981). 
Lianas and epiphytes are also less abundant in Africa rainforests compared to in other tropical 
regions (Jacobs, 1981).  
 Few tropical genera are pantropical and endemism is much higher in this biome 
than in the temperate or boreal forest biomes (UNEP, 1995). For example, in fourteen areas 
2
within exceptionally high species richness in the tropics, on about 300,000 km ., more than 
37,000 plant species can be found (Myers, 1990). Tree species richness declines as altitude 
increases and as climate becomes more seasonal (Orians et al., 1996). The mixture of many 
tree species, with few individuals of each, in a given forest area is a key feature of tropical 
forests and one which distinguishes them from forests in the boreal and temperate biomes. 
This feature is significantly related to a predominance of dioecious species and to a seed 
dispersal relationship with animals in the tropics, compared to boreal and temperate forests 
where wind is often the medium of seed dispersal (Orians et al., 1996). Low density of 
individual species has particular consequences with respect to the necessity for large areas for 
preserving populations. (Wardle et al 2011)  
 Where tropical forests with single dominants do occur (usually dry forest), there 
are no corresponding species among the regions. In the Americas, Eperua and Mora 
39 
 
 
dominate such tropical forests, in Africa, Gilbertiodendron is a common dominant, 
dipterocarps dominate in areas of Southeast Asia, in Indo-Malaysia, Agthis is sometimes 
dominant, while in tropical Australia, Eucalyptus is dominant genus in low richness stands 
(Whitmore, 1990). In rainforests, epiphytes, although common to all regions, are highly 
distinct and certain families predominate (Gentry, 1992), Bromiliaceae and Cactacae in 
Americas; and Orchidacae, Asclepiadacae and Rubiaceae, in indo-Malaysia: Lianas are 
another important component of the structure of tropical rainforests, absent from other 
biomes Gentry, 1992. They make up 8% of the species (in Borneo 150 genera exists) and are 
indicators of an undisturbed state of forests (Jacobs, 1981). Twelve genera and some 470 
species of  the family Dipterocarpaceae are found in the rainforests of the Indo-Malaysian 
region, ranging from Seychelles through Sri Lanka to the south of peninsular India, east to 
India, Bangladesh, Myanmar, Thailand, Indo-China, to continental South China (Yunnan, 
Kwangsi, South Kwangtung, Hainan) and through Melanesia (natural botanical kingdom 
comprising peninsular Malaysia, Sumatra, Java, Lesser Sunda Islands, Borneo, the 
Philippines, Celebes, the Moluccas, New Guinea and Solomons)(Ashton, 1982).  
 With the exception perhaps of New Guinea and the eastern part of the region, the 
tropical rainforests of Indo-Malaysian region are characterized by family dominance of the 
Dipterocarpaceae. Tropical dry forests generally host lower species richness, with fewer 
endemics than tropical moist forests, although still significantly higher than in temperate 
forests. The richest dry forests, found in northeast Mexico and southeast Bolivia, have an 
average of 90 tree species per hectare (WCMC, 1992). Dry forests are more similar in species 
richness to their moist counterparts in terms of mammal and insect species. Tropical dry 
forests are noted for their highly endemic mammal populations, especially insectivores and 
rodents. An important feature of cloud forests and some other montane forests lies in their 
high species richness of epiphytes, shrubs, herbs, llianas, and ferns (Gentry, 1992).  
40 
 
 
 These species increase with altitude in the humid tropics whereas in the warmer, 
lowland tropical forest types, they tend to be less frequent. In addition, cloud forests often 
contain high numbers of rare endemic plant and animal species or subspecies, such as 
mountain gorilla (Gorilla gorilla beringei) in Central/East Africa, and the quetzal 
(Pharamachrus miccino) of Central America (IUCN, 1995). The percentage of endemic 
species is even higher in cloud forests on Island Mountains, such as those in Hawaii and in 
the French overseas territories of Reunion Island and New Caledonia. Mangroves may form 
very extensive and productive forests. Throughout the tropics, there are about 60 species of 
trees and shrubs that are exclusive to the mangrove habitat, the important genera being 
Avicennia, Bruguiera, Rhizophora, Sonneratia and Xylocarpus. There are also important, 
non-exclusive associated with the mangroves, including the fern Acrostichum spp., and trees 
such as Barringtonia racemosa, Hibiscus spp. and Thespesia species.  
 High species richness in the tropical biome may be the result of the large range of 
available microhabitats and niches, the absence of mountain systems or their north-south 
orientation permitting ease of migration and a lengthy period without major disturbance (e.g. 
glaciations) (UNEP, 1995). Terborgh (1986, 1989) reported that many avian guilds were 
abundant in the tropics but entirely absent in temperate or boreal biomes including terrestrial 
frugivores, dead leaf gleaners, army ant followers, and many of the frugivores. High 
productivity is sustained annually, as opposed to seasonally, in many tropical areas which 
allows multiple breedings and results in less movement away from home ranges to avoid 
seasonality (Margaleaf, 1968). Further, in places such as Madagascar and large number of 
tropical island habitats of Southeast Asia and the Caribbean, a high level of endemism is 
found because of their isolation (Margaleaf, 1968). 
41 
 
 
 As an important aspect in tropical forests, differing from boreal and temperate 
forests is the high degree of dioeciousness among trees. The coevolution of tree reproduction 
with pollinators and seed-dispersing organisms is an important and crucial functional linkage 
in tropical forests. Elimination of certain tree species through selective logging can lead to 
loses in animal species closely or obligately tied to the trees (e.g. Terborgh, 1989). The 
forests of South  America and Asia maintain very high animal species richness compared to 
the African tropical forests (UNEP, 1995). The rivers of the Amazon Basin host the most 
diverse fish populations present in its canopy also have high species richness (WCMC, 1999). 
Wilson (1992) recorded 43 species of ants, belonging to 26 genera, on a single tree in Peru, 
about the same number of species as the entire ant fauna of the British Isle. It is not unusual 
for a square kilometer of forest in Central or South America to contain several hundred 
species of birds and many thousands of species of butterflies, beetles and other insects 
(Wilson, 1992). Stattersfield et al. (1998) noted that of the total world forest avifauna, 88% 
are endemic to tropical forests, and of those, more than half are found in wet forest types. 
 Large numbers of species is endangered in tropical areas, despite incomplete 
taxonomy. The IUCN (2000) Red list reports that the majority of threatened species are often 
from tropical areas, and that high levels of species endangerment occurs in southeastern Asia 
(Malaysia 805 species, Indonesia 763 species, Philippines 387 species). Further, other small 
tropical states have high proportions of their species endangered, for example Cuba (206 
species), Jamaica (240 species), Madagascar (302 species), and Papua/New Guinea (263 
species). High numbers of endangered species are listed for some countries with large areas 
of tropical forest including Brazil (608 species) and Mexico (418 species). 
 
 
 
42 
 
 
2.2 CAUSES OF FOREST BIOLOGICAL DIVERSITY LOSS 
2.2.1 Threats to biological diversity 
 It is important to distinguish between underlying or ultimate causes for loss of 
forest biodiversity from the direct causes. The underlying (or ultimate) causes of forest 
destruction are the factors that motivate humans to degrade or destroy forests; complex causal 
chains are usually involved. The underlying causes originate in some of the most basic social, 
economical, political, cultural and historical features of society. They can be local, national, 
regional or global, transmitting their effects through economic or political actions such as 
trade or incentive measures (WWF, 1998). The direct (or proximate) causes of biodiversity 
loss in forests are human induced actions that directly destroy the forests (such as conversion 
of forest land, continuous overexploitation or large scale logging) or reduce their quality (by, 
for instance, unsustainable forest management or pollution).  
 The driving forces behind direct human impact on forest degradation and 
deforestation and, consequently, on biodiversity loss are both numerous and interdependent 
(e.g., McNeely et al., 1995; Contreras-Hermosilla, 2000; Hoffman et.al 2010). Forest 
biodiversity is directly linked to the existence of forest and to the way forests are managed, 
and that deforestation and forest degradation including unsustainable logging, slash and burn 
agriculture, the building of infrastructure such as dams and roads, pollution, fires, infestation, 
and effects of invasive species are themselves the main proximate causes for loss of forest 
biodiversity. Some of these proximate causes, such as climate change or agricultural 
development, can also act as underlying causes, (Benton, 1996). 
 The interactions between direct and underlying causes are very complex: the cause-
effect relationships will vary considerably from country to country and/or over time and there 
can therefore be no overall hierarchy between the causes; they do not interact linearly, but  
rather in a circular fashion with many feedback loops. Even a single force such as agricultural 
43 
 
 
intensification, may operate in a very different way under one set of circumstances than it 
would in a different situation with other variables involved. Accordingly, remedial measures 
need to be tailored to the very specific situation to which they will be applied. There are no 
simple solutions to this complex phenomenon. (Sunderlin and Resosudarmo,1999). 
  The distinction between direct and underlying causes of forest degradation is often 
not as clear as it appears. In reality, there are long, complex causation chains that eventually 
lead to deforestation. Causes may be hierarchical. For example, a hypothetical chain of 
causes and effects may operate in this way: shifting cultivators deforest because they need to 
provide a means of survival for their families. This is because they are poor and have few 
alternatives to deforestation. They are poor because present power structures discriminate 
against a large number of people who therefore have little or no means of survival. Present 
power structures originated in historical arrangements such as colonization and runs through 
unequal control over key resources, to poverty and the need to survive and finally, to forest 
decline. 
 Causal factors are likely to vary over time, sometimes drastically. At certain stages 
of development, rapid income growth could promote decline by, for example, increasing 
demand for forest products and by enhancing human capacity to alter forests. When 
economies reach a certain threshold, the process is reversed. At this point, increases in the 
level of income per capita begin to be associated with factors such as technological 
improvements, better functioning of government institutions, urbanisation and less relative 
dependence on agricultural and forest production. That leads also a change in the composition 
of demand for goods and services with greater demand for environmental services of forests 
and for uses, such as recreation, that do not necessarily lead to the loss of forest cover 
(Contreras-Hermosilla, 2000). 
 
44 
 
 
2.2.2 Lack of capacity, technical and financial resources 
 Despite all the efforts of donors to provide money and technology necessary to 
help conserve and sustainable manage forests, the lack of technical expertise and financial 
resources remains an important cause of forest decline. Understaffed forest authorities, lack 
of knowledge about forest biological diversity and related goods and services and the lack 
available qualified personnel lead to little or no application or enforcement of forestry laws. 
Gabon, for example, only 100 agents were available to monitor and inspect 322 logging 
2
concessions covering 86,00Km  (Global Forest watch, 2000). Another underlying cause for 
poor forest management is the lack of appropriate forest management plan and their 
implementation. Again in Gabon, only five of 200 logging companies have initiated work on 
a management plan (Global Forest Watch, 2000). 
 
2.2.3 Lack of secure land tenure and land rights and uneven distribution of ownership 
 The lack of secure land tenure and the inadequate recognition of the rights and 
needs of forest-dependent indigenous and local communities have also been recognised as 
major underlying causes of forest decline (UN Econ. And Soc. Coun., 2000).  Weak property 
rights reduce the incentive for sustainably managing the forests and unsecured land tenure is 
often directly related to deforestation.  Local communities and indigenous people have, in 
many cases, traditional ways of sustainably managing the forests, ensuring that they remain 
viable for use by future generations.  Increasing inequality of land ownership often leads to 
the breakdown of such common property management schemes.  The rapid depletion of 
species and destruction of habitats occur in many countries where a minority of the 
population may own or control most of the land.  Quick profits from excessive logging can 
flow to a small group of people, while the forest dependent local communities pay the price.  
Clear ownership rights are one of the prerequisites for developing sustainable management 
45 
 
 
plans and applying regulations for ensuring the conservation and sustainable management of 
forests.  Forest land often has a smaller value than agricultural land and, in the absence of 
laws that forbid deforestation; it is, therefore, cleared following privatization.  On the other 
hand, privatization can be a prerequisite for ensuring sufficient investments in order to ensure 
the sustainable management of the forest.  
 It is well established that the existence of complete, exclusive, enforced and 
transferable property rights is a prerequisite for the efficient management of natural 
resources.  Rights must be complete and exclusive to avoid disputes over boundaries and 
access.  They must be enforceable to prevent others from usurping them and they must be 
transferable (there must be customary or full market in them) to ensure that land is allocated 
to its best use.  The effects of incomplete or no property rights show up most clearly in the 
lack of incentive to invest in conservation and sustainable land uses.  Regardless of the 
„paper‟ designation of forest land rights, many forests are de facto open access resources i.e. 
resources for which there are no owner.  Other forests are common property and are managed 
by a defined group of households with rules and regulations about access, use and 
transferability.  Provided common property resources are not subject to external forces that 
lead to the breakdown of the communal rules of self-management, common property is a 
reliable and reasonably efficient use of forest land.  Factors causing common property 
breakdown include rapid population growth and interference in traditional communal 
management by central authorities.  Traditional, customary and, sometimes, even legally 
recognized land rights of indigene nous peoples can be hard to establish and are often ignored 
or violated. 
 Establishing property rights in the form of communal or private ownership regimes 
is a prerequisite to efficient land use, but may still not guarantee the desirable level of forest 
protection.  This will be the case where the forest values take the fore of „public goods‟ i.e. 
46 
 
 
services and goods the benefits of which accrue to a wide community of stakeholders and for 
which no mechanism exists to charge them for the benefits.  Forest dwellers may then have 
no incentive to conserve forests for their benefits to downstream fisheries or water users, 
since they receive no benefit for these services.  Institutional change designed to compensate 
forest users for these services can often be devised (see below), effectively establishing 
property rights in the unappropriated benefits of forest services. 
 
 
47 
 
 
 Examples of policy failures that may lead to forest decline 
Direct government investment *Road construction 
in the forest sector or in related sectors *Hydropower investments 
Government command and control *Conservation area protection 
regulations *Obligation to replant harvested areas 
*Prohibition to harvest without permit 
*Obligation to prepare forest management plans as 
condition for intervening in forest areas log export bans 
Fiscal, price or monetary policies *Subsidies affecting forest raw materials or other inputs 
*Subsidies affecting competitive uses of lands such as 
cattle ranching 
*Plantation subsidies 
*Price controls 
*Subsidies affecting forest harvesting or manufacturing 
*Price controls 
*Forest products taxes 
*Foreign exchange policies affecting competitive uses 
of lands 
Provision of services *Delimitation, demarcation and land titling 
*Actions to promote exports 
*Settlement of frontier areas 
 
  
Source: (Sunderlin and Resosudarmo, 1999) 
 
 
 
 
 
48 
 
 
2.2.4 Lack of good governance 
 The lack of good governance, rampant corruption and fraud are major 
underlying causes of forest decline as they surround illegal logging and other related 
crimes, such as arson and poaching. Politicians and civil servant may misuse the 
public power entrusted to them by, for instance, sale of logging concessions for 
personal enrichment, by not enforcing laws and regulations and by partaking in other 
illegal and corrupt activities. This generally weakens the administrative apparatus, 
deprives the government of income, generate incentives for „cut and run‟ logging 
operations and increases investment risks, thereby reducing incentives for sustainable 
forest management. The consequence in terms of forest biological loss and loss of 
related goods and services is often dramatic. 
 
2.2.5 Ill-defined regulatory mechanism and lack of law enforcement 
 In some countries, the rise of corporate power has gone hand in hand with a 
breakdown in the rule of law. Economic hardship and a growing underclass have 
combined to create a rapid increase in illegal activity, including illegal logging, 
animal poaching and illegal trade. Lack of law enforcement is also linked to the lack 
of adequate financial resources allocated to the implementation of the regulations. 
Many national laws are too weak to provide adequate controls and when this is not the 
case, governments are often too weak to implement these. Property rights are more 
likely to be granted to those who clear the forests or live in the cities than to forest 
dwellers living by the sustainable harvest of natural products (Arnold and Bird, 1999). 
This favours extraction of marketable products (e.g. timber) over the sustainable 
harvesting of products with a limited market value. The range of ill-defined 
regulations can cover all aspects of the causes of forest decline. As an example, in 
49 
 
 
some countries there are governments guidelines used to promote forest management 
activities that are detrimental to forest biodiversity. For instance, regulations of the 
former Latvian government for the management of cultivated forest areas required 
that every piece of dead wood be remove. 
 
2.2.6 Illegal logging 
 A number of recent publications have revealed the extent of the wide range of 
illegal activities to be one of the major causes of forest decline (Jepson et al., 2001, 
FOE, 2000, Glastra, 1999, de Bohan et al., 1996). In the 1980s, the Philippines lost 
about US$1.6 billion per year, a large share of the country‟s gross domestic product, 
to illegal logging. In 1993, Malaysian log exports to Japan were under-declared by as 
much as 40%. Up to one-third of the volume of timber harvested in Ghana may be 
illegal and observers indicate that money injected into the country as part of a SAP 
led to illegal practices on a massive scale (Contreras-Hoermosilla, 2000). An internal 
report by the Cameroon Ministry of Environment and Forests (MINEF, 1999; see also 
FERN, 2001) provides clear evidence of large scale, illegal activities by logging 
companies in Cameroon.  Six companies that are amongst the largest loggers of 
Cameroon forests are said not to respect basic requirements of sustainable forest 
management.  For example, they do not prepare management plans and have no 
respect for environmental laws. 
 In Indonesia, illegal logging has been recognised as the most important cause 
3
of forest decline, about half to two thirds (30 – 50 million M ) of wood consumed 
each year comes from illegal sources.  It is exacerbated by bad governance and 
corruption, which often include the direct involvement of military, police and forest 
officials (Forest Liaison Bureau, 2000).  If the current rate of deforestation continues 
50 
 
 
in Indonesia, the lowland forest of the Sunda Shelf, some of the richest forests on 
earth, will be completely degraded by 2005 on Sumatra, and by 2010 in Kalimantan 
(Jepson et al., 2001).  Global Witness (1998) described the scale of corrupt forest 
activities in Cambodia and stated that in 1997 much of the estimated US$184 million 
worth of timber felled in the country went into the pockets of corrupt officials.  Illegal 
logging could mean the complete disappearance of Cambodia‟s forests in only five 
years time.  All these studies strongly suggest a close link between illegal and corrupt 
activities on one hand and forest decline on the other.  Greenpeace launched a series 
of press releases that provide evidence of the import of illegally logged wood 
products into the United States, Japan and European countries.  According to one of 
their studies (Greenpeace, 2000), 80% of all wood logged in the Amazon is taken 
illegally. 
 The forestry sectors of tropical countries are particularly susceptible to illegal 
operations and corruption.  There are several reasons for this: 
(a) In most tropical countries, forest activities take place in remote areas, away 
from the press, the public and official scrutiny. 
(b) Wood, particularly in tropical countries, is valuable but not inventoried.  It is 
thus difficult to determine how much wood is illegally extracted. 
(c) Frequently, officials have substantial discretionary power.  High timber values 
and high discretionary power held by poorly paid government officials are ideal 
conditions for corruption (Contreras-Hersoilla, 2000). 
(d) Investment in enforcement is minimal owing to other priorities. 
 Illegal logging is not limited to tropical countries but also occurs in other 
countries facing political and/or economic changes such as the Russian Federation, 
where an unknown, but probably substantial amount of timber is illegally logged and 
51 
 
 
traded and exported, mainly to Chinese and Japanese markets but also to western 
Europe ( FOE, 2000). 
 
2.2.7 Lack of scientific knowledge and inadequate use of local knowledge 
 In many cases there is an inadequate knowledge of natural ecosystems (their 
components, structure and functioning).  Furthermore, destruction and decline of 
cultures that possess a traditional understanding of nature is resulting in a permanent 
loss of important complementary information on ecosystems.  These gaps in 
knowledge arise from an insufficient research effort in the study and monitoring of 
forest ecosystems.  Such research is necessary in order to improve understanding of 
how various components interact, to improve information on traditional use and 
knowledge of biodiversity and to implement appropriate changes in ecosystem use. 
 In Indonesia, illegal logging has been recognised as the most important cause 
3
of forest decline, about half to two thirds (30 – 50 million ) of wood consumed each 
year comes from illegal sources.  It is exacerbated by bad governance and corruption, 
which often include the direct involvement of military, police and forestry officials 
(Forest Liaison Bureau, 2000).  If the current rate of deforestation continues in 
Indonesia, the lowland forest of the Sunda Shelf, some of the richest forests on earth, 
will be completely degraded by 2005 on Sumatra, and by 2010 in Kalimantan (Jepson 
et al., 2001).  Global Witness (1998) described the scale of corrupt forest activities in 
Cambodia and stated that in 1997 much of the estimated US$184 million worth of 
timber felled in the country went into the pockets of corrupt officials.  Illegal logging 
could mean the complete disappearance of Cambodia‟s forests in only five years time.  
All these studies strongly suggest a close ling between illegal and corrupt activities o 
one hand and forest decline on the other.  Greenpeace launched a series of press 
52 
 
 
releases that provide evidence of the import of illegally logged wood products into the 
United States, Japana and European countries.  According to one of their studies 
(Greenpeace, 2000), 80% of all wood logged in the Amazon is taken illegally. 
 The forestry sectors of tropical countries are particularly susceptible to illegal 
operations and corruption.  There are several reasons for this: 
(a) In most tropical countries, forest activities take place in remote areas away 
from the press, the public and official scrutiny. 
(b) Wood, particularly in tropical countries, is valuable but not inventoried.  It is 
thus difficult to determine how much wood is illegally extracted. 
(c) Frequently, officials have substantial discretionary power.  High timber values 
and high discretionary power held by poorly paid government officials are ideal 
conditions for corruption (Contreras-Hermosilla, 2000). 
(d) Investment in enforcement is minimal owing to other priorities. 
 Illegal logging is not limited to tropical countries but also occurs in other 
countries facing political and/or economic changes such as the Russian Federation, 
where an unknown, but probably substantial amount of timber is illegally logged and 
traded and exported, mainly to Chinese and Japanese markets but also to western 
Europe ( FOE, 2000). 
 
2.2.8 Under –valuation of forest biological diversity goods and services 
 Many forest products are consumed directly and never enter markets.  For 
instance, sawn timber, pulpwood, rattan and gums may be marketed, while food, 
fuelwood and medicinal plants harvested by local people will usually be consumed 
directly by them. Biodiversity benefits are in large part “public goods” that no single 
owner can claim. The benefits of biodiversity are so diffuse that no market incentives 
53 
 
 
for biodiversity conservation ever develop, which „justifies‟ government policies that 
further encourage conversion of the forest to other use with greater direct market 
values. Thus biodiversity will probably continue to decline while it remains 
undervalued or not valued. A challenge is to develop ready means of attaching greater 
value to it in order to provide an incentive for sustainable management. 
 One of the features underlying comparisons of relative profitability of 
different forest land uses is the role of the discount rate. High discount rates favour 
conventional logging over sustainable timber management, slash-and-burn agriculture 
over agro-forestry and so on. The issue is therefore one of knowing how large 
discount rates are in such contexts. Existing research suggests that local communities 
often have high discount rates of well over 10% and up to 30 or 40%, reflecting their 
urgent need to address subsistence and security needs now rather than in the future 
(Poulos and Whittington, 1999). While this conclusion should not be exaggerated – 
there are many examples of poor communities investing in conservation practices – 
the available evidence supports the traditional view that many have high discount 
rates that these contribute to „resources mining‟. 
 
 
2.2.9 Lack of cultural identity and spiritual values 
 As cultural homogenization sweeps across the world, the vast range of human 
knowledge, skills, beliefs and responses to biological diversity is eroded, leading to 
great impoverishment in the fund of  human intellectual resources. Loss of cultural 
diversity, as a result of globilisation, leads to loss of biological diversity by 
diminishing the variety of approaches to the coexistence of humans, other animals and 
54 
 
 
plants that have been successful in the past. Loss of the different cultures also reduces 
the possibility of imaginative new approaches being developed in the future. 
 
