CONTENTS

Acknowledgement

List of Tables

List of Figure

CHAPTER- INTRODUCTION 1-12

1.1 Background

1.2.1 Study

1.2.1.1 (Axis axis)

1.2.1.2 Sambar ( unicolor)

1.2.1.3- (Boselaphus tragocamelus)

1.2.1.4- ( gaurus)

1.2.1.5- Chowsingha (Tetraceros quadricornis)

1.2 Literature review

1.3 Objectives & Hypothesis

CHAPTER 2- STUDY AREA 13-20

2.1 Introduction

2.2 History and management units

2.3 Physical attributes

2.3.1 Topography

2.3.2 Soil types

2.3.3 Drainage

2.4 Climate

2.5 Biological attributes (flora and fauna)

2.6 Fire

2.7 Socio-economic attributes

2.8 Intensive Study Area

2.8.1. Teak Forest

2.8.2. Teak Mixed forest

2.8.3. Mixed forest type

2.8.4. Grassland type

2.8.5. Bamboo forest

CHAPTER 3- VEGETATION STRUCTURE 21-79 3.1- Introduction 3.2- Methodology 3.2.1- Data Collection 3.2.2- Data analysis 3.3- Results 3.3.1- Vegetation Profile in Pench National park 3.3.1.1- Vegetation profile in different administrative beats of PTR 3.3.1.1.1 Floral Community Structure 3.3.1.1.2 - Percent Vegetation Cover 3.3.1.2- Vegetation profile in different administrative circles of PTR 3.3.1.2.1- Floral Community Structure 3.3.1.2.2- Percent Vegetation Cover 3.3.2- Vegetation profile in different habitats 3.3.2.1- Vegetation profile in different habitats of PTR during post monsoon season 3.3.2.1.1- Floral Community Structure 3.3.2.1.2- Percent vegetation Cover 3.3.2.2- Vegetation profile in different habitats of PTR during summer season 3.3.2.2.1- Floral Community Structure 3.3.2.2.2- Percent vegetation cover 3.3.2.3- Vegetation profile in different habitats of PTR during winter season 3.3.2.3.1- Floral Community Structure 3.3.2.3.2- Percent vegetation cover 3.3.2.4- Vegetation profile in different habitats of PTR in different seasons 3.3.2.4.1- Floral Community Structure 3.3.2.4.2- Percent vegetation cover 3.4 Discussion

CHAPTER 4- STATUS OF IN PTR 80-87 4.1 Introduction 4.2 Methodology 4.2.1 Data collection 4.2.2 Data analysis 4.3 Results 4.3.1 Mammals density in different administrative beats of PTR 4.3.2 Mammals density in different administrative circles of PTR 4.4 Discussion

CHAPTER 5- ABUNDANCE & POPULATION STRUCTURE OF UNGULATES 88-119 5.1 Introduction 5.2 Methodology 5.2.1 Data collection 5.2.1.1 Line Transect 5.2.1.2- Indirect evidences 5.2.2 Data analysis 5.2.2.1- Line transect 5.2.2.2 Indirect evidences 5.2.2.3 Population structure 5.2.2.4 Group size 5.3 Results 5.3.1- Line transect 5.3.1.1- Cheetal 5.3.1.2- Sambar 5.3.1.3- Nilgai 5.3.1.4- Gaur 5.3.2- Indirect evidences 5.3.3- Population structure 5.4 Discussion

CHAPTER 6-HABITAT UTILIZATION AND HABITAT SUITABILITY INDEX MODELLING 120-217 6.1 Habitat Utilization Pattern 6.1.1 Introduction 6.1.2 Methodology 6.1.2.1 Data collection 6.1.2.2 Data Analysis 6.1.3 Results 6.1.3.1- Factors affecting the selection of habitats by cheetal in different seasons 6.1.3.2 Factors affecting the selection of habitats by Chowsingha in different seasons 6.1.3.3- Factors affecting the selection of habitats by Sambar in different seasons 6.1.3.4 Factors affecting the selection of habitats by Nilgai in different seasons 6.1.3.5 Factors affecting the selection of habitats by Gaur in different seasons 6.1.3.6- Relationship between habitat parameters with fecal matter densities of different ungulate species during different season 6.1.3.6.1- Relationship during post monsoon season 6.1.3.6.2- Relationship during summer season 6.1.3.6.3- Relationship during winter season 6.1.4 Discussion 6.2 Habitat Suitability Index Modelling 6.2.1 Introduction 6.2.2 Materials 6.2.3 Methodology 6.2.3.1 Factors influencing the habitat suitability 6.2.3.2 Data collection and data processing 6.2.3.3 Field Survey 6.2.3.4 Post-field analysis Land use/Land cover 6.2.3.5 Database preparation for Habitat Suitability

Analysis

6.2.4 Results and Discussion

6.2.4.1 Habitat Suitability Index for Chowsingha

6.2.4.2 Habitat Suitability Index for Cheetal

6.2.4.3 Habitat Suitability Index for Sambar

6.2.4.4 Habitat Suitability Index for Gaur

6.2.4.5 Habitat Suitability Index for Nilgai

CHAPTER 7- FOOD AND FEEDING HABIT 218-287

7.1 Introduction

7.2 Methodology

7.2.1 Data collection

7.2.2 Data analysis

7.3 Results

7.3.1- Chowsingha diet

7.3.2- Gaur diet

7.3.3- Sambar diet

7.3.4- Feeding on Lantana by different ungulates

7.4 Discussion

CHAPTER 8- RESOURCE PARTITIONING 288-296

8.1 Introduction

8.2 Methodology

8.2.1- Data Collection

8.2.2- Data Analysis

8.3 Results

8.4 Discussion

CHAPTER 9- MANAGEMENT AND CONSERVATION STRATIGIES 297-299

Conservation threats and management mitigation CHAPTER 10 – CONCLUSION 300-303

References 304-332

Appendices I-XI Photo plate I am sincerely thankful to Department of Science and Technology, SERB, Ministry of Science and Technology, Government of for providing the financial assistant to work in Pench Tiger Reserve.

I sincerely acknowledge and thanks to the forest department Madhya Pradesh for giving permission to carry out field work and their valuable support during the study. The Forest Department of Pench Tiger Reserve, Seoni, Madhya Pradesh is as helpful and cooperative as I feel no other authority could be. Especially thanks to Mr. Aalok Kumar (Field Director PTR Seoni), Mr Jagdish Chandra (Former D.F.O.), Dr. Kiran Bisen (D.F.O.) Late Mr. Sumukh Joshi (A.C.F.), Range Officers and field staff of Karmajhiri range.

I would also like to thanks to Rafat mam librarian of the Department of Wildlife Sciences. I can’t imagine to complete my work and this document without their help, affection and their valuable support.

My special thanks are extended to all non teaching staff of Department of Wildlife Sciences for their support.

To say thanks to my seniors Mr. Serajuddin Majumdar, Azram aapa, Zarreen Aapa, Belal Bhai, Rohit Singh Bhai, Masood Bhai, seems to have no value in front of the immense support and guidance, that provide me throughout my work, from the time of field work till the compiling of the report.

I thank to all my colleagues Mr. Rohit Chaudhary, Mr. Tanveer Ahmad, Mr. Khursheed Aalam Khan, Mr. Tahir and Miss. Iffat Yasmeen for helping me always.

I would like to thanks my juniors specially Miss Shaizah Tazdar, Maryam Juveria Khan, Prachi Kulshrestha and Sartaj Ahmad for their support and best wishes.

I would like to thank all the beat guard of PTR specially Deepak Mishra, Meshram, Tekaam Mahesh etc. Thanks are also to Budday bhaiyya, Naresh, Jagdish, Sushpal and Shantabai, and the field assistant, Balgovind and driver Om and Krishna for their active involvement in the study.

Presence of Mr. Shaheer Khan, Miss Farah Akram, Miss Talat Parveen and Mr. Najumuddin Ansari by my side gave me the strength to go on during hard times, without making me feel alone at any step during the field time. Thanks a lot for being with me. I would also like to thanks to Aniruddha da and Krishnendu da for giving me their valuable time and guidance during my field work.

I would like to acknowledge my other friends for their moral support and motivation, which drives me to give my best. Nandan, Rishi, Dr, Abdullah, Dr, Faizul, Azeem, Zubair, Shad Rao, Muneer…the list is endless…thanks to one and all. My special gratitude to Javed, Aasim and Ekhlaq for being with me in thicks and thins of life, I find myself lucky to have friends like them in my life.

My heartiest gratitude to Mr. Hashim and Nushrat Bhabhi for their moral support. Thanks are also to Nashra and Hamza for entertaining me on odd times and giving me homely atmosphere away from my home. Their precious prayers contribute a lot to my success.

The eternal foundation of blessing, moral support and never ending sacrifice of beloved parents, my father Mr. Asrar Ahmad, my mother Mrs. Saida Bano, my younger brother Mr. Danish Ahmed, Mr. Monish Ahmed and my loving sister Mrs. Sana Parveen, they have lit the flame of learning in me and enabled me to reach the footsteps of my long cherished goal, are highly acknowledged. Without their constant inspiration, this milestone would have eluded me.I also extend my respect to my Bade Papa, Badi Ammi, Nadeem Bhai, Zakiya Bhabhi and Saleem for their love and blessing. I don’t imagine a life without their love and blessings. Thank you everyone for showing faith in me and giving me liberty to choose what I desired.

Over the last four year, since the study started, many individuals have helped in various ways whose names I might have missed out. I thank each and every one of them.

Abdul Haleem

LIST OF TABLE

Table-3.1 Mean density, diversity and richness of tree in different 37 administrative beat Table-3.2 Mean density, diversity and richness of seedling in 38 different administrative beat Table-3.3 Mean density, diversity and richness of sapling in 39 different administrative beat Table-3.4 Mean density, diversity and richness of shrub in different 40 administrative beat Table-3.5 Mean density, diversity and richness of herbs in different 41 administrative beat Table-3.6 Mean density, diversity and richness of grasses in 42 different administrative beat Table-3.7 Percent vegetation covers in different administrative beat 43 Table-3.8 Mean density, diversity and richness of tree in different 44 administrative circle Table-3.9 Mean density, diversity and richness of seedling in 44 different administrative circle Table-3.10 Mean density, diversity and richness of sapling in 45 different administrative circle

Table-3.11 Mean density, diversity and richness of shrub in different 45 administrative circle Table-3.12 Mean density, diversity and richness of herbs in different 46 administrative circle Table-3.13 Mean density, diversity and richness of grasses in 46 different administrative circle

Table- 3.14 Mean density/ha, diversity and richness of seedling 47 during post monsoon season Table-3.15 Mean density/ha, diversity and richness of sapling during 47 post monsoon season Table-3.16 Mean density/ha, diversity and richness of shrub during 48 post monsoon season Table-3.17 Mean density/m2, diversity and richness of herb during 48 post monsoon season

Table-3.18 Mean density/m2, diversity and richness of grass during 49 post monsoon season Table-3.19 Mean density/ha, diversity and richness of seedling 49 during summer season Table-3.20 Mean density/ha, diversity and richness of sapling during 50 summer season Table-3.21 Mean density/ha, diversity and richness of shrub during 50 summer season Table-3.22 Mean density/m2, diversity and richness of herb during 51 summer season Table-3.23 Mean density/m2, diversity and richness of grass during 51 summer season Table-3.24 Mean density/ha, diversity and richness of seedling 52 during winter season Table-3.25 Mean density/ha, diversity and richness of sapling during 52 winter season Table-3.26 Mean density/ha, diversity and richness of shrub during 53 winter season Table-3.27 Mean density /m2, diversity and richness of herb during 53 winter season Table-3.28 Mean density/m2, diversity and richness of grass during 54 winter season

Table 3.29 Mean Density/ha ±SE, Diversity and Richness of trees in 54 different habitat of PTR

Table 3.30 Density/ha ± SE of different species of trees in different 55-57 habitat of Pench Tiger Reserve

Table 3.31 Density/ha ± SE of different species of seedling in 58 different habitat of PTR during Post monsoon season

Table 3.32 Density/ha ± SE of different species of seedling in 59 different habitat of PTR during summer season

Table 3.33 Density/ha ± SE of different species of seedling in 60 different habitat of PTR during winter season

Table 3.34 Density/ha ± SE of different species of sapling in 61 different habitat of PTR during post monsoon season

Table 3.35 Density/ha ± SE of different species of sapling in 62 different habitat of PTR during summer season Table 3.36 Density/ha ± SE of different species of sapling in 63 different habitat of PTR during winter season

Table 3.37 Overall Density/ha ± SE of different species of seedling 64-65 and sapling in PTR

Table 3.38 Density/ha ± SE of different species of shrub in different 66 habitat of PTR during post monsoon season

Table 3.39 Density/ha ± SE of different species of shrub in different 66 habitat of PTR during summer season

Table 3.40 Density/ha ± SE of different species of shrub in different 67 habitat of PTR during winter season

Table 3.41 Overall Density/ha ± SE of different species of shrub in 67 PTR

Table 3.42 Density/m2 ± SE of different species of herb in different 68 habitat of PTR during post monsoon season

Table 3.43 Density/m2 ± SE of different species of herb in different 69 habitat of PTR during summer season

Table 3.44 Density/m2 ± SE of different species of herb in different 70 habitat of PTR during winter season

Table 3.45 Overall Density/m2 ± SE of different species of herb in 71 PTR

Table 3.46 Density/m2 ± SE of different species of grasses in 72 different habitat of PTR during post monsoon season

Table 3.47 Density/m2 ± SE of different species of grasses in 73 different habitat of PTR summer season

Table 3.48 Density/m2 ± SE of different species of grasses in 74 different habitat of PTR winter season

Table 3.49 Overall Density/m2 ± SE of different species of grasses 75 in PTR Table-4.1 Mean density (Density/ha) ± SE of different mammalian 85 species in different administrative beats of PTR Table-4.2 Mean density (Density/ha) ± SE of different mammalian 86 species in different administrative circles of PTR Table 5.1 Densities (Individuals Per km2) of Cheetal in Pench Tiger 107 Reserve, Madhya Pradesh, during winter and summer (2013 to 2015) Table 5.2 Densities (Individuals Per km2) of Sambar in Pench Tiger 108 Reserve Table 5.3 Densities (Individuals Per km2) of Nilgai in Pench Tiger 109 Reserve

Table 5.4 Densities (Individuals Per km2) of Gaur in Pench Tiger 110 Reserve Table 5.5 Density and Group density of different ungulate species in 111 Pench Tiger Reserve Table 5.6 Seasonal variation in Density ± Standard deviation of 112 different ungulate species in different habitats of Pench Tiger Reserve Table 5.7 Proportions of different age and sex classes among 113 different ungulate species in Pench Tiger Reserve Table 5.8 Seasonal variation in age-sex ratios among different 113 ungulate species in Pench Tiger Reserve Table 6.1.1 Selection of 13 variables for cheetal during post monsoon 149 season, on the first five components Table 6.1.2 Selection of 13 variables for cheetal during summer 150 season, on the first four components Table 6.1.3 Selection of 10 variables for cheetal during winter 151 season, on the first four components Table 6.1.4 Selection of 16 variables for chowsingha during post 152 monsoon season on the first five components Table 6.1.5 Selection of 11 variables for chowsingha during summer 153 season on the first four components Table 6.1.6 Selection of 12 variables for chowsingha during winter 154 season on the first four components Table 6.1.7 Selection of 15 variables for sambar during post 155 monsoon season on the first five components Table 6.1.8 Selection of 11 variables for sambar during summer 156 season on the first four components Table 6.1.9 Selection of 12 variables for sambar during winter season 157 on the first four components Table 6.1.10 Selection of 13 variables for nilgai during post monsoon 158 season on the first five components Table 6.1.11 Selection of 11 variables for nilgai during summer season 159 on the first four components Table 6.1.12 Selection of 12 variables for nilgai during winter season 160 on the first four components Table 6.1.13 Selection of 11 variables for gaur during post monsoon 161 season on the first five components Table 6.1.14 Selection of 11 variables for gaur during summer season 162 on the first four components Table 6.1.15 Selection of 12 variables for gaur during winter season 163 on the first four components Table 6.1.16 Logistic Regression Model for cheetal during post 164 monsoon season

Table 6.1.17 Logistic Regression Model for cheetal during summer 164 season Table 6.1.18 Logistic Regression Model for cheetal during winter 164 season Table 6.1.19 Logistic Regression Model for chowsingha during post 165 monsoon season Table 6.1.20 Logistic Regression Model for chowsingha during 165 summer season Table 6.1.21 Logistic Regression Model for chowsingha during winter 165 season Table 6.1.22 Logistic Regression Model for sambar during post 166 monsoon season Table 6.1.23 Logistic Regression Model for sambar during summer 166 season Table 6.1.24 Logistic Regression Model for nilgai during summer 166 season Table 6.1.25 Logistic Regression Model for nilgai during winter 166 season Table 6.1.26 Logistic Regression Model for gaur during summer 167 season Table 6.1.27 Logistic Regression Model for gaur during winter season 167 Table 6.1.28 Correlation between faecal matter densities of different 168 ungulate species with habitat variables during post monsoon season Table 6.1.29 Correlation between faecal matter densities of different 169 ungulate species with habitat variables during summer season

Table 6.1.30 Correlation between faecal matter densities of different 170 ungulate species with habitat variables during winter season

Table 6.2.1 Rank allocated to different layers 171

Table 6.2.2 Pair-wise comparison scale for Analytical Hierarchy 171 Process preferences (Saaty 1980)

Table 6.2.3 Pair-wise comparison matrix of LULC map for 172 Chowsingha

Table 6.2.4 Pair-wise comparison matrix of LULC map for Cheetal 173

Table 6.2.5 Pair-wise comparison matrix of LULC map for Sambar 174

Table 6.2.6 Pair-wise comparison matrix of LULC map for Gaur 175

Table 6.2.7 Pair-wise comparison matrix of LULC map for Nilgai 176

Table 6.2.8 Synthesis matrix of LULC map Class for Chowsingha 177

Table 6.2.9 Synthesis matrix of LULC map Class for Cheetal 178

Table 6.2.10 Synthesis matrix of LULC map Class for Sambar 179

Table 6.2.11 Synthesis matrix of LULC map Class for Gaur 180

Table 6.2.12 Synthesis matrix of LULC map Class for Nilgai 181

Table 6.2.13 Pair-wise comparison matrix of different layers for 182 Chowsingha

Table 6.2.14 Synthesized Matrix of different layers for Chowsingha 182

Table 6.2.15 Pair-wise comparison matrix of different layers for 183 Cheetal

Table 6.2.16 Synthesized Matrix of different layers for Cheetal 183

Table 6.2.17 Pair-wise comparison matrix of different layers for 184 Sambar

Table 6.2.18 Synthesized Matrix of different layers for Sambar 184

Table 6.2.19 Pair-wise comparison matrix of different layers for Gaur 185

Table 6.2.20 Synthesized Matrix of different layers for Gaur 185

Table 6.2.21 Pair-wise comparison matrix of different layers for 186 Nilgai

Table 6.2.22 Synthesized Matrix of different layers for Nilgai 186 Table 6.2.23 Area occupied by each land use/cover classes 187 Table 6.2.24 Accuracy assessment for vegetation and land cover 188 classes in Pench tiger Reserve, Madhya Pradesh

Table 6.2.25 Area wise Habitat Suitability status for Chowsingha in 189 PTR

Table 6.2.26 Area wise Habitat Suitability status for Cheetal in PTR 189

Table 6.2.27 Area wise Habitat Suitability status for Sambar in PTR 190

Table 6.2.28 Area wise Habitat Suitability status for Gaur in PTR 190

Table 6.2.29 Area wise Habitat Suitability status for Nilgai in PTR 191 Table-7.1 Chowsingha diet in different season occurrence of 236-239 fragments of tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season. Table-7.2 Gaur diet in different season occurrence of fragments of 240-243 tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season. Table-7.3 Sambar diet in different season occurrence of fragments 244-247 of tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season.

Table-7.4 Bonferroni confidence limits for for available (Pi0) and 248-251 utilised proportion of different species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during summer season.

Table-7.5 Bonferroni confidence limits for for available (Pi0) and 252-255 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during post monsoon season.

Table-7.6 Bonferroni confidence limits for for available (Pi0) and 256-259 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during winter season.

Table-7.7 Bonferroni confidence limits for for available (Pi0) and 260-263 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the gaur during summer season. Table-7.8 Bonferroni confidence limits for for available (Pi0) and 264-267 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the gaur during post monsoon season.

Table-7.9 Bonferroni confidence limits for for available (Pi0) and 268-271 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the gaur during winter season.

Table-7.10 Bonferroni confidence limits for for available (Pi0) and 272-275 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the sambar during summer season.

Table-7.11 Bonferroni confidence limits for for available (Pi0) and 276-279 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the sambar during post monsoon summer season.

Table-7.12 Bonferroni confidence limits for available (Pi0) and 280-283 utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the sambar during winter summer season. Table-8.1 Habitat overlaps between different ungulate species of 294 Pench Tiger Reserve in different seasons Table-8.2 Dietary overlaps among different food items between 295 Chowsingha, Sambar and Gaur in Pench Tiger Reserve throughout different seasons

LIST OF FIGURES

Figure 1.1 Distribution map of cheetal (Source IUCN) 5 Figure 1.2 Distribution map of sambar (Source IUCN) 6 Figure 1.3 Distribution map of nilgai (Source IUCN) 7 Figure 1.4 Distribution map of gaur (Source IUCN) 7 Figure 1.5 Distribution map of chowsingha (Source IUCN) 9 Figure 2.1. Map of study area 20 Figure-3.1 Percent vegetation covers in different administrative 76 circle Figure-3.2 Percent Vegetation covers in different habitats during 76 post monsoon Figure-3.3 Percent vegetation covers in different habitats during 77 summer season Figure-3.4 Percent vegetation covers in different habitats during 77 winter season Figure - 3.5 Mean, seeding density, sapling density, shrub density, 78 herb density and grass density in different habitats in different seasons Figure - 3.6 Mean vegetation covers (%) in different habitats in 79 different seasons Figure-4.1 Map of Study area showing different surveying sites 87 (Not to scale) Figure 5.1 Overall Individual and Group density of ungulates of 114 PTR Figure 5.2 Probability of cheetal detections from line transect 115 sampling (winter) Figure 5.3 Probability of cheetal detections from line transect 115 sampling (summer) Figure 5.4 Probability of sambar detections from line transect 116 sampling (winter) Figure 5.5 Probability of sambar detections from line transect 116 sampling (summer)

Figure 5.6 Probability of nilgai detections from line transect 117 sampling (winter)

Figure 5.7 Probability of nilgai detections from line transect 117 sampling (sum Figure 5.8 Probability of gaur detections from line transect 118 sampling (winter) Figure 5.9 Probability of gaur detections from line transect 118 sampling (summer) Figure 5.10 Mean size of pellets of different ungulate species in PTR 119 Figure 5.11 Seasonal variation in mean group size of different 119 ungulate species in Pench Tiger Reserve (2013 to 2015). Figure 6.1.1 Ordination of available and utilized plots for cheetal 192 during post monsoon season in Pench Tiger Reserve Figure 6.1.2 Ordination of available and utilized plots for cheetal 193 during summer season in Pench Tiger Reserve Figure 6.1.3 Ordination of available and utilized plots for cheetal 194 during winter season in Pench Tiger Reserve Figure 6.1.4 Ordination of available and utilized plots for 195 chowsingha during post monsoon season in Pench Tiger Reserve Figure 6.1.5 Ordination of available and utilized plots for 196 chowsingha during summer season in Pench Tiger Reserve Figure 6.1.6 Ordination of available and utilized plots for 197 chowsingha during winter season in Pench Tiger Reserve Figure 6.1.7 Ordination of available and utilized plots for sambar 198 during post monsoon season in Pench Tiger Reserve Figure 6.1.8 Ordination of available and utilized plots for sambar 199 during summer season in Pench Tiger Reserve Figure 6.1.9 Ordination of available and utilized plots for sambar 200 during winter season in Pench Tiger Reserve

Figure 6.1.10 Ordination of available and utilized plots for nilgai 201 during post monsoon season in Pench Tiger Reserve Figure 6.1.11 Ordination of available and utilized plots for nilgai 202 during summer season in Pench Tiger Reserve Figure 6.1.12 Ordination of available and utilized plots for nilgai 203 during winter season in Pench Tiger Reserve Figure 6.1.13 Ordination of available and utilized plots for gaur during 204 post monsoon season in Pench Tiger Reserve Figure 6.1.14 Ordination of available and utilized plots for gaur during 205 summer season in Pench Tiger Reserve Figure 6.1.15 Ordination of available and utilized plots for gaur during 206 winter season in Pench Tiger Reserve Figure 6.2.1 Slope Map of Pench Tiger Reserve 207 Figure 6.2.2 Map of Euclidean distance from Water body 208 Figure 6.2.3 Map of Euclidean distance from Road 209 Figure 6.2.4 Detailed methodology (Schematic) 210 Figure 6.2.5 False Colour Composite (FCC) of the study area 211 Figure 6.2.6 Land Use/Land Cover of Pench Tiger Reserve 212 Fig 6.2.7 Chowsingha Habitat Suitability map 213 Fig 6.2.8 Cheetal Habitat Suitability map 214 Fig 6.2.9 Sambar Habitat Suitability map 215 Fig 6.2.10 Gaur Habitat Suitability map 216 Fig 6.2.11 Nilgai Habitat Suitability map 217 Figure 7.1 Percentage of occurrence of tree, climber, shrubs, herbs 284 and grasses in the diet of chowsingha in different seasons Figure 7.2 Percentage of occurrence of identified browse and grass 284 in the diet of chowsingha in different seasons Figure 7.3 Percentage of occurrence of tree, climber, shrubs, herbs 285 and grasses in the diet of gaur in different seasons Figure 7.4 Percentage of occurrence of identified browse and grass 285 in the diet of gaur in different seasons

Figure 7.5 Percentage of occurrence of tree, climber, shrubs, herbs 286 and grasses in the diet of sambar in different seasons Figure 7.6 Percentage of occurrence of identified browse and grass 286 in the diet of sambar in different seasons Figure 7.7 Proportion of unidentified monocot and dicot in the diet 287 of chowsingha, gaur and sambar in different seasons Figure 7.8 Proportion of Lantana camera in the diet of chowsingha, 287 gaur and sambar in different seasons Figure-8.1 Niche Breadth of different ungulate species of Pench 296 Tiger Reserve in different seasons

Chapter- Introduction

Chapter -1

CHAPTER- INTRODUCTION

1.1 Background

Pench Tiger Reserve (here after PTR) is India’s 19th tiger reserve and there are many justification for its status. The entire Pench region has long been known for its floral and faunal richness. The area is a part of significant bio-geographic region represented by tropical dry deciduous and slightly moist teak and mixed forest. Encompassed by a contiguous and extensive forest belt, and thus creating compact biomass with relatively low biotic pressure, it promises to be another potential habitat of different species of mammals. The term habitat refers to an area which meets all the basic requisites of the animals such as food, water, space and cover (Giles 1978). A number of independent variables play a role in the formation of a particular habitat. Based on the spatio-temporal variation of such independent habitat variables, animals utilize a particular habitat (Norman et al. 1975). Most of the Wildlife habitats are shared by more than one species of wild ungulates. Wild ungulates co-existing in the same habitat use it differently, with reference to resource, space and time, as a result of resource competition or overlap (Lamprey 1963, Rosenzweig 1981, Scogings et al.1990). Even among the two species using the same habitat, there is a certain degree of temporal or dietary separation. Seasonal changes in habitat use also occur in most wild ungulates (Green 1985, Chakraborty 1991, Khan 1996, Sankar 1994). The knowledge about differential use of habitat in space and time by ungulates can play very important role as far as conservation and management of species are concern (Seidensticker 1976). Moreover, changes in the vital habitat requisites such as food, cover, water have long term implications for the management of threatened species and their habitats (Gilpin and Soule 1986), especially wild ungulates which form the prey base for many endangered and threatened large carnivores. Thus a sound understanding of the habitat requirements of ungulates and the habitat parameters affecting their distribution becomes very essential. The same is true for the Indian scenario, where most wildlife habitats are shared by two or more ungulates species.