2.2.10 Deficiencies in the flow of information in decision makers and to local 
communities 
 Where scientific or traditional knowledge exits, it does not necessarily flow 
efficiently to decision-makers, who may in consequence often fail to develop policies 
that reflect the full values of biodiversity. Information also fails to flow efficiently 
between central decision-makers and local communities. To complicate things further, 
there is a strong public reluctance to accept policies that reduce excessive resource 
consumption, no matter how logical or necessary such policies may be. 
 
2.2.13 Lack of Environmental Impact Assessments or Strategic Environmental  
Assessments 
 Infrastructure development projects, structural adjustment programmes, 
development programmes and trade agreements have been identified as possible 
direct and underlying causes of forest biodiversity loss. The problem is exacerbated 
by the fact that very often no Environmental Impact Assessment (EIA) or Strategic 
Environmental Assessment (SEA) accompanies the development of these projects. In 
addition, many EIAs or SEAs that are undertaken do not include a concrete analysis 
of the impact of the projects on the quality, size and management of the forests that 
may be affected. 
 
 
 
 
55 
 
 
 Consequences of forest biodiversity loss from the perspectives of  
different segments of society 
Societal Group Implications of continued Forest Biodiversity loss 
Forest-dwelling indigenous *Loss of spiritual values. 
communities *Disruption of traditional structures and communities, breakdown of family 
values, and social hardship. 
*Loss of traditional knowledge of use and protection of forests in sustainable 
ways. 
*Reduced prospects for preservation of forest environmental and aesthetic 
functions of interest and potential benefit to society as a whole. 
*Loss of forest products providing food, medicine, fuel and building materials. 
Forest farmers and shifting *For shifting cultivators, an immediate opportunity to survive 
cultivators *Forest deregulation and declining soil fertility 
*Loss of access to forest land and the possibility of food crop production and 
reduced possibility for harvesting forest products, both for subsistence and 
income generation. 
*Prospects of malnutrition or starvation. 
*Disruption of family structures and considerable social hardship. 
Poor and landless local *Decreased availability of essential fruits, fuelwood, fodder and other forest 
communities living outside products. 
forests *Reduced agricultural productivity, through loss of the soil and water protection 
potential of remnant woodlands and on-farm trees and loss of shelterbelt 
influence leading to reduced crop yield. 
*Reduced income generation and possibilities to escape poverty. 
Urban dwellers *In developing-country situations, reduced availability (and /or overpriced) of 
essential forest products such as fuelwood, charcoal, fruits, building materials 
and medicinal products. 
*Loss of the amenity and recreational values of urban forests and parks and 
those afforded by national forest parks and wilderness areas. 
*Reduced prospects for assured supplies of clean drinking water and clean air. 
Commercial forest *Immediate large profits. 
industries and forest *In the long-term, loss of company business and forced closure of forest 
worker communities operations. 
*Loss of jobs for forest-dependent communities, social disruption and hardship. 
*Loss of income and possible negative social implications of reduced of 
shareholders with significant savings invested in forest industrial company 
56 
 
 
stock. 
Environmental Advocacy *Loss of the essential functions of forests, including biodiversity, climate 
groups and conservation regulation, preservation of water catchments and fishery values, that these 
agencies groups are concerned with preserving. 
*Loss of cultural values and social hardship for the underprivileged 
communities whose welfare these groups are committed to protect. 
*Increased problems of environmental pollution 
*Loss of those forest values that could be of vital importance and/or interest to 
the survival and welfare of future generations. 
Mining, oil exploration and *Improved access to potentially profitable mineral, oil or other commercially 
other industrial interests valuable products located under forests. 
*Increased profitability of company operations and returns to company 
shareholders. 
*politically negative impact on company operations of criticism by 
environmentally concerned groups. 
The global Community *Prospects that continued forest destruction will accelerate global warming and 
potentially negative consequences for human welfare and survival. 
*Continuing biotic impoverishment of the planet, loss of genetic resources, and 
all that implies for sustainable food production and loss of potentially valuable 
medicinal and other products. 
*Increasing pollution and toxicity of forest soils, contributing to declining forest 
health. 
National government and *Immediate escape from political pressures when impoverished populations 
planners and decision migrate to frontier forest areas. 
makers  *Loss of potential source of development revenues with consequences of 
reduced employment and opportunities, sustainable trade and economic 
development. 
*Loss of the wide range of environmental functions that forests provide in 
contributing to societal needs and an habitable earth. 
*Loss of political support in situations where forestry loss and degradation 
adversely affect the welfare of many citizens. 
 
Source: (Sunderlin and Resosudarmo, 1999) 
 
 
 
57 
 
 
2.2.11 Perverse incentives and subsidies and ill-defined developmental  
programmes 
 Governments world-wide provide incentive systems that affect natural 
resource use. While usually conceived with good intentions, they often have 
deleterious effect on natural resources. Notable examples include the $800 billion 
spent each year on subsidizing certain economic activities, especially agriculture 
($400 billion). Most subsidies are in the developed economies, where agricultural 
subsidies are responsible for some reduction in woodland area, the woodland being 
removed to capture the subsidies, which are often on a per hectare basis (Porter 1997, 
Pearce and von Finklestein 1999, Sizer, 2000). In some parts of the developing world 
subsidies exist for the clearance of forest land, and in some cases title to the land 
cannot be secured without a given percentage of the land being cleared (Porter, 1997). 
Other subsidies are more subtle, and may take the form of preferential logging 
concessions and low royalty relative to what could be charged without deterring 
logging companies. Low charges increase the „rent‟ to be secured from the land. The 
result is a competition that uses up resources to no productive purpose. Ensuring a 
good share of rent can involve corrupt practices such as bribes to officials and 
politicians. In turn, this can result in more extensive logging outside „official‟ 
concessions and more intensive logging inside concessions as those responsible for 
enforcement secure greater rewards from the bribes than they do from normal 
employment. Unsustainable logging is more immediately profitable and hence there is 
a financial incentive to override or ignore regulations designed to secure sustainable 
forest management. The extent of „illegal‟ logging is not known with any accuracy 
but is clearly very large and may, in some countries, greatly exceed the officially 
declared rates of logging. Tackling illegal logging is immensely complex since it 
58 
 
 
effectively involves tackling the corruption involved. Countervailing power in the 
form of NGOs and citizens‟ groups can help, an can a free media and international 
disapproval. Statistical studies suggest that political freedom may be linked to reduced 
deforestation, but the evidence is not firm (Kaimowitz and Angelsen, 1998). Overall, 
though, there are powerful incentives for illegal logging and deforestation generally 
(Porter 1997). 
 Many other sectoral governmental fiscal, monetary and other subsidies and 
incentives also create driving force for deforestation and forest degradation. For 
example, transportation policies often promote the construction of roads; agricultural 
policies tend to promote the conversion of forests into agricultural land; resettlement 
programmes are frequently detrimental to forest areas; and government subsidies 
promoting mining and hydrological infrastructure are often available. Those 
government incentives are regularly supported through ill-defined development aid 
projects. Furthermore, direct or indirect subsidies are given to economic forest 
operations that can damage biodiversity, such as the drainage of forests and the 
logging of old growth forests (Sizer and Plouvier, 2000). The more common and 
important type of subsidy in the forest sector is that implicit in the low forest charges 
paid by timber concessionaires. Although justified on the grounds of promoting local 
development and employment, they can sometimes lead to a “boom-and-bust” 
situation with consequent excessive and wasteful forest degradation (Contreras-
Hermosilla, 2000), and poor forest regeneration. 
 
2.2.12 Poverty 
 Poverty is both a consequence and an underlying cause of forest decline. The 
case of Haiti is just one of many examples showing how total deforestation, followed 
59 
 
 
by soil erosion has deprived rural populations of their basis for livelihood (Paskett and 
Philoctete, 1990). Poverty often leads to deforestation and forest degradation. Poor 
people are frequently forced to slash and burn or otherwise degrade forests in 
response to population growth, economic marginalisation and environmental 
degradation. However, linkages between the rural poor and forest resources they draw 
upon are complex and poverty does not necessarily lead to forest decline. Many poor 
people are able to adopt protective mechanism through collective action which 
reduces the impacts of demographic, economic and environmental changes. 
 
2.2.13 Population Change 
Brown and Pearce (1994) reviewed the econometric studies that link 
deforestation rates to explanatory factors. They found that population growth is 
generally linked to deforestation, although the patterns of interaction are complex. 
However, though simple statements that „population growth causes deforestation‟ are 
also unquestionably false, many models show that population change is important 
(Kaimowitz and Angelsen, 1998). As current population levels rise from 6 billion 
people to a predicted 9 billion in 2050, with much of the increase in tropical countries, 
pressures on forest areas must be expected grow. Lowland-upland migrations and 
officially induced transmigration will add to the pressure. 
 Another billion people are likely to be added to the world population for each 
of the next decades. This population increase will occur mainly in developing 
countries, creating a strong demand for agricultural lands, forest products and “forest 
crops” (cocoa, coffee, bananas, etc.) To meet the associated food demand, crop yields 
will need to increase consistently, by over 2% every year throughout this period 
(Walker and Steffen, 1997). While possible responses to the food supply issue may 
60 
 
 
The improvements in technology, better distribution of food purchasing 
possibilities, better nutritional education and health care, it is likely that most 
immediate response will be converting more forest ecosystems to agricultural land.  
However, it is important to mention that the link between forest decline and 
population pressure remains unclear due to the complexity of the factors involved.  
Most studies indicate a positive relationship between population and 
deforestation, but most analysts are almost very careful to indicate that there other 
factors that obscure this linkage. For example, many authors note that loggers first 
make forests accessible and then settlers occupy lands. If this is the case, then 
population density is the result of logging and associated initial deforestation or forest 
degradation, not the other way round. In addition, unless reliable information on the 
changes in forest cover is available, it is difficult to see the links clearly (Sunderlin 
and Resosudarmo, 1999). At the global level, it is obvious that the enormous and still 
increasing demand for forest resources (timber, paper, etc) by developed countries, 
which do not now face population growth, is another cause of forest loss. 
 
2.2.14 Globilization 
 At present, a fifth of the world‟s population uses 85% of its resources. The 
globalisation of trade and these demands from developed world for paper, timber, 
minerals and energy provide the incentive to exploit natural resources in the 
developing world. The financial and political power of large companies adds 
dramatically to pressures in forest ecosystems that had previously been too remote to 
attract attention, such as some Central African‟s rainforests and the taiga in far-
eastern Russia.  
61 
 
 
 In addition, the global exchange economy is based on principle of comparative 
advantage and specialization and has increased in both uniformity and 
interdependence. In forest areas, the rapid and total conversion of forest into 
monocultural cash crops is widespread. But when the price of palm oil, coffee or 
cocoa drops, the plantation cannot quickly revert to the biologically diverse forest that 
proceeded it, even if when left alone. This is particularly the case where large-scale 
clearing has occurred, e.g. in south Sumatran oil palm plantations. 
 If environmental and social externalities (costs and benefits) are not 
internalized, then market prices do not reflect true social values, causing allocative 
inefficiency. Where externalities are not internalized, the increased economic growth 
from liberalised trade and investment will serve only to exacerbate, rather than 
address environmental problems, especially in those countries that depend on the 
export of natural resources – e.g. forest products. The liberilisation of exchange and 
trade policies can improve the terms for agriculture expansion and therefore promote 
the clearance of forest for agricultural crops. The solution is to correct market 
distortions through sound environmental and sustainable development policies and in 
addition, measures identified to ensure conservation and sustainable use of forest 
biological diversity must be implemented before bilateral and multilateral trade 
agreements. 
 International trade, investment, debt and technology transfer issues foster 
inequity between developed and developing countries that resemble or often reinforce 
those found within countries. For example, most export credit agencies and 
investment agencies, which finance numerous development projects, are not subject to 
environmental or social guidelines or standards that would ensure that they do not 
contribute to ecologically or socially harmful projects. 
62 
 
 
 Another effect of globalisation is the increasing activity of transitional logging 
companies. These activities often result in an expansion of destructive logging 
operations, violation of indigenous rights and, sometimes, widespread corruption. 
Most of the new investment focuses on short-term activities and economic benefits to 
the exporting country are usually very low. In addition, the forests are often mined 
rather than managed, resulting in high levels of damage and increased access to 
previously untouched areas (Sizer and Plouvier, 2000). 
 
2.2.15 Unsustainable production and consumption patters 
 Agenda 21 of the World Conservation Strategy notes that the major cause of 
the continued deterioration of the global environment is the unsustainable pattern of 
consumption and production, particularly in industrialised countries. It further notes 
that while consumption is very high in certain parts of the world, the basic consumer 
needs of a large section of humanity are not being met. Changing consumption patters 
towards sustainable development will require a multi-pronged strategy focusing on 
meeting basic needs and improving the quality of life, while reorienting consumer 
demands towards sustainably produced goods and services. Per capita consumption 
increased as real gross domestic product (GDP) grew at 2.9% per year while 
population growth was 1.4% per year. A closer look at economic trends, however, 
shows large disparities between and within regions. As noted in the UN Human 
Development Report (1998), 20% of the world‟s population, in the high-income 
countries, account for 86 per cent of total private consumption expenditures, while the 
poorest 20 per cent, in low-income countries, consume a mere 1.3%. Annual 
consumption per capita in industrialised countries has increased steadily at about 
2.3% over the past 25 years, it has increased very rapidly in East Asia at around 6.1%, 
63 
 
 
and at a rising rate in South Asia at around 2.0%. On the other hand, the consumption 
expenditure of the average African household is 20% less than it was 25 years ago 
(UN, 2001, Jachman, 2008). The effects of these consumption patterns on forest 
biodiversity need to be analyzed further. 
 As income rise, so the demand for natural resources increases. The 
relationship is a complex one, however. For some forest services, the income-demand 
relationship can be such that as incomes grow the demand for those services 
decreases. An example might be the switch from wood fuels to liquid fuels as 
incomes grow. At the global level, however, higher income countries do consume 
larger absolute amounts of raw materials. This has led to the view that deforestation is 
linked to excessive consumption in rich countries. The issue is complex because the 
efficiency of raw materials use, i.e. the ratio of raw materials to income, tends to be 
lower in richer countries than in poor countries. Rich countries utilize natural 
resources more efficiently, but the scale of their incomes means that the absolute level 
of consumption is higher than in poor countries. Since the aim of development is to 
raise per capita income, reducing that income is not a realistic policy option, nor is it 
clear what policies would bring this about without damaging the factors giving rise to 
income growth – education, technology etc. But it is legitimate to ask that rich 
countries greatly increase their use efficiency. This will then translate into reduced 
demand for raw materials, including forest products imported from developing 
countries. Care has to be taken that this does not damage the export potential of 
forested countries, but clearly there is scope for making this transition. Additionally, 
richer countries can afford to pay premiums on forest products to discriminate 
between sustainably managed products.(CBD, 2002) 
 
64 
 
 
2.2.16 Political unrest and war 
 One of the most important waves of large-scale forest destruction in Europe, 
th th
occurring from the 15  to the 17  century, was due to the need for wood for military 
ship building. At the same time, dwindling wood resources for the navy prompted a 
number of forest protection, conservation, restoration and management measures in a 
number of European countries that present generation will benefit from. There is clear 
evidence that armed conflicts or political instabilities still correlate with an 
accelerated rate of forest destruction. Cambodia, Congo, Indonesia, Laos, Liberia and 
Sierra Leone are just a few of the countries where forest are logged for quick cash 
needed to purchase military weapons and where the authorities have lost control over 
natural resources enabling specific actors such as the army to deplete the forests, 
either illegally or legally. A recent report commissioned by the UN Security Council 
(2001) on illegal exploitation of natural resources and other forms of wealth in the 
Democratic Republic of Congo demonstrates that illegal logging is linked to armed 
conflicts and suggests concrete measures to reduce trade in so-called “conflict 
timber”. Forests are also being destroyed (e.g. by herbicides) in order to eradicate 
sheltering places for guerilla forces, as was common practice during the Vietnam war. 
In addition, armed conflicts cause increasing pressure on non-timber forest products, 
particularly bush meat for food for either the armed forces or populations that have 
been forced to move from conflict areas, such as in Central Africa. This places some 
already threatened species, e.g. gorilla, in a very dangerous situation. On the other 
hand, creating military security zones has in many areas left large areas outside 
economic activities. In future, many of these areas may be suitable for designation as 
protected areas. 
 
65 
 
 
2.2.17 Conversion of forests to agricultural land 
 The major causes of deforestation are the expansion of subsistence agriculture 
and large economic development programmes involving agriculture. The conversion 
of forests into agricultural land has been the major historical cause for deforestation in 
Europe, Asia, and North America and still is a major driving force today in the 
tropical and sub-tropical areas. The current agents vary from small farmers practicing 
shifting cultivation or clearing forests for subsistence needs to large agricultural 
concerns that clear vast tracts of forest lands in order to establish cattle ranches or 
agro-industrial plantations such as soya beans in Latin America and oil palm in 
Indonesia/Malaysia.(WRI et al., 1992;WCMC, 1992; Stedman-Edwards, 1998; 
Thomas et al 2007). 
 
2.2.18 Dismantling of agro-forestry system 
 An emerging and rather insidious threat to biological diversity and tree genetic 
resources is posed through the dismantling of agro-forestry systems, i.e. the removal 
or failure to plant trees in agricultural and horticultural systems. This is usually 
associated with intensified, often monocultural, agricultural and livestock husbandry 
practices that eliminate trees from rural and urban agricultural areas. In Tonga, 
especially on Tongatapu, successive phases of unsustainable cash cropping have led 
to the elimination of trees in agro-ecosystems. In parts of Africa many useful tree 
species now as exists only as scattered individuals or highly fragmented non-viable 
populations in agro-ecosystems, and are likely to disappear within the next few 
decades (IUCN, 2000). Tress in agro-ecosystems may disappear either directly 
through cutting and clearing, or through establishment for regeneration and 
recruitment of remnant tree species. 
66 
 
 
 
2.2.20 Overgrazing 
 Overgrazing is increasingly a major threat to biodiversity in both tropical and 
temperate forests. The main impacts are damage to the topsoil, destruction of 
understory vegetation and/or replacement with a narrower range of unpalatable 
species and selective browsing of regenerating tree species, which may eventually 
result in the elimination of particular species.(CBD, 2002). 
 
2.2.21 Natural Hazards and Forest Fires 
 Natural hazards, such as storms and hurricane damage, forest fires, floods and 
pests are natural disturbance regimes in forests. They can often have a positive impact 
on biological diversity. These disturbances, on a small or large scale, can create 
specific habitats that are important for the survival of a plethora of flora and fauna; 
they should therefore, be mimicked or maintained in forest management (Angelstam, 
1998). However, many human induced activities exacerbate these disturbances in a 
way that makes them an increasing threat to forest biodiversity. 
 Natural fires are a crucial element for the succession of many forests, 
especially in boreal areas. Prescribed burning, mimicking wildfires should be used to 
a greater extent in restoration of forests in conservation areas and also in some 
managed forests. With a changing climate, however, natural and human-caused fires 
can have deleterious impacts on forest biological diversity; for instance, after the 
predicted prolonged periods of drought. These fires have destroyed many important 
fire refugia on which many forest species intolerant to fire are dependent. Both the 
unusual frequency and new regional occurrence of fires may be attributed to climate 
change. 
67 
 
 
 Lack of fire in habitats where fire is part of the ecological process of 
regeneration (e.g. savannah woodlands or boreal forests) can have a deleterious effect 
on biological diversity and its processes in the longer term. However, extreme 
climatic events generating fire can have devastating impacts on forest biological 
diversity. For example, a prolonged or abnormally severe drought can be followed by 
uncontrolled fire, which can destroy sensitive forest communities and species. In 
recent decade forest fires have been particularly severe and very widespread (in, for 
instance, Australia, Brazil, Central America, Colombia, Indonesia, Kenya, Mexico, 
Mongolia, Papua  New Guinea, Peru, Russia, Rwanda, Spain, USA and western 
Canada). Fires devastated large forest areas that normally do not get burnt. Such 
unprecedented frequency and unusual occurrences of fires may be attributed to 
climate change. Fragmentation may prevent or inhibit recolonisation of burnt forest 
patches by fire-sensitive animal and plant species, thereby aggravating the negative 
impacts of increased fire frequency and intensity on forest biological diversity. In 
Samoa, two severe tropical cyclones in the early 1990s ravaged the remaining 
lowland rainforests, which had been opened up to greater destruction through heavy 
logging. These “secondary” forests are now in a state of arrested regeneration, mostly 
smothered by the rampant native climber (Merremia peltata) and increasingly subject 
to periodic wildfires during El Nino drought years. Merremia has also become a 
problem in the Solomon Islands and Malaysia following both fire and logging (Bacon 
1982, Pinard and Ptuz 1994). This example illustrates the point that forest biological 
diversity is especially vulnerable to the interactions of multiple threat factors. 
 
 
 
68 
 
 
2.2.22 Actions and priorities for conservation and sustainable use of biodiversity 
 The necessity of ensuring that utilization of an ecosystem or species is 
sustainable varies with a society‟s dependence on the resource in question. For a 
subsistence society, sustainable utilization of most, if not all, its living resources is 
essential. The greater the diversity and flexibility of the economy, the less the need to 
utilize certain resources sustainably – but by the same token the less excuse not to. 
Sustainable utilization is also necessary for the rational planning and management of 
industries dependent on the resources concerned (for example, timber, fish). 
Sustainable utilization is somewhat analogous to spending the interest while keeping 
the capital. A society that insists that all utilization of living resources be sustainable 
ensures that it will benefit from those resources virtually indefinitely. Unfortunately, 
most utilization aquatic animals, of wild plants and animals of the land, of forests and 
of grazing lands is not sustainable. According to Convention on Biological Diversity 
(2002), actions for improvement of conservation and sustainable utilization of 
biodiversity are grouped under the following headings: 
(a) Assessment and monitoring 
(b) Conservation and sustainable use 
(c) Institutional and socio-economic enabling environment. 
 
2.2.23 Assessment and monitoring 
 Biological diversity is a scaled consideration, ranging from genes of individual 
organisms, to large forest landscapes, to global biological diversity. Therefore, 
classification, monitoring and reporting must occur on all scales and must involve all 
stakeholders (in particular the indigenous and local forest communities and not only 
the scientific community in proper contexts. 
69 
 
 
2.2.24 Conservation and sustainable use 
 Conservation and, where appropriate, enhancement of forest biological 
diversity should be an important aspect of conservation and sustainable use of all 
types of forests. This applies to the whole range of forest categories, from protected 
primary forests, secondary forests, plantations, agro-forests to other ecosystems that 
include elements of forest biological diversity. 
 The development and implementation of the ecosystem approach, as described 
in decision V/6 of the conference of the Parties, should be guiding principle to 
achieve the conservation and sustainable use of forest biological diversity and it 
should be applied to the full continuum of forests, from protected areas to plantations. 
Application of the ecosystem approach to forest management should be based on both 
science and adaptive experience. 
 Critical levels of biological diversity loss/change that affect forest ecosystem 
functioning, and, in turn, the goods and services provided by forests are still largely 
unknown among forest types. This uncertainty emphasizes the value of applying the 
precautionary approach. As stated in the Preamble of the Convention on biological 
Diversity, lack of full certainty should not be used as a reason for postponing 
measures to avoid or minimize the threat of significant reduction or loss of biological 
diversity. 
 
2.2.25 Institutional and socio-economic enabling environment 
 To identify and propose measures to halt and reverse global forest biological 
diversity loss, both the direct and underlying causes of forest decline must be 
addressed. Political and economic decisions taken in forestry and other forest-related 
70 
 
 
sectors should safeguard forest biological diversity and result in a fair distribution of 
associated costs and benefits among resource users. 
 Creating an enabling legal, policy, economic, and institutional environment to 
address the causes of forest biological diversity loss is a fundamental and urgent 
prerequisite for the conservation and sustainable use of forest biological diversity. The 
Convention on Biological Diversity should place increased emphasis on this matter in 
its work programme, and each country should engage in a process to establish an 
enabling environment that is conducive to the conservation and sustainable 
management of forest biological diversity. The process should be specific to the 
country, the land-use and context. Key actions necessary to establish such an enabling 
environment can be summarized as follows: (a) increase political will; (b) provide 
adequate institutional, human and financial resources; (c) ensure adequate 
involvement all stages of indigenous peoples and local communities in forest 
management; (d) ensure integration of forest biological diversity conservation and 
sustainable use into all relevant sectors; (e) secure a permanent forest estate and an 
adequate land tenure and forest use system; (f) provide a national and global 
economic environment conducive to the conservation and sustainable use of forest 
biological diversity; and (g) establish and enforce appropriate legislation. 
  
 
 
 
 
 
71 
 
 
CHAPTER THREE 
 
3.0                                MATERIALS AND METHODS 
 
3.1                                          THE STUDY AREA 
The study area is contained in the 9,700 hectare land of the University of 
Agriculture, Abeokuta, situated north-eastern of Abeokuta, along Alabata road, 
o o ! o ! o
(fig.1). The site is located between latitude 7  and 7  58  And Longitude 3  3  And 3  
!
37  Generally, the site gently undulating with mild slopes but punctuated in part by 
ridges, isolated residual hills, valleys and lowlands, all of which present a good 
landscape for aesthetics. 
There is a general drop in elevation from the eastern to the western part 
towards Ogun river flood plain where the seasonal stream network within the sitew 
empties their content. Six soil series have been identified in the area. These are 
Egbeda series (Oxic paleudults), Asejire series (typic psammaquent), Iregun series 
(Oxic ustropept),  
Balogun series (Psamentic Hapludults), and Iwo series (Oxic paleudalts). The 
soil are mainly sandy to sandy loam with medium depth underlain by crystalline 
basement complex. The soils have low to moderate organic matter and essential 
nutrients (Anon, 1992).  
 
 
 
72 
 
 
N
W E
S
SNR
Academic Core
Strict Nature Reserve
Campus road network
University boundary
2 0 2 Kilometers
 
Fig. 1: Map Of the University of Agriculture showing the Study Area. 
 
 
 
 
 
 
 
73 
 
 
 
S N R  3 ° 2 3 ' 0 0 " 3 ° 2 3 ' 2 0 "7 ° 1 6 ' 0 0 " 7 ° 1 6 ' 0 0 "
 
 
 
 
N
W E
 S
 
7 ° 1 5 ' 4 0 " 7 ° 1 5 ' 4 0 "
 
S t r i c t  N a t u r e  R e s e r v e
 0 . 1 0 0 . 1 K i l o m e t e r s
3 ° 2 3 ' 0 0 " 3 ° 2 3 ' 2 0 "
 
 
 
 
Fig. 2: Map of Study Area. 
 
 
 
 
 
 
 
 
 
74 
 
 
3.1.1                     LAND USE HISTORY  
 Before the acquisition of the land by the University in 1998, the most 
extensive land use was arable farming. Other land uses included quarrying for 
sandstone. Consequently, with the acquisition of the site, farming activities have 
decreased considerably. Nevertheless, the following agricultural crops still doted the 
site, maize, cassava, pepper, poorly maintained cocoa, citrus, cashew, banana and 
plantain. 
 
3.1.2   VEGETATION 
 The area comprises of various vegetation types ranging from a large portion of 
derived savanna, secondary rain forest and riparian types. The derived savanna is 
climatically similar to rainforest zone, but a combination of farming, lumbering and 
burning have resulted in clearings in the forest which have been colonized by grasses 
and fire resistant savanna trees. The grasses are burnt annually so that clearings are 
maintained and the rainforest trees, which are susceptible to fire, cannot re-establish. 
This has encouraged the spread of derived savanna. Relics of former rainforest occur 
along some river valleys and in localities unsuitable for cultivation. The commonest 
species of trees in the area are:- Daniella oliverii, Cussonia barteri, Annogeissus 
leiocarpus, Pterocarpus spp, Ficus exaspirata, Ficus thonningii, Bambusa vulgaris, 
Afzelia africana, Annona senegalensis, Anarcardium occidentale, Bridelia micrantha, 
Bridelia ferruginea, etc 
 The common grasses belong to the general Andropogon, Hyparrhenia and 
Pennisetum. The grasses include:- Andropogon gayanus, Andropogon tectorum, 
Pennisetum spp, Paspalum nonantum, Impereta cylindrical, Panicum maximum, etc, 
75 
 
 
while the shrubs include:- chromoliana odorata, Aspilia Africana, Commelina 
nudiflora, Waltheria spp, etc. 
3.1.3  CLIMATE 
 The site falls within the humid tropical lowland region with two distinct 
seasons. The longer wet season lasts for eight (8) months, from March – October and 
the shorter dry season lasts for four (4) months from November – February. The area 
normally witness high rainfall at two periods of the year, i.e the peak period of June – 
July and September – October. It has a mean annual rainfall of 1250 to 2500mm. 
 The mean monthly temperature ranges between 25.7oC in July and 30.2oC in 
February. The lowest temperature is recorded in June and September. The relative 
humidity is high all year round. The most humid months coincides with the rainy 
season, spanning between March and October and the figure ranges between 60% and 
80% from December to February.. Fig 2 shows the climatic diagram and temperature 
pattern in the study site. 
 
 
3.2  SAMPLING PROCEDURES 
Twenty (20) sample plots of 25m x 25m (0.062ha) were laid at random over 
the total area of the study site for data collection. The plots were distributed according 
to the observed richness in vegetation cover. For accuracy and ease in data collection, 
each plot of 25m x 25m was partitioned into 5 quadrates of equal sizes at the left and 
right sides of the centerline of each plot. 
 
 
 
76 
 
 
3.2.1 Data Collection 
The importance of reliable and adequate data collection for policy formulation 
and planning for the purpose of sustainable use and biodiversity conservation cannot 
be over emphasized (Ojo, 1996). The collection of data was based on these 
categorization:- plants and animal surveys. 
 
3.2.2 Vegetation Survey  
The vegetation survey was divided into two types : 
(a) the tree and shrub enumeration 
(b) ground flora enumeration 
(a) Tree and Shrub Enumeration:- Total enumeration would be  carried out in 
each sample plot for all the trees and shrubs. A tree is taken to be any vascular 
stem with a girth of  ≥ 5cm and does not fork before 1.3m mark. All the 
measurements to be taken are indicated below:- 
Diameter at breast height of trees 
Height of trees at first branch (Marchantable height) 
Total height of trees 
The diameter at breast height was taken using girthing tape while the height 
was measured by Spiegel relascope.  These provided the floristic data for the study. 
The specimens that cannot be identified on the field were taken to a standard 
herbarium for proper identification. 
(b) Ground Flora Inventory:- All ground flora with height below 1m and dbh of  ≤  
5cm were enumerated for their percentage abundance in each plot.  
 
 
77 
 
 
 
3.3 ANIMAL (VERTEBRATES) SURVEY 
 King Census and Line Transect methods were modified fo this study using 
direct and indirect modes of wildlife stock assessment for an accurate collection of 
data due to the dense nature of the vegetation in some areas. 
Direct count method was used for all animals sighted during the laying of plots. 
Animal survey was carried out within the plots and a checklist of all animal 
species found in the study area was made. 
The indirect method of sampling was also used. All indicators of animal 
presence or activities in the plots sampled were recorded. The signs or indicators used 
for assessing the presence of animals include: 
a. Animal droppings 
b. Call counts 
c. Nest counts 
d. Body parts dropping (e.g. feathers, hairs) 
e. Dens and Burrows 
f. Tracks and trails 
g. Foot print 
h. Feeding remnants 
 
 
 
3.4: SOIL SURVEY 
 
Soil samples at 0 – 15cm, 15 – 30cm, and 30 – 45 cm depths was collected for 
each plot. This was done randomly at three points at the centre line for each plot and 
the sample from each depth was bulked together and air-dried and analyzed for pH, 
organic carbon, nitrogen and the particle size distribution using standard methods.  
78 
 
 
 
 
3.5: HUMAN INTERFERENCE  
  Structured questionnaire were administered randomly to 20 individuals in 4 
farm settlements (five in each settlement) close to the study area to assess the level of human 
interference. 
 
3.6:  CLIMATIC DATA  
 
   Information on climate for the study period; 2006 – 2008 was obtained from the 
Department of Agricultural Meteorology and Water Resources Management of the University 
of Agriculture, Abeokuta. 
 
3.7: METHODS FOR DATA ANALYSIS 
 
The materials of biodiversity data collection and analysis are as diverse as the types 
collected. In this study, the indices discussed bellow was used in determining plant and 
animal diversity in the study area. 
• Biodiversity Determination 
• Simpson‟s diversity index 
       s 
• ∑  ni  (ni − 1) 
   i=1   N  (N − 1) 
  ni is the number of individual of specie i which are counted and N is the total of all 
individuals counted 
Shannon‟s diversity index 
        s  
H = ∑  pi ln pi 
     i=1  
Pi is the fraction of individuals belonging to the  i-th species 
 
CANOCO program after Ter Braak (1998) was be used for analysis of plant and 
animal species and soil data. The floristic gradient of the study site was explored with 
79 
 
 
Detrended Correspondence Analysis. The purpose of using ordination is to explore possible 
gradients and association between soil/site and interacting species in the area. In addition an 
important use of ordination technique is to arrange the interacting species and sites in such a 
way that similar plant species or animals are arranged far apart. The data generated after the 
analysis was used to plot ordination diagram for generating hypothesis about the relationship 
between community composition and the environmental factors that determine such 
association (Greing-Smith, 1983). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
80 
 
 
 
 
 
 
 
                            CHAPTER FOUR 
 
4.0                                                        RESULTS 
 
4.1            PLANT FREQUENCY DISTRIBUTION AND RELATIVE ABUNDANCE 
 The plant average frequency of plants in the study area is shown in table 2. 
One hundred and eighteen (118) plant species belonging to forty – four (44) 
families were enumerated. The most abundant tree species were Daniella 
oliveri, Anona selegalensis, Bradelia micrantha and Ficus capensis in that 
order. The commonest ground flora recorded were Andropogon tectorum, 
Andropogon gayanus, Chromolaina odorata and Aspilia africana.  
 
 
 
 
 
 
 
 
 
81 
 
 
 
 
 
 
 
Table:1 Scintific Names and Codes of Plants in the Study Site 
Couplet No Scientific Name Code 
1 Abelmoschus esculentus ABES 
2 Abrus precatorius ABPR 
3 Abutilon ABMA 
4 Acacia kamerunesis ACKA 
5 Acacia sieberina ACSI 
6 Acalyphyta ciliate ACCI 
7 Acanthospermum hispidum ACHI 
8 Acanthus montanus ACMO 
9 Achyranthes aspera ACAS 
10 Acridocarpus smeathhniamii ACSM 
11 Adansonia digitata ADDI 
12 Adenopus brevflorus ADBR 
13 Afromorsia laxiflora AFLA 
14 Afzelia Africana AFAF 
15 Agelea oblique AGOB 
16 Agerantum conysoides AGCO 
17 Albizia adianthifolia ALAD 
18 Albizia coriara ALCO 
19 Albizia feruginea  ALFE 
20 Albizia zygia ALZY 
21 Albizia lebbeck ALLE 
22 Alchornea cordifolia  ALCD 
23 Alchornea laxiflora ALLA 
24 Allophyllus africanus ALAF 
82 
 
 
25 Alstonia boonei ALBO 
26 Alstonia congensis ALCG 
27 Amaranthus spinosus AMSP 
28 Amaranthus hybridis AMHY 
29 Anarcardium occidentate  ANOC 
30 Ananas comosus ANCO 
31 Aneilema beniniense ANBE 
32 Anchomamis difformis ANDI 
33 Ancistrocapus densisipinosus ANDE 
34 Andropogen gayanus ANGA 
35 Andropogen teetorum ANTE 
36 Anogeisus leiocarpus ANLE 
37 Anona senegalensis ANSE 
38 Antana Africana ANAC 
39 Anthocleista vogeillii ANVO 
40 Anthocleista djalonesis ANDJ 
41 Anthonotha macrophylla ANMA 
42 Anthephora ampilliaceae ANAM 
43 Antiaris Africana ANAF 
44 Antiaris toxicaria ANTO 
45 Asparagus flagellaris ASFL 
46 Aspillia Africana ASAF 
47 Aspillia busei ASBU 
48 Asystatsia gangetica ASGA 
49 Azadirachta indica AZIN 
50 Axonopus compressus  AXCO 
51 Bambussa vulgaris BAVU 
52 Bidens pilosa BIPI 
53 Blepharis maderoapatensis BLMA 
54 Blighia sapida BLSA 
55 Blighia welwetehii BLWE 
56 Boerharia coccinea BODI 
57 Boerharia deflexa BOCO 
83 
 
 
58 Bombax buanopozense BOBU 
59 Brachiera deflexa  BRDE 
60 Brachystegia eurycoma BREU 
61 Bridelia feruginea BRFE 
62 Bridelia micrantha BRMI 
63 Burkea Africana BUAF 
64 Cajanus cajan CACA 
65 Calotropis procera CAPR  
66 Canavalium ensiformis CAEN 
67 Canhium vulgera CAVU 
68 Carica papaya CAPA 
69 Carpolobea lutea CALU 
70 Cassia alata CAAL 
71 Cassia monosoides  CAMI 
72 Cassia podocarpa CAPO 
73 Cassia siamea CASI 
74 Ceiba pentadra  CEPE 
75 Celosia argentea CEAR 
76 Celtis zenkeri CEZE 
77 Centrocema puebescens  CEPU 
78 Chamaecrista mimosoides CHMI 
79 Chloris pilosa CHPO 
80 Chassalia kolly CHKO 
81 Chrosopogon aciculatus  CHAC 
82 Cissampelos mucronanta CIMU 
83 Chromalaena odoratum CHOD 
84 Chrysophyllum albidum CHAL 
85 Citrus sinensis CISI 
86 Clappertoniana ficifolia CLFI 
87 Cleistopholis paten  CLPA 
88 Cleoma viscose  CLVI 
89 Cnestis feruginea CNFE 
90 Cocos nucifera CONU 
84 
 
 
91 Cochlospermum planchonii COPL 
92 Coffea brevipas  COBR 
93 Cola afzelii COAF 
94 Cola gigantean  COGI 
95 Cola milleni COMI 
96 Cola nitida CONI 
97 Combretum  bracteaunm  COBC 
98 Combretum hispidum COHI 
99 Combretum racemosum CORA 
100 Combretum molle  COMO 
101 Combretum zenkeri  COZE 
102 Commelina benghalensis COBE 
103 Commelina nodiflora CONO 
104 Conyza sumatrensis  COSU 
105 Corchorus olitorius COOL 
106 Croton lobatus  CRLO 
107 Crotolaria retusa  CRRE 
108 Crassocephalum rubens CRRU 
109 Crescentia  CRCU 
110 Cucurbita pepo CUPE 
111 Cucumeropsis manni  CUMA 
112 Cussonia barteri CUBA 
113 Cyanolis lanata CYLA 
114 Cymbopogon giganteus CYGI 
115 Cyathula prostrata  CYPR 
116 Cynodon dactylon CYDA 
117 Cynometra megalophylla  CYME 
118 Cyperus articulatus  CYAR 
119 Cyperus esculentus  CYES 
120 Cyperus iria CYIR 
121 Dactyloctenium aegyptium DAAE 
122 Daniella olliverii DAOL 
123 Deloni regia  DERE 
85 
 
 
124 Deinbollia pinnata DEPI 
125 Desmodium salcifolium DESA 
126 Detarium macrocarpum DEMA 
127 Dialium guinensis  DIGU 
128 Discorea prahensilis DIPR 
129 Dioseorea alata DIAL  
130 Discorea cayenensis DICA 
131 Diospyros mesipiliformis DIME 
132 Diospyros monbutensis DIMO 
133 Dichrostachys cinerea DICI 
134 Diplazium sammatii DISA 
135 Distemonanthus benthamanus DIBE 
136 Dracaena fragranus DRFR 
137 Eclipia alba ECAL 
138 Elaeisi guinensis ELGU 
139 Eleusine indica ELIN 
140 Entanda Africana ENAF 
141 Eragrostis tremula ERTR 
142 Erythrina senegalensis  ERSE 
143 Erythrophleum suaveolensis  ERSU 
144 Euphorbia hirta EUHI 
145 Euphorbia lateriflora EULA 
146 Ficus capensis  FICA 
147 Ficus exasperata  FIEX 
148 Ficus mucoso FIMU 
149 Ficus thioningii FITH 
150 Ficus sycomorus FISY 
151 Funtumia elastic FUEL 
152 Gardenia trenifolia GATE 
153 Gardenia aqaulla GAAQ 
154 Gliricidia sepium  GLSE 
155 Glyphaea brevipes GLBR 
156 Gmelina arboreus GMAR 
86 
 
 
157 Gossypium barbadense GOBA 
158 Grevia carpinifolia GRCA 
159 Grevia flavescens GRFL 
160 Greivia mollis GRMO 
161 Guarea cedrata GUCE 
162 Harrisonia abyssinica HAAB 
163 Hedranthera barteri  HEBA 
164 Heinsia crinita  HECR 
165 Hewittia sublobata HESU 
166 Hibiscus asper  HIAS 
167 HIBIscus sabdarrifa HISA 
168 Hibiscus rostellatus  HIRO 
169 Hiprocratea patten  HIPA 
170 Hollarhena floribunda  HOFL 
171 Holoptelia grandis  HOGR 
172 Homalium letestui HOLE 
173 Hyparhenia involucrate HYIN 
174 Hyparhenia rufa HYRU 
175 Hmneocardia acida  HYAC 
176 Icacinia tricantha  ICTR 
177 Imperata cylindrical IMCY 
178 Indigofera capitata INCA 
179 Irvingia gabonensis IRGA 
180 Irvingia wombolu IRWO 
181 Ipomea asarifolia IPAS 
182 Jatropha carcass JACU 
183 Justicia flava JUFL 
184 Khaya ivorensis  KHIV 
185 Kigelia africana  KIAF 
186 Lannea nigritana LANI 
187 Lannea welwetehii LAWE 
188 Lannea taraxacifolia  LATA 
189 Lagenaria sicerania  LASI 
87 
 
 
190 Laportea aestanus  LAAE 
191 Leersia hexandra LAHE 
192 Lactuca capensis LACP 
193 Lantana camara LACA 
194 Lecaniodiscus cupanioides LECU 
195 Lonchocarpus cyanescens LOCY 
196 Lonchocarpus griffonianus LOGR 
197 Lophira lanceolata LOLA 
198 Lovoa trichiloides  LOTR 
199 Ludiwigia deeuirens LUDE 
200 Macaranga barterii MABA 
201 Machrosphyra longistyla MALO 
202 Malotus oppositifolius MAOP 
203 Malancantha alnifolia MAAL 
204 Magnifera indica MAIN 
205 Malvastrum corimandelianum MACO 
206 Manihot esculenta MAES 
207 Maniophyton fulvum MAFU 
208 Maytenus senegalensis MASE 
209 Magariteria discoideae MADI 
210 Microdesmis puberula MIPU 
211 Milicia excels MIEX 
212 Mimosa pudica MIPD 
213 Manscus alternifolius MAAF 
214 Manscus flabelloformis MAFL 
215 Mitragyna inermis MIIN 
216 Melanthra scandens MESC 
217 Momordica charantai MOCH 
218 Mimosa invisa MIIV 
219 Morinda lucida MOLU 
220 Monodorna tennifolia MOTE 
221 Moringa oleifera MOOL 
222 Mucuna prurens  MUPR 
88 
 
 
223 Mucuna sloanei MUSL 
224 Musa sapientum MUSA 
225 Musa paradisiacal MUPA 
226 Myrianthus arboreus MYAR 
227 Nauchlea latifolia NALA 
228 Newbouldia laevis NELA 
229 Ocimum grattasimum OCGR 
230 Oryza longistanimata ORLO 
231 Ocimum canum OCCA 
232 Olax subarolata OLSB 
233 Olax subscorpoidea OLSU 
234 Opillia celtidifolia OPCE 
235 Panicum maximum PAMA 
236 Panicum laxum PALA 
237 Parinari robusta PARO 
238 Parinari polyandra PAPO 
239 Parkia becolor PABI 
240 Parkia biglobosa PABG 
241 Parinari glabra PAGL 
242 Parquettina nigreseen PANI 
243 Palisota hirsute PAHI 
244 Paspalum norranthus PANO 
245 Pennisetum pedicellatum PEPE 
246 Pennisetum purpureum PEPU 
247 Phyllanthus discoides PHDI 
248 Pilostigma thoningii PITH 
249 Poulilzozia giunensis POGU 
250 Paullinia pinnata PAPI 
251 Physalis micrantha PHMI 
252 Prosopis Africana PRAF 
253 Psorospermum febrifugum PSFE 
254 Paspalum conjugatum PACO 
255 Pterocarpus santalinoides PTSA 
89 
 
 
256 Pupalia lappacea PULA 
257 Psidium guajava PSGU 
258 Peperomia pellucid PEPL 
259 Pterocarpus erinaceus PTER 
260 Pterocarpus mildbraedii PTMI 
261 Pennisetum violacea PEVI 
262 Raphia hookerii RAHO 
263 Reissantia indica RAIN 
264 Rhynchospora corymbosa RHCO 
265 Rauvolvisa vomitoria RAVO 
266 Ricinodendron heudelotii RIHE 
267 Ricinus communis  RICO 
268 Rinoria dentrata RIDE 
269 Rothmania longiflora ROLO 
270 Sansevierasenegambica SASE 
271 Sanseviera liberica SALI 
272 Securidaca longipendiculata SELO 
273 Schramkia leptocarpa SCLE 
274 Securinega virosa SEVI 
275 Scleria verrucosa  SCVE 
276 Sesamium indicum SEIN 
277 Senna hirsute SEHI 
278 Sida acuta SIAC 
279 Sida corymbosa SICO 
280 Smilax krausiana SMKR 
281 Solanum aethiopicum SOAE 
282 Seteria megaphylla SEME 
283 Solanum americanum SOAM 
284 Solanum dasyphyllum SODA 
285 Solenostemon monostachyus SOMO 
286 Solanum erianthum SOER 
287 Solanum macrocarpum SOMA 
288 Spathoidea campanulata SPCA 
90 
 
 
289 Spondias mombim SPMO 
290 Sphenocentrum jollyanum SPJO 
291 Sterculia tragacantha STTR 
292 Struchium sparganophora STSP 
293 Syndrella nodiflora SYNO 
294 Tamarindus indica TAIN 
295 Talinum triangulare TATR 
296 Tectona grandis  TEGR 
297 Tephrosia braceolata TEBR 
298 Tephrosia pedicellata TEPE 
299 Terminalia glaucesceus TEGL 
300 Terminalia superb TESU 
301 Theobroma cacao TACA 
302 Tithonia divesifolia  TIDI 
303 Trema orientalis  TRDR 
304 Tridax procumbens  TRPR 
305 Triplochiton sclerotylon TRSC 
306 Trumtet cordifolia TRCO 
307 Uvaria chamae UVCH 
308 Urenia lobata URLO 
309 Vernonia amygdalina VEAM 
310 Vernonia ambigua VEAB 
311 Vernonia anercii VEAN 
312 Vernonia perrottetii VEPE 
313 Vitex doniana VIDO 
314 Waltheria indica WAIN 
315 Xylopia quintasii XYDU 
316 Zanthoxylum zanthoxyloides ZAZA 
317 Vitellaria paradoxa VIPA 
 
 
 
91 
 
 
 
 
 
 
 