Satellite remote sensing is very essential tool and plays a crucial role in generating information about habitat preferences of wildlife species. (Imam et al., 2012). Remote sensing has proved to be an extremely valuable tool in resources mapping, resources targeting, resources management, environmental monitoring, weather forecasting and

1

Chapter -1

disaster location and monitoring. The most important applications include geological, geo-morphological, mineral, groundwater, snow melt runoff, soil, land use/land cover, land degradation mapping and monitoring, forest mapping, management of water and agriculture resources. The availability of multi-temporal remote sensing data enables earth resource scientists to monitor, at periodic intervals the location of zones affected by disaster such as floods, drought, cyclones, landslides, forest fires, pests and diseases of crops, and environmental degradations due to soil erosion, shoreline erosion, deforestation, shifting cultivation, soil salinity/alkalinity, desertification and pollution.

Pench Tiger Reserve (PTR) represents tropical dry deciduous and tropical moist deciduous ecosystem in Central India. Earlier studies which concentrated on the quantitative and qualitative description of vegetation structure in PTR included Sankar et al. (2001), Areendran (2007) and Basu (2012). Basu (2012) studied Evaluation of impact of landscape changes on large Habitats in Pench Tiger Reserve, Madhya Pradesh, India, in which vegetation types of PTR were evaluated and developed habitat suitability model for all the large mammals including spotted , Sambar, wild boar and carnivores like tiger, leopard, and dhole.

The geospatial technology, including remote sensing (RS) and geographic information system (GIS), is a very effective measurement for the assessment of natural. Remotely sensed data provides capabilities for frequent, real-time assessment, monitoring and management of large areas (Kushwaha et, al. 2004). The U.S. Fish and Wildlife Service has developed habitat suitability index (HSI) models for the number of species, and these models plays very important role for the management of wildlife and their habitats (Davis et al 1990, Kushwaha and Roy 2002 & Park and Lee 2003). The aim of habitat suitability model is to evaluate an area on the basis of the sustainability of important habitat factors for the given species. In other words it is to assess the detailed ecological information about the species and with help of that the characteristics of the habitat can be evaluated (Kushwaha et. al., 2000, Kushwaha and Roy 2002). As evident from the earlier review, a large number of studies have been carried out to evaluate the habitat of different species in various parts of the world as well as Indian subcontinent (Hizrel et. al., 2001, Park and Lee, 2003, Roy et. al., 1995, Kushwaha et. al., 2000, Kushwaha et. al., 2004, Imam et. al., 2009 and Ali 2014).

2

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PTR is an area which offers opportunity for research on large mammal’s population and their habitats. Large mammals by virtue of their bigger size and home range are relatively more prone to extinction as a consequence of fragmentation and degradation of habitat. This leads to increase concern among scientist and researchers. Studies of mammals always fascinate scientist, researchers as well as common people because man himself belongs to this class. Studying Tigers leopards, bears, ’s are more fascinating because they are key-stone species of jungle. Large carnivore predators like tiger (Panthera tigris), leopard (Panthera pardus), Snow-leopard (Uncia uncia), Sloth bear (Melursus ursinus) and their prey base mainly Sambar (Rusa unicolor), Barking deer (Muntiacus muntjak) spotted deer (Axix axis), Indian gaur (Bos gaurus), are extensively studied and their morphology, habitat, distribution and all other attributes are accounted all over the country (De et al., 1966; Cairns et. al., 1980; Karanth & Sunquist 1999; Ilyas 2001; Pasha et. al., 2002; Karanth et. al., 2004; Ambika et. al., 2005; Srivastva et. al., 2009). These species should be studied more and more for their better conservation. As far as the existing literature is concerned, Pench Tiger Reserve (PTR) owing to the rich diversity of the flora and fauna a lot of studies has been carried out PTR (Diwedi et. al., 1988; Shukla 1990; Karanth et. al., 1998; Sankar et. al., 2000). Large number of studies has been concentrated on tiger (Karanth et. al., 1998; Biswas et. al., 2002) as well as on ungulate species (Sankar et. al., 2000; Pasha et. al., 2002). Available resources of PTR encompass eight wild ungulates species namely Gaur (Bos gaurus) Chital (Axis axis), Sambar (Rusa unicolor), Nilgai (Boselaphus tragocamelus), Barking deer (Muntiacus muntjack), Chausinga (Tetraceros quadricornis), Chinkara (Gazella benetti) and Wild (Sus scrofa). Resource partitioning among potential competitors takes a central place in ecological research. Ungulates have received much attention in these studies because of the high numbers of coexisting species. Resource partitioning is the differential use of resources such as food and space by species in the same community (Schoener 1974, Voeten & Prins 1999). Resource partitioning between species has been described for many taxa in various ecosystems (Gordon & Illius 1989, Hansen & Reid 1975, Jarman & Sinclair 1979, Leuthold 1978, McDonald 2002, Mysterud 2000, Putman 1996, Voeten & Prins 1999). Species coexist despite overlaps in fundamental niches provided the overlap in potential resource use is incomplete (Putman 1996). Each species can occupy a distinct and non overlapping ‘realized’ or

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‘post-interactive’ niche in the presence of the other potentially competing species (Putman 1996).

1.2 Literature review

Ungulates are any members of a diverse clade of primarily large mammals that includes odd-toed ungulates such as horses and rhinoceroses, and even-toed ungulates such as cattle, , giraffes, , deer and hippopotamuses. Most terrestrial ungulates use the tips of their toes, usually hoofed, to sustain their whole body weight while moving. The term means, roughly, "being hoofed" or "hoofed animal". As a descriptive term, "ungulate" normally excludes cetaceans (whales, dolphins, porpoises); as they do not possess most of the typical morphological characteristics of ungulates, but recent discoveries indicate that they are descended from early artiodactyls (Ursing and Arnason, 1998). Ungulates are typically herbivorous (though some species are omnivorous, such as pigs), and many employ specialized gut bacteria to allow them to digest cellulose, as in the case of . They inhabit a wide range of habitats, from jungles to plains to rivers.

The ungulate species are extensively studied in different parts of world as well as Indian sub continents. There have been numerous studies on ungulates in the Indian sub-continent, notable among those of Schaller (1967), Eisenberg and Lockhart (1972), Berwick (1974), Dinerstein (1979), Mishra (1982),Green (1985), Balakrishnan and Easa (1986), Haque (1990), Shukla (1990), Bhatnagar (1991), Chakraborty (1991), Karanth and Sunquist (1992), Khan (1993), Sankar (1994), Bhat and Rawat (1995), Acharya (1997), Raman (1997) and Ilyas and Khan (2005).

1.2.1 Study animals

1.2.1.1 Chital (Axis axis):

The chital or spotted deer is the third largest deer inhabiting the plains and undulating terrain of the Indian sub-continent. Its distribution ranges from the foot hills of Himalayas throughout the peninsular India in the forested areas, and from western Assam up to Eastern Rajasthan and Gujarat (Figure1.1). It is found in a variety of forest types ranging from dry deciduous forests to moist deciduous, thorn and scrub jungles and also in mangals.

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The chital prefer flat terrain and valley habitats, frequenting eco-tones with a high diversity of palatable grass and herb species and early to middle successional stages of vegetation (Schaller 1967, Dinerstein 1979, Mishra 1982, Bhat, 1995, Khan, 1996). They are highly dependent on water and shade (Schaller 1967). Being more dependent on grass (Schaller 1967, Jhonsingh and Sankar 1991), the chital avoids areas of heavy cattle grazing (Khan 1996). Relatively more information is available on Chital through some other quantitative studies e.g. De & Spillit (1966), Schaller (1967), Berwick (1974), Jhonsingh (1983), Haque (1990), Karanth & Sunquist (1992), Khan (1993), Sankar (1994), Khan et. al. (1995), Raman (1997), Raman (1998), Biswas & Sankar (2002), Bagchi et al.(2003).The definitive research on these species on the other parts of the world such as Hawaii (Graf and Nichols 1966), Texas (Ables and Ramsey 1974), Argentina and Australia (Lever 1985).

Figure 1.1 Distribution Map of Cheetal (Source IUCN)

1.2.1.2 Sambar (Rusa unicolor):

Sambar deer is the largest deer species native to South East Asia. It has an exceedingly wide geographical distribution, including India, Myanmar and Sri Lanka extending through Indo-China and Malay countries and eastward to Philippines and beyond (Prater 1965) (Figure 1.2).

In spite of wide distribution of sambar, and its importance as prey animal (Schaller 1967, Jhonsingh 1983, Jhonsingh et. al. 1993, Mukherjee et. al. 1994, Sankar 1994), there have been few detail studies (Schaller 1967, Green 1985, Khan 1993, Sankar 1994) on its population biology or habitat requirements. Berwick (1976) and

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Jhonsingh &Sankar (1991) studied feeding habit of sambar in Gir national park and Mundanthurai Plateau, Tamilnadu respectively. A lot of information has been generated from studies on sambar introduced into Texas (e.g. Richardson II 1972), Florida (e.g. Shea et. al. 1990) and the Thai population (e.g. Ngampongsai 1987). Probably no other wild ungulates in the Indian subcontinent have adapted itself to such a wide variety of condition and habitat types as the sambar (Schaller 1967, Rodgers 1988). Nevertheless, it definitely avoids disturbed areas (Schaller, 1967, Prater 1965, Khan 1993, Sankar 1994). Availability of water and forest on hill slopes are believed to be essential components of sambar habitat (Schaller 1967, Dinerstein 1979, Jhonsingh 1983, Bhatnagar 1991). Sambar densities in the moist deciduous forest and teak dominated plantation forests are higher than in dry deciduous forest (Karanth & Sunquist 1992).

Figure 1.2 Distribution Map of Sambar (Source IUCN)

1.2.1.3- Nilgai (Boselaphus tragocamelus):

The nilgai occurs in more open forests from the Himalayan foot hills including , southward through central India (Prater 1971) (Figure 1.3).

Nilgai occur in relatively open areas with undulating or flat terrain (Berwick 1974), avoiding dense forests (Daniel 1994) and preferring scrublands, with low tree and shrub densities (Chakraborty 1991, Sankar 1994, Khan 1996). They are reported to tolerate scarcity of water (Bohra et al. 1992). Some other studies on nilgai include those by Schaller (1967), Berwick (1974), Berwick (1976), Haque (1990), Khan

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(1993) and Sankar (1994). Introduced nilgai populations occur in Texas (Sheffield et al.1983), Mexico and in South Africa (Lever 1985).

Figure 1.3 Distribution Map of Nilgai (Source IUCN) 1.2.1.4- Gaur (Bos gaurus):

The gaur favour hilly terrain (Schaller 1967), and occur in the Western Ghats, hill forests of central India and south-eastern peninsular India, north east India and parts of Nepal (Figure 1.4).

Gaurs in central highlands of India are essentially hill- forest dwellers that descend to the lowland valley and plains close to water sources away from disturbance (Schaller 1967). They are known to favour cool, dense forest cover. Different aspect of ecology and behaviour of Gaur is studied by Schaller (1967), Sahai (1972), Guin & Pal (1982), Chandiramani (1984), Imam (1985), Dwivedi & Shukla (1988), Sankar et al (2000) and Pasha . (2002).

Figure 1.4 Distribution Map of Gaur (Source IUCN)

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1.2.1.5- Chowsingha (Tetraceros quadricornis):

Four horned Antelope (Tetracerus quadricornis) is only member of this group with two pairs of horns of which front pair is always shorter than the back (Prater, 1971). Males have horns whereas in females horns are absent. It is an endemic to the Indian subcontinent. Approximately 95% of its current global population occurs in India (Rahmani, 2001), with the remaining 5% found in Nepal (Krishna et al.2009). It is distributed in Peninsular India south to the Himalayas where the country is wooded and hilly, but not too densely forested areas (Prater, 1971). The four horned antelope prefers the dry deciduous forests in hilly areas with open canopy (Baskaran et. al. 2009) and tree-savanna deciduous habitat sub type with a high degree of deciduousness (Krishna et. al. 2008, 2009). It has been reported that the four-horned antelopes are distributed in all of the Indian States south from Uttar Pradesh except Kerala (Rice, 1990) (Figure 1.5).

Despite being widely distributed in India, this species has received very little scientific attention. A review of literature shows that the species is generally given only a brief treatment in accounts of multi-species studies (Krishnan, 1972; Sharatchandra and Gadgil, 1975; Karanth and Sunquist, 1992). A community study on wild ruminants in the Gir forest ecosystem by Berwick (1974) was the first study that furnished information on population density, age structure, and food consumption of this species. Another significant report was by Rice (1990) on the status of four- horned antelope based on information collected through questionnaires from various sources. Few studies have addressed biology and ecology of this antelope. There have been only few species specific studies under taken so far: (1) at Bandipur National Park in South India ( Krishna, 2006); (2) at Panna Tiger Reserve in Central India (Sharma et al. 2005); and (3) at Mudumalai Wildlife Sanctuary in South India (Baskaran and Desai 1999). Singh & Swain (2003) try to investigate distribution of species in Simlipal Tiger Reserve. The IUCN lists the four horned antelope as Vulnerable. Many aspects of this species remain unclear.

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Figure 1.5 Distribution Map of Chowsingha (Source IUCN)

Geospatial technologies including remote sensing and GIS hold the potential for less time and cost effective assessment of nature and natural resources. Remote Sensing and GIS can be useful tools to gather, store, transform and to characterize the spatial environmental data from real world. GIS also be applied for the betterment of wild species and their habitat. The wildlife habitat evaluation model was first ever developed by Thomas et al. (1979) for rocky mountain elk ( elaphus nelson) in the blue mountains of north-eastern Oregon. The U. S. Fish and Wildlife Service developed Habitat Suitability Index (HSI) models in 1981 (U. S. Fish and Wildlife Service (1981).

Habitat modelling has now become well accepted tools for assessing the potential habitat of different species, assessing the habitat quality and for developing wildlife management strategies (Verner et al. 1986). As far as the literature is concerned numerous studies has been conducted on the habitat suitability index modeling on number of species in the different parts of world. Debeljak et. al., 2001 assessed the potential habitats of a population of red deer in South-central Slovenia using existing data on the deer population spatial distribution and size. Hizrel et. al., 2001 compares two different habitat-suitability assessing techniques, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. Park et, al., 2003 devoloped the habitat suitability model for wild boar Sus scrofa in the Mt. Bakwoonsan region of of Korea. Chadwick et. al., 2006 published general technical report on the development of landscape-level habitat suitability model for ten wildlife species in the central hardwood region of the

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Midwestern United States. Hizrel et. al., 2006 Evaluated the ability of habitat suitability models to predict species presences.

Roy et. al., 1995 have used remote sensing and geospatial modeling for (Nemorhaedus goral) habitat evaluation in Rajaji NP and Porwal et. at., 1996 has done habitat suitability analysis of sambar Rusa unicolor in Kanha NP and Pant et. al., 2000 in Corbett National Park. Kushwaha and Roy 2002 reviews the developments on wildlife habitat evaluation and management utilization in remote sensing and GIS tools. Kushwaha et. al., 2004 evaluate the potential habitats of sambar (Rusa unicolor) and muntjak (Muntiacus muntjak) using geospatial techniques in the Chaubatia Reserve Forest of Ranikhet. Imam et. al., 2009 evaluates habitat suitability for tigers (Panthera tigris tigris) in Chandoli National Park, India along with the habitat suitability map of gaur (Bos gaurus), sambar (Rusa unicolor) and muntjak (Muntiacus muntjak). Ali (2014) evaluated the habitat suitability modeling for exploration of the spatial distribution of Kashmir in Dachigam National Park, Kashmir.

There are numbers of studies has been also conceded as far as PTR are concern such as Long-term research in Pench was initiated on the study of Bison (Dwivedi & Shukla 1988). It was followed on the interactions between wild animal and their habitat in the Pench Sanctuary by Shukla (1990). This was followed by a tiger-prey estimation study by Karanth and Nichols (1998). Since 1995 the Wildlife Institute of India has initiated a series of studies beginning with a long-term radio-telemetry study on the gaur Bos frontalis (Sankar et. al., 2000), Creation of a spatial mapping database for the Tiger Reserve (Sankar et. al., 2000) and Prey abundance and food habit of tigers (Panthera tigris tigris) in Pench National Park (Biswas et. al., 2002). Debarking of teak by Bison Bos gaurus (Pasha et. al., 2002), ecology of the Dhole or Asiatic wild dog (Cuon alpinus) in Pench Tiger Reserve (Acharya 2007), feeding habits and temporal activity patterns of golden jackal Canis aureus and the jungle cat Felis chaus (Majumder et. al. 2011), prey selection, food habits and population structure of sympatric carnivores: tiger Panthera tigris tigris (L.), leopard Panthera pardus (L.) and dhole Cuon alpinus (pallas) in Pench Tiger Reserve, MP (Majumder 2011), evaluation of Impact of Landscape Changes on Large Mammal Habitats in Pench Tiger Reserve (Basu, 2012), Quantifying Land Use Land Cover Change in Pench Tiger Reserve (Madhya Pradesh, India): A Landscape Approach (Areendran et. al.,

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2012), feeding ecology of four horned antelope Tetraceros quadricornis (Haleem et. al., 2014). Short-term studies have also been carried out on avifauna (Jayapal 1997 and Parveen 2014), herbivores (Acharya 1997, Sartaj 2009, Khan 2015 and Ansari 2015).

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1.3 Objectives & Hypothesis

Objectives

1- To investigate the vegetation structure of the study area. 2- To investigate the status, distribution and abundance of ungulates in PTR. 3- To investigate the seasonal abundance and habitat utilisation pattern of ungulates in PTR. 4- To investigate the feeding ecology of ungulates to understand the resource partitioning in PTR. 5- To prepare a conservation strategy for the ungulate community in PTR

Hypothesis

1. Floral composition of PTR is similar throughout different seasons. 2. Distribution of mammals is similar in different administrative circles/beats of PTR. 3. There is uniform distribution of ungulates in PTR. 4. There is similar habitat utilization pattern among ungulates in PTR. 5. There is no difference in food and feeding habits of ungulates in PTR. 6. There is no habitat overlap among ungulates of PTR.

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Chapter 2- Study Area

Chapter -2

CHAPTER 2- STUDY AREA

2.1 Introduction Pench Tiger Reserve (PTR) (77° 55' W to 79° 35' E & 21° 08' S to 22° 00' N), lies in the south-western region of the state of Madhya Pradesh (Figure 2.1).The Tiger Reserve comprises of the Sanctuary and the National Park of the same name, covering an area of 757.85 sq. km Pench Tiger Reserve is located 98 km. north of Nagpur(Maharashtra) and 12 km. from Khawasa (Madhya Pradesh) on the National Highway No. 7. 2.2 History and management units This part of Central India has been a major place of attraction not only for naturalists but for sport hunters also. Descriptions of its natural wealth and richness were appearing in the writings of the 14th Century. Ain-I-A-Akbari and the references available on Deogarh Kingdom of the sixteenth century also give insights on the wildlife of this area respectively. Forsyth 1889, Sterndale 1887, Brander 1923, have given a good detailed account of the distribution of the flora, fauna and the local inhabitants of Central Indian Highlands and this tract. The well known work of Kipling, The Jungle Book too revolves around this tract of Satpura. In 1862, Col. Pearson, the Officer-in-Charge of the Armed Police stationed at Seoni was appointed as the first Conservator of Forests of Sagar and Harda, which included the forests of Seoni and Chindwara. In 1863, the first Inspector General of forests India, Dr Brandis, laid down the policies for these forests after visited the area. These forests were declared as Reserve Forests in 1929. Till 1970-71, hunting permits were given. An area of 449.39 sq. km. was notified as the Pench Game Sanctuary; vide Madhya Pradesh State Forest Department Memo No. F/15/77-10(3) Bhopal, dated 30.09.1977. During March 1983, the Government of Madhya Pradesh notified its purpose to constitute an area of 292.85 sq. km. as Pench National Park, to be carved out of the pre-existing Pench Sanctuary area vide notification No. 15/5/82-10(2) Bhopal dated 01.03.1983.The present Pench Tiger Reserve was included into the stream of the Tiger Reserves in 1992. The Pench River, from which the reserve derives the name, flows through the centre of the park dividing it into the west Chindwara and the east Seoni block. The total area of the National Park is 292.85-sq. km. out of which 145.24 sq. km. lies in Seoni District and the rest in Chindwara District. On the southern end of the river, there is

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dam. In 1987, under the Pench Hydro-electric Power Project this dam was constructed on the Pench river, which resulted in the submergence of 74 sq. km. of the forested area, out of which 54 sq. km. lies in PTR (Madhya Pradesh) and rest one in the Maharashtra. This dam demarks the State boundary between Madhya Pradesh and Maharashtra. Looking towards natural history it was observed that Pench forests have been the key place in of central India in general and for wildlife in particular. Famous naturalist captain J. Forsyth, A.A.D. Brander and Rudyard Kipling also presented a vivid account of this area in their famous books, Highlands of central India Wild animals in central India and Jungle book respectively.

2.3 Physical attributes

2.3.1 Topography PTR lies in the southern lower reaches of Satpura Hill ranges. The terrain is gently undulating and criss-crossed by small streams and nullahs; most of these are seasonal. The Pench river runs through the Park covering a length of 24 km. and bisecting it into almost equal halves. The hills have gradual to steep slopes with almost flat tops. The detailed terrain outlay of PTR is represented in the Chapter 9, Figure 9.1. Chindimatta, Khumbadeo, Khairbanmatta, Arjalmatta and Kalapahar are some major prominent hills of the study area. The term matta is used by local’s tribe which basically implies the hills. Kalapahar is the highest hill in the area with an altitude of 650 m. Chindwara block on the west bank of Pench, the land has risen and resulted in the formation of the plateau between Jamtara, Naharjhir and Gumtara villages. A number of valleys and ravines have resulted because of much dissected terrain. The mean altitude of the reserve is 550 m and maximum height is up to 640 msl.

2.3.2 Soil types The sandy loam, Red soil, Kankar and Saline soil and the Alluvial soil are four major types of soils found in the PTR. 1. Sandy loam soil: Most of the area inside the reserve is full of sandy loam soil. The soil is the result of weathering of granites gneisses. This soil type most commonly occurs on gentle slopes and valleys.

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2. Red soil: The higher elevated areas of the PTR are predominantly covered with red soil. The soil varies from the shallow, poor, gravely and light coloured varieties on elevated areas to much more deep, fertile and darker varieties occupying the lower plain sand valleys. 3. Kankar and saline soil: In the foothills, areas where the canopy cover and forest gaps are less, are covered with this type of soil. They hold large proportions of silica and orthoclase quartz with low water holding capacity. They are generally mineral deficient and have low productive value. 4. Alluvial soil: This type of soil is mainly confined to the stream banks and Pench river. Alluvial soil consists mostly of the siliceous debris, washed down from the hills, mixed with humus. This soil varies to a great extent in the chemical and physical properties owing to the variation in the rocks, which form the debris from place to place. The colour is yellowish brown. This soil supports good population of teak (Tectona grandis) along the Pench river.

2.3.3 Drainage The area is crisscrossed by numerous seasonal streams and nullahs - jhors. The Pench River flowing through the central line of the Reserve is dry by the end of April but a number of water pools locally known as dohs/khassas are found, which serve as waterholes for wild animals. A few perennial springs also exist in this area. In the monsoon, the area can be flooded with water: areas being cut off and nalas flowing in full spate. But it is the dry season that highlights the importance of water as a limiting factor. In spite of the efforts by the forest department, who have constructed numerous check-dams and waterholes associated with smaller troughs close to hand- pumps, the dry season can be quite taxing. That is when all major nalas are bone-dry and the talabs, if not completely without water have pitifully small amounts of it and thus the Pench reservoir at the centre of the Reserve is the only major water source during the pinch period.

During the summer season the water resource becomes limited and scattered. To combat with this problem the management authorities of PTR has constructed artificial water hole sat several places. They also installed hand pumps near the vicinity of these artificial water holes through which water is supplied. Furthermore many anicuts and check dams have also been built to trap the available water.

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2.4 Climate The area has four seasons: Summer (March-June), Monsoon (July-August), Post- monsoon (September-October) and winter (November-February), with temperature 0 0 ranging from 0 C in peak winter to 45 C in the peak summer; it receives an average annual rainfall of 1400 mm (Sankar et al. 2000).

2.5 Biological attributes (flora and fauna) Visibility in the area is extremely dynamic and changes from a bare minimum in the monsoon to a situation where one can see the sun rising through the trees in the dry season. It is a dry deciduous forest with all the trees shedding their leaves in the post- monsoon and winter season. With the water receding steadily after the monsoon in the submergence zone and through the winter, more and more areas open up for the animals. Initially weeds like Parthenium hysterophorus and Dhatura indica come up but these eventually die out as the dry season progresses. Visibility, though, in the open as well as in the forest may nonetheless be limited by the terrain. The leaves begin to sprout as summer sets in with Diospyros melanoxylon and then Madhuca indica being the first ones. Throughout the dry season, a number of fruits ripen in the forest and attract the herbivores to forage under the trees. Madhuca indica flowers and Aegle marmelos fruits are few examples.