 
Table 2: Average Frequency of Plants in the Study Area 
 
                   Plant Specie Frequency Percent Valid Percent Cumulative 
Percent 
        
   8 .7 .7 .8 
Acalypha ciliate 
   10 .9 .9 1.8 
Afzelia Africana 
   2 .2 .2 1.9 
Albizia lebeck 
   1 .1 .1 2.0 
Albizia zygia 
   10 .9 .9 3.0 
Alchornea cordifolia 
   14 1.3 1.3 4.3 
Alstonia boonei 
   20 1.9 1.9 6.1 
Amaranthus hybridus 
   2 .2 .2 6.3 
Anacardium occidentalis 
   15 1.4 1.4 7.7 
Anchomaiamis difformis 
   22 2.0 2.0 9.7 
Andropogon gayanus 
   22 2.0 2.0 11.8 
Andropogon tectorum 
   5 .5 .5 12.2 
Annona senegalensis 
   5 .5 .5 12.7 
Anogeisus leiocarpus 
   3 .3 .3 13.0 
Anthoclesta vogelii 
   6 .6 .6 13.5 
Antiaris Africana 
   18 1.7 1.7 15.2 
Aspilia Africana 
   4 .4 .4 15.6 
Astonia boonei 
   1 .1 .1 15.6 
Azadirachta indica 
   3 .3 .3 15.9 
92 
 
 
Barhania monodora 
   8 .7 .7 16.7 
Bidiens pilosa 
   8 .7 .7 17.4 
Blighia welwetchii 
   14 1.3 1.3 18.7 
Boerhavia coccinea 
   8 .7 .7 19.4 
Boerhavia diffussa 
   4 .4 .4 19.8 
Borreria veticulata 
   1 .1 .1 19.9 
Bridelia ferruginea 
   30 2.8 2.8 22.7 
Bridelia feruguinea 
   16 1.5 1.5 24.2 
Bridelia micrantha 
   6 .6 .6 24.7 
Bridellia micrantha 
   2 .2 .2 24.9 
Canthium volgeri 
   6 .6 .6 25.5 
Carica papaya 
   11 1.0 1.0 26.5 
Carpolobia lurea 
   2 .2 .2 26.7 
Casia mimosoides 
   6 .6 .6 27.2 
Casia podocarpa 
   9 .8 .8 28.1 
Cassia mimosoides 
   9 .8 .8 28.9 
Cassia podocarpa 
   1 .1 .1 29.0 
Ceiba pentandra 
   15 1.4 1.4 30.4 
Centrosema puebescen 
   25 2.3 2.3 32.7 
Chromolaena odoratum 
   6 .6 .6 33.2 
Cissampelos micronantha 
   1 .1 .1 33.3 
Cissus arguata 
   11 1.0 1.0 34.4 
Cleome viscose 
   1 .1 .1 34.4 
Cnestis ferruginea 
   2 .2 .2 34.6 
Cochlospermum 
planchonii 
   16 1.5 1.5 36.1 
Coehlospermum planchoni 
   8 .7 .7 36.9 
Cola millenii 
   12 1.1 1.1 38.0 
Combretum hispidum 
93 
 
 
   18 1.7 1.7 39.6 
Combretum molle 
   2 .2 .2 39.8 
Combretum nigerica 
   6 .6 .6 40.4 
Combretum racemosum 
   8 .7 .7 41.1 
Combretum zenkerii 
   8 .7 .7 41.9 
Commelina benghalensis 
   15 1.4 1.4 43.2 
Commelina nodiflora 
   9 .8 .8 44.1 
Corchorus olitoriuos 
   9 .8 .8 44.9 
Cussonia barterii 
   6 .6 .6 45.5 
Cymbopogon giganteus 
   4 .4 .4 45.8 
Cynodon dactylon 
   8 .7 .7 46.6 
Cynometra megallophylla 
   7 .6 .6 47.2 
Cyperrus articularius 
   21 1.9 1.9 49.2 
Daniella olliveri 
   4 .4 .4 49.5 
Delonix regia 
   6 .6 .6 50.1 
Desmodium salutolium 
   2 .2 .2 50.3 
Detarium macrcapum 
   7 .6 .6 50.9 
Diplazium samatii 
   2 .2 .2 51.1 
Elaeis guineensis 
   13 1.2 1.2 52.3 
Eleusine indica 
   1 .1 .1 52.4 
Entada abicinica 
   4 .4 .4 52.8 
Entanda Africana 
   4 .4 .4 53.1 
Eragrostis tremula 
   4 .4 .4 53.5 
Euphorbia hirta 
   5 .5 .5 54.0 
Euphorbia laterflora 
   17 1.6 1.6 55.6 
Ficus capensis 
   21 1.9 1.9 57.5 
Ficus exasperate 
   1 .1 .1 57.6 
Ficus sur 
   9 .8 .8 58.4 
Ficus sycommorus 
94 
 
 
   6 .6 .6 59.0 
Funfumia elastic 
   4 .4 .4 59.4 
Gardenia aqualla 
   3 .3 .3 59.6 
Gardenia rubiscens 
   3 .3 .3 59.9 
Holarrhena floribunda 
   12 1.1 1.1 61.0 
Hymenocardia acida 
   1 .1 .1 61.1 
Hypocrata pallens 
   1 .1 .1 61.2 
Hyptis suaveolens 
   17 1.6 1.6 62.8 
Imperata cylindrical 
   4 .4 .4 63.1 
Indigofera capitata 
   10 .9 .9 64.1 
irvingia wombolu 
   7 .6 .6 64.7 
Jatropha curcas 
   6 .6 .6 65.3 
Lantana camara 
   7 .6 .6 65.9 
Lantema camoma 
   3 .3 .3 66.2 
Lonchocarpus cyacems 
   1 .1 .1 66.3 
Lonchocarpus sericens 
   7 .6 .6 66.9 
Macarange barrteri 
   4 .4 .4 67.3 
Magaritaria discoides 
   2 .2 .2 67.5 
Malacantha alnifolia 
   1 .1 .1 67.6 
Mangifera indica 
   1 .1 .1 67.7 
Mucuna prurens 
   14 1.3 1.3 69.0 
Myrianthus arboreus 
   1 .1 .1 69.1 
Nuclea latifolia 
   4 .4 .4 69.4 
Occimum canon 
   8 .7 .7 70.2 
Occimum gratissimum 
   7 .6 .6 70.8 
Olax secopoides 
   13 1.2 1.2 72.0 
Panieum maximum 
   6 .6 .6 72.6 
Parinari glabra 
   4 .4 .4 73.0 
Parinari polyandra 
95 
 
 
   4 .4 .4 73.3 
Parinari robusta 
   17 1.6 1.6 74.9 
Parkia bicolor 
   7 .6 .6 75.6 
Parkia biglobasa 
   8 .7 .7 76.3 
Parkia biglobosa 
   6 .6 .6 76.9 
Parkia biglobossa 
   9 .8 .8 77.7 
Paspalum conjugatum 
   2 .2 .2 77.9 
Paspalum nonathum 
   6 .6 .6 78.4 
Pauridiantah hirttela 
   3 .3 .3 78.7 
Pauridiantha hirttela 
   1 .1 .1 78.8 
Pavetta corymbosa 
   19 1.8 1.8 80.6 
Pennisetum pedicellatum 
   8 .7 .7 81.3 
Prosopis Africana 
   8 .7 .7 82.0 
Psarospermum febrifuga 
   12 1.1 1.1 83.1 
Securidaea longipendicula 
   2 .2 .2 83.3 
Sema hirsute 
   7 .6 .6 84.0 
Senna hirsute 
   1 .1 .1 84.1 
Sinolax crucicina 
   3 .3 .3 84.4 
Smilax kruciana 
   12 1.1 1.1 85.5 
Solanum eriantum 
   6 .6 .6 86.0 
Solanum macrocarpum 
   8 .7 .7 86.8 
Solenostrenum 
monostachyc 
   14 1.3 1.3 88.1 
Spandias mombim 
   6 .6 .6 88.6 
Sphenocentron jollyanum 
   2 .2 .2 88.8 
Spondias mombim 
   10 .9 .9 89.7 
Sterculia tragacantha 
   5 .5 .5 90.2 
Stragia spp 
   10 .9 .9 91.1 
Syndrella nodiflora 
   10 .9 .9 92.0 
96 
 
 
Tectona grandis 
   10 .9 .9 93.0 
Tephrosia braceolata 
   10 .9 .9 93.9 
Tephrosia pedicellata 
   18 1.7 1.7 95.6 
Terminalia  glaucescens 
   10 .9 .9 96.5 
Vernonia amygdalina 
   8 .7 .7 97.2 
Vipellaria paradoxa 
   4 .4 .4 97.6 
Vitellaria paradoxa 
   9 .8 .8 98.4 
Vitex doniana 
   1 .1 .1 98.5 
Vittelaria paradoxum 
   16 1.5 1.5 100.0 
Waltheria indica 
   1080 100.0 100.0   
Total 
 
Source: Field Survey (2005 – 2008) 
 
 
 
Analysis of Variance of Plants Abundance for Raining Season 
 
 Sum of            Df     Mean     F Sig. 
Squares Squares 
Between 231393.500             12    1932.792 5330.741 0.000 
Groups       386.867         1067          0.363  
Within  23580.367         1079 
Groups 
Total 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 Analysis of Plants Abundance for Dry Season 
 
 Sum of       Df     Mean      F   Sig. 
Squares Squares 
Between   16057.7550       12    1338.129  1535.663   0.000 
Groups 
Within 
groups 
Total 
 
Source: Field Survey (2005 – 2008) 
 
 
 
97 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 3: Percentage Average Relative Abundance of Plant Species in the Study Area  
Source:  Field Survey (2005 – 2008) 
 
 
 
 
 
98 
 
 
 
 
 
 
 
800
600
400
200
Mean =2.09

Std. Dev. =4.675

N =1,080
0
0 10 20 30 40 50 60
Number of Plants
 
Fig. 4: Average Raining Season Plant Species Frequency of Abundance 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
99 
 
Frequency
 
 
800
600
400
200
Mean =1.88

Std. Dev. =3.968

N =1,080
0
0 10 20 30 40 50
Number of Plants
 
 
Fig. 5 : Average Dry Season Plant Species Frequency of Abundance 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
100 
 
Frequency
 
 
 
 
Fig. 6:  Rainy Season Mean Number of Plants per plot in the Study Area 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
101 
 
 
 
 
 
Fig. 7: Dry Season Mean Number of Plants per plot in the Study Area 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
102 
 
 
 
4.2 ANIMAL FREQUENCY DISTRIBUTION AND RELATIVE ABUNDANCE 
  
 In all 0ne thousand eight hundred and twenty – four (1824) animals were observed 
either by direct sighting and indices during the study. The animals belong to forty (40) 
species from thirty – one (31) family. The average frequency of animals in the study area is 
shown in table 4. The monthly abundance of animals is shown in table 7. The cane rat 
(Thryonomys swinderianus) was the most abundant species followed by Ground squirrel 
(Xerus erythrocepus), Maxwell duiker (Cephalopus maxwelli) and Giant rat (Cricetomys 
gambianus).  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
103 
 
 
Table 3: Scientific names and Codes of Animals in the Study Site 
COUPLET NO. SCIENTIFIC NAME  ENGLISH NAME CODE 
       1 Actophilornis africana  Lily rotter ACAF 
       2 Agama agama  Agama lizard AGAG 
       3 Ardea cinera Grey heron  ARCI 
       4 Arvicanthus niloticus  Nile rat  ARNI 
       5 Artheris chloraechis Brown snake  ARCH 
       6 Anthus leucophrys Plainbacked pipit ANLE 
       7 Bitis gabonica Gabon viper BIGA 
       8 Bostrichia hagedash Hadada ibis BOHA 
       9 Bothropthalmus ,ineatum Sidestripe brown snake BOLI 
      10 Bulbulcus ibis  Cattle egret BUIB 
      11 Burhinus senegalensis Senegal thick snale BUSE 
      12 Carprimulgus spp Night jar CASP 
      13 Centropus grilli Black coucal CEGR 
      14 Centropus senegalensis Senegal coucal  CESE 
      15 Cephalophus maxwellii Maxwell duiker CEMA 
      16 Cephalophus rufilatus Red flanked duiker CERU 
      17  Cephalophus spp  Duiker CESP 
      18  Cercopitheecus mona Mona monkey   CEMO 
      19  Ceryle rudis Pied king fisher  CERU 
      20  Ciconia abdmii Abdim stork   CIAB 
      21  Cisticola cantan Lanceolated warbier  CICA 
      22  Cisticola galactotes Grass wabler  CIGA 
       23   C,amator glandarius Great spottted cukoo  CLGA 
       24   Clamator jacobinus Jaccobin cukoo   CLJA 
       25   Clamator levallanti Levaillantafrican cukoo  CLLE 
       26   Coracias abysinica Abysinia roller  COAB 
       27   Coracias cyanogaster  Bleud bellied roller  COCY 
       28   Corvinella corvine Long tail shrike   COCO 
       29   Corvus albus Pied cow  COAL 
       30  Corythaeola cristata Blue plantain eater  COCR 
       31  Cricetomys gamianus  Giant rat  CRGA 
       32  Crinifer piscator  Grey plantain eater    CRPI 
       33 Cypsiuurus parvus African palm swift  CYPA 
104 
 
 
       34 Dendroaspis virindis Green mamba   DEVI 
          35 Dendrocygna viduata White faced tree duck DEVD 
          36 Dendrohyrax dorsalis Tree hyrax DEDO 
          37 Dendropicos fuscescens  Cardinal woodpecker DEFU 
         38 Epixerus ebii Red headed tree squirrel EPEB 
        39 Erythrocebus patas Patas monkey ERPA 
       40 Estrilda melpoda Orange cheeked waxbill ESME 
      41 Euplectes orix Red bishop EUOR 
     42 Euplectes macrourus Yellow mantle whydah EUMA 
     43 Francolinus bicalcaratus  Francolin (Bush fow) FRBI 
    44 Fraseria ocreata Fraser forest flycatcher FROC 
    45 Genetta macullatta Forest genet (Maloko) GEMA 
   46 Genetta trigrina Serval cat (Ogbo) GETR 
   47 Gypohierax angolensis Plamnut vulture  GYAN 
  48 Halcyon leucocephala Grey headed kingfisher HALE 
  49 Halcyon malimbica  Blue breasted kingfisher HAMA 
  50 Hacyon senegalensis  Sengal kingfisher HASE 
  51 Haliatus vocifer Fish (River) Eagle  HAVO 
  52 Heliosciurus puncatus Small forest swallow HEPU 
  53 Hirundo semirufa Rufuos chested swallow HISE 
  54 Hirundo senegalensis Mospue swallow HISG 
  55 Hylochoerus minertzhageni Bush pig  HYMI 
  56 Hystrix cristata Crested porcupine HYCR 
  57 Indicator indicator Greater honey guide  ININ 
  58 Indicator minor Lesser honey guide INMI 
  59 Kaupifalco monogrammiscus Lizard Buzzard KAMO 
  60 Logonosticta senegala Senegal fire finch  LASE 
  61 Lamptotornis spp Glossy starlings  LASP 
  62 Laniarus artoflavus Yellow billed shrike LAAR 
  63 Lemniscormys striatus Spotted grass mouse LEST 
  64 Lepus capensis  Hare  LECA 
  65 Lonhura bicolor Black and white manikin LOBI 
  66 Lonchura cucullata  Bronse manikin LOCU 
  67 Lophuromys sikapusi Rufuos bellied rat  LOSI 
  68 Lybius veilliot  veilliot barbet LYNE 
  69 Macronyx crocent  Yellow throated long claw MACR 
  70 Merops albicolis White throated bee eater  MEAL 
  71 Merops malimbicus Rosy bee eater  MEMA 
  72 Merops muellenii Black headed bee eater  MEMU 
105 
 
 
73 Merops nubicus Carmine bee eater  MENU 
74 Micropus caffer White rumped swift MICA 
75 Milvus migrans Black kite  MIMI 
76 Motacilla flava Yellow wagtail MOFL 
77 Mungos obsciurus Long nose mongoose MUOB 
78 Mus minutoides Pigmy mouse MUMI 
79 Musophaga violacea Violet plantain eater MUVI 
80 Naja melanoleuca Black cobra NAME 
81 Numida meleagris Giunea fowl NUME 
82 phoeniculus atterimus  Lesser (Green) wood hoope PHAT 
83 Phylloscopus trochillus Wilow warbler  PHTR 
84 Ploceus cucullatus Village weaver bird PLCU 
85 Ploceus melanocephalus  Black headed weaver  PLME 
86 Pogonileus subsulpheus Yellow rumped tinker bird POSU 
87 Poicephalus senegalus Senegal parrot POSE 
88 Polyboroides radiates Harrier hawk PORA 
89 Procavia ruficeps Rock hyrax PRRU 
90 Protexerus aubinni Slender tailed squirrel PRAU 
91 Protexerus strangerii Gaint forest squirel  PRST 
92 Psamophis sibilans Yellow stripe snake PSSI 
93 Psamophis sibilans philipsii Yellow snake PSSP 
94 Pyconotus barbatus Common garden bulbul PYBA 
95 Python sebae Rock python  PYSE 
96 Rattus natalensis  Muiltimammate rat RANA 
97 Rousethus smithii Fruit bat ROSM 
98 Schoenicola platyura Fan tailed swamp barbler SCPL 
99 Scopus umbretta Hammerkop SCUM 
100 Sphenoeacus mentalis  Moustached grass warbler SPME 
101 Streptopelia decipens African (morning) dove  STDE 
102 Streptopelia senegalensis  Laughing dove  STSE 
103 Streptopelia semitorquata Red Eyed dove  STSQ 
104 Streptopelia turtur European turtle dove  STTU 
105 Streptopelia vinacea Veinaceous dove STVI 
106 ateri kempi Kemps gerbil TAKE 
107 Thryonomys swinderianus Grasscutter  THSW 
108 Tockus erthorhyncus African hornbill TOER 
109 Tockus nasutus Afrcan grey hornbill TONA 
110 Tragelaphus scriptus  Bush buck TRSC 
111 Teron australis  Green pigeon fruit TRAU 
106 
 
 
112 Turdoides reinwardii Black cap barbler TURE 
113 Turdus Pelios West African thrush TUPE 
114 Tyto alba Owl TYAL 
115 Veranus  examthematicus Short tailed Nile monitor VEEX 
116 Veranus niloticus  Monitor lizard  VENI 
117 Viverra civetta Civet cat VICI 
118 Vidua macroura Pin tailed whydah VIMA 
119 Xerus erythropus  White stripe ground squirel  XEER 
120 Xerus sp Plain body ground squirel  XESP 
121 Zosterops senegalensis  Yellow white eye  ZOSE 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
107 
 
 
Table 4 : Average Frequency of Animals in the Study Area 
                     Name of Animal Frequenc Percent Valid Cumulative 
y Percent Percent 
  Anthus leueophrys 24 1.3 1.3 1.3 
Arvicauthus niloticus 122 6.7 6.7 8.0 
Bothrophthalmus lineatus 4 .2 .2 8.2 
Bulbulcus ibis 58 3.2 3.2 11.4 
Centropus senegalensis 56 3.1 3.1 14.5 
Cephalophus maxwellii 22 1.2 1.2 15.7 
Cephalophus spp 89 4.9 4.9 20.6 
Cercopithecus mona 12 .7 .7 21.2 
Corvus albus 48 2.6 2.6 23.8 
Cricetomys gambianus 73 4.0 4.0 27.9 
Cypsiurus parvus 24 1.3 1.3 29.2 
Epixerus ebii 12 .7 .7 29.8 
Francolinus bicalcaratus 107 5.9 5.9 35.7 
Hylochocrus minertzhage 12 .7 .7 36.3 
Kaupifalco 44 2.4 2.4 38.8 
monogrammicus 
Lemniscomys striatus 36 2.0 2.0 40.7 
Lephuromys sikapusi 24 1.3 1.3 42.1 
Lepus capensis 116 6.4 6.4 48.4 
Lonchura cucullata 24 1.3 1.3 49.7 
Merops malimbicus 24 1.3 1.3 51.0 
Milvus migrans 24 1.3 1.3 52.4 
Mungos obscures 12 .7 .7 53.0 
Numida meleagris 60 3.3 3.3 56.3 
Otus senegalensis 12 .7 .7 57.0 
Ploceus capensis 5 .1 .1 57.0 
Ploceus cucullatus  35 1.9 1.9 58.9 
Protexerus aubinnii 12 .7 .7 59.6 
Protexerus strangerii 11 .6 .6 60.2 
Psammophis sibilous 12 .7 .7 60.9 
Philip 
Sphenoeacus mentalis 12 .7 .7 61.5 
Streptopelia turtur 24 1.3 1.3 62.8 
Tateri kempi 12 .7 .7 63.5 
Thryonomys swinderianus 319 17.5 17.5 81.0 
Tockus nasutus 12 .7 .7 81.6 
Tragelaphus scriptus 72 3.9 3.9 85.6 
Treron australis 23 1.3 1.3 86.8 
Varanus niloticus 12 .7 .7 87.5 
Viverra civeta 61 3.3 3.3 90.8 
Willow warbler ** 24 1.3 1.3 92.2 
Xerus erythropus 143 7.8 7.8 100.0 
     
Total 1824 100.0 100.0   
Source: Field Survey (2005 – 2008) 
 
 
108 
 
 
 
 Analysis of Variance of Distance of Sighting Animals and Season 
 
    Model Sum of        Df Mean Squares           F        Sig. 
Squares 
Regression 29933290        2           54.140           0  
a
Residual 5.03E+08 1821 14966645.097 .000  
Total 5.33E+08 1823         
276445.152 
 
a. Predictors: (Constant), dry , wet 
b. Dependent Variable: Distance 
 
Source: Field Survey (2005 – 2008) 
 
 
 Analysis of Variance of Animal Order and Season 
 
     Model Sum of       Df   Mean          F      Sig. 
Squares Squares 
a
Regression    52.514         2         26.257     28.758        0 .0000  
Residual 1662.643   1821             .913 
Total 1715.158   1823 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
109 
 
 
Table 5: Mode of Animal Identification 
 
Mode of Animal Frequen Percen Valid Cumulative 
Identification cy t Percent Percent 
  Direct 793 43.1 43.1 44.0 
  Dung pol 36 2.0 2.0 46.0 
  Egg shel 12 0.7 0.7 46.6 
  Fd & pt 333 18.1 18.1 64.7 
  feacal p 34 1.8 1.8 66.6 
  Feather 24 1.3 1.3 67.9 
  Ft prt 208 11.3 11.3 79.2 
  Hole 128 6.9 6.9 86.1 
  HYMI 6 0.3 0.3 86.4 
  Nest 49 2.7 2.7 89.1 
  Nest cou 22 1.2 1.2 90.3 
  Pellet 118 6.4 6.4 96.7 
  Reptile 1 0.1 0.1 96.7 
  Sand bat 12 0.7 0.7 97.4 
  Stand bi 12 0.7 0.7 98.0 
  Trail 36 2.0 2.0 100.0 
  Total 1842 100.0 100.0   
 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
110 
 
 
Table 6: Crosstabs of Animal Abundance and Distance 
 
    Animal                                                                                   Distance 
Code Name 250.00 750.00 1250.00 1750.00 Total 
ANLE 0 0 12 12 24 
ARNI 90 20 12 0 122 
BOLI 0 4 0 0 4 
BUIB 13 20 13 12 58 
CEMA 0 0 4 18 22 
CEMO 1 0 2 9 12 
CESE 3 19 19 15 56 
CESP 1 27 46 15 89 
COAL 1 1 34 12 48 
CRGA 3 32 10 28 73 
CYPA 0 0 10 14 24 
EPEB 1 10 1 0 12 
FRBI 16 50 40 1 107 
HYMI 1 0 4 7 12 
KAMO 0 9 11 24 44 
LECA 29 29 44 14 116 
LESI 14 10 0 0 24 
LEST 25 0 11 0 36 
LOCU 24 0 0 0 24 
MEMA 1 22 1 0 24 
MIMI 0 0 7 17 24 
MUOB 1 0 8 3 12 
NUME 0 1 15 44 60 
OTSE 1 2 9 0 12 
PLCA 1 0 0 0 1 
PLCU 35 0 0 0 35 
PRAU 1 0 8 3 12 
PRST 1 0 4 6 11 
PSSI 1 0 0 11 12 
SPME 1 0 0 11 12 
STTU 0 0 1 23 24 
TAKE 12 0 0 0 12 
THSW 50 130 61 78 319 
TONA 1 0 2 9 12 
TRAU 0 17 6 0 23 
TRSC 1 23 1 47 72 
VANI 12 0 0 0 12 
VICI 1 11 49 0 61 
WIWA 1 23 0 0 24 
XEER 43 43 35 22 143 
Total 386 503 480 455 1824 
Source: Field Survey (2005 – 2008) 
 