Teak (Tectona grandis), and associated species such as Madhuca indica, Diospyros melanoxylon, Terminalia tomentosa, Buchanania lanzan, Lagerstroemia parviflora, Ougeinia dalbergoides, Miliusa velutina and Lannea coromandalica, occur on flat terrain. The undulating terrain and hill slopes have patches of Mixed Forest dominated by Boswellia serrata and Anogeissus latifolia. Species like Sterculia urens and latifolia are found scattered on rocky slopes. Bamboo forests occur in the hill slopes and along streams. Some of the open patches of the Park are covered with tall grasses interspersed with Butea monosperma and Zizyphus mauritiana. Evergreen tree species like Terminalia arjuna, Syzygium cumini and Ixora parviflora are found in riparian vegetation along nullahs and river banks. Cleistanthus collinus dominant patches are also found in some parts of the Tiger Reserve.

The carnivore fauna is represented by the tiger (Panthera tigris), leopard (Panthera pardus), dhole (Cuon alpinus), jungle cat (Felis chaus), and small Indian civet (Viverricula indica). Wolf (Canis lupus) occurs on the fringes and outside the Reserve

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limits. Striped hyena (Hyaena hyaena), sloth bear (Melursus ursinus), jackal (Canis aureus), and common palm civet (Paradoxurus hermaphroditus) make up the rest of the carnivore fauna of the Reserve.

Chital (Axis axis), sambar (Rusa unicolor), gaur (Bos frontalis), nilgai (Boselaphus tragocamelus), wild pig (Sus scrofa), barking deer (Muntiacus ) and chowsingha (Tetraceros quadricornis), are the wild ungulate species found in the study area. The common langur (Presbytis entellus) and rhesus macaque (Macaca mulatta) represent the primate fauna of the area. The Indian porcupine (Hystrix indica), two species of mongoose i.e. common mongoose (Herpestes edwardsii) and ruddy mongoose (Herpestes smithii), and black-naped hare (Lepus nigricollis nigricollis) also occur in this Tiger Reserve.

As far as the avifauna is concerned a total of 262 species of birds have been recorded from PTR (Pasha et al. 2003). Parveen (2015) record 221 species of birds in and around PTR. Among the lower vertebrates there are 19 species of snakes, 4 species of turtles, 5 species of amphibians and 18 species of fishes (Kumar 1990). 2.6 Fire PTR, being mainly a dry deciduous habitat, experiences fire annually in the summer. The River Pench has been rigorously affected in the past due to increased anthropogenic activities. The fire occurring in the reserve can be broadly called as below canopy fires. These fires largely remain restricted to the shrub layer and because of this ground herbage is depleted. These fires also pretence a serious threat to the ground dwelling fauna, as most of the species breed at this time of the year. To overcome these situations, construction of fire lines as a prevention measure is created by the management authorities of the PTR. 2.7 Socio-economic attributes Several tribes, among whom the Gonds are most prominent and inhabit this part of the central India. In the beginning of 17th century the Gonds were politically very active and ruled much of this territory (Rangarajan 1996). They ruled over the plateau region of Satpura in 17th century. Jataba was the first gond king who built the fort of Deogarh and formed the gond Kingdom. It is the gonds who have given their name to this country the Gondwana Land (Forsyth 1889). The lingual similarity between gonds and Tamil folks of southern India bear the testimony that the gonds are a part of the same lineage as the Dravidians of South India (Forsyth 1889). In the past, gonds

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inhabiting the interiors of the forest and hills were largely hunter-gatherers while those in the foothills and on the fringes of the woods took to agriculture (Areendran, 2007). At present there are 99 villages within five-kilometre radius of the periphery of the Pench Tiger Reserve (Areendran, 2007).

2.8- Intensive Study Area

The intensive study area for long term study is located around core zone of PTR. Selection of intensive study area is based on extensive survey and indentifying different habitat for long term study

2.8.1. Teak Forest The teak forest is mainly composed of Tectona grandis, the circular plots which have more than 75% of teak tree comes under this. Along with teak Lagerstroemia parviflora, Grewia latlifolia are also present. Teak forests are stretched along the Tikadi beat, Kumbhadev beat, Karmajhiri beat, and Satosa beat as well as Turai beat.

2.8.2. Teak Mixed forest These are the mixed forest which is comprises of different species such as Tectona grandis, Lagerstroemia parviflora, Grewia latifolia, Schleicher aoleosa, Aegl emarmelos, Ziziphus xylopyra, Ziziphus jujube, Syzygium cumini, Acacia catechu, Acacia nilotica, Terminalia tomentosa, Cassia fistula, Semecarpus anacardium, Buchanania lanzan and a few climbers such as Butea superb, Butea parvifiora. Tikadi beat, Karmajhiri beat, Byson camp, Kumbhadev, Khamreeth, Bodanala, Sapath, Piyothadi, Alikatta, Chikhlakhari, Avarghani and turia cover the teak miscellaneous forest.

2.8.3. Mixed forest type This type of habitat generally found on the hilly slopes of PTR and mainly composed of cholroxylon which is locally known as bherra forest. Along with the Cholroxylon Dalbergia paniculata, Madhuka indica, Lagerstroemia parviflora, Bridelia retusa. Shrubs like Lantana camara, Hilectrux izora. Telia, Avarghani, Chindimatta, Bodanala beat of PTR having this type of forest.

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2.8.4. Grassland type A very few patches of this type of habitat found in the Khamreeth, Bodanala, Satosa and Piyorthadi beats of PTR. The grassland habitat dominated by Imperata cylindrical, Eragrostis tenella, Pennisetum pedicelatum, Thysanolaena maxima and Hateropogon contorts in winter season. The members of poeceae family, cyperaceae family and Ericaulaceae family are in scarcity which changes seasonally.

2.8.5. Bamboo forest A very few patches of PTR covered with the bamboo which is entirely dominated by the Arundineria species. In the area of Karmajhiri and Baghdev beat this type of habitat.

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Figure 2.1. Map of study area

20

Chapter 3- Vegetation Structure

Chapter -3

CHAPTER 3- VEGETATION STRUCTURE

3.1- Introduction

Pench Tiger reserve is a tropical dry deciduas and tropical moist deciduous forest, with variety of the tree, shrub, herb and grass species. The Tropical dry forests form a major biome in India by covering around 38% of total forest area of the country (Thakur and Khare, 2006). The tropical dry forests are one of the most extended tropical forested ecosystems, and yet have received only recent attention from the scientific community. This ecosystem is also scarcely represented in the international protection schemes, which perhaps causes increased vulnerability of this ecosystem to the tropical fingerprint of global human development. Tropical forest ecosystems are one of the richest terrestrial ecosystems which support a variety of life forms and maintain huge global biodiversity (Shi and Singh 2002). The phytosociology is one of the important aspects for analyzing the structure, composition and phytodiversity for thoroughly understanding the vegetation dynamics. Both structure and diversity of vegetation have strong functional role in controlling ecosystem processes like biomass production, cycling of water and nutrients (Gower. 1992). The strong correlation also exists between structural diversity and species diversity (Sahu, 2008).

The study of floristic composition of vegetation is crucial for biodiversity conservation and management by providing habitats for wildlife and contributing to the ecologically sustainable management of natural resources. Considering its importance, the vegetation profile of PTR is discussed in present chapter.

3.2- Methodology

3.2.1- Data Collection Tree species density was assessed by plot method which is one of the most widely used method for investigating vegetation (Parthasarathy 2001, Ilyas and Khan 2005, Kumar and Bhatt, 2006, Sahu .2007, Anitha et. al. 2008). Random plots of 10m radius were selected and distances between two plots were maintained by 200 meter. At each plot all the tree species with their individuals were counted which comes under 10 meter radius. The canopy cover was measured at four points of each sampling plot, using a mirror of 25x25cm which is divided into 100 equal grids squire. The mirror was kept horizontally at 1.25m above the ground level, and grid squares which covered more than 50% by tree foliage were counted. 21

Chapter -3

The tree species less than 50cm height were considered seedling and tree species more than 50 cm but less than 3m height as well as GBH less than 30 cm were considered saplings were counted in 5m radius plot. Along with the regenerating tree species the shrub species and their individuals were also counted in 5m radius circular plots. Shrub cover, herb cover and grass cover was measured by ocular estimation.

3.2.2- Data analysis Densities for tress, seedlings, saplings, shrubs, herbs and grasses were calculated for every plot and based on that the mean densities were calculated for different beats, circles as well as for different identified habitats in different seasons, using descriptive statistics. To test the significant difference between mean densities of vegetation in different habitat and different season One Way ANOVA was performed. All statistical tests were performed following Zar (1984) using computer programme SPSS 6.1 (Norusis, 1994).

3.3- Results

3.3.1- Vegetation Profile in Pench National park 3.3.1.1- Vegetation profile in different administrative beats of PTR 3.3.1.1.1: Floral Community Structure:

The mean tree density (Density/ha ± SE) was found maximum (664.01± 43.97) in Karmajhiri and minimum (323.24± 18.08) in Alikatta. Mean tree diversity (Diversity/ha+SE) was maximum (1.78± 0.0751) in Gaur Camp and mininum (1.2318± 0.0811) tree diversity was recorded from Kumbhadev. Mean tree richness were found maximum (2.73± 0.0963) in Turia and minimum (1.49± 0.1247) in Kumbhadev (Table 3.1).

Similarly mean seedling and sapling density (Density/ha ± SE) were found maximum (592.35± 99.54 and 402.86± 103.82) in Byson camp and Tikadi and minimum (136.94± 28.86 and 3.18± 2.19) in Kumbhadev and Sapat respectively. Mean Seedling diversity and richness were found maximum (1.04± 0.0689 & 1.008± 0.1561) in Bodanala and Baghdev and minimum (0.3483± 0.0895 and 0.3175± 0.0887) were recorded from Alikatta (Table 3.2).

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Likewise mean sapling diversity and richness were recorded maximum (0.7386± 0.0834, 0.7120± 0.0905) from Tikadi and minimum (0.0346± 0.0346, 0.0721± 0.0721) from Alikatta (Table 3.3).

Mean shrub density (Density/ha ± SE) was found maximum (705.41± 114.15) at Khamreeth and minimum (7.96± 7.96) at Chindimatta. Similarly mean shrub diversity and richness were reported maximum (0.5044± 0.0952, 0.3644± 0.0849) in Turia and minimum (0.0116± 0.0116, 0.0144± 0.0144) in Alikatta (Table 3.4).

Mean Herb density (Density/m2 ± SE), diversity and richness were found maximum (68.70± 7.45, 1.009± 0.1103, 1.25± 0.0944) in Baghdev, Piyorthadi and Avarghani and minimum (13.75± 1.85, 0.4595± 0.0920, 0.3385± 0.0721) in Sathosa and Karmajhiri respectively (Table 3.5).

The mean grass density (Density/m2 ± SE) was recorded maximum (51.60± 6.23) from Gaur Camp and minimum (13.05± 1.57) from Tikadi. Chindimatta and Bodanala were found more (0.7554± 0.0693, 0.6295± 0.0822) diverse and rich while Avarghani shows least (0.2234± 0.0591, 0.2640± 0.0607) diversity and richness respectively (Table 3.6).

3.3.1.1.2 : Percent Vegetation Cover

Mean tree cover was recorded maximum (43.75± 2.45) at Teliya and minimum (20.75± 3.32) in Khamreeth. Similarly mean shrub cover was found maximum (26.30± 6.21) in Alikatta and minimum (0.25± 0.25) in Chindimatta. Likewise mean herb and grass cover were recorded maximum (22.00± 2.09, 38.25± 2.76) from Baghdev and Karmajhiri and minimum (5.35 ± 0.71, 13.35± 2.02) from Satosha and Chindimatta (Table 3.7).

3.3.1.2- Vegetation profile in different administrative circles of PTR 3.3.1.2.1- Floral Community Structure:

Mean tree density (Density/ha ±SE) was found maximum (566.08± 21.06) in Karmajhiri and minimum (397.29± 19.30) in Alikatta. Mean tree diversity and richness were found maximum (1.55± 0.0461, 2.03± 0.0740) at Turia and minimum (1.51± 0.0464, 1.92± 0.0746) in Alikatta and Karmajhiri respectively (Table 3.8).

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Mean seedling density (Density/ha±SE) and diversity were found maximum (353.50± 26.21, 0.7962± 0.0512) in Baghdev and minimum (293.39± 35.57, 0.4558± 0.0422) in Karmajhiri and Alikatta respectively. Similarly mean seedling richness was found maximum (0.7861± 0.0554) at Turia and minimum (0.4348± 0.0436) at Alikatta (Table 3.9). Similarly mean sapling density, diversity and richness were found maximum (195.85± 31.55, 0.4927± 0.0490, 0.5078± 0.0546) in Karmajhiri and minimum (46.57± 10.73, 0.0793± 0.0244, 0.1055± 0.0343) in Alikatta (Table 3.10).

Mean Shrub density (Density/ha±SE) was found maximum (333.99± 49.82) in Baghdev and minimum (225.71± 70.84) in Alikatta. Likewise mean shrub diversity and richness were found maximum (0.2575± 0.0406, 0.2324± 0.0411) from Turia and minimum (0.0154± 0.0128, 0.0164± 0.0133) from Alikatta (Table 3.11).

Mean herb and grass density density (Density/m2 ± SE) were found maximum (33.98± 3.00, 41.27± 2.47) in Karmajhiri and Baghdev and minimum (25.22± 1.53, 26.88± 1.72) in Turia and Alikatta. Similarly mean herb diversity and richness were reported maximum (1.22± 0.0454, 1.17± 0.0508) from Turia and minimum (0.6356± 0.0424, 0.5253± 0.0369) from Karmajhiri (Table 3.12). Likewise mean grass diversity and richness were found maximum (0.6232± 0.0384, 0.4965± 0.0338) in Alikatta and Baghdev and minimum (0.4855± 0.0423, 0.3591± 0.0355) in Turia and Karmajhiri (Table 3.13).

3.3.1.2.2- Percent Vegetation Cover

Mean tree cover was found maximum (38.37± 1.90) in Turia and minimum (27.26± 1.54) in Karmajhiri. In the same way mean shrub cover and herb cover was found maximum (6.97± 1.97, 15.01± 0.858) in Alikatta and Turia and minimum (6.53± 1.08, 11.88± 1.04) in Turia and Baghdev respectively. Similarly mean grass cover was recorded maximum (29.93± 1.86) in Turia and minimum (25.83± 2.01) in Baghdev (Figure 3.1).

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Chapter -3

3.3.2- Vegetation profile in different habitats: 3.3.2.1- Vegetation profile in different habitats of PTR during post monsoon season 3.3.2.1.1- Floral Community Structure

Mean seedling density (Density/ha±SE), diversity and richness were found significantly higher (1528.66± 212.54, 0.7601± 0.087, 0.8171± 0.098) in Teak miscellaneous forest and minimum (348.36± 73.80, 0.1756± 0.047, 0.2101± 0.058) in

miscellaneous forest respectively (F4 164 = 15.462, P<0.05, F4 164 = 10.001, P<0.05, F4

164 = 8.334, P<0.05) (Table 3.14).

Similarly mean sapling density (Density/ha±SE), diversity and richness were maximum (860.10± 147.18, 0.4643± 0.062, 0.5395± 0.074) in miscellaneous forest and minimum (276.00± 57.32, 0.1422± 0.060, 0.1602± 0.071) in teak forest and the

results were found significant F4 164 = 4.586, P<0.05, F4 164 = 2.909, P<0.05, F4 164 = 2.910, P<0.05) (Table 3.15).

Mean shrub density (Density/ha±SE) was found maximum (1970.27± 233.19) in bamboo forest and minimum (955.41± 182.29) in teak forest with a significant result

(F4 164 = 7.939, P<0.05). Shrub diversity and shrub richness were found maximum (0.6801± 0.072, 0.5088± 0.053) in grassland and minimum (0.1102± 0.046, 0.086±

0.035) in teak forest and the results were found significant (F4 164 = 15.792, P<0.05, F4

164 = 15.160, P<0.05) (Table 3.16).

Mean herb density (Density/m2±SE) was maximum (35.56± 2.55) in teak miscellaneous forest and minimum (21.16± 2.17) with a significant result (F4 164 = 5.332, P< 0.05).Similarly mean herb diversity and herb richness were found maximum (1.59± 0.032, 1.39± 0.043) in teak miscellaneous forest and minimum (0.9716± 0.059, 0.8470± 0.058) in miscellaneous forest respectively with a significant

result (F4 164 = 11.234, P<0.05, F4 164 = 8.237, P<0.05) (Table 3.17).

Similarly mean grass density(Density/m2±SE), diversity and richness were found significantly maximum (69.63± 3.46, 1.03± 0.087, 0.605± 0.053) in grassland and

minimum (18.73± 1.86, 0.331± 0.073, 0.225± 0.050) in bamboo forest (F4 164 = 44.66,

P< .005, F4 164 = 14.332, P< .005, F4 164 = 10.209, P< .005) (Table 3.18).

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Chapter -3

3.3.2.1.2- Percent vegetation Cover:

Mean tree cover and shrub cover were found maximum (46.03± 2.63, 58.66± 2.62) in bamboo forest and minimum (20.86± 1.46, 6.80± 1.82) in teak miscellaneous and teak

forest respectively. However results were found significant (F4 164 = 15.325, P<0.05,

F4 164 = 89.420, P<0.05). Similarly mean herb cover and grass cover were found maximum (15.80± 1.09, 56.63± 3.00) in teak miscellaneous forest and grassland and minimum (9.23± 0.9846, 11.63± 1.22) in grassland and teak forest respectively with a

significant result (F4 164 = 4.934, P<0.05, F4 164 = 77.921, P<0.05) (Figure 3.2).

3.3.2.2- Vegetation profile in different habitats of PTR during summer season 3.3.2.2.1- Floral Community Structure

Mean seedling density (Density/ha±SE) was significantly higher (852.08 ± 90.97) in

teak mixed forest and lower (297.23 ± 39.59) in teak forest (F4 514 = 15.804, P< 0.05). On the contrary mean seedling diversity and richness were found maximum (0.627 ± 0.047, 0.745 ± 0.057) in teak mixed forest and minimum (0.231 ± 0.037, 0.270 ±

0.043) in grassland, showing significant results (F4 514 = 15.800, P< 0.05, F4 514 = 14.614, P< 0.05) (Table 3.19).

Mean sapling density (Density/ha±SE) was found maximum (736.02 ± 65.68) in bamboo forest and minimum (424.62 ± 40.95) in teak forest with a significant result

(F4 514 = 4.027, P< 0.05). The results also reveals that mean sapling diversity and richness were maximum (0.574 ± 0.050, 0.656 ± 0.059) in teak mixed forest and minimum (0.386 ± 0.049, 0.470 ± 0.061) in teak forest but the results were not found

significant (F4 514 = 2.114, P> 0.05, F4 514 = 1.462, P> 0.05) (Table 3.20).

Mean shrub density (Density/ha±SE) and diversity were maximum (2519.46 ± 149.19, 0.570 ± 0.046) in bamboo forest and minimum (1149.32 ± 103.15, 0.333 ±

0.041) in teak forest and mixed forest respectively, showing significant results (F4 514

= 22.459, P< 0.05, F4 514 = 5.918, P< 0.05). Mean shrub richness was reported maximum (0.405 ± 0.082) from teak forest and minimum (0.274 ± 0.027) from mixed

forest however results were not found to be significant (F4 514 = 1.902, P> 0.05) (Table 3.21).

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Chapter -3

Mean herb density (Density/m2±SE) was maximum (194.03 ± 16.00) in teak forest and minimum (128.36 ± 9.94) in bamboo forest with a significant result (F4 514 = 6.190, P< 0.05). Mean herb diversity and richness were found significantly maximum (1.57 ± 0.023, 1.29 ± 0.028) in teak mixed forest and minimum (1.24 ± 0.051, 0.949 ±

0.037) in bamboo forest and teak forest respectively (F4 514 = 9.746, P< 0.05, F4 514 = 9.385, P< 0.05) (Table 3.22).

The results shows that mean grass density (Density/m2±SE) and diversity were maximum (196.14 ± 12.07, 0.992 ± 0.036) in grassland and minimum (102.73 ± 8.28,

0.570 ± 0.046) in bamboo forest with significant results (F4 514 = 11.728, P< 0.05, F4

514 = 15.837, P< 0.05). Grass richness was significantly maximum (0.547 ± 0.025) in

teak mixed forest and minimum (0.374 ± 0.0330 in bamboo forest (F4 513 = 6.831, P< 0.05) (Table 3.23).

3.3.2.2.2- Percent vegetation cover:

During summer season mean tree cover and shrub cover were significantly maximum (38.80 ± 1.64, 38.80 ± 2.26) in bamboo forest and minimum (27.85 ± 1.11, 9.34 ±

0.85) in teak mixed forest and grassland respectively (F4 513 = 7.749, P< 0.05, F4 513 = 70.165, P< 0.05). The results also reveals that mean grass cover and herb cover were found maximum (49.39 ± 2.53, 18.43 ± 0.75) in grassland and teak mixed forest and minimum (15.13 ± 1.06, 13.37 ± 0.76) in teak forest and grassland respectively and the results were found to be significant (F4 513 = 69.719, P< 0.05, F4 513 = 4.005, P< 0.05) (Figure 3.3).

3.3.2.3- Vegetation profile in different habitats of PTR during winter season 3.3.2.3.1- Floral Community Structure

Seedling density(Density/ha±SE), diversity and richness were maximum (379.84 ± 42.13, 0.366 ± 0.039, 0.482 ± 0.053) in mixed forest and minimum (138.0 ± 28.77,

0.114 ± 0.036, 0.158 ± 0.052) in grassland respectively with a significant result (F4 345

= 5.754, P< 0.05, F4 345 = 5.425, P< 0.05, F4 345 = 4.587, P< 0.05) (Table 3.24).

Mean sapling density (Density/ha±SE) was found maximum (787.49 ± 60.52) in mixed forest and minimum (609.34 ± 54.76) in teak forest but the result was found insignificant (F4 345 = 1.105, P> 0.05). Mean sapling diversity and richness were reported maximum (0.681 ± 0.062, 0.835 ± 0.078) from teak forest and minimum

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Chapter -3

(0.510 ± 0.055, 0.565 ± 0.063) from bamboo forest however results were found

insignificant (F4 345 = 1.792, P> 0.05, F4 345 = 3.049, P> 0.05) (Table 3.25).

During winters mean shrub density(Density/ha±SE), diversity and richness were found significantly maximum (2511.67 ± 162.27, 0.646 ± 0.050, 0.444 ± 0.036) in bamboo forest and minimum (828.02 ± 108.43, 0.199 ± 0.043, 0.177 ± 0.038) in

grassland (F4 345 = 19.586, P< 0.05, F4 343 = 11.361, P< 0.05, F4 343 = 5.731, P< 0.05) (Table 3.26).

Mean herb density (Density/m2±SE) was found maximum (196.32 ± 9.81) in mixed forest and minimum (86.13 ± 4.95) in grassland with a significant result (F4 345 = 17.344, P< 0.05). Herb diversity was found maximum (1.39 ± 0.045) in teak forest and minimum (1.20 ± 0.051) in grassland however result was found insignificant (F4

345 = 3.237, P> 0.05). Mean herb richness was significantly maximum (1.25 ± 0.047)

in teak forest and minimum (0.956 ± 0.061) in bamboo forest (F4 345 = 6.359, P< 0.05) (Table 3.27).

Mean grass density(Density/m2±SE), diversity and richness were found maximum (248.53 ± 10.48 ±, 0.899 ± 0.031, 0.519 ± 0.033) in grassland, mixed forest and grassland and minimum (149.80 ± 7.94, 0.534 ± 0.053, 0.316 ± 0.034) in teak forest, bamboo forest and bamboo forest respectively and the results were found to be

significant (F4 345 = 22.817, P< 0.05, F4 345 = 11.235, P< 0.05, F4 345 = 6.680, P< 0.05) (Table 3.28).

3.3.2.3.2- Percent vegetation cover

During winters mean tree cover and herb cover were maximum (39.11 ± 1.78, 21.60 ± 0.99) in mixed forest and teak mixed forest and minimum (32.05 ± 1.33, 15.48 ± 0.92) in teak mixed forest and grassland respectively however results were not found

significant (F4 345 = 2.618, P> 0.05, F4 345 = 3.336, P> 0.05). Mean shrub cover and grass cover were found maximum (30.30 ± 2.24, 44.43 ± 3.24)in bamboo forest and grassland and minimum (8.77 ± 1.08, 18.20 ± 1.48) in mixed forest and teak forest

respectively with a significant result (F4 345 = 30.821, P< 0.05, F4 345 = 25.190, P< 0.05) (Figure 3.4).

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3.3.2.4- Vegetation profile in different habitats of PTR in different seasons 3.3.2.4.1- Floral Community Structure

Mean tree densities (Density/ha±SE) in different habitats were similar throughout different seasons. No remarkable seasonal changes in tree density were observed during the study period. Therefore seasonal comparison for tree density is not discussed. The result revealed that mean tree density ± SE was found maximum (576.43 ± 27.53) in teak forest followed by teak mixed forest (572.18 ± 28.33) and minimum (401.27 ± 24.91) in bamboo forest (Table 3.29).

Considering regeneration it was found that mean seedling density (Density/ha±SE) was maximum (1528.66 ± 212.54, in teak mixed forest during post monsoon season, followed by teak mixed forest (852.08 ± 90.97) during summer and minimum (138.0 ± 28.77) in grassland during winter season. Mean Sapling density (Density/ha±SE) was maximum (1016.50 ± 134.42) in mixed forest during post monsoon season, followed by bamboo forest (836.51 ± 154.59) in post monsoon and minimum (276.0 ± 57.32) in teak forest during post monsoon season. Similarly shrub densities (Density/ha±SE) were found maximum (2519.46 ± 149.19) in bamboo forest during summer and minimum (828.02 ± 108.43) in grassland during winter. Herb density (Density/m2±SE) was reported maximum (196.32 ± 9.81) from mixed forest in winter and minimum (21.16 ± 2.17) from grassland during post monsoon season. In the same way mean grass density (Density/m2±SE) were maximum (248.53 ± 10.48) in grassland during winter, followed by mixed forest (214.18 ± 7.90) in winter and minimum (18.73 ± 1.86) in bamboo forest during post monsoon season (Figure 3.5).

Vegetation sampling revealed that the intensive study area is composed of forty families of plant species.

Considering density of trees it was recorded that Tectona grandis was having maximum density (20.56±5.41), followed by Legerstromia parviflora (12.82±3.83), Diospyros melanoxylon (7.44±2.56), whereas Careya arboria, Ficus benghalensis, Ficus glomerata, Ficus infectoria, Garuga pinnata, Pterocarpus marsupium, Randia dumetorum, Schrebera swietenioides, Sterculia urens and Terminalia arjuna were minimum (0.03±0.07) (Table 3.30)

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When seedling density was considered in post monsoon season it was found that Diospyros melanoxylon was having maximum density (41.99±10.20) followed by Lagersroemia parviflora (40.57±8.23),where as Aegle marmelos and Kydia calycina were minimum (0.47±0.58). During summer season it was recorded that Diospyros melanoxylon was having maximum density (139.86±14.72) however Careya arborea and Kydia calycina was having minimum density (0.26±0.25). In winter again Diospyros melanoxylon was maximum (70.23±9.78) and Casearia tomentosa was minimum (0.31±0.34) (Table 3.31 to Table 3.33).