 
 
 
111 
 
 
Table 7: Monthly Abundance of Animals  
 
                                                                      Month Total 
CodeName Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
ANLE 2 2 2 2 2 2 2 2 2 2 2 2 24 
ARNI 10 10 10 10 11 11 10 10 10 10 10 10 122 
BOLI 1 1 1 1 0 0 0 0 0 0 0 0 4 
BUIB 4 4 5 4 4 4 6 6 6 6 5 4 58 
CEMA 2 1 2 2 1 2 2 2 2 2 2 2 22 
CEMO 1 1 1 1 1 1 1 1 1 1 1 1 12 
CESE 4 4 4 4 5 5 5 5 5 5 5 5 56 
CESP 7 9 8 8 8 7 7 7 7 7 7 7 89 
COAL 4 4 4 4 4 4 4 4 4 4 4 4 44 
CRGA 6 6 6 7 6 6 6 6 6 6 6 6 73 
CYPA 2 2 2 2 2 2 2 2 2 2 2 2 24 
EPEB 1 1 1 1 1 1 1 1 1 1 1 1 12 
FRBI 8 9 9 9 9 9 9 9 9 9 9 9 107 
HYMI 1 1 1 1 1 1 1 1 1 1 1 1 12 
KAMO 3 3 3 3 4 4 4 4 4 4 4 4 44 
LECA 9 10 9 10 9 9 10 10 10 10 10 10 116 
LESI 2 2 2 2 2 2 2 2 2 2 2 2 24 
LEST 3 3 3 3 3 3 3 3 3 3 3 3 36 
LOCU 2 2 2 2 2 2 2 2 2 2 2 2 24 
MEMA 2 2 2 2 2 2 2 2 2 2 2 2 24 
MIMI 2 2 2 2 2 2 2 2 2 2 2 2 24 
MUOB 1 1 1 1 1 1 1 1 1 1 1 1 12 
NUME 5 5 5 5 5 5 5 5 5 5 5 5 60 
OTSE 1 1 1 1 1 1 1 1 1 1 1 1 12 
PLCA 1 0 0 0 0 0 0 0 0 0 0 0 1 
PLCU 2 3 3 3 3 3 3 3 3 3 3 3 35 
PRAU 1 1 1 1 1 1 1 1 1 1 1 1 12 
PRST 1 0 1 1 1 1 1 1 1 1 1 1 11 
PSSI 1 1 1 1 1 1 1 1 1 1 1 1 12 
SPME 1 1 1 1 1 1 1 1 1 1 1 1 12 
STTU 2 2 2 2 2 2 2 2 2 2 2 2 24 
TAKE 1 1 1 1 1 1 1 1 1 1 1 1 12 
THSW 25 26 26 27 27 27 26 27 27 27 27 27 319 
TONA 1 1 1 1 1 1 1 1 1 1 1 1 12 
TRAU 1 2 2 2 2 2 2 2 2 2 2 2 23 
TRSC 6 6 6 6 6 6 6 6 6 6 6 6 72 
VANI 1 1 1 1 1 1 1 1 1 1 1 1 12 
VICI 6 5 5 5 5 5 5 5 5 5 5 5 61 
WIWA 2 2 2 2 2 2 2 2 2 2 2 2 24 
XEER 11 12 12 12 12 12 12 12 12 12 12 12 143 
Total 146 150 151 153 152 152 153 154 154 154 153 152 1824 
Source: Field Survey (2005 – 2008) 
 
 
 
 
112 
 
 
Table 8: Crosstab of Distance and Order 
 
                                                                          Order Total 
Distance Mammal Reptile Bird 
250.00 248 13 125 386 
750.00 324 4 175 503 
1250.00 276 0 204 480 
1750.00 242 11 202 455 
Total 1090 28 706 1824 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
113 
 
 
Table 9: Regression Analysis of Distance of Sighting and Season 
 
 Distance Wet Dry 
Pearson Correlation  1.000 -.169 -.144 
Distance -.169 1.000 -.122 
                                  Wet -.144 -.122 1.000 
                                  Dry 
Sig. (1-tailed)           . .000 0.000 
Distance .000       . 0.000 
                                 Wet .000 .000       . 
                                 Dry  
N                              1824 1824 1824 
Distance 1824 1824 1824 
                                 Wet 1824 1824 1824 
                                 Dry 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
114 
 
 
Table 10: Land Use Changes in the Study Area (1984 - 2008) 
 
Land use Categories Area Extent (ha) Percentage of Amount of Percentage of 
1984       2008 Total (%) Change from Change of 
1984          2008 1884 – 2008 the land % 
Agricultural tree crop 0.00          106.15 0.00          1.03 106.15 100.00 
Built up areas 0.00          108.85 0.00          1.06 108.85 100.00 
Disturbed Forest 2051.32    1537.72 20.00        14.99 -513.6 -33.4 
Extensive farmland 1538.49    1394.90 15.00        13.60 -143.59   -10.30 
Intensive farmland 1846.19    1517.98 18.00        14.80 -328.21 -21.6 
Road 82.05          206.67 0.80           2.02 124.62 60.3 
 
Source: Planning Unit UNAAB (2009) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
115 
 
 
 
 
 
Fig. 8: Percentage Average Abundance of Animals in the Study Area 
 
 
Source: Field Survey (2005 - 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
116 
 
 
 
 
Fig.9 : Average Frequency of Animals Sighted in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
117 
 
 
 
 
 
Fig. 10: Order of Animals Sighted in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
118 
 
 
 
 
 
Fig. 11: Percentage Average Monthly Animal Abundance in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
119 
 
 
 
 
 
Fig. 12: Animal Sighting Indicator of the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
120 
 
 
 
 
 
Fig. 13: Average Animals Sighted in the Rainy Season in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
121 
 
 
 
 
 
 
Fig. 14: Average Animals Sighted in the Dry Season in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
122 
 
 
 
400
300
200
100
Mean =9.63

Std. Dev. =5.13

N =1,824
0
0.00 5.00 10.00 15.00 20.00 25.00
Transect Number
 
Fig. 15: Average Frequency of Animals along Transects in the Study Area 
 
Source: Field Study (2005 – 2008) 
 
 
 
 
 
123 
 
Frequency
 
 
1,200
1,000
800
600
400
200
Mean =5.39

Std. Dev. =14.114

N =1,824
0
0 20 40 60 80 100 120
Rainy Season Animal Abundance
 
Fig. 16  Average Rainy Season Abundance of Animals in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
124 
 
Frequency
 
 
1,500
1,000
500
Mean =3.65

Std. Dev. =11.459

N =1,824
0
0 20 40 60 80 100 120
Dry Season Animal Abundance
 
Fig. 17: Average Dry Season Abundance of Animals in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
125 
 
Frequency
 
 
 
 
Fig. 18: Modes of Animal Identification in the Study Area 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
126 
 
 
 
4.3  SOIL ANALYSIS 
 Table 11 shows the pH, percentage Carbon, percentage Nitrogen, percentage Organic 
matter, Silt and Sand of the various plots in the study area. Plot 3 has the most Orgaic matter 
with while polts 2, 6 and 7 had the least with 13.07 percent. The pH of the plots were almost 
constant ranging between 5.18 and 6.62 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
127 
 
 
 
 
 
 
Table 11: Soil characteristics parameter of the study site 
 
Plot  pH  %C  %N  %OM  Clay  Silt   
Plot        
pH Percentage Percentage Percentage Clay Silt Sand 
Carbon Nitrogen Organic Matter 
1  6.11  36.31  3.63  62.60  4.00  4.80  90.40  
2  6.12  7.58  0.78  13.07  4.80  4.80  83.20  
3  5.66  40.30  4.03  69.48  9.60  7.20  89.60  
4  6.10  38.52  3.85  66.41  4.80  8.80  87.20   
5  6.15  35.11  3.51  60.53  4.80  8.80  89.60  
6  5.37  7.58  0.76  13.07  7.20  3.20  90.40  
7  5.64  7.58  0.76  13.07  4.80  4.80  78.29  
8  6.30  75.01  7.50  19.32  9.14  12.57  
87.20  
9  5.85  25.94  2.59  44.71  5.60  7.20  84.00  
10  5.72  12.77  1.28  22.01  8.80  7.20  84.80  
11  5.41  55.06  5.51  94.93  5.60  9.60  82.40  
12  5.18  61.85  6.18  16.62  5.60  12.00  
88.80  
47.20  
13  6.29  23.14  2.31  39.90  4.80  6.40  
14  6.16  15.56  1.56  26.83  5.60  47.20  69.60  
15  6.16  33.12  3.31  57.09  4.80  25.60  90.40  
16  5.93  12.77  1.28  22.01  4.80  4.80  90.40  
17  6.62  26.73  2.67  46.09  5.60  4.00  86.40  
18  6.29  38.70  3.87  66.72  5.60  8.00  89.60 
19 5.61  9.98  0.99  17.20  4.80  5.60  
89.60 
20 5.60  11.57  1.20  20.64  5.60  4.80  89.60 
   
 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
128 
 
 
 
 
4.4 DIVERSITY INDICES, ANALYSIS OF VARIANCE AND CORRELATION 
 The Animal and plant diversity indices are shown in tables 23 and 24 respectively. 
The rainy season plant analysis of variance at p = 0.05 was 0.2579 and 0.0005266 for the dry 
season. The plants were positive and significantly correlated r = 0.96661 (p = 0.05). 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
129 
 
 
 
 
Table 12: Animal Diversity Indices of the Study Area 
 
INDICES  RAINY SEASON DRY SEASON 
 
Dominance_D 0.004305 0.005938 
Shannon_H  0.6065 0.5741 
Simpson_1-D  0.9957  0.9941 
Evenness_e^H/S 0.4016 0.4162 
Equitability_J  0.8692  0.8675 
Fisher_alpha  0.3063 0.2162 
Berger-Parker  0.01037 0.01531 
 
Source: Field Survey (2005 – 2007) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
130 
 
 
 
 
 
 
Table 13: Plant Diversity Indices of the Study Area 
 
INDICES  RAINY DRY SEASON 
SEASON 
Dominance_D  0.005534 0.005032 
Shannon_H  0.6308  0.625 
Simpson_1-D 0.9945 0.995 
Evenness_e^H/S 0.5137  0.5503 
Equitability_J  0.9045 0.9128 
Fisher_alpha 0.7905  0.6797 
Berger-Parker  0.02653 0.02458 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131 
 
 
 
 
 
 
Table 14: Problems confronting the Nature Reserve based on respondents observation 
 
Problem Percentage 
Burning   46 
Development   - 
Farming   - 
Hunting   20 
Grazing   34 
 
Source: Field Survey (2005 – 2008) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
132 
 
 
 
 
 
 
Table 15: Means of meteorological Observations of the Study Area (2005 -2008)  
 
Parameter JAN FEB MA APR MA JUN JUL AUG SEP OCT NOV DEC 
R Y Y 
MEAN TEMP OC 2.03 29.1 28.6 29.3 23.87 22.9 26.4 25.7 26.0 27.3 28.4 28.0
7 3 3 7 7 0 8 8 5 
RAINFALL(mm) 1.51 20.6 24.4 65.0 29.2 98.3 41.77 25.0 91.3 25.2 14.5 4.51 
7 3 3 7 7 5 7 
REL.HUMUDITY(% 6.13 71.1 73.3 70.4 69.07 68.7 85.53 85.7 85.9 82.3 77.0 70.3
) 3 3 0 3 3 3 7 7 7 
WIND RUN 22.0 9.28 9.28 15.5 5.49 5.54 5.96 6.54 4.13 1.45 0.96 1.38 
(Km/Day) 9 3 
SUNSHINE 1.28 2.42 1.92 1.73 6.07 2.56 2.06 0.77 1.08 1.17 1.09 1.41 
DURATION(Hrs) 
 
Source: Department of Agro Meteorology UNAAB 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
133 
 
 
100
THSW ALERCNAI
90 wet
80 LECA LECA LECA
70
60
50
40 CESP CESP
30 CYPA PLCUCYPA
THSW
20 XEER THSW ILECA CXVIEOCEAIRL
ALERSNTI
NPCSTEULRCTSMGUPAE NTLERUCAMACUE CLTERSI
10 X OCETSPE FRAB
GUIA LESII
NFMKLCTEA
ECEMARO FCR BETPUIEBCB
TBCXOHREOUIIMESSBNIABREWIIALE VKCTLEICC
SBIAPI TTAXOMFHHRERTEBSNMRWWEIIA ATTFHRRSBNWII
PNHNTSLCLAATBOPHRHNNYSUEUCCAAPUL
transwct CESP
FMPSXPRSIEBMSMNIILGLAGSBARWCWECIIIIIITTAUIIILEEIOI LTFMPBSALXO
HAEROIMSGAWILOmorodnerth XCLECESARE THSW
FKTA LTAHTV
C
BI CBXFMPNH
VEEIIPIHIHOUUR
RYYNC
UCSEEBMSNIALIIOgraidienttificatBioISBTARREPWETIAUIALIOEIB ACTKTXHHR
UERnESIGNBWA XCTHEESREW
0 CPTSRTELCTSGUPCA ATFLCKXHANCICSCEMSBBIIINNGLAIIBOTAAREWPWIIIIIUIIILLAOIB AFLCATXKCW
BVLBTVXFAI
AIIHIREUUENOOIIC
CWCCESBIIII
M
IIMGNNGLL
IIIBARRPIPEWIIIIIAO BTTXUIB
XLECEAR ENOSXTPREUTTEAMRPEBUE TFELPHRCBSEAIBAARIPWWEBIIIIIIALO TAWL
VAHEEKSNI REWI
LXCOEOTCESAAREL EBTAFNMXPERUECASBIENMBARPCBIUIIIEA FCPTLXMHV
BSLOABXTPMHIO
HAPEUREULSRLYICWC
CEI U
CYPA LCE GI A ETOHPECEK
BSSMI
I
IIANGI
BAWII
TCHYPWA CLAPFERL
NMBOTARRAIECPWTIIIUIIIAEIOBA
CESP CESP THSW CALERO
TCSBSNGAIIAUWCBEIIIAL
-10 CESP SNTIXPELCERU
-20
LECA dry LECA XLECEAR
-30 THSW
AXERENRII
-190-0180-0170-0160-0150-0140-0130-0120-0110-0100-0900-800-700-600-500-400-300-200-100 0
Component 1
 
Fig.19: Principal Component Analysis of the distribution of Animals species encountered in 
the Study Area. 
Source: (Field Survey 2005 – 2008) 
 
 
 
 
134 
 
Component 2
 
 
 
 
 
20
LLBLOUOCICBUU
TPTHLHCSSAWW
10
TVBTHAUSNIBWI BLLOUOI
BTHUSI I
CIBCUU
IIBW
dAPArRNI
TTHSW
yLLCUIII
VBAICIATURINBIIITCTHSWMFRLTE
N
AWA
HERHESRICWB
WISE
BUN
MAWI
IIBAIII
A BUBTIUHB
XREAEURI PAwLTReCHtNUSII W
ISBW
TXVATTCTHAHSNSWIPW I0 ATFVMWNLXBFE
ENRHOEPREENLASBERIEILIIITTHABHSRUSIWNIWBI
CFATSLLTCNLPFOMXTBC
LSHLAIHEDX
RNItioBOUPHCLMXTUR
HIIEdLrLWANVTFPHMTWXTLALTCMEIEEULYORSFTCAa
BS
irL
NI
LHREeSBXCTAMIV
SMCI
TEAHBOSCMLBSFEONOIHPUYITPIYIUSTsENPOHI
dE
ASKB
OBT
MRUNITSMI
nBGNEAWIIKBUITIITSIRI
AICW
IEeCIStIKAPWEHNIIAL
BsIAOURIBNI
PKVAISRCIGLIIIBOPEIMTCRUI
IIaIIISBEtNrBiEeSfNASTiIIcIRWIIUHIIM tationIIWCEPB
RIIIIELNAIITLAIIILLSUMEIIBWRITUIOIIIPInIA
IIWEILIIIIUEAcIIAe
0 10 20
Axis 1
 
Fig.20: Ordination Diagram of Animals in the Study Area 
Source: (Field Survey 2005 – 2008) 
135 
 
Axis 2
 
Row and Column Points
Symmetrical Normalization
2
37.00 Distance
18.00 wet26.00
1
24.00 4.00
102.00 7.00 2.00 1250.00
33.00 11.00
250.00 5.00 1750.00
0 .00
16.00 1.003.00
15.00
23.00 8.00
-1 750.00 12.00
83.00
13.00
10.00
-2
-3 99.00
-4
-4 -3 -2 -1 0 1 2
Dimension 1
 
Fig.21: Sighting of Animals According to Distance from Transects in the Wet Season 
Source: (Field Survey 2005 – 2008) 
 
 
 
 
 
 
 
 
136 
 
Dimension 2
 
Row and Column Points
Symmetrical Normalization
2
18.00 Distance
26.00
dry
1 14.00 7.00 2.00 11.00 37.00
4.00
33.00 1750.00
1250.00
250.00
0 16.00
.00 5.00
8.0012.0023.00
750.00
-1 15.00
10.00 13.00
83.00
-2
-3
99.00
-4
-4 -3 -2 -1 0 1 2
Dimension 1
 
Fig.22: Sighting of Animals According to Distance from Transects in the Dry Season 
Source: (Field Survey 2005 – 2008) 
 
 
 
 
 
 
 
 
 
137 
 
Dimension 2
 
CHAPTER FIVE 
 
5.0   DISCUSSION 
5.1  Discussion 
5.11  Species Diversity, Component Analysis  
The level of species diversity recorded for plants and animals in the study area is 
high; one hundred and eighteen (118) plant species from 44 families and 40 animal species 
from 31 families. According to Richards (1952), the humid tropical forest has the richest and 
most heterogeneous faunal and floristic diversity which developed largely because of the 
favourable conditions of climate and other factors that favours the abundance of species in all 
seasons. The study area has the diversity of plants recorded because it is free from hunting 
pressures, thus serving as a refuge for the animals. Onadeko and Meduna (1985) reported 
abundance of animals in the protected sites than sites that were unprotected. Also the high 
plant species diversity recorded in the study area can be attributed to the absence of 
agricultural practices and other development activities. Grasscutters and Giant rats were most 
abundant in the study area because there were favourable food resources as well as cover 
adequate for their requirments were present. 
The results of this study indicate that Daniella oliveri, Anona senegalensis, Bridelia 
micrantha and Ficus capenssis were the most abundant tree species. According to Kupchella 
et al (1993), the edaphic, climatic and topographic factors determine the type and distribution 
of plant species that will survive in an area. The plants in turn control these factors and create 
a microclimate that ensures a normal physical environment that promotes their survival. 
Happold (1987), also reported that in certain cases, the animals present in a vegetation could 
be a major determinant of the type of vegetation that will persist in an area because of their 
mode of utilization of the plants for food and cover. Therefore, the relationship that exists 
138 
 
 
between most of the plants and animals indicated by the biplots promotes a stable ecological 
system for their survival. 
Animals in the order rodentia, especially Cane rat (Thryonomys swinderianus), Giant 
rat (Cricetomys gambianus) and Ground squirrel (Xerus erythropus) were the most abundant 
in the study area. Indices of their activities include feeding remains, droppings and burrows. 
The Maxwell duiker (Cephalopus maxwelli) was also recorded in appreciable portion. 
Happold (1973) and Roberts (1986) stated that the trophic ecology and need for protection 
against predators of animal species in an area explains basis for their habitat distribution. 
Dasmann (1985) and Onadeko (1995) also reported that the availability of food, water 
and cover are the major determinants of wild animal occurrence and distribution in an area. 
This explains the distributions of animals on the biplot based on their feeding and cover 
requirements. 
The Cane rats were predominant in areas with dense grasses and rampant herbaceous 
vegetation where there is also good cover. They feed on thick stemmed grasses and 
occasionally on tree barks (Happold, 1987) as shown by their runways, feacal droppings and 
feeding remains. The Giant rat (Cricetomys gambianus) feed on fruits, vegetables, seeds, 
maize, yams, and oil palm nuts and this explains their abundance because some of these 
requirements are in abundant supply in the study area. 
Also, the Ground squirrel, found widely in the study area live habitually on the 
ground especially in burrows and feed on seeds, roots and bulbs (Ewer, 1969). The areas 
were they are mostly found in the study area is rich in these requirements. The Maxwell 
duiker lives in wooded and grassland savanna where there are small thickets and undergrowth 
where they can seek cover (Happold, 1973). Their diet consists of leaves and herbs and 
young plant. These food and cover requirements abound in the study area where they browse 
on the young stems of these trees and shrubs and hide in the dense undergrowth. 
139 
 
 
The Hares (Lepus capensis) live in drier habitats where the vegetation is heavily 
grazed and grasses are short and spouting (Happold, 1987). They are found to predominate in 
such vegetation on the study site. This habitat preference causes them to live in areas 
otherwise uninhabitable for other browsers and grazers and explains the large dispersion of 
their position on the northern portion of the study site where they occur away from the other 
wildlife species occurring in the dense wooded vegetation at the southern part of the study 
site. 
The Principal component analysis (fig. 19) and Ordination (fig. 20) shows that the 
ecosystem of the study site is not stable yet. This can be observed from the clustering of the 
animal species together in an attempt to make the best use of the environment. This may be 
due to the fact that the Strict Nature Reserve is recently demarcated and requires some time to 
settle away from the previous land use pattern of the area. The bulk of animal species wthin 
transects, combed during the survey were encountered during the dry season, while few were 
encountered during the wet season. Along the transects, gradients, distribution of most of the 
species were closely tied to the season and are related either in the movement or other activity 
pattern, but some other also show a wide dispersion from the effect of the major component 
i.e  dry season. Animals such as Cephalopus spesies, Lepus capensis, some Arvicauthus 
niloticus and Thryonomys swinderianus are in this group. These were found at the extremes 
of dry and wet season within the space. 
Ordination of animal species distribution in transects and season revealed that the 
gradation is discontinuous but concentrated in the ordination space at around 12.0‟clock and 
3.0‟clock and between 9-12 0‟clock again. What this translates into is that every animal 
species that are found within the same quarter space are close and have almost the same 
factors influencing their distribution. Within the same quarter it was also noticed that 
Lonchura cucullata and Thryonomys swinderianus are closer and a bit separated from the 
140 
 
 
bulk, thus it can be suspected that a kind of ecological or biological relationship is occurring 
between them. Relationship between the animal species and environmental variables 
measured (seasons) indicate a very strong association between the factors and animal species 
thus, distribution, performance and survival of the species may be directly influenced by 
these variables. 
Gradient distribution of animal species in wet season indicative of the point of contact 
with the animal along the transect gradient as well as the abundance values of the animal 
species encountered. The least abundance value of animal species (5.0) was encountered 
within the quadrant 1750 while the highest (102) was found in quadrant 250, so also in the 
dry season, the least (11.00) was encountered in quadrant 1750 but the highest abundance of 
(99.00) was found within 750 gradient. 
The disappearance of many plant species due to human activities is depleting the 
world‟s genetic resources and is putting man‟s heritage of biodiversity under serious threat. 
There is therefore the urgent need to preserve genetic diversity including plant resources of 
known and unknown economic importance which will guarantee the availability of all 
potentials for use in the benefit of our children and grandchildren (Olowokudejo, 1987). The 
human race in their quest for economic development and improvement of their conditions of 
life must come to terms with the realities of resource limitations and the carrying capacity of 
ecosystem must also take account of the needs of future generation. This is the central 
message to modern conservation. Biological diversity must be treated seriously as a global 
resource, be indexed, used and above all preserved. Three circumstances make it imperative 
for this to be given an unprecedented urgency particularly in West Africa. Firstly, exploding 
human populations are seriously region. Secondly, science is discovering new uses for 
degrading the environment at an alarming rate in the sub biological diversity in ways that 
relieve both human suffering and environmental destruction. Thirdly, much of the diversity is 
141 
 
 
being irreversibly lost through extinction caused by the destruction of natural habitats, which 
occurs more in Africa than elsewhere (Wilson, 1988). Dasman et al., (1973) agreed that forest  
exploitation leads to the extinction of animals and plants whose genetic resources are of 
considerable value to future generations (Round Table, 1969). Forest depletion has 
destabilized the natural environment and eroded genetic resources throughout the southern 
part of Nigeria in order to meet the sustenance of the population and financial requirements 
of government i.e. the social, economic, demographic and political needs of the people. 
Exploitation of forests therefore appears to be split about vegetation depletion which is 
considered as a inevitable considering the above. Opinions are however loss of natural 
heritage. According to some scientists (Harvey and Hallet, 1977) it may not be beneficial to 
conserve resources for future generation at all costs because the future demands, aspirations, 
lifestyles and needs of rural people cannot be adequately defined now. Must we then wait for 
the needs to be defined before we conserve? Definitely not, because all of these genetic 
resources would have disappeared before the needs are identified. As such, conservation is 
basic to human welfare and indeed to human survival (Allen, 1980). Lack of conservation 
measures will amount to an increase in the number of endangered species and this will 
ultimately result in extinction, which is the gradual but sure elimination of taxa (Allaby, 
1998). Many of the species that are already endangered are faced with the risk of eventual 
extinction if human activities such as land development, logging and pollution are not 
checked. Gbile et al. (1981, 1984) revealed that about four hundred and eighty plant species 
of the Nigerian flora have been described as endangered or rare, out of which many of these 
are being studied at the Forestry Research Institute of Nigeria, Ibadan. Apart from the gradual 
loss of biodiversity, the devastating environmental disasters in urban and rural areas of 
Nigeria indicate that these environments are under stress and require urgent intervention 
(Oguntala, 1993). While developmental activities continue on the campus it will be a sound 
142 
 
 
scientific judgment to protect a representative sample of vegetation for posterity, hence the 
idea of the idea of UNAAB Strict nature Reaserve. This is the practice in most developed 
countries of the world. The Omo Biosphere Reserve and the International Institute for 
Tropical Agriculture (IITA) at Ibadan, Nigeria has such an area which now serves as an 
example of a typical tropical Rain forest in south Western Nigeria. 
 Burning from wild fire is the greatest problem being faced by the Nature Reserve 
according to respondents (Table 25), making up 46% of problems confronting the site. 
Another big problem is the illegal grazing by nomadic Fulani herds men that have settled 
around Opeji ( a town close to the Alabata area), these herds men are traditionally difficult 
and stubborn, but they are being engaged through there leaders. Hunting is minimal at 20% 
according to respondents and this may be due to conservation awareness among the settlers 
around the nature Reserve emanating from the efforts of the Department of Forestry and 
Wildlife Management of the University field staff. 
 