Considering overall seedling density it was found that Diospyros melanoxylon were recorded maximum (93.09±7.88), followed by Tectona grandis (47.81±4.18), Lagersroemia parviflora (34.84±3.62), Syzygium cumini (33.41±3.66) and Careya arborea was minimum (0.10±0.11) (Table 3.37)

When sapling density was observed it was found that during post monsoon season Tectona grandis was maximum (56.50±10.66) whereas Acacia catechu, Aegle marmelos, Buchanania lanzan and Terminalia tomentosa were having minimum density (0.49±0.60). In summer season again Tectona grandis was found maximum (101.95±12.35) followed by Diospyros melanoxylon (90.71±11.63) however Acacia catechu, Gardinia gummifera, Gymnosporia spinosa, Ixora arborea, Ougeinia oojeinensis and Terminalia tomentosa were minimum (0.39±0.43). Similar to post monsoon and summer season Tectona grandis was again found maximum (87.15±12.03) during winter followed by Diospyros melanoxylon (72.59±11.46), where as Ixora arborea,and Madhuca indica was minimum (0.17±0.24) (Table 3.34 to Table 3.36).

Considering overall sapling density it was recorded that Tectona grandis was maximum (88.17.±7.40), followed by Diospyros melanoxylon (75.25±6.96), Lagersroemia parviflora (66.90±5.45), Syzygium cumini (43.77±6.17) however Lenia coromandalica was minimum (0.07±0.09) (Table 3.37)

The result reveals that among shrubs Lantana camera was most abundant species in post monsoon, summer and winter and their density was 487.78.±63.77, 510.24±44.74 and 459.95±48.87 respectively whereas Grewia spp, Holarrhena

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antidysenterica and Vitex negundo was least abundant and their density was 1.42±2.03, 4.13±1.80 and 0.01±0.04 respectively (Table 3.38 to Table 3.40).

When overall shrub density was considered it was found that Lantana camera was recorded maximum (485.57.±29.05) and Holarrhena antidysenterica was minimum (2.55.±0.95) (Table 3.41)

When herb density (/m2) was considered it was found that in post monsoon season Marsilea quadrifolia, was recorded maximum (1.57±0.41), whereas Desmodium spp, was minimum (0.04±0.04). In summer season Casia tora was maximum (1.71±0.22) followed by Marsilea quadrifolia (1.64±0.30) and Xanthium strumaxium was found minimum (0.03±0.02). Similar to post monsoon Marsilea quadrifolia, was again maximum (2.18±0.44) during winter where as Desmodium triflorum was minimum (0.02±0.01) (Table 3.42 to Table 3.44).

Considering overall herb density it was observed that Marsilea quadrifolia, was maximum (1.83±0.22) whereas Desmodium spp. and Xanthium strumaxium was minimum (0.02±0.01) (Table 3.45).

In case of grasses Eragrostis tenella was most abundant grass during post monsoon, summer and winter and their density was 9.45±1.15, 9.33±0.3 and 8.97±0.75 respectively however Cyperus scariosus was minimum (0.02±0.03) in post monsoon , Pennisetum pedicellatum was minimum (0.10±0.06) in summer whereas Apluda mutica was minimum (0.03±0.02) in winter (Table 3.46 to Table 3.48).

Considering overall density of grasses it was observed that Eragrostis tenella was most abundant (9.22±0.45) and Cyperus scariosus was least abundant (0.02±0.01) (Table 3.49)

3.3.2.4.2- Percent vegetation cover

Mean tree cover was maximum (46.03 ± 2.63) in bamboo forest during post monsoon season and minimum (20.86 ± 1.46) in teak mixed forest during post monsoon season. Similarly mean shrub cover was maximum (58.66 ± 2.62) in bamboo forest during post monsoon season, followed by again bamboo forest (38.80 ± 2.26) in summer and minimum (6.80 ± 1.820 in teak forest during post monsoon season. Grass cover were maximum (56.63 ± 3.0) in grassland during post monsoon and minimum (11.63 ±

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1.22) in teak forest in post monsoon season. In the same way herb cover was reported maximum (21.60 ± 0.99) from teak mixed forest during winter, followed by teak forest (19.25 ± 1.24) in winter and minimum (9.23 ± 0.984) from grassland during post monsoon season (Figure 3.6). On the basis of findings null hypothesis was rejected except for trees.

3.4 Discussion The study on vegetation has focused largely on floral structure, composition and diversity of the forest in Pench tiger reserve. Bamboo forest, grasslands, mixed forest, teak forest and teak mixed forest were the major forest types identified in the area. In the present study vegetation profile of Pench National Park was assessed at different administrative beats and circles. Along with this, vegetation structures of different forest type throughout different seasons were also looked. The comparison of data for different administrative beats and circles has been restricted due to lack of information’s, however for different forest types it was compared with available information’s. The study revealed that tree density was maximum in Karmajahiri beat and probably it may be due to high abundance of trees in the area. The finding is similar to the observation recorded in management plan of PTR (Anonymous 2008). Tree diversity and richness was maximum in Turia which is a mixed forest. The result is similar to the outcome reported by Aye et al. (2014), where they suggested that mixed forest is more diverse than other types of forest. Furthermore it was recorded that regeneration of tree species were found higher in Byson camp and Bodanala beat. It may be due to presence of moisture and nutrient soil (Grigaliūnas and Ruseckas 2005). Since Alikatta beat was largely covered by Lantana camera therefore shrub density in this area was very high. It is suggested that floral density influences the vegetation cover of an area and probably this could be the reason that the shrub over of this area is also high. The result is also supported by management plan of the PTR (Anonymous, 2008). Most of the area of Baghdev beat is covered with herbs and the possible reason is that the area is open and enriched with large number of water sources. Apart from this, the area is having little bit difficult terrain therefore grazing by ungulates and cattle is minimal. It was found that in Teliya beat most of the dominant tree species were of broad leaf forest and probably due to this the canopy cover recorded in this area was high. A part from this it is suggested that where species richness is high the

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chances of canopy cover will also be high. In the study area species richness was fairly good and probably this influences the canopy cover.

After pooling the beat data into circle level it was found that density, diversity and richness of trees were maximum in Karmajhiri circle. Similarly tree regeneration was also fairly high in this region. Furthermore it was observed that herbs were maximum in this circle. Karmajhiri circle is located in Pench National park area and falls under core zone of tiger reserve therefore maximum conservation attention has been given to this area by park management and probably due to this the area is having dense forest. A part from this, the villages located around the park are having least anthropogenic impact as they are jointly involved in conservation with forest department under Eco-development program. Baghdev circle is dominantly covered with bamboos and different species of grasses therefore it is obvious that shrubs and grass density will also be high in this area.

When seasonal comparison was done it was found that the floral composition of bamboo, grasslands, mixed forest, teak forest and teak mixed forest was more or less similar during all three seasons, however, a change in regenerating species (seedling and saplings) was observed. In Pench National Park tree density was found to be maximum in teak forest during all three seasons and it is obvious because the forest type of this area is tropical deciduous forest and the most dominant species is Tectona grandis (Anonymous 2008). The regeneration of tree species was maximum in teak mixed forest and minimum in teak forest in all seasons. Grigaliūnas and Ruseckas (2005) suggested that moisture and soil type play greater role in vegetation regeneration. Probably this could be the reason that teak mixed forest is having high regeneration, as Pench river flows through this area. Whereas, teak forest is having low regeneration as its landscape is hilly with less water resources. It was found that shrubs density was more in bamboo forest throughout different seasons because as bamboo are considered to shrub. Furthermore it was observed that bamboos were not allowed to be extracted by villagers, therefore their population were found to be similar throughout the year. Management plan developed by PTR also suggested similar trend (Anonymous 2008). The composition of herbs and grasses in the study area shows similar trends during the season of post monsoon, summer and winter. The abundance of herbs and grasses should be more after rainy season however findings

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of the present study is contrary to this and probably small sample size could be the reason.

Water play essential role in the distribution of vegetation (Araya and Gonzalo, 2014). Probably this may be the reason for higher abundance of herbs in teak mixed forest after rainy season. Apart from this river Pench flows through this area and provides sufficient moisture to soil. Contrary to this, the abundance of herbs was more in teak forest in summer season which may be due to less grazing pressure of wild ungulates on teak forest during this season. In summers ungulates are having more potential alternate food resources with compared to teak forest and foraging model suggested that animals prefer those food habitats where they have to spend less energy in grabbing the food (Bergman et. al. 2001). In winter herbs were more abundant in mixed forest and it may be due to diverse nature of mixed forest (Aye et al. 2014).

When grasses are considered it was found that their density was recorded maximum from grasslands during all seasons. It is obvious that density of grasses will be more in grassland. Management plan of the park state that the grassland was completely free from grass extraction and livestock grazing (Anonymous 2008). Probably this may also have played role in high abundance of grasses.

Vegetation composition of study area is also discussed at species level describing tree density and their regeneration. Regeneration of any species is confined to a peculiar range of habitat conditions and the extent of those conditions is a major determinant of its geographic distribution (Grubb, 1977). The population structure of a species in a forest can convey its regeneration behaviour. The population structure, characterized by the presence of sufficient population of seedlings, saplings and adults, indicates successful regeneration of forest species (Saxena and Singh, 1984).Regeneration of a particular species can be said to be poor if seedlings and saplings are much less than the mature trees (Khan, et al. 1987). Regeneration of tree species and survival and growth of their seedlings and sprouts depend upon the interactive influence of biotic and abiotic factors of the forest environment. In the present study regeneration of Tectona grandis, Diospyros melanoxylon and Lagersroemia parviflora was found maximum among others and the possible reason could be influence of biotic and abiotic factors. Although the effect of certain factors like light intensity (Bazzaz and Pickett, 1980; Augspurger, 1984; Burton and Dombois, 1984; Connell et al., 1984), 34

Chapter -3 temperature (Sorensen and Ferrell, 1973), soil nutrients (Kliejunas and Ko, 1974; Mullin and Browdery, 1977; Mullin, 1978; Driessche, 1982) and pathogens (Dombois et al., 1983, Augspurger, 1984, Connell et al., 1984) has been studied on survival and growth of tree seedlings in other tropical, sub-tropical and temperate forests, but there is conspicuous lack of studies on population behaviour of tree seedlings of this region. The study of regeneration of forest trees has important implications for the management of natural forests, and is one of the thrust areas of forestry. Regeneration is the process of silvigenesis by which trees and forests survive over time. Research in this field contributes to planning, conservation and decision making in forest resources management programmes.

In the present study it was observed that among trees the most dominant species was Tectona grandis. The possible reason could be its high regeneration. In the present study it was recorded that sapling density of Tectona grandis was fairly good, whereas, the seedling regeneration of Tectona grandis was second most dominant but moderately high. The outcome is also supported by Umashankar (2001) and Mishra et al. (2003) where they suggested that density of trees are governed by their regeneration in the forest.

Among shrubs, density of Lantana camera was maximum and probably it is due to its fast growing and invasive nature. Lantana is considered to be world’s 100 most invasive species and abundant throughout the India particularly dry-moist forest (IUCN, 2002; Prasad, 2006). It is also true for PTR where Lantana is a major invasive species and abundant throughout the reserve. The finding of the present study is also supported by management plan and LULC map developed for the area (Anonymous, 2008, Ilyas and Haleem 2015).

While considering herbs, Marsilea quadrifolia was found to be most abundant species among herbs in PTR. Marsilea quadrifolia is an aquatic and amphibious plant with roots embedded in the soil, mud or in shallow pools (Strat, 2012). The plant prefers light (sandy) and medium (loamy) soils. It can grow in semi-shade (light woodland) or no shade and requires moist or wet soil and also grow in water .Furthermore its adaptation and survival level is considered to be very high therefore it is widely distributed throughout the India (Soni and Singh 2012). The PTR provides this type of habitat and probably due to this it is most abundant.

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Among grasses Eragrostis tenella was most abundant and distributed through entire reserve. The finding is supported by management plan of the PTR (Anonymous, 2008).

The overall vegetation cover follows similar trends during three different seasons of post monsoon, summer and winter. It was also observed that there was no anthropogenic disturbance from outside villages. Very few incidences of forest fire and NTFP collection including grasses were reported from the park area. A part from that, this PA is one of the best managed parks not only in Madhya Pradesh but also in India.

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Table-3.1 Mean density, diversity and richness of tree in different administrative beat

Mean Density± SE (ha.) S.No Beats Tree Density Tree Diversity Tree Richness

1 Tikadi 616.24± 21.21 1.62± 0.0675 1.95± 0.1174

2 Kumbhadev 512.73± 47.51 1.2318± 0.0811 1.49± 0.1247

3 Karmajhiri 664.01± 43.97 1.52± 0.0746 1.78± 0.5942

4 Byson Camp 471.33± 38.42 1.78± 0.0751 2.48± 0.1343

5 Baghdev 388.53± 37.15 1.47± 0.0738 1.94± 0.1097

6 Khamreeth 535.03± 37.15 1.60± 0.0884 2.09± 0.1397

7 Bodanala 539.80± 53.81 1.66± 0.0666 2.12± 0.1126

8 Satosha 493.63± 50.16 1.36± 0.0755 1.80± 0.1254

9 Sapat 364.64± 36.05 1.52± 0.0609 1.99± 0.5298

10 Chindimatta 363.05± 33.51 0.1119 1.67± 0.1665

11 Piyorthadi 538.21± 44.67 1.79± 0.0881 2.49± 0.1802

12 Alikatta 323.24± 18.08 1.50± 0.0618 1.94± 0.1116

13 Chikhlakhari 444.26± 37.21 1.41± 0.1122 1.90± 0.1631

14 Avarghani 439.49± 34.85 1.45± 0.0578 1.84± 0.0901

15 Turia 544.58± 33.94 1.90± 0.05802 2.73± 0.0963

16 Teliya 488.75± 10.86 1.42± 0.0845 1.66± 0.1067

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Table-3.2: Mean density, diversity and richness of seedling in different administrative beat

Mean Density± SE (ha.) S.No Beats Seedling Density Seedling Diversity Seedling Richness

1 Tikadi 222.92± 52.07 0.5389± 0.1054 0.6196± 0.1471

2 Kumbhadev 136.94± 28.86 0.3558± 0.0953 0.4263± 0.1306

3 Karmajhiri 221.33± 32.63 0.6419± 0.0815 0.7427± 0.1112

4 Byson Camp 592.35± 99.54 0.7722± 0.1008 0.7523± 0.1099

5 Baghdev 358.28± 52.83 0.9410± 0.1260 1.008± 0.1561

6 Khamreeth 230.89± 36.66 0.5378± 0.0837 0.5912± 0.0959

7 Bodanala 512.73± 44.61 1.04± 0.0689 0.8956± 0.0930

8 Satosha 312.10± 55.57 0.6537± 0.0824 0.5832± 0.0888

9 Sapat 265.92± 41.47 0.4709± 0.0739 0.4828± 0.0926

10 Chindimatta 326.43± 71.76 0.4007± 0.0956 0.3786± 0.0901

11 Piyorthadi 300.95± 57.27 0.6032± 0.0714 0.5671± 0.0703

12 Alikatta 304.14± 110.52 0.3483± 0.0895 0.3175± 0.0887

13 Chikhlakhari 363.05± 53.56 0.6021± 0.0798 0.5699± 0.0989

14 Avarghani 323.24± 38.12 0.7636± 0.0695 0.8792± 0.1023

15 Turia 292.99± 62.70 0.7494± 0.1165 0.8814± 0.1371

16 Teliya 415.60± 54.10 0.8898± 0.0952 0.8140± 0.0923

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Table-3.3: Mean density, diversity and richness of sapling in different administrative beat

Mean Density± SE (ha.) S.No Beats Sapling Density Sapling Diversity Sapling Richness

1 Tikadi 402.86± 103.82 0.7386± 0.0834 0.7120± 0.0905

2 Kumbhadev 167.19± 35.18 0.4105± 0.0933 0.4496± 0.1095

3 Karmajhiri 179.93± 26.87 0.3343± 0.0902 0.3711± 0.1074

4 Byson Camp 33.43± 11.19 0.4875± 0.1065 0.4984± 0.1204

5 Baghdev 162.42± 25.50 0.5390± 0.1027 0.5610± 0.114

6 Khamreeth 100.31± 17.48 0.4540± 0.0821 0.6450± 0.1241

7 Bodanala 130.57± 30.55 0.2661± 0.0691 0.2431± 0.0646

8 Satosha 114.64± 33.83 0.2231± 0.0925 0.2725± 0.1213

9 Sapat 3.18± 2.19 0 0

10 Chindimatta 7.96± 3.91 0 0

11 Piyorthadi 119.42± 24.97 0.2828± 0.0759 0.3499± 0.1003

12 Alikatta 55.73± 28.47 0.0346± 0.0346 0.0721± 0.0721

13 Chikhlakhari 130.57± 28.94 0.1671± 0.0590 0.1635± 0.0597

14 Avarghani 103.50± 25.19 0.3618± 0.0973 0.5656± 0.1593

15 Turia 95.54± 32.59 0.1581± 0.0710 0.1994± 0.0924

16 Teliya 162.42± 27.13 0.5014± 0.09802 0.5788± 0.1124

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Table-3.4: Mean density, diversity and richness of shrub in different administrative beat

Mean Density± SE (ha) S.No Beats Shrub Density Shrub Diversity Shrub Richness

1 Tikadi 184.71± 39.72 0.1755± 0.0641 0.2496± 0.1046

2 Kumbhadev 388.53± 86.77 0.1127± 0.0562 0.0954± 0.0447

3 Karmajhiri 261.14± 164.45 0.1012± 0.0581 0.0747± 0.0475

4 Byson Camp 277.07± 69.00 0.3252± 0.0837 0.3598± 0.0971

5 Baghdev 363.05± 106.56 0.2085± 0.0695 0.1492± 0.0483

6 Khamreeth 705.41± 114.15 0.0418± 0.0235 0.0476± 0.0260

7 Bodanala 128.98± 63.08 0.0685± 0.0434 0.0856± 0.0504

8 Satosha 138.53± 37.34 0.0719± 0.0407 0.0886± 0.0520

9 Sapat 23.88± 10.80 0 0

10 Chindimatta 7.96± 7.96 0 0

11 Piyorthadi 46.17± 19.11 0.0502± 0.0502 0.0514± 0.0514

12 Alikatta 824.84± 240.18 0.0116± 0.0116 0.0144± 0.0144

13 Chikhlakhari 82.80± 38.41 0.0904± 0.0496 0.0880± 0.0597

14 Avarghani 135.35± 30.72 0.2072± 0.0735 0.2754± 0.1085

15 Turia 681.52± 178.79 0.5044± 0.0952 0.3644± 0.0849

16 Teliya 144.90± 23.15 0.2279± 0.0751 0.2017± 0.0669

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Table-3.5: Mean density, diversity and richness of herbs in different administrative beat

Mean Density± SE (m2 ) S.No Beats Herb Density Herb Diversity Herb Richness

1 Tikadi 31.45± 3.85 0.5918± 0.07422 0.4509± 0.0521

2 Kumbhadev 44.10± 6.63 0.7718± 0.0766 0.6171± 0.0718

3 Karmajhiri 17.90± 5.07 0.4595± 0.0920 0.3385± 0.0721

4 Byson Camp 42.50± 6.49 0.7192± 0.0836 0.6946± 0.0747

5 Baghdev 68.70± 7.45 0.7879± 0.1050 0.7294± 0.1169

6 Khamreeth 17.05± 2.19 0.8138± 0.0921 0.7915± 0.1088

7 Bodanala 33.10± 5.21 0.9310± 0.0601 0.6961± 0.05886

8 Satosha 13.75± 1.85 0.6112± 0.0944 0.5077± 0.0693

9 Sapat 33.15± 4.44 0.5049± 0.0874 0.4476± 0.0614

10 Chindimatta 33.30± 2.93 0.7735± 0.0768 0.6477± 0.0508

11 Piyorthadi 26.90± 3.04 1.009± 0.1103 0.9334± 0.1153

12 Alikatta 29.80± 2.23 0.8945± 0.0828 0.8215± 0.08955

13 Chikhlakhari 26.30± 2.67 1.21± 0.0731 1.17± 0.0741

14 Avarghani 24.40± 3.19 1.23± 0.0818 1.25± 0.0944

15 Turia 19.60± 2.88 1.18± 0.1099 1.21± 0.1411

16 Teliya 30.60± 3.18 1.26± 0.0993 1.06± 0.0873

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Table-3.6: Mean density, diversity and richness of grasses in different administrative beat

Mean Density± SE (m2) S.No Beats Grass Density Grass Diversity Grass Richness

1 Tikadi 13.05± 1.57 0.3058± 0.08006 0.2683± 0.0723

2 Kumbhadev 41.95± 6.10 0.4390± 0.1120 0.3830± 0.0978

3 Karmajhiri 25.45± 5.76 0.5966± 0.09107 0.3754± 0.0656

4 Byson Camp 51.60± 6.23 0.6285± 0.0553 0.4097± 0.0363

5 Baghdev 38.80± 2.87 0.7295± 0.0945 0.5469± 0.0679

6 Khamreeth 50.10± 6.99 0.5439± 0.0787 0.4220± 0.0605

7 Bodanala 37.10± 2.93 0.7006± 0.09401 0.6295± 0.0822

8 Satosha 39.10± 5.52 0.4708± 0.0708 0.3876± 0.0445

9 Sapat 27.25± 2.66 0.5115± 0.0725 0.3589± 0.0471

10 Chindimatta 23.40± 2.42 0.7554± 0.0693 0.5988± 0.0582

11 Piyorthadi 22.70± 3.06 0.5468± 0.0879 0.3831± 0.0568

12 Alikatta 34.20± 4.75 0.6819± 0.0671 0.4994± 0.0659

13 Chikhlakhari 31.50± 2.97 0.6576± 0.0676 0.5387± 0.0567

14 Avarghani 38.80± 33.06 0.2234± 0.0591 0.2640± 0.0607

15 Turia 33.30± 3.90 0.6011± 0.1053 0.4704± 0.0692

16 Teliya 44.85± 5.23 0.4598± 0.0690 0.4032± 0.0524

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Table-3.7: Percent vegetation covers in different administrative beat

Mean Vegetation Cover ± SE (%) S.No Beats Tree Cover Shrub Cover Herb Cover Grass Cover

1 Tikadi 31.75± 2.64 12.65± 2.90 14.85± 2.49 24.50± 4.29

2 Kumbhadev 31.35± 4.34 11.95± 2.78 7.40± 1.01 13.90± 2.61

3 Karmajhiri 24.50± 2.37 4.85± 1.95 11.80± 1.68 38.25± 2.76

4 Byson Camp 21.45± 2.03 8.30± 1.67 13.90± 1.35 26.90± 2.81

5 Baghdev 25.25± 2.84 8.25± 2.79 22.00± 2.09 23.15± 2.13

6 Khamreeth 20.75± 3.32 20.30± 3.56 8.25± 1.28 33.25± 4.66

7 Bodanala 39.00± 2.77 4.35± 2.43 11.95± 1.82 23.40± 3.01

8 Satosha 33.40± 4.88 6.80± 3.57 5.35 ± 0.71 23.55± 5.35

9 Sapat 32.50± 3.70 0.60± 0.28 12.40± 1.51 15.00± 2.63

10 Chindimatta 40.00± 3.90 0.25± 0.25 13.45± 1.64 13.35± 2.02

11 Piyorthadi 37.75± 4.99 0.75± 0.35 11.00± 1.96 22.60± 5.86

12 Alikatta 24.40± 2.44 26.30± 6.21 17.65± 1.96 32.50± 2.91

13 Chikhlakhari 36.50± 3.51 2.70± 0.86 13.20± 1.48 20.35± 2.47

14 Avarghani 42.75± 4.44 4.10± 0.90 18.10± 1.62 35.00± 2.85

15 Turia 30.50± 4.00 15.90± 3.40 13.00± 1.81 29.65± 4.22

16 Teliya 43.75± 2.45 3.45± 0.60 15.75± 1.78 34.75± 4.28

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Table-3.8: Mean density, diversity and richness of tree in different administrative circle

Mean Density± SE (ha.) S.No Circle Tree Density Tree Diversity Tree Richness

1 Karmajhiri 566.08± 21.06 1.53± 0.0430 1.92± 0.0746

2 Baghdev 489.25± 23.20 1.52± 0.0397 1.99± 0.0617

3 Alikatta 397.29± 19.30 1.51± 0.0464 2.02± 0.0795

4 Turia 502.38± 19.21 1.55± 0.0461 2.03± 0.0740

Table-3.9: Mean density, diversity and richness of seedling in different administrative circle

Mean Density± SE (ha.) S.No Circle Seedling Density Seedling Diversity Seedling Richness

1 Karmajhiri 293.39± 35.57 0.5772± 0.0506 0.6352± 0.0633

2 Baghdev 353.50± 26.21 0.7962± 0.0512 0.7719± 0.0590

3 Alikatta 299.36± 36.75 0.4558± 0.0422 0.4348± 0.0436

4 Turia 348.72± 26.43 0.7512± 0.0465 0.7861± 0.0554

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Table-3.10: Mean density, diversity and richness of sapling in different administrative circle

Mean Density± SE (ha.) S.No Circle Sapling Density Sapling Diversity Sapling Richness

1 Karmajhiri 195.85± 31.55 0.4927± 0.0490 0.5078± 0.0546

2 Baghdev 126.99± 13.75 0.3706± 0.0453 0.4304± 0.0565

3 Alikatta 46.57± 10.73 0.0793± 0.0244 0.1055± 0.0343

4 Turia 123.00± 14.32 0.2971± 0.0438 0.3768± 0.0591

Table-3.11: Mean density, diversity and richness of shrub in different administrative circle

Mean Density± SE (ha.) S.No Circle Shrub Density Shrub Diversity Shrub Richness

1 Karmajhiri 277.86± 50.27 0.1786± 0.0341 0.1949± 0.0406

2 Baghdev 333.99± 49.82 0.0976± 0.0243 0.0927± 0.0226

3 Alikatta 225.71± 70.84 0.0154± 0.0128 0.0164± 0.0133

4 Turia 261.14± 53.40 0.2575± 0.0406 0.2324± 0.0411

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Table-3.12: Mean density, diversity and richness of herbs in different administrative circle

Mean Density± SE (m2) S.No Circle Herb Density Herb Diversity Herb Richness

1 Karmajhiri 33.98± 3.00 0.6356± 0.0424 0.5253± 0.0369

2 Baghdev 33.15± 3.38 0.7860± 0.0457 0.6812± 0.0465

3 Alikatta 30.78± 1.62 0.7956± 0.0490 0.7098± 0.0458

4 Turia 25.22± 1.53 1.22± 0.0454 1.17± 0.0508

Table-3.13: Mean density, diversity and richness of grasses in different administrative circle

Mean Density± SE (m2) S.No Circle Grass Density Grass Diversity Grass Richness

1 Karmajhiri 33.01± 3.08 0.4925± 0.0451 0.3591± 0.0355

2 Baghdev 41.27± 2.47 0.6112± 0.0434 0.4965± 0.0338

3 Alikatta 26.88± 1.72 0.6232± 0.0384 0.4601± 0.0301

4 Turia 37.11± 1.97 0.4855± 0.0423 0.4176± 0.0316

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Table- 3.14: Mean density/ha, diversity and richness of seedling during post monsoon season

Habitat Seedling Density Seedling Diversity Seedling Richness

Bamboo Forest 437.36 ± 104.49 0.258 ± 0.071 0.277 ± 0.077

Grassland 785.56 ± 131.86 0.398 ± 0.078 0.426 ± 0.085

Mixed Forest 348.36 ± 73.80 0.175 ± 0.047 0.210 ± 0.058

Teak Forest 411.88 ± 83 0.315 ± 0.085 0.361 ± 0.100

Teak Mixed Forest 1528.66 ± 212.54 0.760 ± 0.087 0.817 ± 0.098

Table-3.15: Mean density/ha, diversity and richness of sapling during post monsoon season

Habitat Sapling Density Sapling Diversity Sapling Richness

Bamboo Forest 836.51 ± 154.59 0.365 ± 0.085 0.380 ± 0.095

Grassland 768.57 ± 117.87 0.360 ± 0.071 0.392 ± 0.088

Mixed Forest 1016.50 ± 134.42 0.464 ± 0.062 0.539 ± 0.074

Teak Forest 276.00 ± 57.32 0.142 ± 0.060 0.160 ± 0.071

Teak Mixed Forest 751.59 ± 131.33 0.377 ± 0.072 0.437 ± 0.085

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Table-3.16: Mean density/ha, diversity and richness of shrub during post monsoon season

Habitat Shrub Density Shrub Diversity Shrub Richness

Bamboo Forest 1970.27 ± 233.19 0.319 ± 0.064 0.203 ± 0.040

Grassland 1651.80 ± 152.87 0.680 ± 0.072 0.508 ± 0.053

Mixed Forest 1398.67 ± 239.46 0.130 ± 0.041 0.102 ± 0.031

Teak Forest 955.41 ± 182.29 0.110 ± 0.046 0.086 ± 0.035

Teak Mixed Forest 1817.40 ± 255.86 0.313 ± 0.064 0.272 ± 0.058

Table-3.17: Mean density/m2, diversity and richness of herb during post monsoon season

Habitat Herb Density Herb Diversity Herb Richness

Bamboo Forest 25.36 ± 2.35 1.030 ± 0.103 0.953 ± 0.107

Grassland 21.16 ± 2.17 1.190 ± 0.090 1.033 ± 0.090

Mixed Forest 27.22 ± 1.95 0.971 ± 0.059 0.847 ± 0.058

Teak Forest 30.23 ± 1.95 1.178 ± 0.065 0.902 ± 0.058

Teak Mixed Forest 35.56 ± 2.55 1.597 ± 0.032 1.390 ± 0.043

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Table-3.18: Mean density/m2, diversity and richness of grass during post monsoon season

Habitat Grass Density Grass Diversity Grass Richness

Bamboo Forest 18.73 ± 1.86 0.331 ± 0.073 0.225 ± 0.050

Grassland 69.63 ± 3.46 1.038 ± 0.085 0.605 ± 0.053

Mixed Forest 38.75 ± 2.93 0.665 ± 0.040 0.463 ± 0.028

Teak Forest 23.06 ± 1.90 0.524 ± 0.074 0.356 ± 0.052

Teak Mixed Forest 42.73 ± 2.96 0.788 ± 0.070 0.557 ± 0.051

Table-3.19: Mean density/ha, diversity and richness of seedling during summer season Habitat Seedling Density Seedling Diversity Seedling Richness

Bamboo Forest 444.44 ± 46.26 0.350 ± 0.043 0.418 ± 0.053

Grassland 396.31 ± 56.62 0.231 ± 0.037 0.270 ± 0.043

Mixed Forest 376.55 ± 29.82 0.298 ± 0.029 0.359 ± 0.036

Teak Forest 297.23 ± 39.59 0.239 ± 0.040 0.298 ± 0.050

Teak Mixed Forest 852.08 ± 90.97 0.627 ± 0.047 0.745 ± 0.057

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Table-3.20: Mean density/ha, diversity and richness of sapling during summer season

Habitat Sapling Density Sapling Diversity Sapling Richness

Bamboo Forest 736.02 ± 65.68 0.489 ± 0.045 0.567 ± 0.054

Grassland 704.88 ± 65.88 0.472 ± 0.046 0.508 ± 0.053

Mixed Forest 679.40 ± 56.83 0.450 ± 0.034 0.555 ± 0.044

Teak Forest 424.62 ± 40.95 0.386 ± 0.049 0.470 ± 0.061

Teak Mixed Forest 689.31 ± 50.79 0.574 ± 0.050 0.656 ± 0.059

Table-3.21: Mean density/ha, diversity and richness of shrub during summer season

Habitat Shrub Density Shrub Diversity Shrub Richness

Bamboo Forest 2519.46 ± 149.19 0.570 ± 0.046 0.399 ± 0.033

Grassland 1230.00 ± 100.58 0.391 ± 0.041 0.330 ± 0.035

Mixed Forest 1478.18 ± 112.68 0.333 ± 0.033 0.274 ± 0.027

Teak Forest 1149.32 ± 103.15 0.383 ± 0.043 0.405 ± 0.082

Teak Mixed Forest 2298.65 ± 1427.62 0.506 ± 0.043 0.373 ± 0.030

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Table-3.22: Mean density/m2, diversity and richness of herb during summer season

Habitat Herb Density Herb Diversity Herb Richness

Bamboo Forest 128.36 ± 9.94 1.24 ± 0.051 1.04 ± 0.053

Grassland 137.76 ± 10.39 1.29 ± 0.039 1.02 ± 0.040

Mixed Forest 130.65 ± 7.50 1.35 ± 0.036 1.12 ± 0.036

Teak Forest 194.03 ± 16.00 1.29 ± 0.035 0.949 ± 0.037

Teak Mixed Forest 138.83 ± 9.47 1.57 ± 0.023 1.29 ± 0.028

Table-3.23: Mean density/m2, diversity and richness of grass during summer season

Habitat Grass Density Grass Diversity Grass Richness

Bamboo Forest 102.73 ± 8.28 0.570 ± 0.046 0.374 ± 0.033

Grassland 196.14 ± 12.07 0.992 ± 0.036 0.532 ± 0.024

Mixed Forest 152.84 ± 7.11 0.805 ± 0.024 0.488 ± 0.017

Teak Forest 158.80 ± 12.21 0.766 ± 0.046 0.452 ± 0.027

Teak Mixed Forest 131.44 ± 8.24 0.864 ± 0.032 0.547 ± 0.025

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Table-3.24: Mean density/ha, diversity and richness of seedling during winter season

Habitat Seedling Density Seedling Diversity Seedling Richness

Bamboo Forest 299.36 ± 38.87 0.250 ± 0.047 0.346 ± 0.067

Grassland 138.00 ± 28.77 0.114 ± 0.036 0.158 ± 0.052

Mixed Forest 379.84 ± 42.13 0.366 ± 0.039 0.482 ± 0.053

Teak Forest 237.79 ± 42.05 0.208 ± 0.047 0.269 ± 0.062

Teak Mixed Forest 377.91 ± 44.60 0.354 ± 0.054 0.409 ± 0.063

Table-3.25: Mean density/ha, diversity and richness of sapling during winter season

Habitat Sapling Density Sapling Diversity Sapling Richness

Bamboo Forest 677.28 ± 67.12 0.510 ± 0.055 0.565 ± 0.063

Grassland 764.33 ± 100.15 0.526 ± 0.063 0.614 ± 0.076

Mixed Forest 787.49 ± 60.52 0.598 ± 0.040 0.649 ± 0.045

Teak Forest 609.34 ± 54.76 0.681 ± 0.062 0.835 ± 0.078

Teak Mixed Forest 692.14 ± 57.63 0.667 ± 0.059 0.807 ± 0.071

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Table-3.26: Mean density/ha, diversity and richness of shrub during winter season

Habitat Shrub Density Shrub Diversity Shrub Richness

Bamboo Forest 2511.67 ± 162.27 0.646 ± 0.050 0.444 ± 0.036

Grassland 828.02 ± 108.43 0.199 ± 0.043 0.177 ± 0.038

Mixed Forest 1332.94 ± 124.66 0.343 ± 0.038 0.285 ± 0.032

Teak Forest 1118.89 ± 111.83 0.397 ± 0.048 0.323 ± 0.041

Teak Mixed Forest 1783.43 ± 165.70 0.417 ± 0.045 0.334 ± 0.037

Table-3.27: Mean density /m2, diversity and richness of herb during winter season

Habitat Herb Density Herb Diversity Herb Richness

Bamboo Forest 174.40 ± 12.51 1.21 ± 0.066 0.956 ± 0.061

Grassland 86.13 ± 4.95 1.20 ± 0.051 1.03 ± 0.058

Mixed Forest 196.32 ± 9.81 1.32 ± 0.032 1.00 ± 0.034

Teak Forest 191.40 ± 14.48 1.39 ± 0.045 1.25 ± 0.047

Teak Mixed Forest 140.33 ± 8.64 1.37 ± 0.041 1.17 ± 0.044

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Table-3.28: Mean density/m2, diversity and richness of grass during winter season

Habitat Grass Density Grass Diversity Grass Richness

Bamboo Forest 152.06 ± 10.13 0.534 ± 0.053 0.316 ± 0.034

Grassland 248.53 ± 10.48 0.896 ± 0.049 0.519 ± 0.033

Mixed Forest 214.18 ± 7.90 0.899 ± 0.031 0.501 ± 0.021

Teak Forest 149.80 ± 7.94 0.762 ± 0.052 0.494 ± 0.037

Teak Mixed Forest 154.66 ± 8.67 0.819 ± 0.038 0.482 ± 0.031

Table 3.29 Mean Density/ha ±SE, Diversity and Richness of trees in different habitat of PTR

Habitat Tree Density/ha Tree Diversity Tree Richness

Bamboo Forest 401.27 ± 24.91 1.23 ± 0.052 1.35 ± 0.068

Grassland 477.70 ± 29.14 1.46 ± 0.047 1.85 ± 0.073

Mixed Forest 535.03 ± 16.09 1.76 ± 0.029 2.34 ± 0.061

Teak Forest 576.43 ± 27.53 1.39 ± 0.036 1.59 ± 0.059

Teak Mixed Forest 572.18 ± 28.33 1.49 ± 0.049 1.84 ± 0.074

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Table 3.30 Density/ha ± SE of different species of trees in different habitat of Pench Tiger Reserve

S.No Density ±SE (ha) 1 Tree Species Teak Forest Grassland Teak Mixed Forest Mixed Forest Bamboo Forest Overall 2 Acacia catechu 0 1.99 ± 3.17 0 0.77 ± 1.24 0 0.63±0.55 3 Acacia leucophloca 0 0.90 ± 1.15 0 0 0 0.15±0.19 4 Adina cordifolia 0 0 0.16 ± 0.42 0 0.48 ± 1.02 0.09±0.16 5 Aegle marmelos 1.17 ± 2.23 0.36 ± 0.60 0 0.23 ± 0.38 0 0.33±0.40 6 Alangium salviifolium 0 0 0 0 0.97 ± 2.04 0.11±0.29 7 Albizia odoratissima 0 0 0 0 1.22 ± 1.33 0.15±0.19 8 Anogeissus latifolia 0.58 ± 1.36 3.07 ± 3.61 2.01 ± 3.14 3.17 ± 2.10 0 2.17±1.10 9 Bahunia barigata 2.34 ± 3.07 0.18 ± 0.43 1.00 ± 1.32 1.31 ± 1.08 2.20 ± 2.53 1.34±0.75 10 Bombax ceiba 0.19 ± 0.45 0 0 0.07 ± 0.22 0 0.06±0.10 11 Boswellia serrata 0 0 0 0.38 ± 0.72 0 0.15±0.24 12 Buchanania lanzan 0.97 ± 1.19 1.99 ± 2.17 5.53 ± 4.85 3.33±2.46 0.24 ± 0.51 2.76±1.27 13 Butea monosperma 0.19 ± 0.45 2.17 ± 1.82 0.50 ± 0.72 0.30 ± 0.69 0 0.63±0.42 14 Careya arborea 0.19 ± 0.45 0 0 0 0 0.03±0.07 15 Casearia tomentosa 0.19 ± 0.45 0 2.71 ± 2.24 0.15 ± 0.31 2.20 ± 2.63 0.74±0.56 16 Cassia fistula 3.71 ± 3.44 0 1.84 ± 3.46 1.23 ± 1.01 5.87 ± 3.38 2.08±1.01 17 Chloroxylon swietenia 1.75 ± 1.74 10.31 ± 8.33 0 0.30 ± 0.53 0 2.08±1.46 18 Cleistanthus collinus 0.19 ± 0.45 0.36 ± 0.82 0.16 ± 0.42 0.07 ± 0.22 0 0.06±0.14 19 Delbergia peniculata 0 0.72 ± 1.06 0 0.30 ± 0.69 0 0.27±0.30 20 Diospyros melanoxylon 10.15 ± 6.36 6.51 ± 4.45 14.91 ± 11.03 3.95 ± 2.77 5.38 ± 3.91 7.44±2.56 21 Diospyros montana 0 0 0 0 2.44 ± 3.87 0.30±0.56 22 Emblica offcinalis 0.78 ± 1.11 0.90 ± 2.19 2.51 ± 2.67 2.86 ± 2.15 0 1.82±0.95

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23 Ficus benghalensis 0 0 0.16 ± 0.42 0 0 0.03±0.07 24 Ficus glomerata 0 0 0 0 0.24 ± 0.51 0.03±0.07 25 Ficus hispida 0 0 0 0 0.97 ± 2.04 0.12±0.29 26 Ficus infectoria 0 0 0.16 ± 0.42 0.07 ± 0.22 0 0.03±0.07 27 Flacourtia indica 0 3.07 ± 2.30 0 7.43 ± 3.26 0 0.24±0.25 28 Gardinia latifolia 0 1.44 ± 2.39 0 0.30 ± 0.69 0 0.36±0.46 29 Garuga pinnata 0 0 0.16 ± 0.42 0 0 0.03±0.07 30 Grewia latifolia 2.34 ± 1.89 0.36 ± 0.61 1.00 ± 1.02 1.93 ± 1.78 2.93 ± 3.35 1.70±0.85 31 Gymnosporia spinosa 0 2.77 ± 3.14 0 0 0 0.36±0.53 32 Hymenodictyon excelsum 0 0.18 ± 0.43 0 0 0 0.06±0.10 33 Ixora arborea 0 11.03 ± 8.93 0 1.16 ± 1.43 0 2.26±1.59 34 Kydia calycina 0 0 1.50 ± 1.72 1.31 ± 1.65 0 0.77±0.63 35 Lagerstroemia parviflora 24.61 ± 14.04 3.43 ± 3.70 14.7 ± 9.51 11.39 ± 6.20 13.47 ± 9.66 12.82±3.83 36 Lenia coromandalica 3.52 ± 3.20 3.77 ± 2.81 2.51 ± 2.67 5.57 ± 3.04 0.97 ± 1.43 3.87±1.34 37 Madhuca indica 0.78 ± 1.11 0.54 ± 0.97 1.34 ± 1.31 0.85 ± 0.84 0.24 ± 0.51 0.80±0.44 38 Milosa tomentosa 7.03 ± 4.14 0 6.20 ± 3.96 0 11.75 ± 8.04 5.98±1.79 39 Mitragyna parvifolia 0.79 ± 1.50 0.90 ± 1.80 0.50 ± 0.72 0.61 ± 1.02 0.73 ± 1.13 0.71±0.55 40 Ougeinia oojeinensis 0.58 ± 1.01 0 0.33 ± 0.84 2.63 ± 2.50 0.97 ± 1.00 1.28±0.88 41 Pterocarpus marsupium 0.19 ± 0.45 0 0 0 0.24 ± 0.15 0.03±0.07 42 Randia dumetorum 0 0 0 0.07 ± 0.22 0 0.03±0.07 43 Schleichera oleosa 0.58 ± 0.78 0 1.34 ± 2.98 0 1.22 ± 1.52 0.48±0.57 44 Schrebera swietenioides 0 0 0 0.07 ± 0.22 0 0.03±0.07 45 Semecarpus anacardium 0.19 ± 0.45 0.36 ± 0.86 1.17 ± 1.25 1.31 ± 1.17 0 0.83±0.48 46 Soymida febrifuga 0 3.25 ± 2.85 0 0.23 ± 0.66 0 0.62±0.53

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47 Sterculia urens 0 0 0 0.07 ± 0.22 0 0.03±0.07 48 Syzygium cumini 0.19 ± 0.45 0 0.50 ± 0.94 0.07 ± 0.22 4.89 ± 3.58 0.74±0.56 49 Tectona grandis 36.14 ± 18.56 17.19 ± 12.03 22.12 ± 13.30 11.62 ± 7.87 31.60 ± 13.07 20.56±5.41 50 Terminalia arjuna 0 0 0 0.077 ± 0.22 0 0.03±0.07 51 Terminalia tomentosa 2.41 ± 2.64 2.35 ± 2.15 6.03 ± 5.13 3.56 ± 2.63 0.24 ± 0.51 3.21±1.38 52 Ziziphus xylopyra 1.75 ± 1.74 0.73 ± 1.06 0.33 ± 0.59 1.78 ± 1.42 0 1.13±0.59

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Table 3.31 Density/ha ± SE of different species of seedling in different habitat of PTR during Post monsoon season

Post Monsoon Season S.No. Seedling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall Density 1 Cassia fistula 8.93±9.70 0 13.84±12.25 29.01±16.64 34.74±23.48 19.34±7.21 2 Acacia catechu 0 0 0 3.78±5.13 0 1.41±1.75 3 Aegle marmelos 2.23±0.08 0 0 0 0 0.47±0.58 4 Anogeissus latifolia 0 0 0 2.52±3.42 0 0.94±1.17 5 Bahunia barigata 0 0 0 3.78±3.80 0 1.41±1.30 6 Buchanania lanzan 0 0 0 10.09±8.65 0 1.41±1.30 7 Butea monosperma 0 11.59±8.95 11.07±9.60 6.30±8.55 15.44±16.21 3.77±2.98 8 Careya arborea 0 0 0 0 0 7.54±4.27 9 Chloroxylon Swietenia 13.40±12.96 30.88±17.49 0 11.35±11.42 0 10.85±5.28 10 Cleistanthus collinus 0 7.72±8.10 0 0 0 0.94±1.17 11 Diospyros melanoxylon 24.58±12.78 50.18±23.24 49.84±23.78 50.45±23.32 27.04±18.18 41.99±10.20 12 Emblica offcinalis 11.17±12.66 0 8.30±7.60 10.09±6.28 0 7.54±3.47 13 Grewia latifolia 2.23±3.08 0 11.07±9.60 10.09±8.31 0 6.13±3.34 14 Ixora arborea 0 77.20±27.27 0 22.70±14.04 0 17.92±6.45 15 Kydia calycina 0 0 0 1.26±1.71 0 0.47±0.58 16 Lagerstroemia parviflora 51.40±21.49 0 27.69±12.90 58.01±18.59 27.02±13.97 40.57±8.23 17 Lenia coromandalica 13.40±11.37 0 0 13.87±7.25 0 8.02±3.31 18 Madhuca indica 2.23±3.08 0 0 8.82±8.51 0 3.77±2.98 19 Milosa tomentosa 26.81±14.42 7.72±5.64 30.46±22.05 15.13±8.52 7.72±5.64 16.98±5.48 20 Ougeinia oojeinensis 8.93±9.70 0 0 16.39±13.42 0 8.02±4.98 21 Syzygium cumini 33.52±20.88 34.74±20.39 44.30±19.77 16.39±13.24 115.80±38.77 32.55±8.74 22 Tectona grandis 42.46±19.84 15.44±9.66 44.30±19.77 40.36±18.01 54.04±23.31 36.80±8.46 23 Terminalia tomentosa 2.23±3.08 7.72±8.10 5.53±4.80 7.56±5.33 0 5.18±2.39 24 Ziziphus xylopyra 2.23±3.08 0 3.78±3.80 0 1.88±1.43

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Table 3.32 Density/ha ± SE of different species of seedling in different habitat of PTR during summer season

Summer Season S.No. Seedling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Cassia fistula 36.39±12.06 0 2.69±3.07 14.44±5.32 33.36±12.55 15.27±2.76 2 Aegle marmelos 9.09±6.05 0 0 4.18±3.10 0 5.95±1.82 3 Bahunia barigata 0 3.44±3.82 8.97±4.94 1.04±1.10 15.17±7.41 5.95±1.71 4 Buchanania lanzan 0 0 0 3.13±2.45 0 0.77±0.50 5 Butea monosperma 0 15.49±7.18 10.77±8.87 3.13±2.45 12.13±11.32 8.28±2.52 6 Careya arborea 0 0 0 0 1.51±1.80 0.26±0.25 7 Casearia tomentosa 0 0 30.50±27.01 1.04±1.10 0 9.09±4.97 8 Chloroxylon Swietenia 3.63±2.87 15.49±9.41 0 35.50±16.39 15.17±8.99 14.24±4.20 9 Diospyros melanoxylon 87.35±22.68 223.79±52.17 195.57±59.51 95.02±19.16 77.34±30.23 139.86±14.72 10 Emblica offcinalis 0 0 0 6.27±4.10 0 1.55±0.94 11 Ficus hispida 0 0 0 0 16.68±19.75 2.84±2.77 12 Flacourtia indica 0 0 8.97±8.23 0 0 2.51±1.58 13 Gardinia latifolia 0 0 1.79±2.76 0 0 0.51±0.50 14 Grewia latifolia 0 3.44±2.68 8.97±9.33 1.04±1.10 1.51±1.80 3.62±1.77 15 Ixora arborea 0 0 0 11.49±6.44 0 2.84±1.48 16 Kydia calycina 0 0 0.89±1.38 0 0 0.26±0.25 17 Lagerstroemia parviflora 25.47±20.93 1.72±1.91 7.94±11.36 55.34±16.60 31.85±14.20 28.22±5.46 18 Lenia coromandalica 0 0 1.79±1.94 4.09±2.20 0 1.55±0.79 19 Madhuca indica 0 0 1.79±1.95 5.22±4.52 0 1.81±1.09 20 Milosa tomentosa 32.70±18.08 3.58±2.73 8.35±4.62 0 8.26±2.69 21 Mitragyna parvifolia 0 5.16±3.26 0 1.04±1.10 0 1.03±0.50 22 Ougeinia oojeinensis 0 0 1.79±1.95 7.31±5.90 0 2.33±1.40 23 Randia dumetorum 0 1.72±1.91 57.42±34.76 0 0 16.82±6.44 24 Schleichera oleosa 0 1.72±1.91 0 0 3.03±3.60 0.77±0.56 25 Syzygium cumini 61.87±23.09 24.10±11.45 14.35±8.34 12.53±6.51 80.38±28.77 33.40±5.68 26 Tectona grandis 61.87±22.15 43.03±18.53 68.18±21.71 59.52±16.83 63.69±19.30 60.83±7.16 27 Terminalia tomentosa 0 8.60±4.97 3.13±2.45 0 2.07±0.87 28 Ziziphus xylopyra 0 0 0 3.13±3.30 0 0.77±0.75

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Table 3.33 Density/ha ± SE of different species of seedling in different habitat of PTR during winter season Winter Season S.No. Seedling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Acacia catechu 0 0 0 0 0 0.95±1.02 2 Aegle marmelos 0 0 16.48±11.29 6.60±4.61 0 7.01±2.78 3 Anogeissus latifolia 0 0 0 8.32±5.73 0 3.19±1.98 4 Bahunia barigata 0 0 7.49±5.87 7.49±3.50 0 4.77±1.68 5 Buchanania lanzan 0 3.53±2.74 0 3.33±3.12 0 1.59±1.12 6 Butea monosperma 0 0 8.99±6.61 0 10.47±7.45 3.83±1.79 7 Casearia tomentosa 0 0 1.49±1.78 0 0 0.31±0.34 8 Cassia fistula 0 0 2.99±3.57 11.87±5.02 20.94±8.61 11.78±2.96 9 Chloroxylon Swietenia 0 17.69±11.23 0 14.90±9.09 17.45±11.58 10.53±3.95 10 Diospyros melanoxylon 0 67.23±19.06 103.40±21.62 73.26±22.85 29.66±9.73 70.23±9.78 11 Emblica offcinalis 0 0 1.49±1.78 6.60±4.61 0 4.14±1.88 12 Ficus hispida 0 0 0 0 22.68±26.19 4.15±4.43 13 Flacourtia indica 0 0 2.99±3.57 0 0 0.63±0.68 14 Gardinia gummifera 0 7.07±5.48 0 0 0 0.63±0.68 15 Grewia latifolia 0 3.53±2.74 1.49±1.78 2.49±2.97 0 1.59±1.12 16 Ixora arborea 0 24.76±11.67 0 4.16±2.95 0 3.83±1.79 17 Lagerstroemia parviflora 0 21.23±11.47 34.46±13.53 44.96±12.98 34.90±12.26 38.95±6.03 18 Lenia coromandalica 0 0 2.99±3.57 0.83±0.99 0 1.27±0.83 19 Madhuca indica 0 0 0 3.33±2.79 0 1.91±1.17 20 Milosa tomentosa 0 0 14.15±5.54 0 4.78±1.68 21 Mitragyna parvifolia 0 10.61±4.61 0 2.49±2.20 0 1.91±0.96 22 Ougeinia oojeinensis 0 0 2.99±3.57 8.32±4.13 0 4.15±1.62 23 Randia dumetorum 0 3.53±2.74 13.48±8.43 0.83±0.99 0 3.51±1.69 24 Syzygium cumini 0 33.38±12.81 29.97±11.81 12.48±6.08 61.07±17.24 33.84±5.65 25 Tectona grandis 0 28.38±8.89 31.46±11.86 35.80±9.15 48.86±14.80 39.27±5.76 26 Terminalia tomentosa 0 3.53±2.74 0 7.49±2.20 0 3.19±1.43 27 Ziziphus xylopyra 0 0 0 1.66±1.39 0 0.63±0.48 60

Chapter -3

Table 3.34 Density/ha ± SE of different species of sapling in different habitat of PTR during post monsoon season