5.12 Soil structure, texture and chemical composition 
 The structure, texture, consistence and chemical composition of the soil determine the 
type of plants and consequently the animals it will support (Russell, 1957; Happold, 1973). 
These are the factors that determine the fertility of any soil. (Forth, 1978), explains that the 
humus and clay contents of soil dictates its ability to absorbs and retain nutrients. The sandy-
loam soil of the study area has an appreciable proportion of organic matter and clay. 
According to Bohn et al (1979), the pH of a soil determines the percentage composition of 
organic matter in it. Soil with high pH value allows a high microbial activity hence, 
increasing biological degradation (Brady, 1974). Also, a highly leached soil allows high 
mineral synthesis and hence, high clay content. The leached soil of the study area containing 
143 
 
 
plenty organic matter and having a high pH value supports a large proportion of plant species 
(Table 11). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
144 
 
 
CHAPTER SIX 
 
6.0  CONCLUSION AND RECOMMENDATION 
 
6.1  Conclusion 
 Many scholars and some multinational organizations such as the World Bank, which 
have long linked high population growth with poverty and underdevelopment, have now 
turned their attention to uncovering linkage between population and environmental 
degradation. According to World Bank (1992), rapidly growing populations have led to 
“overgrazing, deforestation, depletion of water resources and loss of natural habitat”. In a 
separate report, the World Resources Institute, IUCN- the World Conservation Union, and 
the United Nations Environmental Programme also identified “unsustainable high rates of 
human Population growth and natural resources consumption” as the first of the six 
fundamental causes of biodiversity loss (IUCN/UNEP/WWF 1992) 
 The maintenance of a healthy ecosystem is largely dependent on its management and 
control of activities of man and animals. Human interference such as hunting, grazing, 
farming, bush burning and clearing for construction and development of physical facilities 
will influence survival and relative abundance of plant and animal species available in an 
area.  
 Climate change with is attendant effect on temperature levels and pattern of rainfall 
will also determine the survival of wildlife in a given area. Because the rate at which the 
climate is changing makes it difficult for biodiversity to adapt, as temperatures keeps 
changing with time. 
145 
 
 
The stability of the soil is also determined largely by these activities. It is therefore 
expedient to consciously manage the plants, animals and soil components of the study site 
and their complex interactions to ensure a healthy environment. 
 
6.2  Recommendation 
 The strict nature reserve should be managed on an environmentally sound sustainable 
principle. The incidence of annual fire that currently ravages the area should be reduced 
drastically. This would enable the ecosystem of the study site to stabilize. 
 There should be continuous awareness education on the Strict Nature Reserve, by 
means of awareness campaigns conducted through the mass media and also organized talks, 
film shows and seminars, so that more reverence would be accorded the site. 
 Establish a data-base to show the diversity, distribution and status of biological 
diversities (both flora and fauna) in the study area. 
 There should be employment of dedicated security to enforce the entrance law of the 
site, the arrangement of overseeing the area by the existing University Security has proved to 
be inadequate. 
 
 
 
 
 
 
 
 
 
146 
 
 
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156 
 
 
APPENDIX 1 
 
DEPARTMENT OF WILDLIFE AND FISHERIES MANAGEMENT 
FACULTY OF AGRICULTURE AND FORESTRY 
UNIVERSITY OF IBADAN,IBADAN 
WILDLIFE RESEARCH QUESTIONAIRE IN SOME SELECTED EXTENSION VILLAGES 
AROUND UNAAB, ABEOKUTA 
 
1Name of village     2Location of village  
 
3Estimated Population size of village/settlement 
 
4 Name of respondent 
 
5 Age group of respondent  
(a) 10-20years 
(b) 21-30years 
(c) 31-40years 
(d) 41-50years 
(e) above 50years 
6 Marital statuses: Single/ Married  7 Sex: male/female 
 
8 Household Sizes 
 No of wife(s)    No of Children 
9 Nationality     10 State of origin 
 
11Occupation 
 (a)farming (b)hunting (c)trading (d)farming+hunting 
  
(e)government or paid employment  (f) others 
12Mention your major source of income 
 
13How many years of experience 
 (a) as a farmer 
 (b) as an Hunter 
 ©  as a trader 
 (d) as a paid worker 
14 Highest Educational statuses obtained  
(a) No formal school attended 
(b) primary school 
©   Secondary school 
 (d) tertiary institution 
 (e) others (specify) 
15 What motivated you into hunting 
 
16 What motivated you into farming 
 
17 What motivated you into trading 
157 
 
 
 
18What are the method(s) of hunting that you use  
(a) Traps            List types 
 (b) Dogs 
 (c )Chasing   
   (d) traditional (describe) 
 (e) Fire 
 (f)sole hunting  (g) group hunting 
 
 
19 What types of weapons do you hunt with  
(a) modern firearms 
 (b) traditional firearms eg dane guns 
 ©   Cutlass 
 (d) combination of-----------------------------and---------------------------------- 
 (d) others 
 
20 How do you get your weapons?  
(a) Made by self 
 (b) Local purchase from blacksmith 
© Local purchase from ready-made shop  
(d) others 
21 What animal species do you     List species 
(a) kill for sale only 
 (b) kill and eat only 
  ©  kill to sell part and consume part 
 (d) kill and you do not consume    why?----------------   
22 Around where do you hunt? 
 
23 When do you prefer to hunt  
(a) season 
(b) time of day  
1 early morning 
2 afternoons 
3 late evening 
(c) night 
24 What season do you kill more animals:  
(a) rainy season 
(b) dry season 
(c) full moon 
(d) half moon 
(e) no moon 
 25 List types of Animals hunted 
 
 26 Is there any laws that guide the hunting operation? 
 
 27 What is the distance of your hunting site from home (approximate km) 
 
 
28 In What type of vegetation do you prefer to hunt 
158 
 
 
(a) On my farm 
(b) In the natural bush 
© Anywhere 
 
29 topography of the area  
(a) rocky outcrop       animals mostly found  
 (b) Flat terrain            animals mostly found 
 ©   Wetland(riparian) animals mostly found 
 
30 Average no of wildlife SPECIES (types) hunted/day 
 
31 How often do you see animals in the bush?  
(a) during the day 
 (b) at night 
 
32How are the animals sold?  
(a) whole  
(b) part 
33 Selling price of hunted animals?  
List  (a) species                                        selling price/whole animal   
 
34 Who are your customers?  
(a) Co-villagers  
(b) civil servants  
(c) Traders(buy and re-sell)  
(d)  Consumers (buy and consume) 
(e)  others 
35 Does Government influence the prices? Yes/no 
 
36 What animal species do people demand for 
 
37 Why do they demand for such animals?  
(a) price  
(b) taste  
38 Is there any taboo on  
(a)consumption of any animal in the village What is the taboo? and list species 
affected 
(b)killing of any in the village What is the taboo? and list species affected 
 
39 Is there any protocol in sharing hunted animals. 
(a) by group hunters 
(b) by family members 
(c) by villagers 
40 If offered any other job, can you leave hunting?. 
 
41 Do you belong to any farmers‟ association/cooperative? Yes/No       
Name if Yes 
If no why  
 
 
159 
 
 
42 Do you belong to any hunters‟ association/cooperative? Yes/No       
Name if Yes 
       If no why    
 
 
43 List benefits from your association/cooperatives 
 farmers‟ association/ cooperatives 
 
 hunters‟ association/ cooperatives 
 
44 What type of crop(s) do you plant on your farm 
 
45 Which crop(s) is(are) most affected by wildlife? List 
 
46 Which Wildlife SPECIES attack your farm most. 
 Species                                            Crop and part affected 
 
 
47 Problems encountered in carrying out  
(a)hunting activities.   
   
(b)farming activities 
 
48Suggest what you will like done for you to encourage your hunting activities 
 
49Suggest what you will like done for you to encourage your farming activities 
 
Thank You. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
160 
 
 
APPENDIX 2 
 
 
NAMES, CODE AND TAXONOMIC CHARACTERISTICS OF PLANT IN THE STUDY  AREA 
Couplet No Scientific Name Code Family Name Form  
1 Abelmoschus esculentus ABES Malvaceae Shrub  
2 Abrus precatorius ABPR Papillionaceae Climber 
3 Abutilon ABMA Malvaceae Shrub  
4 Acacia kamerunesis ACKA Mimosaceae Tree 
5 Acacia sieberina ACSI Mimosaceae Tree 
6 Acalyphyta ciliate ACCI Malvaceae Shrub  
7 Acanthospermum hispidum ACHI Acanthaceae Herb 
8 Acanthus montanus ACMO Acanthaceae Shrub  
9 Achyranthes aspera ACAS Amaranthaceae Herb 
10 Acridocarpus smeathhniamii ACSM Malphishiaceae Shrub  
11 Adansonia digitata ADDI Bombacaceae Tree 
12 Adenopus brevflorus ADBR Apocynaceae Climber 
13 Afromorsia laxiflora AFLA Papillionaceae Shrub  
14 Afzelia Africana AFAF Caesalpinioideae Tree 
15 Agelea oblique AGOB Connaraceae Shrub  
16 Agerantum conysoides AGCO Asteraceae Herb 
17 Albizia adianthifolia ALAD Mimosoideae Tree 
18 Albizia coriara ALCO Mimosoideae Tree 
19 Albizia feruginea  ALFE Mimosoideae Tree 
20 Albizia zygia ALZY Mimosoideae Tree 
21 Albizia lebbeck ALLE Mimosoideae Tree 
22 Alchornea cordifolia  ALCD Euphorbiaceae Shrub  
23 Alchornea laxiflora ALLA Euphorbiaceae Shrub  
24 Allophyllus africanus ALAF Sapindaceae Shrub  
25 Alstonia boonei ALBO Apocynaceae Tree 
26 Alstonia congensis ALCG Apocynaceae Tree 
27 Amaranthus spinosus AMSP Amaranthaceae Herb 
28 Amaranthus hybridis AMHY Amaranthaceae Herb 
29 Anarcardium occidentate  ANOC Anarcardiaceae Tree 
161 
 
 
30 Ananas comosus ANCO Palmae Shrub  
31 Aneilema beniniense ANBE Commelinaceae Climber 
32 Anchomamis difformis ANDI Araceae Herb 
33 Ancistrocapus densisipinosus ANDE Tiliaceae Shrub  
34 Andropogen gayanus ANGA Poaceae Grass 
35 Andropogen teetorum ANTE Poaceae Grass 
36 Anogeisus leiocarpus ANLE Combretaceae Tree 
37 Anona senegalensis ANSE Annonaceae Shrub  
38 Antana Africana ANAC Mimosoideae Shrub  
39 Anthocleista vogeillii ANVO Loganiaceae Shrub  
40 Anthocleista djalonesis ANDJ Loganiaceae Shrub  
41 Anthonotha macrophylla ANMA Cesalipinioideae Shrub  
42 Anthephora ampilliaceae ANAM Poaceae Shrub  
43 Antiaris Africana ANAF Moraceae Tree 
44 Antiaris toxicaria ANTO Moraceae Tree 
45 Asparagus flagellaris ASFL Caesalpinioideae Tree 
46 Aspillia Africana ASAF Asteraceae Herb 
47 Aspillia busei ASBU Asteraceae Herb 
48 Asystatsia gangetica ASGA Acanthaceae Shrub 
49 Azadirachta indica AZIN Azadirachtaceae Tree 
50 Axonopus compressus  AXCO Poaceae Grass 
51 Bambussa vulgaris BAVU Poaceae Grass 
52 Bidens pilosa BIPI Asteraceae Herb 
53 Blepharis maderoapatensis BLMA Acanthaceae Shrub 
54 Blighia sapida BLSA Sapindaceae Tree 
55 Blighia welwetehii BLWE Sapindaceae Tree 
56 Boerharia coccinea BODI Nyctagmaceae Tree 
57 Boerharia deflexa BOCO Nyctagmaceae Tree 
58 Bombax buanopozense BOBU Bombacaceae Tree 
59 Brachiera deflexa  BRDE Poaceae Grass 
60 Brachystegia eurycoma BREU Caesalpinioideae Tree 
61 Bridelia feruginea BRFE Euphorbiaceae Tree 
62 Bridelia micrantha BRMI Euphorbiaceae Tree 
162 
 
 
63 Burkea Africana BUAF Caesalpinioideae Tree 
64 Cajanus cajan CACA Poaceae Shrub  
65 Calotropis procera CAPR  Bombacaceae Shrub  
66 Canavalium ensiformis CAEN Papillionaceae Climber 
67 Canhium vulgera CAVU Rabiaceae Grass 
68 Carica papaya CAPA Caricaceae Pseudo tree 
69 Carpolobea lutea CALU Polygalaceae Shrub/Herb  
70 Cassia alata CAAL Caesalpinioideae Tree 
71 Cassia monosoides  CAMI Caesalpinioideae Tree 
72 Cassia podocarpa CAPO Caesalpinioideae Tree 
73 Cassia siamea CASI Caesalpinioideae Tree 
74 Ceiba pentadra  CEPE Bombacaceae Tree 
75 Celosia argentea CEAR Amaranthaceae Herb 
76 Celtis zenkeri CEZE Ulmaceae Tree 
77 Centrocema puebescens  CEPU Papillionaceae Climber 
78 Chamaecrista mimosoides CHMI Poaceae Grass 
79 Chloris pilosa CHPO Poaceae Grass 
80 Chassalia kolly CHKO Poaceae Grass 
81 Chrosopogon aciculatus  CHAC Poaceae Grass 
82 Cissampelos mucronanta CIMU Menispermaceae Herb 
83 Chromalaena odoratum CHOD Asteraceae Herb 
84 Chrysophyllum albidum CHAL Sapotaceae Tree 
85 Citrus sinensis CISI Rutaceae Tree 
86 Clappertoniana ficifolia CLFI Tiliaceae Shrub 
87 Cleistopholis paten  CLPA Annonaceae Tree 
88 Cleoma viscose  CLVI Cleomaceae Shrub 
89 Cnestis feruginea CNFE Connaraceae Shrub 
90 Cocos nucifera CONU Palmae Tree 
91 Cochlospermum planchonii COPL Cochlospaermaceae Shrub 
92 Coffea brevipas  COBR Rubiaceae Tree 
93 Cola afzelii COAF Sterculiaceae Tree 
94 Cola gigantean  COGI Sterculiaceae Tree 
95 Cola milleni COMI Sterculiaceae Tree 
163 
 
 
96 Cola nitida CONI Sterculiaceae Tree 
97 Combretum  bracteaunm  COBC Combretaceae Tree 
98 Combretum hispidum COHI Combretaceae Tree 
99 Combretum racemosum CORA Combretaceae Tree 
100 Combretum molle  COMO Combretaceae Tree 
101 Combretum zenkeri  COZE Combretaceae Tree 
102 Commelina benghalensis COBE Commelinaceae Tree 
103 Commelina nodiflora CONO Commelinaceae Tree 
104 Conyza sumatrensis  COSU Asteraceae Herb 
105 Corchorus olitorius COOL Tiliaceae Herb 
106 Croton lobatus  CRLO Euphorbiaceae Herb 
107 Crotolaria retusa  CRRE Papillionaceae Shrub  
108 Crassocephalum rubens CRRU Papillionaceae Grass 
109 Crescentia  CRCU Cucurbitaceae Shrub/Tree 
110 Cucurbita pepo CUPE Cucurbitaceae Climber 
111 Cucumeropsis manni  CUMA Cucurbitaceae Climber 
112 Cussonia barteri CUBA Araliaceae Tree 
113 Cyanolis lanata CYLA Amaranthaceae Herb 
114 Cymbopogon giganteus CYGI Poaceae Grass 
115 Cyathula prostrata  CYPR Poaceae Grass 
116 Cynodon dactylon CYDA Poaceae Grass 
117 Cynometra megalophylla  CYME Caesalpinioideae Herb 
118 Cyperus articulatus  CYAR Cyperaceae Sedges 
119 Cyperus esculentus  CYES Cyperaceae Sedges 
120 Cyperus iria CYIR Cyperaceae Sedges 
121 Dactyloctenium aegyptium DAAE Poaceae Grass 
122 Daniella olliverii DAOL Caesalpinioideae Tree 
123 Deloni regia  DERE Caesalpinioideae Tree 
124 Deinbollia pinnata DEPI Sapindaceae Tree 
125 Desmodium salcifolium DESA Papillionaceae Herb 
126 Detarium macrocarpum DEMA Caesalpinioideae Tree 
127 Dialium guinensis  DIGU Caesalpinioideae Tree 
128 Discorea prahensilis DIPR Dioscoreaceae Climber 
164 
 
 
129 Dioseorea alata DIAL  Dioscoreaceae Climber 
130 Discorea cayenensis DICA Dioscoreaceae Climber 
131 Diospyros mesipiliformis DIME Ebenaceae Tree 
132 Diospyros monbutensis DIMO Ebenaceae Tree 
133 Dichrostachys cinerea DICI Mimosoideae Tree 
134 Diplazium sammatii DISA Athyriaceae Tree 
135 Distemonanthus benthamanus DIBE Caesalpinioideae Tree 
136 Dracaena fragranus DRFR Agavaceae Shrub 
137 Eclipia alba ECAL Asteraceae Shrub 
138 Elaeisi guinensis ELGU Palmae Pseudo tree 
139 Eleusine indica ELIN Poaceae Grass 
140 Entanda Africana ENAF Mimosoideae Herb 
141 Eragrostis tremula ERTR Poaceae Grass 
142 Erythrina senegalensis  ERSE Caesalpinioideae Shrub/Tree 
143 Erythrophleum suaveolensis  ERSU Caesalpinioideae Tree 
144 Euphorbia hirta EUHI Euphorbiaceae Herb 
145 Euphorbia lateriflora EULA Euphorbiaceae Herb 
146 Ficus capensis  FICA Moraceae Tree 
147 Ficus exasperata  FIEX Moraceae Tree 
148 Ficus mucoso FIMU Moraceae Tree 
149 Ficus thioningii FITH Moraceae Tree 
150 Ficus sycomorus FISY Moraceae Tree 
151 Funtumia elastic FUEL Apocynaceae Tree 
152 Gardenia trenifolia GATE Rubiaceae Shrub/tree 
153 Gardenia aqaulla GAAQ Rubiaceae Shrub/Tree 
154 Gliricidia sepium  GLSE Papillionaceae Shrub/Tree 
155 Glyphaea brevipes GLBR Tiliaceae Shrub/Tree 
156 Gmelina arboreus GMAR Verbenaceae Tree 
157 Gossypium barbadense GOBA Bombacaceae Tree 
158 Grevia carpinifolia GRCA Tiliaceae Tree 
159 Grevia flavescens GRFL Tiliaceae Tree 
160 Greivia mollis GRMO Tiliaceae Tree 
161 Guarea cedrata GUCE Meliaceae Tree 
165 
 
 
162 Harrisonia abyssinica HAAB Simaroubaceae Tree 
163 Hedranthera barteri  HEBA Simaroubaceae Tree 
164 Heinsia crinita  HECR Rubiaceae Tree 
165 Hewittia sublobata HESU Convolvulaceae Herb 
166 Hibiscus asper  HIAS Malvaceae Shrub 
167 HIBIscus sabdarrifa HISA Malvaceae Shrub 
168 Hibiscus rostellatus  HIRO Poaceae Grass 
169 Hiprocratea patten  HIPA Poaceae Grass 
170 Hollarhena floribunda  HOFL Aprigmaceae Tree 
171 Holoptelia grandis  HOGR Ulmaceae Tree 
172 Homalium letestui HOLE Samydaceae Tree 
173 Hyparhenia involucrate HYIN Poaceae Grass 
174 Hyparhenia rufa HYRU Poaceae Grass 
175 Hmneocardia acida  HYAC Euphorbiaceae Tree 
176 Icacinia tricantha  ICTR Icacimaceae Shrub/Herb  
177 Imperata cylindrical IMCY Poaceae Grass 
178 Indigofera capitata INCA Papillionaceae Herb 
179 Irvingia gabonensis IRGA Ixonamthaceae Tree 
180 Irvingia wombolu IRWO Ixonamthaceae Tree 
181 Ipomea asarifolia IPAS Convolvulaceae climber/Crawler  
182 Jatropha carcass JACU Euphorbiaceae Shrub 
183 Justicia flava JUFL Acanthaceae Climber 
184 Khaya ivorensis  KHIV Meliaceae Tree 
185 Kigelia africana  KIAF Bignoniaceae Tree 
186 Lannea nigritana LANI Anarcardiaceae Tree 
187 Lannea welwetehii LAWE Anarcardiaceae Tree 
188 Lannea taraxacifolia  LATA Asteraceae Tree 
189 Lagenaria sicerania  LASI  Tree 
190 Laportea aestanus  LAAE Urticaceae  
191 Leersia hexandra LAHE Poaceae Grass 
192 Lactuca capensis LACP Asteraceae Shrub 
193 Lantana camara LACA Verbenaceae Shrub/Herb  
194 Lecaniodiscus cupanioides LECU Sapindaceae Tree 
166 
 