Post Monsoon Season S.No. Sapling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall Density 1 Acacia catechu 0 0 0 1.30±1.74 0 0.49±0.60 2 Aegle marmelos 2.23 ± 3.08 0 0 0 0 0.49±0.60 3 Buchanania lanzan 0 0 3.16±4.48 0 0 0.49±0.60 4 Butea monosperma 0 16.84±8.48 6.32±4.68 1.30±1.22 1.13±1.55 5.45±2.44 5 Cassia fistula 49.09±16.33 4.21±5.98 11.58±9.02 11.05±4.88 45.09±14.07 20.32±5.26 6 Chloroxylon Swietenia 11.94±8.62 35.79±15.78 19.50±9.20 21.42±14.57 10.40±4.39 7 Diospyros melanoxylon 41.13±17.27 195.82±50.73 172.76±48.91 42.25±30.23 15.78±10.80 44.11±8.81 8 Ficus hispida 0 0 0 2.60±2.74 9.02±8.99 3.46±1.78 9 Grewia latifolia 0 0 0 0.65±0.87 3.38±4.65 0.99±0.84 10 Lagerstroemia parviflora 61.04±19.05 24.21±11.89 34.74±14.72 158.59±29.88 46.22±22.54 45.60±7.59 11 Madhuca indica 0.65±0.87 0 0 3.38±4.65 0 0.99±0.84 12 Milosa tomentosa 0 0 2.11±2.99 1.95±1.94 0 2.97±1.89 13 Randia dumetorum 5.30±6.98 1.05±1.49 5.26±4.94 3.90±2.43 0 3.56±1.78 14 Syzygium cumini 39.80±16.70 11.58±7.95 21.06±8.05 43.55±24.81 169.10±58.29 29.74±9.48 15 Tectona grandis 70.32±17.69 91.59±27.14 74.75±25.02 126.09±38.70 120.62±32.09 56.50±10.66 16 Terminalia tomentosa 0 0 0 1.30±1.74 0 0.49±0.60

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Table 3.35 Density/ha ± SE of different species of sapling in different habitat of PTR during summer season

Summer Season S.No. Sapling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Acacia catechu 0 0 0 2.69±2.86 0 0.39±0.43 2 Aegle marmelos 5.66±3.95 0 1.81±2.78 1.07±1.12 0 4.13±1.46 3 Annona squamosa 0 5.26±6.15 0 0 1.18±0.93 4 Bahunia barigata 0 20.01±12.20 3.63±4.38 3.23±2.23 0 8.26±2.70 5 Buchanania lanzan 0 0 1.81±2.71 0 0 0.59±0.65 6 Butea monosperma 0 9.47±5.27 12.73±8.67 4.31±3.36 50.28±23.44 4.92±1.49 7 Cassia fistula 48.12±17.28 6.31±6.32 13.64±9.81 12.41±6.22 45.81±14.54 22.24±3.62 8 Chloroxylon Swietenia 13.21±10.13 25.26±13.54 30.76±13.15 24.58±17.36 18.31±4.05 9 Diospyros melanoxylon 41.51±17.27 178.97±51.63 111.91±27.46 43.72±12.14 6.70±4.81 90.71±11.63 10 Ficus hispida 0 0 0 2.69±3.28 3.35±4.62 2.36±1.45 11 Gardinia gummifera 0 2.10±2.99 0 0 0 0.39±0.43 12 Grewia latifolia 0 0 0 2.15±2.51 0 0.78±0.69 13 Gymnosporia spinosa 0 1.05±1.45 0 0 0 0.39±0.43 14 Ixora arborea 0 1.05±1.49 0 0 0 0.39±0.43 15 Lagerstroemia parviflora 59.44±16.66 0 48.22±15.09 106.30±19.7 4.46±4.32 76.19±9.30 16 Milosa tomentosa 0 0 2.72±3.10 7.01±3.77 0 0.98±0.65 17 Mitragyna parvifolia 0.94±1.14 12.63±10.27 0 3.23±3.55 0 4.92±2.28 18 Ougeinia oojeinensis 0 0 21.53±2.78 0.53±0.79 0 0.39±0.43 19 Randia dumetorum 0 1.05±1.49 1.81±1.96 5.93±4.11 0 3.15±1.31 20 Syzygium cumini 30.19±14.80 14.73±8.80 26.38±10.72 32.38±18.58 103.94±35.27 54.73±10.96 21 Tectona grandis 72.65±18.37 73.69±24.66 52.77±18.93 104.17±29.02 124.03±29.16 101.95±12.35 22 Terminalia tomentosa 0 2.10±2.99 0 2.69±2.86 0 0.39±0.43

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Table 3.36 Density/ha ± SE of different species of sapling in different habitat of PTR during winter season

Winter Season S.No. Sapling Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Acacia catechu 0 0 0 0 0 0.85±0.89 2 Aegle marmelos 2.15±3.03 0 0 0 0 1.53±0.82 3 Buchanania lanzan 0 0 0 1.63±1.94 0 0.34±0.49 4 Butea monosperma 0 16.76±9.64 11.58±9.81 1.63±1.94 3.35±3.77 5.98±2.19 5 Cassia fistula 43.18±19.63 3.35±3.77 20.26±13.23 4.89±3.32 33.52±13.99 23.25±4.85 6 Chloroxylon Swietenia 8.63±12.12 16.76±11.05 14.69±8.75 10.05±11.33 20.01±5.78 7 Diospyros melanoxylon 28.06±15.02 100.56±39.47 75.27±20.90 26.30±10.49 13.40±9.04 72.59±11.46 8 Grewia latifolia 0 0 0 3.26±2.73 0 0.68±0.78 9 Ixora arborea 0 13.40±7.24 0 4.89±4.32 0 0.17±0.24 10 Lagerstroemia parviflora 60.45±21.77 10.05±6.36 34.74±14.52 63.69±14.32 33.52±20.74 66.01±8.63 11 Lenia coromandalica 0 0 0 1.63±1.94 0 0.34±0.49 12 Madhuca indica 0 0 0 3.26±2.73 0 0.17±0.24 13 Milosa tomentosa 6.47±6.73 0 0 4.89±4.32 0 2.73±1.31 14 Randia dumetorum 0 0 0 1.63±1.94 0 0.34±0.49 15 Syzygium cumini 32.38±19.14 13.40±11.84 31.84±15.17 26.13±23.73 46.93±21.94 38.98±8.99 16 Tectona grandis 71.25±24.56 33.52±13.99 31.84±15.17 68.95±27.10 60.34±17.74 87.15±12.03

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Table 3.37 Overall Density/ha ± SE of different species of seedling and sapling in PTR

S.No Plant Species Seedling Sapling 1 Acacia catechu 0.66±0.49 0.54±0.40 2 Aegle marmelos 5.05±1.27 2.40±0.72 3 Alangium salviifolium 0 0.31±0.38 4 Annona squamosa 0 0.85±0.57 5 Anogeissus latifolia 1.32±0.71 0 6 Bahunia barigata 4.51±1.01 5.49±1.50 7 Bridelia retusa 0.33±0.25 0 8 Buchanania lanzan 1.76±0.74 0.46±0.36 9 Butea monosperma 6.59±1.55 5.49±1.14 10 Careya arborea 0.10±0.11 0 11 Casearia tomentosa 3.95±2.28 0 12 Cassia fistula 15.05±2.15 22.43±2.61 13 Chloroxylon swietenia 12.20±2.57 17.86±2.96 14 Cleistanthus collinus 0.22±0.23 0 15 Diospyros melanoxylon 93.09±7.88 75.25±6.96 16 Emblica offcinalis 3.85±1.03 0 17 Ficus hispida 2.63±1.97 1.54±0.81 18 Flacourtia indica 1.32±0.73 0.93±0.90 19 Gardinia gummifera 0.44±0.32 0.31±0.27 20 Gardinia latifolia 0.22±0.23 0.77±0.45 21 Grewia latifolia 3.51±1.11 0.77±0.45 22 Gymnosporia spinosa 0 0.23±0.21 23 Ixora arborea 6.70±1.57 0.62±0.30 64

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24 Kydia calycina 0.22±0.16 0.62±0.61 25 Lagerstroemia parviflora 34.84±3.62 66.90±5.45 26 Lenia coromandalica 2.96±0.80 0.07±0.09 27 Madhuca indica 2.31±0.87 0.23±0.16 28 Milosa tomentosa 9.12±1.74 2.08±0.66 29 Mitragyna parvifolia 1.09±0.39 3.40±1.29 30 Ougeinia oojeinensis 4.28±1.29 0.39±0.29 31 Randia dumetorum 8.35±3.01 2.40±0.79 32 Schleichera oleosa 0.33±0.25 0.08±0.09 33 Syzygium cumini 33.41±3.66 43.77±6.17 34 Tectona grandis 47.81±4.18 88.17±7.40 35 Terminalia arjuna 0 0.23±0.29 36 Terminalia tomentosa 3.18±0.78 0.54±0.40 37 Ziziphus xylopyra 0.99±0.47 0

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Table 3.38 Density/ha ± SE of different species of shrub in different habitat of PTR during post monsoon season

Post Monsoon Season S.No Shrub Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Hilectrux izora 49.21 ± 29.32 37.28 ± 21.62 51.69 ± 32.03 29.83 ± 12.03 0 68.15±18.71 2 Bamboo spp. 76.02 ± 56.17 24.85 ± 29.09 0 18.36 ± 15.34 762.60 ± 200.94 179.84±45.41 3 Pheonix aquilis 88.35 ± 46.68 65.24 ± 50.25 171.69 ± 67.74 130.83 ± 45.74 49.92 ± 35.29 107.04±22.81 4 Grewia hirsuta 119.16 ± 52.47 90.1043.33 66.46 ± 36.30 115.91 ± 34.89 118.78 ± 57.99 104.55±19.96 5 Grewia spp 0 0 0 4.59 ± 6.52 0 1.42±2.03 6 Holarrhena antidysenterica 16.43 ± 1153 3.10 ± 3.36 0 8.03 ± 8.74 0 5.70±3.43 7 Lantana camara 295.87 ± 93.32 633.83 ± 143.79 823.41 ± 206.05 521.03 ± 124.39 204.85 ± 84.52 487.78±63.77 8 Vitex negundo 0.043 ± 0.07 0 18.46 ± 20.07 0.07 ± 0.09 0.09 ± 0.14 3.56±3.67

Table 3.39 Density/ha ± SE of different species of shrub in different habitat of PTR during summer season

Summer Season S.No Shrub Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Hilectrux izora 20.03 ± 10.19 0 8.41 ± 8.58 4.79 ± 4.33 173.61 ± 58.32 61.28±12.66 2 Bamboo spp. 85.87 ± 36.19 9.43 ± 0.02 0 5.75 ± 4.45 780.11 ± 132.93 176.04±24.91 3 Pheonix aquilis 75.86 ± 25.75 30.66 ± 18.94 105.76 ± 32.08 6.71 ± 5.25 5.64 ± 6.37 76.43±10.16 4 Grewia hirsuta 57.25 ± 21.77 79.02 ± 23.78 6.01 ± 7.99 154.21 ± 32.58 127.39 ± 41.79 77.35±10.01 5 Grewia spp 35.78 ± 43.61 0 12.02 ± 9.24 107.28 ± 25.58 0 13.77±8.32 6 Holarrhena antidysenterica 0 0 873.69 ± 157.50 33.52 ± 25.89 6.76 ± 6.90 4.13±1.80 7 Lantana camara 412.22 ± 98.34 455.29 ± 94.88 46.87 ± 23.09 746.13 ± 131.02 48.48 ± 44.28 510.24±44.74 8 Vitex negundo 17.17 ± 9.01 8.25 ± 5.16 50.48 ± 33.74 25.86 ± 9.43 12.40 ± 7.92 20.42±4.42

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Table 3.40 Density/ha ± SE of different species of shrub in different habitat of PTR during winter season

Winter Season S.No Shrub Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Hilectrux izora 42.46±15.02 5.53 ± 4.40 14.28 ± 7.93 22.29 ± 8.52 158.29 ± 50.97 54.78±11.47 2 Bamboo spp. 90.40 ± 40.06 16.61 ± 13.21 0 16.61 ± 9.72 672.53 ± 130.28 185.33±31.18 3 Pheonix aquilis 58.90 ± 25.63 48 ± 29.48 129.76 ± 33.22 136 ± 27.73 71.15 ± 9.19 83.42±12.27 4 Grewia hirsuta 102.73 ± 26.72 62.77 ± 18.85 9.52 ± 8.95 113.85 ± 3643 137.69 ± 37.74 91.76±14.49 5 Grewia spp 0 0 0 6.36 ± 5.63 0 1.80±1.66 6 Holarrhena antidysenterica 0 0 0 1.59 ± 1.93 0 0.45±0.57 7 Lantana camara 371.20 ± 95.15 546.47 ± 101.14 755.99 ± 138.04 587.57 ± 110.71 93.66 ± 44.86 459.95±48.87 8 Vitex negundo 0.01 ± 0.02 0 0.02 ± 0.04 0.03 ± 0.03 0.03 ± 0.04 0.01 ± 0.04

Table 3.41 Overall Density/ha ± SE of different species of shrub in PTR

S.No Shrub Density 1 Hilectrux izora 60.45±7.80 2 Bamboo spp. 180.51±17.91 3 Pheonix aquilis 86.50±7.72 4 Grewia hirsuta 89.43±7.85 5 Grewia spp 6.72±3.70 6 Holarrhena antidysenterica 2.58±0.95 7 Lantana camara 485.57±29.05 8 Vitex negundo 8.53±2.06

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Table 3.42 Density/m2 ± SE of different species of herb in different habitat of PTR during post monsoon season

Post Monsoon Season S.No Herb Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Phyllanthus amarus 0.81 ± 0.50 0.9 ± 0.5 1.22 ± 0.81 0.29 ± 0.22 1.07 ± 0.76 0.76±0.23 2 Marsilea quadrifolia 1.77 ± 0.98 0.66 ± 0.52 0.75 ± 0.75 1.80 ± 0.83 2.68 ± 1.35 1.57±0.41 3 Casia tora 0.47 ± 0.75 0.46 ± 0.29 0.71 ± 0.06 2.39 ± 0.87 1.51 ± 0.87 1.45±0.35 4 Vallaris solanacea 0.15 ± 0.13 0.21 ± 0.21 0.43 ± 0.30 0.35 ± 0.17 0.29 ± 0.21 0.30±0.09 5 Tribulus terrestris 0 0 0.18 ± 0.20 0.04 ± 0.05 0.04 ± 0.19 0.07±0.05 6 Elephantopus scaber 0.74 ± 0.33 0.47 ± 0.27 0.97 ± 0.32 0.65 ± 0.45 0.15 ± 0.17 0.62±0.16 7 Guizotia abyssinica 0.87 ± 0.48 0.72 ± 0.39 1.47 ± 0.73 1.13 ± 0.47 0.58 ± 0.33 1.02±0.23 8 Sida acuta 1.91 ± 0.61 1.35 ± 0.62 1.62 ± 0.93 0.73 ± 0.40 1 ± 0.54 1.10±0.27 9 Desmodium spp. 0.021 ± 0.05 0 0.22 ± 0.25 0 0 0.04±0.04 10 Sida spp. 0 0.11 ± 0.14 0.42 ± 0.34 0.56 ± 0.28 0.18 ± 0.17 0.31±0.11 11 Spirodela polyrhiza 0.34 ± 0.32 0.28 ± 0.30 0.045 ± 0.11 0.03 ± 0.06 0.05 ± 0.12 0.13±0.08 12 Sida spp. 0.01 ± 0.56 1.34 ± 0.29 0.13 ± 0.1 0.55 ± 0.32 0.40 ± 0.31 0.83±0.22 13 Ocimum canum 0 0 0.33 ± 0.34 1.01 ± 0.55 0.04 ± 0.06 0.44±0.19 14 Parthenium hysterophorus 0.44 ± 0.9 0.02 ± 0.04 0.70 ± 0.44 0.33 ± 0.20 0.22 ± 0.18 0.36±0.13 15 Ocimum basilicum 0.21 ± 0.26 0.24 ± 0.24 0.07 ± 0.10 0.19 ± 0.19 0 0.15±0.08 16 Desmodium triflorum 0.86 ± 0.62 0.43 ± 0.26 0 0.97 ± 0.55 0.36 ± 0.38 0.83±0.25

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Table 3.43 Density/m2 ± SE of different species of herb in different habitat of PTR during summer season

Summer Season S.No Herb Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Phyllanthus amarus 0.49 ± 0.31 0.51 ± 0.26 1.40 ± 0.43 0.13 ± 0.09 0.17 ± 0.14 0.29±0.07 2 Marsilea quadrifolia 2.55 ± 1.19 0.61 ± 0.41 1.09 ± 0.49 1.17 ± 0.66 2.73 ± 1.14 1.64±0.30 3 Casia tora 2.64 ± 0.88 0.81 ± 0.22 0.81 ± 0.32 2.45 ± 0.51 1.39 ± 0.54 1.71±0.22 4 Vallaris solanacea 0.31 ± 0.24 0.35 ± 0.21 0.27 ± 0.14 0.25 ± 0.11 0.15 ± 0.09 0.26±0.05 5 Tribulus terrestris 0 0 0.46 ± 0.23 0.02 ± 0.03 0.19 ± 0.17 0.16±0.05 6 Elephantopus scaber 0.66 ± 0.30 0.68 ± 0.27 0.77 ± 0.21 0.52 ± 0.11 0.20 ± 0.10 0.56±0.08 7 Guizotia abyssinica 1.69 ± 0.07 1.10 ± 0.43 1.24 ± 0.45 0.78 ± 0.23 1.57 ± 0.59 1.22±0.17 8 Sida acuta 0.71 ± 0.42 0.86 ± 0.47 1.12 ± 0.73 0.36 ± 0.19 0.64 ± 0.34 0.70±0.15 9 Xanthium strumaxium 0.04 ± 0.11 0.02 ± 0.03 0.08 ± 0.11 0.01 ± 0.03 0.004 ± 0.007 0.03±0.02 10 Sida spp. 0.01 ± 0.02 0.003 ± 0.007 0.01 ± 0.02 0.12 ± 0.08 0.09 ± 0.08 0.05±0.02 11 Sida spp. 1.6 ± 0.69 1.96 ± 0.56 1.71 ± 0.44 1.32 ± 0.25 1.07 ± 0.36 1.27±0.16 12 Ocimum canum 0.05 ± 0.07 0.01 ± 0.02 0.21 ± 0.15 0.68 ± 0.16 0.08 ± 0.07 0.23±0.06 13 Parthenium hysterophorus 0.33 ± 0.28 0.47 ± 0.84 0.26 ± 0.23 0.17 ± 0.12 0.17 ± 0.16 0.26±0.08 14 Ocimum basilicum 0.08 ± 0.08 0.11 ± 0.12 0.09 ± 0.09 0.11 ± 0.07 0.07 ± 0.06 0.09±0.03 15 Desmodium triflorum 0.05 ± 0.06 0.03 ± 0.03 0.11 ± 0.11 0 0.23 ± 0.14 0.10±0.03

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Table 3.44 Density/m2 ± SE of different species of herb in different habitat of PTR during winter season

Winter Season S.No Herb Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Phyllanthus amarus 0.25 ± 0.13 0.37 ± 0.16 0.40 ± 0.21 0.12 ± 0.08 0.27 ± 0.18 0.33±0.08 2 Marsilea quadrifolia 3.72 ± 1.53 59 ± 0.32 0.79 ± 0.45 2.1 ± 0.75 3.39 ± 1.29 2.18±0.44 3 Casia tora 1.04 ± 0.38 0.12 ± 0.15 0.21 ± 0.19 0.53 ± 0.31 0.98 ± 0.41 1.14±0.20 4 Vallaris solanacea 0.11 ± 0.07 0.15 ± 0.09 0.28 ± 0.15 0.39 ± 0.15 0.27 ± 0.14 0.26±0.06 5 Tribulus terrestris 0 0 0.11 ± 0.08 0.04 ± 0.04 0.021 ± 0.034 0.03±0.02 6 Elephantopus scaber 0.30 ± 0.12 0.40 ± 0.16 0.70 ± 0.26 0.66 ± 0.22 0.22 ± 0.13 0.49±0.09 7 Guizotia abyssinica 1.28 ± 0.58 0.68 ± 0.26 1.33 ± 0.46 1.39 ± 0.43 1 ± 0.40 1.20±0.20 8 Sida acuta 0.46 ± 0.18 0.88 ± 0.29 1.88 ± 0.97 0.73 ± 0.31 1.02 ± 0.38 0.95±0.21 9 Desmodium spp. 0.008 ± 0.021 0 0.06 ± 0.06 0 0 0.01±0.01 10 Sida spp. 0 0.020 ± 0.041 0.01 ± 0.03 0.13 ± 0.07 0.20 ± 0.15 0.08±0.03 11 Spirodela polyrhiza 0.14 ± 0.17 0 0.01 ± 0.04 0 0.04 ± 0.07 0.05±0.03 12 Sida spp. 0.74 ± 0.27 0.07 ± 0.06 0.12 ± 0.08 0.11 ± 0.13 1.66 ± 0.58 1.04±0.18 13 Ocimum canum 0 0 0.17 ± 0.14 1.20 ± 0.42 0.07 ± 0.07 0.42±0.13 14 Parthenium hysterophorus 0.04 ± 0.06 0 0.86 ± 0.11 0.14 ± 0.09 0.20 ± 0.14 0.11±0.04 15 Ocimum basilicum 0.04 ± 0.06 0.004 ± 0.008 0.05 ± 0.07 0.06 ± 0.05 0 0.02±0.02 16 Desmodium triflorum 0.02 ± 0.03 0.08 ± 0.09 0.05 ± 0.06 0.02 ± 0.02 0.15 ± 0.32 0.02±0.01

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Table 3.45 Overall Density/m2 ± SE of different species of herb in PTR

S.No Herb Density 1 Phyllanthus amarus 0.41±0.06 2 Marsilea quadrifolia 1.83±0.22 3 Spirodela polyrhiza 0.05±0.02 4 Sida spp. 1.08±0.10 5 Casia tora 1.43±0.14 6 Vallaris solanacea 0.28±0.03 7 Parthenium hysterophorus 0.23±0.04 8 Tribulus terrestris 0.09±0.02 9 Elephantopus scaber 0.55±0.05 10 Guizotia abyssinica 1.17±0.11 11 Sida acuta 0.90±0.11 12 Desmodium spp. 0.02±0.01 13 Ocimum basilicum 0.08±0.02 14 Xanthium strumaxium 0.02±0.01 15 Sida spp. 0.12±0.02 16 Desmodium triflorum 0.20±0.05 17 Ocimum canum 0.35±0.06

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Table 3.46 Density/m2 ± SE of different species of grasses in different habitat of PTR during post monsoon season

Post Monsoon Season S.No Grasses Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Eragrostis tenella 8.16 ± 2.54 7.56 ± 2.80 9.06 ± 2.72 10.20 ± 2.11 14.01 ± 3.0 9.45±1.15 2 Imperata cylindrica 0 0.92 ± 1.01 0 0 0.04 ± 0.67 0.31±0.21 3 Pennisetum pedicellatum 6.71 ± 0.71 0.07 ± 0.14 0 0 0.51 ± 0.38 0.16±0.11 4 Cynodon dactylon 1.04 ± 0.66 0.65 ± 0.51 1.07 ± 0.63 1.0 ± 0.42 0.31 ± 0.27 0.87±0.23 5 Themeda quadrivalvis 0.46 ± 0.46 1.26 ± 0.84 0.51 ± 0.45 0.39 ± 0.24 0 0.57±0.21 6 Chloris barbata 0.14 ± 0.19 1.71 ± 1.02 0.14 ± 0.16 0.17 ± 0.17 0 0.47±0.21 7 Cyperus scariosus 0 0 0 0.06 ± 0.12 0 0.02±0.03 8 Dicanthium spp. 0.04 ± 1.24 2.15 ± 1.08 5.27 ± 0.81 3.3 ± .99 0.04 ± 1.37 3.43±0.57 9 Eulaliopsis binata 0.42 ± 0.36 1.04 ± 0.66 0.99 ± 0.59 0.37 ± 0.25 0.64 ± 0.48 0.66±0.20 10 Heteropogon contortus 3.78 ± 1.41 5.61 ± 2.17 1.21 ± 0.69 3.07 ± .93 0.46 ± 0.50 3.12±0.60 11 Apluda mutica 1.56 ± 2.78 0.73 ± 0.49 0.19 ± 0.26 0.61 ± 0.35 0 0.68±0.21

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Table 3.47 Density/m2 ± SE of different species of grasses in different habitat of PTR summer season

Summer Season S.No Grasses Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Eragrostis tenella 9.28 ± 1.85 9.90 ± 2.19 8.64 ± 1.83 9.27 ± 1.33 9.49 ± 1.60 9.33±0.63 2 Imperata cylindrica 0 0.64 ± 0.44 0.03 ± 0.06 0 0.40 ± 0.24 0.19±0.07 3 Pennisetum pedicellatum 0.37 ± 0.33 0.10 ± 0.18 0.02 ± 0.03 0 0.18 ± 0.21 0.10±0.06 4 Cynodon dactylon 0.07 ± 0.08 0.02 ± 0.02 0.35 ± 0.23 0.31 ± 0.14 0.19 ± 0.10 0.19±0.05 5 Themeda quadrivalvis 0.12 ± 0.13 1.08 ± 0.55 0 0.21 ± 0.15 0.23 ± 0.16 0.34±0.09 6 Chloris barbata 0.34 ± 0.23 1.29 ± 0.74 0.65 ± 0.30 0.15 ± 0.10 0.02 ± 0.02 0.49±0.13 7 Dicanthium spp. 2.74 ± 0.90 0.89 ± 0.51 3.74 ± 0.92 2.48 ± 0.56 2.27 ± 0.07 2.40±0.26 8 Eulaliopsis binata 0.26 ± 0.26 1.2 ± 0.52 0.06 ± 0.06 0.17 ± 0.13 0.23 ± 0.17 0.41±0.10 9 Heteropogon contortus 3.14 ± 0.90 7.30 ± 1.75 1.09 ± 0.35 3.54 ± 0.91 0.57 ± 0.30 3.40±0.39

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Table 3.48 Density/m2 ± SE of different species of grasses in different habitat of PTR winter season

Winter Season S.No Grasses Teak Forest Grassland Teak Mixed Mixed Bamboo Overall 1 Eragrostis tenella 8.55 ± 1.74 8.49 ± 1.92 7.53 ± 1.61 8.80 ± 1.34 12.19 ± 2 8.97±0.75 2 Imperata cylindrica 0 0.88 ± 0.62 0 0 0.56 ± 0.28 0.24±0.12 3 Pennisetum pedicellatum 0.40 ± 0.29 0 0 0 0.32 ± 0.23 0.11±0.06 4 Cynodon dactylon 0.28 ± 0.16 0.30 ± 0.20 0.26 ± 0.14 0.50 ± 0.19 0.33 ± 0.17 0.36±0.08 5 Themeda quadrivalvis 0.10 ± 0.11 0.60 ± 0.48 0.06 ± 0.06 0.41 ± 0.20 0 0.25±0.11 6 Chloris barbata 0.018 ± 0.03 1.80 ± 0.88 0.26 ± 0.18 0.12 ± 0.10 0.05 ± 0.05 0.43±0.16 7 Dicanthium spp. 1.27 ± 0.36 0.97 ± 0.32 5.16 ± 1.13 3.02 ± 0.63 3.62 ± 0.94 2.78±0.33 8 Eulaliopsis binata 0.06 ± 0.05 0.41 ± 0.25 0.17 ± 0.11 0.19 ± 0.11 0.35 ± 0.20 0.23±0.07 9 Heteropogon contortus 2.74 ± 0.72 7.20 ± 1.82 0.79 ± 0.33 4.67 ± 0.98 0.09 ± 0.09 3.57±0.49 10 Apluda mutica 0 0.027 ± 0.048 0.03 ± 0.05 0.05 ± 0.07 0 0.03±0.02

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Table 3.49 Overall Density/m2 ± SE of different species of grasses in PTR

S.No. Grasses Density 1 Eragrostis tenella 9.22±0.45 2 Imperata cylindrica 0.24±0.06 3 Pennisetum pedicellatum 0.12±0.04 4 Cynodon dactylon 0.42±0.06 5 Apluda mutica 0.17±0.04 6 Cyperus scariosus 0.02±0.01 7 Themeda quadrivalvis 0.36±0.07 8 Chloris barbata 0.47±0.09 9 Dicanthium spp. 2.79±0.20 10 Eulaliopsis binata 0.40±0.06 11 Heteropogon contortus 3.40±0.27

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45 40 35 30 25 Tree Cover 20 Shrub Cover 15 Herb Cover Grass Cover 10 5 0 Karmajhiri Baghdev Alikatta Turia

Figure-3.1: Percent vegetation covers in different administrative circle

140

120

100

80 Grass Cover Herb Cover 60 Shrub Cover 40 Tree Cover 20

0 Teak Teak Grassland Misleanous Bamboo Misleanous

Figure-3.2 Percent Vegetation covers in different habitats during post monsoon season

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120

100

80 Herb Cover 60 Grass Cover Shrub Cover 40 Tree Cover

20

0 Bamboo Grassland Mixed Forest Teak Forest Teak Mixed Forest Forest

Figure-3.3 Percent vegetation covers in different habitats during summer season

120

100

80 Herb Cover 60 Grass Cover Shrub Cover 40 Tree Cover

20

0 Bamboo Grassland Mixed Forest Teak Forest Teak Mixed Forest Forest

Figure-3.4 Percent vegetation covers in different habitats during winter season

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4500

4000

3500

3000

2500

2000 Grass Density Herb Density 1500 Shrub Density 1000 Sapling Density Seedling Density 500

0

Grassland Grassland Grassland Teak Forest Teak Forest Teak Forest Mixed Forest Mixed Forest Mixed Forest Bamboo Forest Bamboo Forest Bamboo Forest Teak Mixed Mixed Teak Forest Teak Mixed Mixed Teak Forest Teak Mixed Mixed Teak Forest Post monsoon Summer Winter

Figure-3.5 Mean, seeding density, sapling density, shrub density, herb density and grass density in different habitats in different seasons

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140

120

100

80

60 Herb Cover Grass Cover 40 Shrub Cover

20 Tree Cover

0

Grassland Grassland Grassland Teak Forest Teak Forest Teak Forest Mixed Forest Mixed Forest Mixed Forest Bamboo Forest Bamboo Forest Bamboo Forest Teak Mixed Mixed Teak Forest Teak Mixed Mixed Teak Forest Teak Mixed Mixed Teak Forest Post monsoon Summer Winter

Figure-3.6 Mean vegetation covers (%) in different habitats in different seasons

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Chapter 4- Status of Mammals in PTR

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CHAPTER 4- STATUS OF MAMMALS IN PTR

4.1 Introduction

National parks help to take care of places with natural and historical value. Unlike humans, who are extremely good at surviving in all sorts of conditions, some plants and animals find it very difficult, or impossible, to survive in areas where their habitat has been disturbed or changed. National parks preserve habitats for a wide range of native plants and wildlife. Parks maintain biodiversity and protect endangered species. They provide people with opportunities to learn about natural flora and fauna as well as to explore and admire the beauty of diverse environments. For conducting any study in any area, the most essential thing which an investigator has to do is reconnaissance or survey of the area that helps in familiarization of the area to collect the useful information. To achieve above said goal the core area of PTR which comes under Karmajhiri range as well as Indira Priyadarshani National Park was surveyed. The core zone of PTR is divided into 4 circles, each having 4 beats. While surveying the core area abundance of mammalian fauna in different administrative units (circles and beats) was recorded. On the basis of efforts which were made while surveying helps in the selection of intensive study area because it is not possible to cover all the area in a limited period of a time. For long term study selection of intensive study area is very important. The present chapter deals with the density of mammals found in different administrative units of PTR.

4.2 Methodology

4.2.1 Data collection The survey was done to assess the abundance and distribution of mammalian species in different administrative beats of the Karmajhiri range of PTR. Two line transects of two km each were laid and marked in each beat. For abundance of mammals in different beats indirect method was used. At every transect different sampling plots were marked at 200 meter interval and searched for faecal matter/droppings of different mammalian species, as a sign of species presence [Ilyas and Khan, (1998), (2005), Ilyas, (2001), (2006), (2007), Khan, (1993), Haleem et al. (2014a) & (2014b)]. A total of 320 sampling plots were established in the intensive study area and on each sampling plot faecal matter of different mammals were counted in 10 m

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radius circular plots. The faecal matters of different mammalian species were differentiated on the basis of shape, size and colour. 4.2.2 Data analysis Pellet groups/ faecal matter of different mammal’s species were counted at each sampling plot to assess the mammal’s density. Pellet groups/ faecal matter densities were calculated for each and every plot and on the basis of that the mean pellet group densities were calculated for different beats, circles and habitats of Karmajhiri range of PTR. One way analysis of variance was used to test for significant differences in mean density of mammal’s vis-à-vis administrative beats and circles. All statistical tests were performed, using computer programme SPSS 6.1 (Norusis, 1994).

4.3 Results

4.3.1 Mammals density in different administrative beats of PTR Mean pellet group density (Density/ha ± SE) of chital was found significantly

maximum (2003.18±159) in Sapat and minimum (95.54±39.82) in Teliya (F 15 304= 34.977, P<0.05). Mean pellet group density of sambar was recorded maximum (253.18±95.92) from Biosn camp, Chindimatta, and Chikhlakhari and minimum

(84.39±20.05) in Khamreeth with a significant result (F 15 304= 1.790, P<0.05). In the same way dung pile density of gaur was recorded maximum (12.74±4.85) from Tikadi and minimum (1.59±1.59) from Baghdev, Khamreeth, Piyorthadi and Turia and the

results were significant (F 15 304= 3.25, P<0.05). Similarly mean pellet group density of nilgai was found maximum (76.43±20.19) in Turia and minimum (1.59±1.59) in

Chikhlakhari showing a significant result (F 15 304= 4.076, P<0.05). For Wild boar mean faecal matter density was found maximum (25.48±9.41) in Sapat and minimum (1.59±1.59) in Khamreeth, Chikhlakhari and Teliya, however results were found to be

insignificant(F 15 304= 1.326, P>0.05). Mean droppings density of langur was found maximum (92.36±64.36) in Baghdev and minimum (1.59±1.59) in Piyorthadi with an insignificant result (F 15 304= 1.503, P>0.05). Likewise mean faecal matter density of porcupine was recorded maximum (15.92±15.92) in Tikadi and minimum (1.59±1.59)

in Bison camp and Alikatta and the results were found to be insignificant (F 15 304= 0.918, P>0.05). Mean faecal matter density of black naped hare was found significantly maximum (35.03±17.73) in Kumbhadev and minimum (3.18±3.18) in

Karmajhiri and Awarghani (F 15 304= 1.668, P<0.05). In the same pellet group of

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barking deer was only recorded from Baghdev and the mean pellet group density was

(0.32±0.32) showing an insignificant result (F 15 304= 1.0, P>0.05) (Table 4.1). 4.3.2 Mammals density in different administrative circles of PTR Mean pellet group density (Density/ha ± SE) of chital and sambar were found maximum (923.17±96.71, 228.1±15.3) in Alikatta, Turia and minimum (154.86±22.01, 146.07±17.72) in Turia and Baghdev respectively and the results were found to be significant (F 3 316= 34.881, P<0.05, (F 3 316= 3.106, P<0.05)). Similarly mean pellet group density of nilgai was significantly maximum (48.17±6.84) in

Alikatta and minimum (18.31±5.27) in Baghdev (F 3 316= 5.407, P<0.05). Mean dung pile density of gaur was found maximum (5.17±1.54) in Karmajhiri and minimum

(0.39±0.39) in Alikatta with a significant result (F 3 316= 5.191, P<0.05). Mean faecal matter density of wild boar and porcupine were maximum (11.54±3.41, 4.37±3.99) in Karmajhiri and minimum (2.38±1.23, 1.99±1.03) in Turia and Baghdev respectively,

however results were found to be insignificant (F 3 316= 1.252, P>0.05, F 3 316= 0.721, P>0.05).Similarly mean faecal matter density of langur and black naped hare were recorded maximum (29.06±16.44, 9.55±4.71) from Baghdev and Karmajhiri and minimum (9.55±3.12, 3.18±1.45) from Turia and Alikatta respectively with an insignificant results (F 3 316= 0.753, P>0.05, F 3 316= 1.043, P>0.05). Barking deer was only reported from Baghdev and the mean pellet group density was (0.08±0.08) and

the result was found to be insignificant (F 3 316= 1.0, P>0.05) (Table 4.2).

Based on the finding null hypothesis was rejected.

4.4 Discussion Methods to estimate population abundance are broadly classified as direct and indirect. Direct methods are based on surveys or counts of the animals (Focardi et al., 2002; Ward et al., 2004) and generally allow estimating their population structure in addition to abundance, whereas, indirect methods only give an estimate of the overall population abundance (Putman, 1984). Indirect methods for ungulates are frequently based on faecal pellet counts (Ilyas and Khan, 1998, Marques et al., 2001; Ilyas, 2001, Smart et al., 2004, Haleem et al. 2014a, 2014b). Pellet group/faecal matters are considered to be one of the best indicators for the species presence (Ilyas and Khan, 1998, 2005, Ilyas, 2001). To investigate the population abundance of mammalian species present in the tiger reserve, entire National park was surveyed using indirect evidences at administrative beat level and the data were further pooled up at different 82

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administrative circles level for further analysis. During survey it was found that cheetal was most abundant mammal species of the park and their abundance was maximum in Alikatta circle. This may be due to presence of extensive open grassy area in Alikatta (Anonymous 2008). It is further supported by study of Adhikari and Khadka (2009) and Kumar and Subudhi (2013). They also reported that cheetal prefer to live in grasslands. Sambar was more abundant in Turia circle followed by Karmjhairi, because of presence of more hilly terrain in both the circles, as sambar was reported to prefer hilly terrains (Biswas and Sankar, 2002; Kushwahaet. al. 2004; Ilyas, 2001; Trisurat et al, .2010; Simcharoen et al., 2014. Nilgai was found maximum in open area of Alikatta which is similar to the findings of Aryal (2007), Leslie (2008). They also reported nilgai in open areas. Another reason could be their feeding habit as Bhat et al. (2012) found nilgai to feed on grasses. In Alikatta grass abundance was quite high due to grassland management practice by the forest authorities (Anonymous, 2008), which may be another possible reason for Nilgai presence. In case of gaur it was observed that they were more abundant in Karmajhiri circle and within that they were sighted frequently in moist areas of Tikadi beat. Similar trends were also found in Bhadra wildlife sanctuary where gaur used to prefer moist areas of the sanctuary (Anonymous, 2005). The study shows that wild-bore prefer the area near the human habitation (Khan, 2015) and that is why their abundance is maximum in Karmajhiri and within that they were more frequent in Tikadi beat as it is very close to human habitation. Porcupines were reported maximum again from Karmajhiri due to presence of teak dominating forest as well as hilly terrain which the porcupine prefers (Akram, 2015). The studies shows that Langours are present and prefer all kind of habitat and this could be the reason they are abundant more or less in all circles except Turia. Absence in Turia may be due to avoidance of hilly terrain (Anonymous, 2008). The present finding was also supported by Tiwari and Mukhrajee (1992), Kumar et al. (2008), Groves and Molur (2008) and Mir et al. (2015). Black napped hare were reported maximum in Kumbhadev beat of Karmajhiri circles due to presence of favoured micro habitat features in these dense and low land hilly terrains of teak dominating forest, which has been observed by the Prater, (1965), Kirk and Racey, (1992), and Chakraborty et al. (2005) also, they state that hares generally live in dense forest as in hilly areas. Through our surveys of all the area, there was only one record of barking deer that is from Baghdev beat. Barking deer prefers the habitat with dense forest with hilly terrain near water (Ilyas 83

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2001). The overall results suggest that the maximum density and diversity of mammals were recorded from Karmajhiri range out of all four circles of Pench National Park and to conserve the mentioned mammalian species managers have to maintain the mosaic of open and close forest. The present chapter shows the results of the reconnaissance survey to assess the presence of different mammalian species in different part of the national park only. The finding is supporting the enough population of herbivores species which is a good number to support the charismatic carnivore species such as tiger, to maintain the ecology of the environment. Though this was a small effort and many other mammalian species were not assessed, the survey was done only in National Park area which is 292.85 sq km. while the PTR is covering 757.85 sq. km. surveying the whole tiger reserve was not possible because of paucity of funds. Therefore it is recommended to carry out a long term study to assess the population of large and small mammalian species in entire Pench tiger reserve as this data will give a clear picture to understand the population status and distribution of large and small mammals in Pench tiger reserve and also will explain the association among the mammalian community. Along with that it will also explain the predator prey relationship for the sound management of the tiger reserve.

After the rapid assessment of major mammalian community in Pench national park, the major herbivore ungulates species were selected for further detailed ecological studies in different habitats of national park.

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Table-4.1 Mean density (Density/ha) ± SE of different mammalian species in different administrative beats of PTR Mean Density ± SE S.No Beat Barking deer Black naped hare Chital Bison Langur Nilgai Porcupine Sambar Wildboar 1 Tikadi 0.00 0.00 651.27±81.44 12.74±4.85 7.96±3.16 36.62±9.03 15.92±15.92 230.89±36.08 23.89±10.56 2 Kumbhadev 0.00 35.03±17.73 135.35±33.07 3.18±2.19 23.89±15.48 4.78±3.48 0.00 181.53±31.94 6.37±4.96 3 Karmajhiri 0.00 3.18±3.18 219.74±64.19 4.78±2.61 7.96±5.60 31.85±14.43 0.00 184.71±37.65 4.78±3.48 4 Bison camp 0.00 0.00 657.64±151.55 0.00 71.66±42.97 9.55±4.68 1.59±1.59 253.18±95.92 11.15±5.79 5 Baghdev 0.32±0.32 0.00 121.02±36.50 1.59±1.59 92.36±64.36 17.52±7.11 0.00 210.19±54.30 9.55±5.22 6 Khamreeth 0.00 7.96±6.48 318.47±46.55 1.59±1.59 4.78±4.78 4.78±2.61 0.00 84.39±20.05 1.59±1.59 7 Bodanala 0.00 14.33±8.48 272.29±45.60 0.00 9.55±5.22 44.59±18.40 3.18±2.19 202.23±31.62 0.00 8 Satosha 0.00 4.78±2.61 334.39±55.81 0.00 9.55±4.68 6.37±3.73 4.78±3.48 87.58±10.56 22.29±22.29 9 Sapath 0.00 0.00 2003.18±159 0.00 36.62±20.96 47.77±10.71 4.78±2.61 132.17±24.26 25.48±9.41 10 Chindimatta 0.00 0.00 437.89±62.29 0.00 7.96±5.60 65.29±15.57 0.00 253.18±32.22 9.55±4.07 11 Piyorthadi 0.00 6.37±4.38 254.78±30.30 1.59±1.59 1.59±1.59 20.70±9.03 4.78±3.48 245.22±52.54 3.18±2.19 12 Alikatta 0.00 6.37±3.73 996.81±167.03 0.00 23.89±8.61 58.92±16.54 1.59±1.59 203.82±29.62 4.78±4.78 13 Chikhlakhari 0.00 0.00 105.09±34.51 3.18±2.19 11.15±6.23 1.59±1.59 0.00 253.18±38.42 1.59±1.59 14 Avarghani 0.00 3.18±3.18 176.75±63 3.18±2.19 0.00 20.70±7.41 0.00 232.48±30.4. 0.00 15 Turia 0.00 0.00 242.03±23.94 1.59±1.59 17.52±8.16 76.43±20.19 0.00 210.19±26.38 6.37±4.38 16 Teliya 0.00 11.15±4.78 95.54±39.82 0.00 9.55±6.97 55.73±16.96 0.00 216.56±27.20 1.59±1.59

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Table-4.2 Mean density (Density/ha) ± SE of different mammalian species in different administrative circles of PTR

Mean Density± SE S.No Species Karmajhiri Baghdev Alikatta Turia 1 Barking deer 0 0.08±0,08 0 0 2 Gaur 5.17±1.54 0.79±0.55 0.39±0.39 1.99±0.86 3 B.N. Hare 9.55±4.71 6.77±2.75 3.18±1.45 3.58±1.49 4 Chital 416.0±53.14 261.54±24.75 923.17±96.71 154.86±22.01 5 Langur 27.86±11.68 29.06±16.44 17.51±5.93 9.55±3.12 6 Nilgai 20.70±4.67 18.31±5.27 48.17±6.84 38.61±7.48 7 Porcupine 4.37±3.99 1.99±1.03 2.78±1.16 0 8 Sambar 212.57±28.10 146.07±17.72 208.59±18.59 228.1±15.3 9 Wild boar 11.54±3.41 8.36±5.71 10.75±2.99 2.38±1.23

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Figure-4.1 Map of Study area showing different surveying sites (Not to scale)

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Chapter 5- Abundance & Population Structure of Ungulates

Chapter -5

CHAPTER 5- ABUNDANCE & POPULATION STRUCTURE OF UNGULATES

5.1 Introduction: The management and conservation of wildlife are increasingly important concerns in a world of limited resources and increasing human population. To develop effective management and conservation for a given species, the most basic questions that often must be addressed first are ‘how many are there?’ and ‘is the population increasing or decreasing (Marques et al., 2013)? Furthermore, relationships between wild animals and their habitats provides basic datum of the greatest importance and estimating density is one of the strongest way of defining this relation. Population density is one of the simple and systematic ways of expressing relationship between animal population and their habitat. Shukla (1990) suggested that population study refers to the determination of total number of individuals of a species in an area and it is the most widely used parameter to ascertain the ecosystem. It is suggested that population density become more meaningful when expressed as number of individuals per km2 (Majumder, 2011). Additionally, density is an important index in making quantitative comparisons between the areas or ecosystems. The abundance ratio and percent contribution are also important quantitative measures in population studies. The former signifies the relative abundance of an animal species within a population whereas the latter renders an idea of contribution of a species to the total animal population and also to an extent the role of a species population in the community (Shukla, 1990). Animal population structure refers to the classification of age distribution and sex ratio of a species. Population structure is therefore regarded as an important requisite for the first hand information of reproductive potentiality. Apart from this, population helps in defining conservation status of a species (IUCN, 2003). Therefore estimating abundance is essential to implement management and conservation practices.

This chapter deals with the abundance, densities and population structure of ungulates in Pench Tiger Reserve.

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5.2 Methodology

5.2.1 Data collection

5.2.1.1 Line Transect

Direct estimation of ungulates densities in tropical forests is difficult mainly because of poor visibility and relatively low density of these populations, resulting in inadequate sample sizes for statistically precise result (Majumder, 2011). With the development of sophisticated statistical methods of sampling animal densities (Burham et al. 1980), this difficulty has been reduced to a large extent. In the present study Line transect method was used for assessing population structure of different ungulate species. Line transect method (Burham et al. 1980, Buckland et al. 1993) is one of the robust and scientifically accepted method used for estimating abundance of animals (Karanth and Sunquist 1992, Khan 1993, Ilyas 2001, Syed et al. 2010). Line transect sampling is practical, efficient and relatively inexpensive for estimation of many biological populations. One of the assumptions of the Line Transect Method is that objects on the line are seen with probability 1 and the probability decreases in some way as object are sighted away from the line. The way this probability decreases is known as the probability density function (PDF), (Burnham et al.1980) having a particular shape. Burnham et al. (1980) provided a monograph on line transect sampling theory and application. Their extensive work provided a review of previous methods and presented guidelines for field use and identified a small class of estimators that seemed generally useful (Majumder, 2011). Usefulness of line transect is based on four criteria: Model robustness, Pooling robustness, Shape criterion and Estimator efficiency. Line transect method involves collection of data on the existing forest trails and transects which will be marked and laid in the different study sites but it has to be ensured that distance between two transect should be at least 500m to avoid overlapping in the data. Selected forest trails and transects were monitored on daily basis in early morning and late evening to gather data on status of different wildlife fauna. For each detection, species, group size, group composition (age classes and sex, whenever possible) and perpendicular distance from the transect line were recorded. Sighting distances were measured using a laser rangefinder (LRM 1500, Newcon Optik), thus preventing a bias in the density estimation process due to heaping (Buckland et al. 1993). The best model was selected on basis of the lowest Akaike Information Criteria (AIC) (Burnham et al. 1980; Buckland et al. 1993). 89

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During this study identified habitat type was considered as sampling unit and transect were laid in each habitat of the intensive study area. A total of 15 line-transect (2 km each) were laid in 5 different identified habitat (each having 3) which were monitored 8 times (four time during dawn and four time during dusk) during summer (total effort = 2160 km) and winter seasons (total effort = 960 km) for investigating population structure and abundance of different ungulate species.

5.2.1.2- Indirect evidences

To assess the status and distribution of mammals during different seasons, the data was also collected through indirect method using faecal matter as a sign of species presence. Faecal matter is the best indicator for the presence of herbivore species in a particular habitat. (Ilyas and Khan, 1998, 2005, Ilyas, 2001, 2006, 2007, Khan, 1993, Haleem et al. 2014 a & b). Three line transects of two km was laid in each identified habitat and 10 m circular plots were marked at 200m interval resulting a total of 30 sampling plot in each habitat type. A total of 150 sampling plots were established in five different habitat types for long term study. Since Chowsingha pellets were not recorded on transects hence for Chowsingha random plots were searched in different seasons for presence of faecal matter in miscellaneous forest of Pench Mowgli Sanctuary. The elevation and the latitude & longitude location at each sampling plot were recorded through GPS (e-trex 10). On each sampling point; faecal matter of different ungulate species was counted in 10 m radius circular plots in each season. The faecal matter of different ungulates was differentiated on the basis of shape, size and colour. Aerospace Digital calliper (0-150mm) was used to measure the diameter of pellet groups

5.2.2 Data analysis

5.2.2.1- Line transect

Line transects data were analyzed using program DISTANCE 5.0 (Laake, 1993). The distribution of the data were firstly examined by assigning very small cut off points to the distance intervals during the curve fitting, to detect evidences of evasive movements by the animals or heaping of data were truncated at suitable distances from the line. After choosing convenient cut-off points for the distance intervals, the best key function (with the appropriate adjustment term, where necessary) was

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selected using the criterion of lowest AIC (Akaike Information Criteria). The AIC is computed as AIC= -2 loge (£) + 2q, where loge (£) is the log likelihood function evaluated at the maximum likelihood estimates of the model parameters and q is the number of parameters in the model. AIC thus chooses the model with the best fit with the least terms (i.e. the most parsimonious model). The model selection were carried out only after the truncation and distance intervals would be decided on since AIC cannot be used to choose between models that have different truncation distances (Buckland, 1993). Estimation of the variance associated with the mean density presented some difficulties since the theoretical variance estimated by program DISTANCE 5.0 is likely to be underestimated in biological populations. The underestimation in biological populations may become more acute with species that are highly clumped. Therefore, an over dispersion factor of 3, estimated to be a reasonable estimate for more biological populations (Buckland, 1993; Burnham, 1980) was applied.

5.2.2.2: Indirect evidences

Mean faecal matter density of different ungulates species was calculated for each and every sampling plot in each season and pooled together to calculate mean pellet group density in different habitats in different seasons. Two way ANOVA was used to test significant differences in mean faecal matter density in different habitats in different seasons using computer program SPSS 6.1 (Norusis, 1994).

5.2.2.3: Population structure

Living in a group can increase chances of foraging ability. The mean group size is a more sensitive measure of changes in group size due to the individuals remaining solitary or joining groups (Majumder, 2011). An understanding of the structure and organization of a population is important for its long term conservation as it gives us information not only on the internal balance between various components (age and sex) of the population of a species, it also explains how various components of a population interact with their environment (Pabla, 1996).The population structure of a species is generally expressed as the proportion (ratio) of different age and sex classes and the changes that take place in these ratios under the influence of various ecological and intra-population factors (Schaller 1967). In any large sample of a population the two sexes are originally conceived in equal numbers although this

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parity may not persist until births because of prenatal mortality (Leopold, 1933). Although some proportions of the sub adult and yearling population of some of the species are known to be capable of breeding (Graf and Nichols 1966; Schaller, 1967) and a certain proportion of adults may not be actually breeding because of malnutrition or old age, in this analysis breeding population has been taken as synonymous with adult population, as both these factors may neutralize each other to some extent (Pabla, 1996). Age classification of chital, sambar and gaur were followed by Schaller (1967), Sankar (1994) and Sankar et al. (2001). Age of female chital deer was categorized as follows; full grown > 30 kg as adults while <30 kg as sub-adults. The male chital deer were classified mainly based on height of antlers as follows; adult (> 1 feet antlers) and sub-adult (spike and < 1 feet antlers). Fawns were considered if the size was equal to the height of the mother's belly. Gaur was classified into; adult males (shiny black coat with heavy horns sweeping sideways and upwards), sub-adult males (dark brown coat with a conspicuous dorsal ridge and small dewlap hanging below the chin, large drapes between the fore legs), yearlings (10-20 months old), adult females (smaller than adult males, pelage is dark brown with more upright horns corrugated inwards than in adult males), sub-adult females (50 - 75 % size of adult female lacking a conspicuous white stocking), female yearlings (light brown coat which were 25 to 50 percent size of sub-adult females), small calves (light brown coloured coat, approximately < 3 months old of < 30 kg), medium calves (light brown coloured coat of approximately 30 to 100 kg) and large calves (dark brown coloured coat which were half the size of yearling females). Percentage of male and young ratio to 100 females was calculated from the group composition.