 
195 Lonchocarpus cyanescens LOCY Papillionaceae Shrub/Herb  
196 Lonchocarpus griffonianus LOGR Papillionaceae Shrub/Herb  
197 Lophira lanceolata LOLA Ochnaceae Tree 
198 Lovoa trichiloides  LOTR Meliaceae Tree 
199 Ludiwigia deeuirens LUDE Onagreceae Tree 
200 Macaranga barterii MABA Euphorbiaceae Tree 
201 Machrosphyra longistyla MALO Rubiaceae Tree 
202 Malotus oppositifolius MAOP Euphorbiaceae Tree 
203 Malancantha alnifolia MAAL Sapotaceae Tree 
204 Magnifera indica MAIN Anarcardiaceae Tree 
205 Malvastrum corimandelianum MACO Malvaceae Tree 
206 Manihot esculenta MAES Euphorbiaceae Shrub/herb  
207 Maniophyton fulvum MAFU Euphorbiaceae Shrub/herb  
208 Maytenus senegalensis MASE Celastraceae Tree 
209 Magariteria discoideae MADI Euphorbiaceae Tree 
210 Microdesmis puberula MIPU Euphorbiaceae Tree 
211 Milicia excels MIEX Moraceae Tree 
212 Mimosa pudica MIPD Mimosoideae Herb 
213 Manscus alternifolius MAAF Cyperaceae Sedges 
214 Manscus flabelloformis MAFL Cyperaceae Sedges 
215 Mitragyna inermis MIIN Moraceae Shrub/Tree 
216 Melanthra scandens MESC Asteraceae Shrub 
217 Momordica charantai MOCH Cucurbitaceae Climber 
218 Mimosa invisa MIIV Mimosoideae Herb 
219 Morinda lucida MOLU Rubiaceae Shrub/Tree 
220 Monodorna tennifolia MOTE Annonaceae Tree 
221 Moringa oleifera MOOL Moringaceae Shrub/Tree 
222 Mucuna prurens  MUPR Papillionaceae Climber 
223 Mucuna sloanei MUSL Papillionaceae Climber 
224 Musa sapientum MUSA Musaceae Pseudo tree 
225 Musa paradisiacal MUPA Musaceae Pseudo tree 
226 Myrianthus arboreus MYAR Moraceae Shrub/Tree 
227 Nauchlea latifolia NALA Rubiaceae Tree 
167 
 
 
228 Newbouldia laevis NELA Bignoniaceae Tree 
229 Ocimum grattasimum OCGR Lamiaaceae Shrub/Tree 
230 Oryza longistanimata ORLO Poaceae Sedges 
231 Ocimum canum OCCA Lamiaaceae Shrub/Tree 
232 Olax subarolata OLSB Olacaaceae Tree 
233 Olax subscorpoidea OLSU Olacaaceae Tree 
234 Opillia celtidifolia OPCE Opilliaaceae Herb 
235 Panicum maximum PAMA Poaceae Grass 
236 Panicum laxum PALA Poaceae Grass 
237 Parinari robusta PARO Rosaaceae Tree 
238 Parinari polyandra PAPO Rosaaceae Tree 
239 Parkia becolor PABI Mimosoideae Tree 
240 Parkia biglobosa PABG Mimosoideae Tree 
241 Parinari glabra PAGL Rosaaceae Tree 
242 Parquettina nigreseen PANI Periplocaaceae Tree 
243 Palisota hirsute PAHI Commelinaceae Herb 
244 Paspalum norranthus PANO Poaceae Grass 
245 Pennisetum pedicellatum PEPE Poaceae Grass 
246 Pennisetum purpureum PEPU Poaceae Grass 
247 Phyllanthus discoides PHDI Euphorbiaceae Herb 
248 Pilostigma thoningii PITH Caesalpinioideae Shrub/tree 
249 Poulilzozia giunensis POGU Poaceae Grass 
250 Paullinia pinnata PAPI Sapindaceae Tree 
251 Physalis micrantha PHMI Euphorbiaceae Tree 
252 Prosopis Africana PRAF Mimosoideae Tree 
253 Psorospermum febrifugum PSFE Hypericaaceae Shrub 
254 Paspalum conjugatum PACO Poaceae Grass 
255 Pterocarpus santalinoides PTSA Papillionaceae Tree 
256 Pupalia lappacea PULA Amaranthaceae Herb 
257 Psidium guajava PSGU Myrtaceae Tree 
258 Peperomia pellucid PEPL Piperraaceae Tree 
259 Pterocarpus erinaceus PTER Papillionaceae Tree 
260 Pterocarpus mildbraedii PTMI Papillionaceae Tree 
168 
 
 
261 Pennisetum violacea PEVI Poaceae Grass 
262 Raphia hookerii RAHO Palmae Pseudo tree 
263 Reissantia indica RAIN Hyppocrateaceae Grass 
264 Rhynchospora corymbosa RHCO Cyperaceae Sedges 
265 Rauvolvisa vomitoria RAVO Apocynaceae Tree 
266 Ricinodendron heudelotii RIHE Euphorbiaceae Herb 
267 Ricinus communis  RICO Euphorbiaceae Climber 
268 Rinoria dentrata RIDE Volaceae Tree 
269 Rothmania longiflora ROLO Rubiaceae Tree 
270 Sansevierasenegambica SASE Agaraceae Grass 
271 Sanseviera liberica SALI Agaraceae Grass 
272 Securidaca longipendiculata SELO Polygalaceae Tree 
273 Schramkia leptocarpa SCLE Mimosoideae Tree 
274 Securinega virosa SEVI Euphorbiaceae Shrub 
275 Scleria verrucosa  SCVE Cyperaceae Herb 
276 Sesamium indicum SEIN Pedoliaceae Herb 
277 Senna hirsute SEHI Caesalpinioideae Herb 
278 Sida acuta SIAC Malvaceae Herb 
279 Sida corymbosa SICO Malvaceae Herb 
280 Smilax krausiana SMKR Smilacaceae Herb 
281 Solanum aethiopicum SOAE Solanaceae Herb 
282 Seteria megaphylla SEME Poaceae Herb 
283 Solanum americanum SOAM Solanaceae Herb 
284 Solanum dasyphyllum SODA Solanaceae Herb 
285 Solenostemon monostachyus SOMO Lamiaaceae Herb 
286 Solanum erianthum SOER Solanaceae Herb 
287 Solanum macrocarpum SOMA Solanaceae Herb 
288 Spathoidea campanulata SPCA Bignoniaceae Tree 
289 Spondias mombim SPMO Anarcardiaceae Tree 
290 Sphenocentrum jollyanum SPJO Menispermaceae Shrub 
291 Sterculia tragacantha STTR Sterculiaceae Tree 
292 Struchium sparganophora STSP Asteraceae Herb 
293 Syndrella nodiflora SYNO Asteraceae Herb 
169 
 
 
294 Tamarindus indica TAIN Mimosoideae Tree 
295 Talinum triangulare TATR Portulacaceae Herb 
296 Tectona grandis  TEGR Verbenaceae Herb 
297 Tephrosia braceolata TEBR Papillionaceae Shrub 
298 Tephrosia pedicellata TEPE Papillionaceae Shrub 
299 Terminalia glaucesceus TEGL Combretaceae Tree 
300 Terminalia superb TESU Combretaceae Tree 
301 Theobroma cacao TACA Sterculiaceae Tree 
302 Tithonia divesifolia  TIDI Asteraceae Herb 
303 Trema orientalis  TRDR Ulmaceae Herb 
304 Tridax procumbens  TRPR Asteraceae Herb 
305 Triplochiton sclerotylon TRSC Sterculiaceae Tree 
306 Trumtet cordifolia TRCO Tiliaceae Shrub 
307 Uvaria chamae UVCH Cucurbitaceae Climber 
308 Urenia lobata URLO Malvaceae   
309 Vernonia amygdalina VEAM Asteraceae Shrub 
310 Vernonia ambigua VEAB Asteraceae Shrub 
311 Vernonia anercii VEAN Asteraceae Shrub 
312 Vernonia perrottetii VEPE Asteraceae Shrub 
313 Vitex doniana VIDO Verbenaceae Tree 
314 Waltheria indica WAIN Sterculiaceae Shrub 
315 Xylopia quintasii XYDU Annonaceae Shrub/Tree 
316 Zanthoxylum zanthoxyloides ZAZA Rutaceae Shrub/Tree 
317 Vitellaria paradoxa VIPA Sapotaceae Tree      
170 
 
 
APPENDIX 3 
 
NAMES, CODE AND TAXONOMIC CHARACTERISTICS OF ANIMAL IN THE STUDY AREA  
COUPLET NO. SCIENTIFIC NAME  ENGLISH NAME CODE CLASS FAMILY 
       1 Actophilornis africana  Lily rotter ACAF Birds  Jacanidae 
       2 Agama agama  Agama lizard AGAG Reptiles  Agamidae 
       3 Ardea cinera Grey heron  ARCI Birds  Ardeidae 
       4 Arvicanthus niloticus Nile rat  ARNI Mamamal  Rattus 
       5 Artheris chloraechis Brown snake  ARCH Reptiles  Colubridae 
       6 Anthus leucophrys Plainbacked pipit ANLE Birds  Motacillidae 
       7 Bitis gabonica Gabon viper BIGA Reptiles  Viperridae 
       8 Bostrichia hagedash Hadada ibis BOHA Birds  Threskionithidae 
       9 Bothropthalmus ,ineatum Sidestripe brown snake BOLI Reptiles  Colubridae 
      10 Bulbulcus ibis  Cattle egret BUIB Birds  Ardeidae 
      11 Burhinus senegalensis Senegal thick snale BUSE Birds  Burhinidae 
      12 Carprimulgus spp Night jar CASP Birds  Caprimulgidae 
      13 Centropus grilli Black coucal CEGR Birds  Cuculidae 
      14 Centropus senegalensis Senegal coucal  CESE Birds  Cuculidae 
      15 Cephalophus maxwellii Maxwell duiker CEMA Mamamal  Cephalophinae 
      16 Cephalophus rufilatus Red flanked duiker CERU Mamamal  Cephalophinae 
     17          Cephalophus spp  Duiker       CESP Mamamal  CCephalophinae 
    18         Cercopitheecus mona Mona monkey       CEMO Mamamal  Cercopithecidae 
    19         Ceryle rudis Pied king fisher       CERU Birds  Alcedinidae 
    20        Ciconia abdmii Abdim stork        CIAb Birds  Ciconidae 
    21        Cisticola cantan Lanceolated warbier       CICA Birds  Sylvidae 
   22       Cisticola galactotes Grass wabler       CIGA Birds  Sylvidae 
   23       C,amator glandarius Great spottted cukoo      CLGA Birds  Campephagidae 
  24      Clamator jacobinus Jaccobin cukoo      CLJA Birds  Campephagidae 
Levaillant african 
25     Clamator levallanti cukoo CLLE Birds  Campephagidae 
26    Coracias abysinica Abysinia roller COAB Mamamal  Coraciidae  
27    Coracias cyanogaster  Bleud bellied roller COCY Mamamal  Coraciidae  
28    Corvinella corvine Long tail shrike  COCO Mamamal  Lanildae 
29    Corvus albus Pied cow COAL Mamamal  Corvidae 
30   Corythaeola cristata Blue plantain eater COCR Mamamal  Musophagidae 
31   Cricetomys gamianus  Giant rat CRGA Mamamal  Cricetidae 
32   Crinifer piscator  Grey plantain eater  CRPI Birds  Musophagidae 
171 
 
 
33 Cypsiuurus parvus African palm swift CYPA Birds  Apodidae 
          34 Dendroaspis virindis Green mamba  DEVI Reptiles  Elapidae 
          35 Dendrocygna viduata White faced tree duck DEVD Birds  Anatidae 
          36 Dendrohyrax dorsalis Tree hyrax DEDO Mamamal  Provaviidae 
          37 Dendropicos fuscescens  Cardinal woodpecker DEFU Birds  Picidae 
         38 Epixerus ebii Red headed tree squirrel EPEB Mamamal  Sciuridae 
        39 Erythrocebus patas Patas monkey ERPA Mamamal  Cercopithecidae 
       40 Estrilda melpoda Orange cheeked waxbill ESME Birds  Estrildae 
      41 Euplectes orix Red bishop EUOR Birds  Estrildae 
     42 Euplectes macrourus Yellow mantle whydah EUMA Birds  Ploceidae 
     43 Francolinus bicalcaratus  Francolin (Bush fow) FRBI Birds  Phasiannidae 
    44 Fraseria ocreata Fraser forest flycatcher FROC Birds  Mucicapidae 
    45 Genetta macullatta Forest genet (Maloko) GEMA Mamamal  Viverridae 
   46 Genetta trigrina Serval cat (Ogbo) GETR Mamamal  Viverridae 
   47 Gypohierax angolensis Plamnut vulture  GYAN Birds  Accipitiridae 
  48 Halcyon leucocephala Grey headed kingfisher HALE Birds  Alcedinidae 
  49 Halcyon malimbica  Blue breasted kingfisher HAMA Birds  Alcedinidae 
  50 Hacyon senegalensis  Sengal kingfisher HASE Birds  Alcedinidae 
  51 Haliatus vocifer Fish (River) Eagle  HAVO Birds  Accipitiridae 
  52 Heliosciurus puncatus Small forest swallow HEPU Birds  Sciuridae 
  53 Hirundo semirufa Rufuos chested swallow HISE Birds  Hirundidae 
  54 Hirundo senegalensis Mospue swallow HISG Birds  Hirundidae 
  55 Hylochoerus minertzhageni Bush pig  HYMI Mamamal  Suidae 
  56 Hystrix cristata Crested porcupine HYCR Mamamal  Hysricidae 
  57 Indicator indicator Greater honey guide  ININ Birds  Indicatoridae 
  58 Indicator minor Lesser honey guide INMI Birds  Indicatoridae 
  59 Kaupifalco monogrammiscus Lizard Buzzard KAMO Birds  Accipitiridae 
  60 Logonosticta senegala Senegal fire finch  LASE Birds  Fringilidae 
  61 Lamptotornis spp Glossy starlings  LASP Birds  Sturnidae 
  62 Laniarus artoflavus Yellow billed shrike LAAR Birds  Lanildae 
  63 Lemniscormys striatus Spotted grass mouse LEST Mamamal  Rattus 
  64 Lepus capensis  Hare  LECA Mamamal  Leporidae 
  65 Lonhura bicolor Black and white manikin LOBI Birds  Estrildae 
  66 Lonchura cucullata  Bronse manikin LOCU Birds  Estrildae 
  67 Lophuromys sikapusi Rufuos bellied rat  LOSI Mamamal  Rattus 
  68 Lybius veilliot  veilliot barbet LYNE Birds  Capitornidae 
  69 Macronyx crocent  Yellow throated long claw MACR Birds  Motacillidae 
  70 Merops albicolis White throated bee eater  MEAL Birds  Meropidae 
  71 Merops malimbicus Rosy bee eater  MEMA Birds  Meropidae 
  72 Merops muellenii Black headed bee eater  MEMU Birds  Meropidae 
172 
 
 
73 Merops nubicus Carmine bee eater  MENU Birds  Apodidae 
74 Micropus caffer White rumped swift MICA Birds  Apodidae 
75 Milvus migrans Black kite  MIMI Birds  Accipitiridae 
76 Motacilla flava Yellow wagtail MOFL Birds  Motacillidae 
77 Mungos obsciurus Long nose mongoose MUOB Mamamal  Viverridae 
78 Mus minutoides Pigmy mouse MUMI Mamamal  Rattus 
79 Musophaga violacea Violet plantain eater MUVI Birds  Musophagidae 
80 Naja melanoleuca Black cobra NAME Reptiles  Elapidae 
81 Numida meleagris Giunea fowl NUME Birds  Phasiannidae 
82 phoeniculus atterimus  Lesser (Green) wood hoope PHAT Birds  Upupidae 
83 Phylloscopus trochillus Wilow warbler  PHTR Birds  Sylvidae 
84 Ploceus cucullatus Village weaver bird PLCU Birds  Ploceidae 
85 Ploceus melanocephalus  Black headed weaver  PLME Birds  Ploceidae 
86 Pogonileus subsulpheus Yellow rumped tinker bird POSU Birds  Pogonidae 
87 Poicephalus senegalus Senegal parrot POSE Birds  Psittacidae 
88 Polyboroides radiates Harrier hawk PORA Birds  Accipitiridae 
89 Procavia ruficeps Rock hyrax PRRU Mamamal  Procaviidae 
90 Protexerus aubinni Slender tailed squirrel PRAU Mamamal  Sciuridae 
91 Protexerus strangerii Gaint forest squirel  PRST Mamamal  Sciuridae 
92 Psamophis sibilans Yellow stripe snake PSSI Reptiles  Colubridae 
93 Psamophis sibilans philipsii Yellow snake PSSP Reptiles  Colubridae 
94 Pyconotus barbatus Common garden bulbul PYBA Birds  Pyconotidae 
95 Python sebae Rock python  PYSE Reptiles  Boidae 
96 Rattus natalensis  Muiltimammate rat RANA Mamamal  Rattus 
97 Rousethus smithii Fruit bat ROSM Mamamal  Chiroptera 
98 Schoenicola platyura Fan tailed swamp barbler SCPL Birds  Timalidae 
99 Scopus umbretta Hammerkop SCUM Birds  Scopidae 
100 Sphenoeacus mentalis  Moustached grass warbler SPME Birds  Sylvidae 
101 Streptopelia decipens African (morning) dove  STDE Birds  Colubridae 
102 Streptopelia senegalensis  Laughing dove  STSE Birds  Colubridae 
103 Streptopelia semitorquata Red Eyed dove  STSQ Birds  Colubridae 
104 Streptopelia turtur European turtle dove  STTU Birds  Colubridae 
105 Streptopelia vinacea Veinaceous dove STVI Birds  Colubridae 
106 ateri kempi Kemps gerbil TAKE Mamamal  Rattus 
107 Thryonomys swinderianus Grasscutter  THSW Mamamal  Thryonomidae 
108 Tockus erthorhyncus African hornbill TOER Birds  Bucerotidae 
109 Tockus nasutus Afrcan grey hornbill TONA Birds  Bucerotidae 
110 Tragelaphus scriptus  Bush buck TRSCm Mamamal  Tragelaphidae 
111 Teron australis  Green pigeon fruit TRAU Birds  Colubridae 
112 Turdoides reinwardii Black cap barbler TURE Birds  Timalidae 
173 
 
 
113 Turdus Pelios West African thrush TUPE Birds  Turbidae 
114 Tyto alba Owl TYAL Birds  Strigidae 
115 Veranus  examthematicus Short tailed Nile monitor VEEX Reptiles  Veramidae 
116 Veranus niloticus  Monitor lizard  VENI Reptiles  Veramidae 
117 Viverra civetta Civet cat VICI Mamamal  Viverridae 
118 Vidua macroura Pin tailed whydah VIMA Birds  Ploceidae 
119 Xerus erythropus  White stripe ground squirel  XEER Mamamal  Sciuridae 
120 Xerus sp Plain body ground squirel  XESP Mamamal  Sciuridae 
121 Zosterops senegalensis  Yellow white eye  ZOSE Mamamal  Zosterpidae 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
174 
 
 
APPENDIX 4 
 
ABUNDANCE AND RELATIVE ABUNDANCE VALUE OF ANIMAL ENCOUNTERED ABUNDANCE AND RELATIVE ABUNDANCE VALUE OF NAIML ENCOUTERED DURING THE  
 IN THE STUDY AREA DRY SEASON IN THE STUDY AREA 
  
      
 S/N Code Total No. of Animal Abundance Rela. Abd. 
 1 ACAF 17 0.08 0.044 ± 1.133 
 2 AGAG 26 0.12 0.068 ± 1.252 
 3 ANLE 266 1.24 0.692 ± 13.925 
 4 ARCI 6 0.03 0.016 ± 0.400 
 5 ARNI 1444 6.75 3.760 ± 15.387 
 6 ATCH 7 0.03 0.018 ± 0.322 
 7 BIGA 5 0.02 0.014 ± 0.322 
 8 BOHA 2 0.01 0.006 ± 0.160 
 9 BOLI 13 0.05 0.034 ±0.571 
 10 BUIB 1399 6.54 3.644 ± 49.183 
 11 BUSE 9 0.04 0.024 ±0.314 
 12 CASP 72 0.34 0.188 ± 2.288 
 13 CEGR 20 0.1 0.052 ± 0.638 
 14 CESE 134 0.64 0.350 ± 5.632 
 15 CEMA 49 0.22 0.128 ± 0.803 
 16 CERU 6 0.03 0.016 ± 0.400 
 17 CESP 169 0.79 0.440 ± 1.579 
 18 CEMO 46 0.22 0.120 ± 2.519 
 19 CERD 22 0.1 0.058 ± 0.753 
 20 CIAB 10 0.05 0.026 ± 0.753 
 21 CICA 7 0.03 0.018 ± 0.381 
 22 CIGA 57 0.27 0.148 ± 2.593 
 23 CLGL 14 0.07 0.036 ± 0.798 
 24 CLJA 42 0.2 0.110 ± 1.945 
 25 CLLE 34 0.16 0.088 ± 1.743 
 26 COAB 29 0.14 0.076 ± 1.181 
 27 COCY 11 0.05 0.028 ± 0.463 
 28 COCO 8 0.03 0.020 ± 0.463 
 29 COAL 413 1.93 1.076 ± 7.640 
 30 COCR 25 0.12 0.066 ± 1.609 
 31 CRGA 136 0.64 0.354 ± 0.900 
 32 CRPI 17 0.08 0.044 ± 0.820 
 33 CYPA 58 0.27 0.152 ± 2.327 
 34 DEVI 25 0.12 0.066 ± 0.671 
 35 DEVD 40 0.19 0.104 ± 3.192 
 36 DEDO 19 0.1 0.050 ± 1.102 
 37 DEFU 42 0.2 0.110 ± 1.782 
 38 EPEB 142 0.66 0.37 ± 2.976 
175 
 
 
 39 ERPA 91 0.43 0.238 ± 3.716 
 40 ESME 7 0.03 0.018 ± 0.399 
 41 EUOR 0 0 0.000 ± 0.000 
 42 EUMA 23 0.11 0.060 ± 1.669 
 43 FRBI 1095 5.12 2.852 ± 12.229 
 44 FROC 6 0.03 0.016 ± 0.276 
 45 GEMA 0 0 0.000 ± 0.000 
 46 GETR 1 0 0.002 ± 0.079 
 47 GYAN 4 0.01 0.010 ± 0.320 
 48 HALE 0 0 0.000 ±  0.000 
 49 HAMA 1 0 0.002 ±  0.079 
 50 HASE 4 0.01 0.010 ±  0.320 
 51 HAVO 3 0.01 0.008 ±  0.171 
 52 HEPU 17 0.08 0.044  ±  0.571 
 53 HISE 10 0.05 0.026  ± 0.798 
 54 HISG 24 0.11 0.062  ± 1.225 
 55 HYMI 77 0.36 0.200 ±  3.589 
 56 HYCR 37 0.17 0.096 ±  1.175 
 57 ININ 11 0.05 0.028 ± 0.795 
 58 INMI 192 0.9 0.500 ±  8.308 
 59 KAMO 96 0.45 0.250 ± 1.965 
 60 LASE 13 0.06 0.034 ±  0.953 
 61 LAAR 9 0.04 0.024 ±  0.393 
 62 LASP 38 0.18 0.098 ± 2.421 
 63 LEST 60 0.28 0.156 ± 0.896 
 64 LECA 457 2.14 1.190 ± 5.865 
 65 LOBI 51 0.24 0.132 ±  0.739 
 66 LOCU 2278 10.66 5.932 ±  27.500 
 67 LOSI 50 0.23 0.052 ± 1.853 
 68 LYVE 15 0.07 0.040 ±  0.809 
 69 MACR 7 0.03 0.018 ± 0.299 
 70 MEAL 11 0.05 0.028 ± 0721 
 71 MENU 21 0.1 0.054 ± 1.143 
 72 MEMA 251 1.22 0.680 ± 8.416 
 73 MEMU 17 0.08 0.044 ± 0.975 
 74 MICA 0 0 0.000 ±  0.000 
 75 MIMI 133 0.62 0.346 ± 4.360 
 76 MOFL 132 0.62 0.344 ± 7.101 
 77 MUOB 12 0.06 0.032 ±0.717 
 78 MUMI 7 0.03 0.018 ± 0.416 
 79 MUVI 77 0.36 0.200 ± 0.731 
 80 NAME 10 0.05 0.026 ± 0.388 
 81 NUME 658 3.08 1.714 ± 12.214 
 82 PHAT 22 0.1 0.058 ± 0.772 
176 
 
 
 83 PHTR 177 0.83 0.462 ± 7.520 
 84 PLCU 1588 7.43 4.136 ±3.904\ 
 85 PLME 230 1.08 0.600 ± 10.152 
 86 POSU 20 0.1 0.052 ± 1.070 
 87 POSE 2 0.01 0.006 ± 0.160 
 88 PORA 4 0.02 0.010 ± 0.320 
 89 PRRU 19 0.09 0.050 ±0.931 
 90 PRAU 17 0.08 0.044 ± 0.854 
 91 PRST 30 0.14 0.078 ±0.870 
 92 PSSI 7 0.03 0.018 ± 0.478 
 93 PSSP 48 0.22 0.126 ± 1.893 
 94 PYBA 7 0.03 0.018 ± 0.322 
 95 PYSE 0 0 0.000 ± 0.000 
 96 RANA 16 0.08 0.042 ± 0.785 
 97 ROSM 759 3.55 1.976 ± 57.632 
 98 SCPL 31 0.14 0.080 ± 2.219 
 99 SCUM 3 0.01 0.008 ± 0.239 
 100 SPME 163 0.76 0.424 ± 8.812 
 101 STDE 9 0.04 0.024 ±0.266 
 102 STSE 90 0.42 0.234 ± 5.502 
 103 STSQ 52 0.24 0.136 ± 1.327 
 104 STTU 158 0.74 0.412 ± 4.823 
 105 STVI 20 0.1 0.052 ± 0.802 
 106 TAKE 12 0.05 0.032 ± 0.289 
 107 THSW 5342 25 13.912 ± 40.871 
 108 TOER 26 0.12 0.068 ± 0.686 
 109 TONA 69 0.32 0.180 ± 1.292 
 110 TRSC 176 0.82 0.458 ± 1.082 
 111 TRAU 596 2.79 1.552 ± 17.995 
 112 TUPE 25 0.12 0.066 ± 1.244 
 113 TURE 10 0.05 0.026 ± 0.715 
 114 TYAL 1 0 0.002 ± 0.079 
 115 VEEX 3 0.01 0.008 ± 0.171 
 116 VENI 9 0.04 0.024 ± 0.443 
 117 VIMA 32 0.15 0.084 ± 1.502 
 118 VICI 136 0.64 0.354 ± 5.452 
 119 XEER 488 2.28 1.270 ± 2.976 
 120 XESP 117 0.55 0.304 ± 6.240 
 121 ZOSE 6 0.03 0.016 ± 0.479 
      
      
      
 
 
 
177 
 
 
APPENDIX 5 
 
ABUNDANCE AND RELATIVE ABUNDANCE VALUE OF ANIMAL ENCOUTERED  
DURING THE WET SEASON IN THE STUDY AREA 
  
Total No. of 
S/N Code Animal Abundance  Rela. Abd.± Se     
1 ACAF 68 0.24 0.178 ±  1.651   
2 AGAG 18 0.07 0.046 ±  0.583   
3 ANLE 391 1.35 1.018±  19.318   
4 ARCI 15 0.05 0.040 ±  0.590   
5 ARNI 1752 5.97 4.492±  23.367   
6 ATCH 8 0.02 0.020 ±  0.216   
7 BIGA 32 0.11 0.084 ± 1.729   
8 BOHA 6 0.02 0.016 ± 0.276   
9 BOLI 13 0.05 0.034 ± 0.558   
10 BUIB 938 3.25 2.442±  20.485   
11 BUSE 33 0.11 0.086 ±  1.429   
12 CASP 81 0.28 0.212 ±  2.820   
13 CEGR 15 0.05 0.040 ±  0.800   
14 CESE 235 0.81 0.612 ±  9.636   
15 CEMA 63 0.22 0.164 ±  9.178   
16 CERU 15 0.05 0.040 ±  0.800   
17 CESP 287 0.99 0.748 ±  3.501   
18 CEMO 38 0.13 0.098 ± 1.863   
19 CERD 36 0.12 0.094 ± 1.054   
20 CIAB 43 0.15 0.112 ±  1.977   
21 CICA 2 0.01 0.006 ± 0.160   
22 CIGA 153 0.53 0.398 ± 8.265   
23 CLGL 15 0.05 0.040 ±  0.698   
24 CLJA 8 0.02 0.020 ±  0.397   
25 CLLE 16 0.06 0.042 ± 0.981   
26 COAB 25 0.09 0.066 ± 1.145   
27 COCY 64 0.22 0.166 ± 1.934   
28 COCO 33 0.11 0.086 ± 1.001   
29 COAL 553 1.91 1.440 ± 9.642   
30 COCR 33 0.11 0.086 ±  1.435   
31 CRGA 198 0.68 0.516 ±  1.698   
32 CRPI 50 0.17 0.130 ±  2.124   
33 CYPA 132 0.46 0.344 ±  3.257   
34 DEVI 34 0.12 0.088 ±  0.725   
35 DEVD 48 0.17 0.126 ±  3.830   
36 DEDO 8 0.02 0.020 ±  0.491   
37 DEFU 56 0.19 0.146 ± 1.676   
38 EPEB 143 0.5 0.72 ±  3.246   
39 ERPA 105 0.36 0.238 ± 3.716   
40 ESME 14 0.05 0.036 ± 0.762   
41 EUOR 73 0.25 0.190 ±  2.691   
42 EUMA 64 0.22 0.166 ±  2.691   
43 FRBI 1105 3.38 2.878 ± 9.818   
178 
 
 
44 FROC 106 0.37 0.276 ±  3.783   
45 GEMA 5 0.02 0.014 ± 0.399   
46 GETR 1 0 0.002 ± 0.079   
47 GYAN 2 0.01 0.006 ±  0.160   
48 HALE 8 0.02 0.020 ± 0.431   
49 HAMA 18 0.06 0.046 ± 0.571   
50 HASE 14 0.05 0.036 ±  0.696   
51 HAVO 53 0.18 0.138 ± 3.888   
52 HEPU 42 0.15 0.110 ±  1.507   
53 HISE 57 0.2 0.148 ±  3.012   
54 HISG 22 0.08 0.058 ±  0.798   
55 HYMI 108 0.37 0.282 ±  4.048   
56 HYCR 12 0.04 0.032 ±  0.565   
57 ININ 45 0.16 0.118 ± 2.334   
58 INMI 448 1.55 1.166±  16.242   
59 KAMO 144 0.5 0.376± 15.0.32   
60 LASE 60 0.21 0.156 ±  2.211   
61 LAAR 11 0.04 0.028 ± 0.478   
62 LASP 34 0.12 0.088 ±  1.723   
63 LEST 79 0.27 0.206 ±  1.931   
64 LECA 549 1.9 1.430 ±  1.921   
65 LOBI 105 0.36 0.274 ±  6.240   
66 LOCU 3389 11.7 8.826142.035   
67 LOSI 59 0.21 0.154 ±  1.014   
68 LYVE 21 0.08 0.054 ±  1.014   
69 MACR 7 0.02 0.018 ±  0.343   
70 MEAL 6 0.02 0.016 ±  0.344   
71 MENU 120 0.42 0.312 ±  9.156   
72 MEMA 396 1.37 1.032 ± 11.618   
73 MEMU 51 0.18 0.132 ±  2.369   
74 MICA 19 0.07 0.050 ±  0.845   
75 MIMI 189 0.65 0.492 ±  4.471   
76 MOFL 170 0.59 0.442 ±  7.511   
77 MUOB 24 0.08 0.062 ±  1.130   
78 MUMI 36 0.12 0.094 ± 1.550   
79 MUVI 26 0.09 0.068 ±  1.013   
80 NAME 15 0.05 0.040 ±  0.410   
81 NUME 913 3.16 2.376±  17.050   
82 PHAT 10 0.04 0.026 ±  0.715   
83 PHTR 321 1.11 0.836±  18.273   
84 PLCU 2296 7.95 5.980±  51.929   
85 PLME 407 1.41 1.060±  15.593   
86 POSU 31 0.11 0.080 ±  1.421   
87 POSE 80 0.28 0.208 ±  2.765   
88 PORA 15 0.05 0.040 ±  0.645   
89 PRRU 8 0.02 0.020 ±  0.558   
90 PRAU 35 0.12 0.092 ±  1.290   
91 PRST 34 0.12 0.088 ±  0.866   
92 PSSI 5 0.02 0.014 ±  0.249   
93 PSSP 35 0.12 0.092 ±  0.879   
179 
 
 
94 PYBA 33 0.11 0.086 ±  1.095   
95 PYSE 1 0 0.002 ±  0.079   
96 RANA 24 0.08 0.006 ±  0.774   
97 ROSM 427 1.48 1.112±  17.315   
98 SCPL 77 0.27 0.002 ±  3.158   
99 SCUM 2 0.01 0.006 ±  0.161   
100 SPME 333 1.15 0.868±  15.627   
101 STDE 21 0.08 0.054 ±  1.107   
102 STSE 53 0.18 0.138 ±  1.554   
103 STSQ 113 0.39 0.294 ±  4.271   
104 STTU 264 0.91 0.688 ±  7.324   
105 STVI 28 0.1 0.078 ±  1.324   
106 TAKE 25 0.09 0.066 ±  0.534   
18.964±  
107 THSW 7282 25.2 37.567   
108 TOER 10 0.04 0.026 ±  0.455   
109 TONA 54 0.19 0.140 ±  2.082   
110 TRSC 305 1.06 0.794 ±  3.059   
111 TRAU 875 3.03 2.278 ±  9.162   
112 TUPE 59 0.2 0.154 ±  2.055   
113 TURE 14 0.05 0.036 ±  0.600   
114 TYAL 2 0.01 0.006 ±  0.161   
115 VEEX 5 0.02 0.014 ±  0.249   
116 VENI 46 0.16 0.120 ±  1.938   
117 VIMA 4 0.01 0.010 ±  0.181   
118 VICI 214 0.74 0.588 ±  9.531   
119 XEER 740 2.56 1.928 ±  6.029   
120 XESP 140 0.48 0.364 ±  7.164   
121 ZOSE 100 0.35 0.260 ±  7.145   
180 
 
 
APPENDIX 6 
 
ABUNDANCE AND RELATIVE ABUNDANCE VALUE OF ANIMAL ENCOUTERED  
DURING THE DRY SEASON IN THE STUDY AREA IN THE STUDY AREA 
Total No. of Rela. Abd. ± 
S/N Code Animal Abundance  Se     
1 ACAF 85 0.17 0.111 ± 2.554   
2 AGAG 44 0.09 0.057 ± 1.541   
3 ANLE 657 1.31 0.856 ± 0.289   
4 ARCI 21 0.04 0.027 ± 0.799   
5 ARNI 3169 6.31 4.126 ± 0.289   
6 ATCH 15 0.03 0.020 ± 0.445   
7 BIGA 37 0.07 0.048 ± 1.906   
8 BOHA 8 0.02 0.010 ± 0.349   
9 BOLI 26 0.05 0.034 ± 0.895   
10 BUIB 2337 4.05 30.43 ± 5.412   
11 BUSE 42 0.08 0.055 ± 1.654   
12 CASP 153 0.3 0.199 ± 4.342   
13 CEGR 35 0.07 0.046 ± 0.966   
14 CESE 369 0.73 0.481 ± 12.663   
15 CEMA 112 0.22 0.146 ± 9.379   
16 CERU 21 0.04 0.027 ± 0.964   
17 CESP 456 0.91 0594 ± 8.302   
18 CEMO 84 0.16 0.109 ± 3.396   
19 CERD 58 0.11 0.076 ± 1.596   
20 CIAB 53 0.11 0.069 ± 2.375   
21 CICA 9 0.02 0.012 ± 0.444   
22 CIGA 210 0.43 0.273 ± 9.398   
23 CLGL 29 0.06 0.38 ± 1.152   
24 CLJA 50 0.1 0.065 ± 2.206   
25 CLLE 54 0.11 0.070 ± 1.848   
26 COAB 75 0.15 0.098 ± 2.475   
27 COCY 41 0.08 0.053 ± 1.352   
28 COCO 966 1.92 1.258 ± 19.545   
29 COAL 58 0.12 0.075 ± 2.322   
30 COCR 334 0.66 0.435 ± 5.632   
31 CRGA 67 0.13 0.087 ± 2.566   
32 CRPI 190 0.38 0.087 ± 5.130   
33 CYPA 132 0.46 0.344 ±  3.257   
34 DEVI 59 0.12 0.077 ± 1.356   
35 DEVD 88 0.18 0.115 ± 5.171   
36 DEDO 27 0.05 0035 ± 1.291   
37 DEFU 98 0.19 0.128 ± 2.891   
38 EPEB 285 0.57 0.371 ± 6.243   
39 ERPA 196 0.39 0.255 ± 30.027   
40 ESME 21 0.04 0.027 ± 0.928   
41 EUOR 73 0.15 0.095 ± 3.173   
42 EUMA 87 0.17 0.113 ± 3.512   
43 FRBI 2200 4.38 2.865 ± 37.590   
44 FROC 112 0.22 0.146 ± 4.493   
181 
 
 
45 GEMA 5 0.01 0.007 ± 0.415   
46 GETR 2 0 0.003 ± 0.117   
47 GYAN 6 0.01 0.008 ± 0.370   
48 HALE 8 0.02 0.010 ± 0.468   
49 HAMA 19 0.04 0.025 ± 0.710   
50 HASE 18 0.01 0.073 ± 2.677   
51 HAVO 56 0.11 0.073 ± 4.078   
52 HEPU 59 0.12 0.077 ± 1.902   
53 HISE 67 0.13 0.087 ± 3.381   
54 HISG 46 0.09 0.060 ± 1.627   
55 HYMI 185 0.37 0.241 ± 6.148   
56 HYCR 49 0.1 0.064 ± 1.659   
57 ININ 56 0.11 0.073 ± 2.667   
58 INMI 640 1.27 0.833 ± 21.23   
59 KAMO 240 0.48 0.313 ± 5.036   
60 LASE 73 0.15 0.095 ± 2.779   
61 LAAR 20 0.04 0.026 ± 0.693   
62 LASP 72 0.15 0.094 ± 3.175   
63 LEST 139 0.28 0.181 ± 3.049   
64 LECA 1006 2 1.310 ± 16.869    
65 LOBI 156 0.31 0.203 ± 7.305   
66 LOCU 5667 11.3 0.739  ± 0.170   
67 LOSI 109 0.22 0.142 ± 3.119   
68 LYVE 66 0.13 0.086 ± 1.364   
69 MACR 14 0.03 0.018 ± 0.504   
70 MEAL 17 0.03 0.022 ± 0.846   
71 MENU 120 0.42 0.28 ± 9.631    
72 MEMA 657 1.31 0.856 ± 17.753   
7 MEMU 68 0.14 0.089 ± 2.832   
74 MICA 19 0.04 0.025 ± 0.952   
75 MIMI 322 0.64 0.419 ± 8.051   
76 MOFL 302 0.6 0.393 ± 13.073   
77 MUOB 36 0.07 0.047 ± 1.465   
78 MUMI 43 0.09 0.056 ± 1.807   
79 MUVI 103 0.2 0.134 ± 2.207   
80 NAME 25 0.05 0.033 ± 0.691   
81 NUME 1571 3.13 2.046 ±32.467   
82 PHAT 32 0.06 0.042 ± 1.181   
83 PHTR 498 0.99 0648 ± 21.360   
84 PLCU 3884 7.73 5.057 ± 87.342   
85 PLME 637 1.27 0.829 ± 21.286   
86 POSU 51 0.1 0.066 ± 1.956   
87 POSE 82 0.16 0.107 ± 3.324   
88 PORA 19 0.04 0.025 ± 0.710   
89 PRRU 27 0.09 0.035 ± 1.178   
90 PRAU 52 0.1 0.068 ± 1.770   
91 PRST 64 0.13 0.083 ± 1.580   
92 PSSI 12 0.03 0.016 ± 0.571   
93 PSSP 83 0.17 0.108 ± 2.462   
94 PYBA 40 0.08 0.052 ± 1.372   
182 
 
 
95 PYSE 1 0 0.001 ± 0.083   
96 RANA 40 0.08 0.052 ± 1.372   
97 ROSM 1186 2.36 1.544 ± 63.180   
98 SCPL 108 0.21 0.141 ± 4.278   
99 SCUM 5 0.01 0.007 ± 0.298   
100 SPME 496 0.99 0.646 ± 18.199   
101 STDE 30 0.06 0.039 ± 1.247   
102 STSE 143 0.28 0.186 ± 6.165   
103 STSQ 165 0.33 0.215 ± 5.254   
104 STTU 422 0.84 0.549 ± 7.803   
105 STVI 48 0.1 0.063 ± 1.787   
106 TAKE 37 0.07 0.048 ± 0.864   
107 THSW 12624 25.1 0.164.± 0.190.   
108 TOER 36 0.07 0.047 ± 1.031   
109 TONA 123 0.24 0.160 ± 3.116   
110 TRSC 481 0.96 0.626 ± 2.934   
111 TRAU 1471 2.93 1.915 ± 30.852   
112 TUPE 84 0.16 0.109 ± 2.793   
113 TURE 34 0.07 0.044 ± 0.993   
114 TYAL 3 0.01 0.004 ± 0.185   
115 VEEX 8 0.02 0.010 ± 0.329   
116 VENI 55 0.11 0.072 ± 2.252   
117 VIMA 36 0.07 0.047 ± 1.681   
118 VICI 350 0.7 0.456 ± 12.314   
119 XEER 1128 2.24 1.469 ± 20.513   
120 XESP 257 0.51 0.335 ± 10.150   
121 ZOSE 106 0.21 0.138 ± 7.784   
 
183 
 
 
 
  
 APPENDIX 7: CHEMICAL AND MECHANICAL ANALYSIS O F 48 PLOTS SAMPLED AT THE PERMANENT SITE OF THE  UNIVERSITY OF AGRICULTURE, ABEOKUTA OGUN STATE 
  
  
  
PLOT  SAND SILT CLAY GRAVEL O.M N P K Mg Ca Na H+AL3- CEC Ph 
 -------- (Cmolkg-
NO. ----------- -       (%)-- ----------- -------- ------- (PPM)- --------- -------- 1) -------- ------------- ------- -------- 
1 87.8 6 6.2 21 3.14 0.18 4.7 0.15 0.97 3.23 0.23 0.13 4.71 6.6 
2 89.2 5.3 5.5 28.1 3.28 0.19 2.4 0.92 1.13 3.29 0.2 0.16 5.7 6.1 
3 85.3 3.7 11 22.4 1.45 0.89 4.3 0.79 1.19 3.13 0.21 0.2 5.52 5.6 
4 80.8 22.8 6.4 10.3 3.64 0.21 0.7 0.21 0.91 3.09 0.32 0.14 4.66 6.5 
5 99 5.6 6.4 23.7 5.12 0.3 0.6 0.16 0.89 3.61 0.39 0.44 5.49 6.7 
6 96.1 4.8 6.4 13.5 3.45 0.2 1.6 0.22 1.06 3.46 0.31 0.13 5.18 6.3 
7 90.3 2.8 9.1 31.3 1.17 0.07 5.7 0.36 1.13 1.91 0.2 0.21 3.78 6 
8 57 7.3 6.9 29.7 1.64 0.09 3.7 0.71 1.08 3.33 0.22 0.12 3.78 6.3 
9 90.4 3.3 5.7 26.6 2.67 0.15 4.1 0.95 0.88 3.65 0.23 0.29 5.46 5.9 
10 87.6 3.2 5.3 30 4.38 0.25 6.4 0.75 1.53 3.42 0.3 0.33 5.95 6.2 
11 95.6 5.2 4.2 21.8 1.28 0.07 5 0.47 0.79 2.94 0.21 0.2 6.33 6.4 
12 56.4 4 9.3 27.9 1.28 0.27 0.5 0.61 1.12 3.38 0.4 0.14 4.61 6.3 
13 94.4 3.5 9.6 17.2 4.71 0.13 4.8 1.02 1.14 2.81 0.38 0.16 5.65 6 
14 -94.4 2 5.5 29.4 2.24 0.11 2.5 0.9 1.27 3.48 0.38 0.47 5.51 5.8 
15 94.4 2.6 13.5 21.8 1.69 0.07 1 0.19 1.42 4 0.27 0.29 6.5 5.7 
16 99.7 2.9 4 13.5 3.41 0.23 1.7 0.53 1.08 3.01 0.26 0.35 6.15 5.2 
17 92 2.4 8.4 22 1.76 0.11 7.5 0.23 1.26 2.48 0.2 0.14 5.23 5.5 
18 83.3 2.9 5.6 27.3 3.52 0.22 1.2 0.69 0.99 2.43 0.23 0.33 4.31 6.4 
19 99.3 2.4 10.3 33.7 2.77 0.16 5.7 0.28 1.82 4.74 0.3 0.29 7.41 5.7 
20 84.5 9.7 8.3 29.6 3.6 0.21 7.8 0.83 1.93 6.1 0.26 0.16 9.28 6.3 
 
184 
 
 
APP ENDIX 8 
  
 PLOT SAND SILT CLA Y GRAVEL  CLASSIFICATION 
               ------
N O.---  (%) ------ -------------- ------------     
1  87.8 6 6.2 21  Gravelly loamy sand 
2  89.2 5.3 5.5 28.1  Gravelly loamy sand 
3  85.3 3.7 11 22.4  Gravelly loamy sand 
4 80.8 12.9 6.4 10.3  Sligghrtalvye lly loamy sand 
5 88 5.6 6.4 23.7  Gravelly loamy sand 
Slightly Gravelly loamy 
6 86.1 4.8 9.1 13.5  sand 
7 90.3 2.8 6.9 31.3  Gravelly sand  
8 87 7.3 5.7 29.7  Gravelly loamy sand 
9 91.4 3.3 5.3 26.6  Gravelly sand  
10 87.6 8.2 4.2 30  Gravelly loamy sand 
11 85.6 5.2 9.2 21.8  Gravelly loamy sand 
12 86.4 4 9.6 27.9  Gravelly loamy sand 
13 90.7 3.5 5.9 17.2  Gravelly sand  
14 79.5 7 13.5 29.4  Gravelly loamy sand 
15 94.4 1.6 4 21.8  Gravelly loamy sand 
16 89.7 1.9 8.4 13.5  Gravelly loamy sand 
17 92 2.4 5.6 22  Gravelly sand  
18 88.8 0.9 10.3 27.3  Gravelly sand  
19 89.3 2.4 8.3 33.7  Gravelly sand  
20 84.5 9.7 5.8 29.6  Gravelly loamy sand 
       
       
       
       
       
       
       
       
       
       
       
       
       
       
       
        
       
       
       
       
       
        
       
       
        
       
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 APPENDIX 9 
   
  
  
  
  
   
 
 
 
Plate 1. Picture of Cattle Egret (Bulbulcus ibis) seen on the site. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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A PPENDIX 10 
 
 
 
Plate2. Picture of expended cartridge located close to the study site. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX 11 
 
 
 
 
Plate 3: Illegal grazing on the site 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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A PPENDIX 12 
 
 
 
Plate 4: Wild fire at the edge of the site 
 
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