5.2.2.4: Group size

The two important features of ungulate population which throws light on the ecology and adaptations of the species are group size as well as group compositions (Majumder, 2011). Data on group size of different ungulate species with their composition were estimated following recommendation by Schaller (1967), Johnsingh (1983) and Karanth and Sunquist (1992). Mean group size was estimated by taking the average of different group sightings and group size was classified into different class intervals for better explanation between different seasons. Observations on ungulates from line transects was pooled for two different seasons (summer and

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winter ) of three years i.e. 2013, 2014 and 2015, to find out the population structure and grouping patterns of different ungulate species.

5.3 Results 5.3.1- Line transect The result revealed that overall density of cheetal was 35.73 individual per km2 where as its group density was 8.04 groups per km2. For sambar the overall density was 3.71 individual per km2 and their group density was 1.44 groups per km2. In case of Nilgai their individual density was found 1.29 individual per km2 where as its group density was 0.90 groups per km2. In the same way overall density of gaur was 1.68 individual per km2 however its group density was 0.99 groups per km2 (Figure 5.1).

The estimated densities of different ungulate species in winter and summer season are given below and summarized in Table 5.1 to Table 5.4 and Figure 5.2 to Figure 5.9.

5.3.1.1- Cheetal

In the intensive study area cheetal were most abundant species in winter as well as in summer season. Total number of observation in winter season before truncation was 155 in 2014, 231 in 2015 and 386 in pooled data and after truncating data up to 45 m the observations was 153 in 2014, 231 in 2015 and 380 in overall data set during winter seasons. Whereas in summer season the observations before truncation was 242 in 2013, 197 in 2014, 191 in 2015 and 630 in overall data and after truncating up to 40 m observations was 230, 197, 181 and 601 for 2013, 2014, 2015 and pooled data set of summer season respectively. Half-normal simple polynomial model was selected for winter season and Half-normal hermite polynomial model for summer season as best fit estimator. The effective strip width for winter season was (23.75±1.02) meter where as for summer season it was (17.94±0.57) meter. The estimated density of cheetal was (50.21 ± 8.03) km -2 in winter 2014, (63.50 ± 11.19) km -2 in winter 2015 and (31.48 ± 3.47) km -2 for overall winter season. However during summer season density was (96.25 ± 10.34) km -2 in 2013, (76.28 ± 11.19) km -2 in 2014, (79.19 ± 9.68) km -2 in 2015 and (39.99 ± 2.73) km -2 for overall summer season (Table 5.1, Figure 5.2 and Figure 5.3). Group densities in different seasons were also presented in Table 5.1.

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The density of cheetal was found maximum (24.01 ± 2.58) km -2 in Teak miscellaneous forest followed by grassland (18.22 ± 2.34), km -2, miscellaneous forest (13.64 ± 1.92) km -2, teak forest (13.06 ± 1.44) km -2and minimum (6.31 ± 0.79) km -2 in Bamboo forest (Table 5.5).

5.3.1.2- Sambar

The total number of sambar in winter season before truncation was 48 in 2014, 32 in 2015 and 80 in pooled data and after truncating data up to 50 m the observations was 48 in 2014, 32 in 2015 and 78 in overall data set during winter seasons. Where as in summer season the observations before truncation was 27 in 2013, 41 in 2014, 45 in 2015 and 113 in overall data and after truncating up to 45 m observations was 27, 41, 40 and 104 for 2013, 2014, 2015 and pooled data set of summer season respectively. Half-normal cosine model was selected for both winter and summer season as best fit estimator. The effective strip width for winter season was (23.66 ± 3.45) m where as for summer season it was (18.67 ± 2.34) m. The estimated density of sambar was (6.93 ± 1.69) km -2 in winter 2014, (4.27 ± 1.05) km -2 in winter 2015 and (3.36 ± 0.71) km -2 for overall winter season. However during summer season density was (10.21 ± 2.58) km -2 in 2013, (15.73 ± 4.88) km-2 in 2014, (8.53 ± 2.48) km-2 in 2015 and (4.06 ± 0.74) km -2 for overall summer season (Table 5.2, Figure 5.4 and Figure 5.5). Group densities of sambar in different seasons were also represented in Table 5.2.

The density of sambar was reported maximum (4.05 ± 0.85) km -2 in Teak forest followed by grassland (2.08 ± 0.54) km -2, miscellaneous forest (1.61 ± 0.47) km -2, bamboo forest (1.06 ± 0.27) km -2and minimum (0.84 ± 0.22) km -2 in Teak miscellaneous forest (Table 5.5).

5.3.1.3- Nilgai

For Nilgai total number of observation in winter season before truncation was 31 in 2014, 50 in 2015 and 81 in pooled data and after truncating data up to 45 m the observations was 31 in 2014, 50 in 2015 and 76 in overall data set during winter seasons. Where as in summer season the observations before truncation and after truncating up to 60 m observations were same and that was 21 in 2013, 39 in 2014, 31 in 2015 and 91 in overall data of summer season respectively. Hazard rate Simple

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polynomial was selected for winter and Uniform cosine was selected for summer season as best fit estimator. The effective strip width for winter season was (31.97 ± 10.66)m whereas for summer season it was (37.63 ± 3.06)m. The density of Nilgai was (2.90 ± 0.75) km -2 in winter 2014, (3.49 ± 0.63) km -2 in winter 2015 and (1.75 ± 0.62) km -2 for overall winter season. However during summer season density was (1.78 ± 0.66) km -2 in 2013, (2.02 ± 0.46) km -2 in 2014, (2.82 ± 0.84) km-2 in 2015 and (0.83 ± 0.12) km -2 for overall summer season (Table 5.3, Figure 5.6 and Figure 5.7). Group densities of Nilgai in different seasons were also represented in Table 5.3.

The estimated density of Nilgai was found maximum (1.25 ± 0.27) km -2 in Teak forest followed by Teak miscellaneous forest (0.55 ± 0.13) km -2, miscellaneous forest (0.43 ± 0.09) km -2, grassland (0.38 ± 0.10) km -2 and minimum in bamboo forest (0.24 ± 0.07) km -2 in (Table 5.5).

5.3.1.4- Gaur

In the intensive study area gaur was the largest ungulate species in winter as well as summer season. Total number of observation in winter season before truncation was 39 in 2014, 36 in 2015 and 75 in pooled data and after truncating data up to 50 m the observations was 39 in 2014, 36 in 2015 and 68 in overall data set during winter seasons. Where as in summer season the observations before truncation was 32 in 2013, 32 in 2014, 44 in 2015 and 108 in overall data and after truncating up to 55 m observations was 32, 32, 44 and 107 for 2013, 2014, 2015 and pooled data set of summer season respectively. Half-normal simple polynomial model was selected for winter season and Half-normal hermite polynomial model for summer season as best fit estimator. The effective strip width for winter season was (29.14 ± 3.06) m where as for summer season it was (31.77 ± 2.92) m. The density of gaur was (4.51 ± 1.18) km -2 in winter 2014, (3.34 ± 0.69) km -2 in winter 2015 and (1.82 ± 0.28) km -2 for overall winter season. Whereas during summer season the estimated density was (4.01 ± 1.16) km -2 in 2013, (2.16 ± 0.57) km -2 in 2014, (6.02 ± 2.27) km -2 in 2015 and (1.54 ± 0.30) km -2 for overall summer season (table 5.4, Figure 5.8 and Figure 5.9). The estimated group densities of gaur in different seasons were also summarized in table 5.4.

The density of gaur was maximum (1.70 ± 0.50) km -2 in Teak miscellaneous forest followed by grassland (1.47 ± 0.35) km -2, teak forest (0.62 ± 0.10) km -2, bamboo

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forest (0.27 ± 0.07) km -2 and minimum (0.22 ± 0.06) km -2 in miscellaneous forest (Table 5.5).

5.3.2- Indirect evidences

The result revealed that maximum size of pellet of chowsingha was found 6.54 mm. and minimum was 4.47mm. For cheetal maximum pellet size was recorded 10.78mm. whereas minimum was 8.20mm. Similarly for sambar maximum diameter of pellet was found 10.70mm however minimum was 9.32m.m. In case of Nilgai maximum diameters of pellets were recorded 15.28m.m. whereas minimum was 12.09m.m. (Appendix 3). Figure 5.10 shows mean size of pellets of different ungulate species of PTR. In the present study it was found that mean ±S.E. of pellets was found maximum for Nilgai (13.35±0.12), followed by sambar (9.91±0.07), cheetal (8.91±0.12) and minimum for chowsingha (5.56±0.11).

Mean pellet group density ± Standard deviation of cheetal was found maximum in Grassland during post monsoon, summer and winter season (167.72±112.49, 278.13±206.05, 192.67±128.52 respectively) and minimum in mixed forest during summer and winter (114.36±132.24, 115.80±143.31 respectively), however during post monsoon season minimum mean pellet group density was estimated from bamboo forest (91.29± 89.58). The result of two way ANOVA reveals that there is a significant difference in mean pellet group density of cheetal in different habitat in 2 different seasons [F 8 1043 = 3.602, ƞ (468070.3), P < 0.05]. Mean pellet group density of cheetal in bamboo forest was significantly different from grassland (P<0.05). In grassland their mean pellet group density was found different from all other habitat types with a significant result (P<0.05). Mean pellet group density of cheetal in mixed forest was found significantly different from grassland and teak mixed forest. Similarly for teak forest it was found that their mean pellet group density was dissimilar from grassland showing a significant result (P<0.05). For teak mixed forest it was reported that the mean pellet group density was significantly different from grassland and teak forest. It was also found that the mean pellet group density of cheetal in post monsoon and summer seasons was found significantly different with each other (P<0.05).

Mean pellet group density of sambar during post monsoon, summer and winter season were maximum (100.84±101.77, 89.87±88.36, 98.19±94.59) respectively in Teak

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forest, teak mixed forest and bamboo forest and minimum (30.78±37.85, 50.24±62.78, 53.07±65.20) in teak mixed, grassland and teak mixed forest respectively. Analysis of two way ANOVA shows significance differences in mean 2 pellet group density in different habitat in different seasons [F 8 1043 = 3.706, ƞ (166748.3), P < 0.05].Post hoc test shows that mean pellet group density of sambar in grassland and teak forest were found significantly different with each other. It also shows that mean pellet group density of sambar shows significant differences between post monsoon season and winter seasons.

Mean pellet group density of nilgai during post monsoon, summer and winter were maximum (72.18±61.98, 73.60±76.18, 95.54±81.03) in grassland and minimum (39.37±39.22, 52.72±65.83, 16.98±32.89) in mixed forest, bamboo forest and teak forest respectively. The analysis of two way ANOVA shows that there were significance differences in mean pellet group densities in different habitat in different 2 seasons [F 8 1043 = 4.527, ƞ (161424), P < 0.05]. The result also shows that mean pellet group density of nilgai in bamboo forest was different from grassland and grassland were different from bamboo forest and teak forest with a significant result.

Mean dung pile density of gaur during post monsoon, winter and summer were found maximum(37.15±38.40, 18.04±33.35, 23.35±33.69) respectively in grassland, teak mixed forest and grassland and minimum (18.04±26.02, 7.96±19.99, 11.61±20.71) respectively in bamboo forest, teak forest and mixed forest respectively. In case of gaur result obtain from two way ANOVA state that there were no significance differences in mean dung pile densities in different habitat in different seasons [F 8 2 1043 = 1.205, ƞ (6030.52), P > 0.05]. However result of post hoc shows that mean dung pile density of gaur in grassland was significantly different from mixed and teak forest. Similarly mean dung pile density of gaur in mixed forest and teak forest were different from grassland with a significant result. Post hoc test also shows that mean dung pile density of gaur in post monsoon season was significantly different from summer and winter season whereas summer and winter season was found different from post monsoon season with a significant result.

Chowsingha was only reported from mixed forest of Pench Mowgli sanctuary and their mean pellet group density was maximum (18.52±27.19) in post monsoon season and minimum (14.02±26.71) in summer season. The result of two way ANOVA,

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shows that there is no significant differences in mean pellet group densities in 2 different habitats in different seasons [F 8 1043 = 0.328, ƞ (72.42), P > 0.05]. Scheffe’s post hoc test shows that mean pellet group of chowsingha in mixed forest is different from other habitat types with a significant result (P<0.05).

5.3.3- Population structure

Distribution of age and sex of four ungulate species in the intensive study area for two different seasons (winter and summer) for three years (2013, 2014 and 2015) are given in table 5.7. During winter a total of 1472 cheetal, 165 sambar, 113 nilgai and 114gaur were observed whereas 3282 cheetal, 341 sambar, 133 nilgai and 213 gaur were observed in summer season.

Among cheetal adult female were observed maximum (%) in both winter (37.64%) and summer (35.31%) followed by adult male (21.12% in summer and 18.75 % in winter). Proportion of sub-adult male and sub-adult female were maximum (14.13% and 10.6%) in winter season and minimum (10.94% and 9.9%) in summer season respectively. Yearlings were observed maximum (22.73%) in summer and minimum (18.88%) in winter.

For sambar it was found that adult males and adult females were observed maximum (30.91% and 52.73%) in winter season and minimum (26.68% and 47.8%) in summer respectively. However in case of sub adult male and sub adult female result was contrary. They were observed maximum (4.11% and 6.16%) in summer and minimum (2.42% and 4.24%) in winter respectively. Observations of yearlings was maximum (15.25%) in summer and minimum (9.7%) in winter season.

In case of Nilgai observation of adult males and adult females were maximum (36.09% and 59.4%) in summer and minimum (28.32% and 59.29%) in winter. Sub adult female and yearlings were observed maximum (3.54% and 6.19%) in winter and minimum (1.5% and 3.01%) in summer respectively. Sub adult male was only observed in winter season and their proportion was 2.65%.

The result of Gaur reveals that adult males and adult females were observed maximum (24.56% and 59.65% respectively) in winter and minimum (20.66% and 53.05%) in summer respectively. In case of yearlings, they were observed maximum

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(26.29%) in summer and minimum (15.79%) in winter. Sub adult male and sub adult female of gaur were not observed during both seasons. The results of sex ratio were found biased towards females in different ungulate species (Table 5.8). For cheetal during winter season, the sex ratio (adult Female: adult Male) was 100:50 and adult female: Yearling ratio was 100:50, whereas in summer these ratios were 100:60 and 100:64. In case of sambar the adult sex ratio was 100:59 and female: yearling ratio was 100:18 in winter whereas in summer these ratios were 100:56 and 100:32 respectively. For nilgai it was found that the adult sex ratio (adult male: adult female) was 100:48 in winter and 100:60 in summer whereas adult Female: yearlings ratios were 100:10 for winter and 100:5 for summer respectively. For gaur during winter season adult sex ratio was 100:41and adult female: yearlings ratio was 100:26 however in summer season these ratio were 100:39 and 100:50 respectively Mean group size ± SE of cheetal in winter was 3.78± 0.13 and in summer was 5.15±0.12. In case of sambar, the mean group size in winter was found 2.08±0.11 and in summer was 3.15±0.18. For nilgai, the estimated mean group size in winter was. 1.42±0.07 and in summer was 1.46±0.07. Similarly in case of gaur, the mean group size in winter was 1.50±0.08, however in summer season it was 1.98±0.15 (Figure 5.11). The results revealed that distribution ungulates in PTR are not uniformly so the null hypothesis was rejected. 5.4 Discussion The methods of estimating size of ungulate population fall into two categories: direct and indirect. The choice of method depends upon a variety of factors such as behaviour of species, terrain, man power available and the accuracy required in the result (Ilyas, 2001). The accuracy of density estimates depends on how well the underlying assumptions were met (Majumder, 2011). There have been rapid advancements in the field of population estimation using direct method such as line transects or more appropriately distance sampling (Burham et al. 1980, Buckland et al. 1993). Line transects have been used widely to estimate populations of ungulates (Biswas and Sankar 2002; Bagchi et al. 2003; Harihar 2005; Ramesh 2010). In most studies different ungulate species estimates are derived for the entire protected area in concern (Karanth and Nichols 1998, 2000). During the present study the data was gathered using a laser rangefinder and compass to estimate the bearing and distance 99

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respectively to the animal group along the line transects for accurate density estimation. Detections near the line, as shown by the low chi-square values for the first distance interval, were as expected for each model for all different ungulate species. Since most species distributions are governed by various environmental variables, it is important to understand these sources of variation and estimate populations accordingly (Ramesh, 2010). Studies in PTR have used habitat of forest beats as the source of variation in animal distributions (Jhala et al. 2008). Thus stratifying the area based on different habitat types helps to arrive at better ecological estimates of ungulate densities. The study area is dominated by open canopy, mixed forest with considerable shrub cover interspersed with small open grassy patches. This condition of high habitat heterogeneity probably favoured the observed high density of browser and grazer (Eisenberg and Seidensticker, 1976) such as sambar and chital respectively. As there is no village inside the intensive study area, competition for food between domestic livestock and wild ungulates was not observed during the present study. This undisturbed availability of food resources may be attributed to the high abundance of wild ungulates in PTR. The most usual indirect method of assessing ungulate population is through faecal matter count. Pellet group or faecal matters are the best sign for the presence of species (Ilyas and Khan, 1998, Ilyas 2001). This methods has been extensively used for assessing densities [Cairrns and Telfer (1980), Forbs and Theberge (1993), Mitchell et al. (1983), Roeland et al. (1984), Ratcliffe (1987), Neff (1968), Stormer et al. (1977), Putman, (1981), Dinerstein and Dublin (1982), Ilyas (2001), Haleem et al. (2014a)].

Cheetal

Among all major ungulates chital by far were the most numerous of the ungulate species in PTR. This species dominates the landscape in Pench in terms of numbers and biomass. Except for the hill-tops, they are found almost everywhere in the Reserve, forming huge aggregations in the green grassy patches sprouting from the receding waters of the reservoir. The teak miscellaneous forest type, with moist deciduous vegetation and gaps in the canopy had lots of grass. It was the favoured areas for chital, demonstrated by their high densities in these forest types.

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However, indirect evidences suggested that population density of cheetal was more in grassland during summer season. In summers the food resources available in other habitat may become scarce therefore they were forced to move in grassland area. Similarly, Adhikari and Khadka (2009) in Khata Corridor Barda National Park also recorded that pellet group of cheetal were more abundant in grassland. Whereas Shekih et al. (2011) observed that in summer, the most preferred habitat was woodland followed by scrubland and grassland. Kumar and Subudhi, 2013 also observed high abundance of cheetal in grassland. Since the Cheetal exploit diverse habitats, such variations of resource utilization is quite normal.

Sambar

There was a difference in pooled densities of sambar between winter and summer. Though they favour dense forest patches as well as hilly terrain (Biswas and Sankar 2002, Kushwaha et al. 2004). Our study also shows similar trend. In present study distribution of sambar was maximum in teak forest because of its hilly terrain as well as dense forest.

During summer, most of the sambars were sighted around the water holes. About 132 artificial and 106 natural water holes were present in the tiger reserve (Anonymous, 2008) and during summer, park management supplement the water requirement of wild animals through filling the partially dried natural holes and digging of artificial water holes. Availability of water in these water holes attracts the animals and probably this was the reason that most of the sighting of sambar in summer was recorded around water holes.

The result obtained from indirect evidence suggests that sambar was more abundant in teak forest, probably due to their preference for hilly terrains. Similar trends for preference of hilly terrain were also recorded by Biswas and Sankar (2002), Kushwaha et al. (2004), Ilyas (2001), Trisurat et al., (2010), Simcharoen et al. (2014).

Nilgai

Nilgai is versatile forager and opportunistic feeder and feeds even in croplands (Biswas and Sankar, 2002). During the present study it was found that nilgai prefers lowland hills of teak forest, which is contrary to the findings of Aryal (2007) and

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Leslie (2008) who suggested that nilgai prefers open areas and grassland. Probably it may be due to the different methods used for population estimation. Furthermore difference in feeding sites, resting sites could also be reason for the same.

It is reported that Nilgai prefer to feed mostly on grasses when green grasses are available in large quantity (Bhat et al. 2012) Similar findings were recorded in the present study where pellet group density of nilgai were abundant in grassland. Leslie (2008) also observed similar results that nilgai prefer open grassland. Aryal (2007) also recorded that Blue bulls generally use open grassland

Gaur

Gaur, the largest of the wild ungulate is more of a forest dwelling species, but also needs open grassy patches for foraging (Schaller 1967; Eisenberg and Seidensticker 1976; Biswas and Sankar 2002). In the study area, they occurred in the areas along the water sources during summer, were they were sighted frequently and this could be the reason for high density of gaur in teak miscellaneous forest, because Pench river flows though teak miscellaneous forest type. They were also found close to roads during the early morning and evening during winter and summer which probably led to their low sightings on the line transects.

The results of indirect evidences suggested that mean dung pile density of gaur were high in grasslands. Gaur is known to feed on grasses (Easa 1998, Ahrestani et al. 2012), hence their presence in grassland is obvious. We further investigated the feeding ecology through micro-histology, which shows that the Gaur prefer the grasses over the browse

Chowsingha

Species presence/absence are commonly used in ecology and conservation management yet they can never be used to confirm that a species is absent from a given location (MacKenzie et al. 2002). According to Gu & Swihart 2004, failure to detect a species presence in an occupied habitat patch is common sampling problem when the population size is small, individuals are difficult to sample, or sampling effort is restricted. Detecting chowsingha is difficult because of its cryptic nature and small body size. Therefore we had to rely on indirect evidences i.e. pellet group count. The study revealed that the mean pellet group density of chowsingha was 15.8/ha.

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The recorded density was low in comparison to other studies (Berwick, 1974, Rice, 1990, Sharma et al., 2005, Biswas and Sankar, 2002). Chowsingha is a habitat generalist (Berwick, 1974; Sharma, 2006), however it mostly lives in dry deciduous miscellaneous forest with hilly terrain and avoids areas with human disturbance (Prater, 1980, Sharma et al., 2005). In Gir National Park, Chowsingha use dense hilly areas as a habitat (Khan et al., 1996) while in Panna National Park, India, it uses all the available habitats, except the human disturbed areas (Sharma et al., 2005). Our result reveals that chowsingha was only reported from miscellaneous forest of sanctuary and no direct as well as indirect evidences were recorded from National Park. The reason behind the complete absence of species from national park area need to be further investigated because the habitat preferred by the Chowsingha is also present in National park area. The national Park as well as sanctuary both the areas are far from the human disturbance and Sharma et al. 2005 also supported the same.

There are records of chowsingha sighting in National park area also and Habitat suitability index of chowsingha in forthcoming chapter also shows the presence of preferred habitat in the National park area. The complete absence of chowsingha from the National park could be due to increase inter-specific competition. The over abundant population of Chital could be one of the reason of the removal of chowsingha from NP?

Dynamics of population density of major ungulates

Comparing densities (± standard error or SE) of ungulates during the study period in winter (Table 5.1-5.4) it was evident that in case of chital, sambar and gaur densities differ in both the years (2013 and 2014). In case of the nilgai, there was a marginal change in density (± SE) / km²) of both the years. In summer cheetal, sambar and gaur densities changed over the period (2013 to 2015) but in case of nilgai density was found more or less same over study period (2013-2015). These changes in densities over the years might be because of clumpy distribution of the population. It is observed that distributions of ungulates in the study area were found to be of clumped type. Biological populations in natural habitat generally exhibit clumped type distribution pattern (Odum, 1996,). This suggests that the ungulates are not distributed equally in all areas. The difference in occurrence of the ungulates means that ungulate prefers different areas of the forest and Floodplain differently, which indicate the

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difference in their preference for different habitats. This means that they do not utilize all the habitats equally and each habitat is not equally favourable to the ungulates. Similar trends were also reported by Adhikari and Khadka, 2009. Sample sizes of gaur and nilgai found to be low comparatively to cheetal and sambar because of their low detection on the line transect over the study period. Therefore a long term ecological study is needed in PTR on these ungulate populations covering their recruitment pattern and growth rate.

Population structure of ungulate species

Living in a group can increase foraging ability. Schaller (1967) and Eisenberg and Lockhart (1972) reported that chital and sambar do not remain in permanent social groups. Group composition of chital was observed to change frequently during feeding periods (Dinerstein, 1980). Chital male: female sex ratio was 0.77: 1 in Hawaii (Graf and Nichols, 1966); 0.69:1in Corbett and 0.70: 1 in Kanha (Schaller 1967), 0.72: 1.0 in Nagarahole (Karanth and Sunquist, 1992) and 1: 0.2 in Gir (Khan, 1996). The average male: female: fawn ratio was 0.57: 1: 0.53 in Royal-Karnali Bardia (Dinerstein, 1980), 0.66: 1: 0.49 in Bandipur (Johnsingh, 1983), 0.47: 1: 0.22 in Sariska (Sankar, 1994), 0.50: 1: 0.27 in Pench (Acharya et al. 2007) and 0.61: 1: 0.15 in Mudumalai (Ramesh, 2010). The chital male: female ratio in PTR was 0.5:1 in winter and 0.6:1 in summer where as adult female: fawn was 1:0.2 in winter and 1:0.6 in summer which is more or less to similar of Majumder, 2011. Chital sex ratio (male: female) in PTR was skewed towards females and similar findings were reported from other studies (Graf and Nichols, 1966; Schaller, 1967; Dinerstein, 1980; Johnsingh, 1983; Karanth and Sunquist, 1992; Khan et al. 1996; Ramesh, 2010, Majumder, 2011). In PTR, the observed sambar male: female ratio was 0.59: 1 in winter and 0.56:1 in summer. The observed sambar low male ratio might be due to selective predation by tiger on male sambar as reported in other studies (Johnsingh, 1983; Schaller 1967; Karanth and Sunquist, 1992). In south Asian ungulates, solitary habits, proneness to injuries from intra-specific aggression, lack of antlers during rut, and dispersal behaviour have been considered as some of the factors which make males more vulnerable to selective predation (Johnsingh, 1983; Schaller, 1967; Karanth and Sunquist, 1992). Sambar male: female sex ratio of present study can be comparable with Gir, 0.5: 1 (Khan et al. 1996), Wilpattu 1.2: 1 (Eisenberg and Lockhart, 1972), Flordia, 0.73:1 (Flynn, 1990) and Ranthambore, 0.83: 1 (Bagchi et al. 2008). The

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Group composition of chital was observed to change frequently during feeding periods (Dinerstein, 1980). Smaller group sizes in forest habitats are presumably a consequence of food being more dispersed and scattered throughout the habitat (Jarman, 1974; Mishra, 1982; Johnsingh, 1983; Karanth and Sunquist 1992; Ramesh, 2010). Sartaj et al. (2010) reported that chital group size was more in open areas. Chital group size may vary from 1 to 150 individuals or more depending upon circumstances (De and Spillit, 1966; Schaller, 1967; Eisenberg and Lockhart, 1972; Krishnan, 1972; Balasubramaniam et al. 1980). H.S.A. Yahya in 1979 counted 350 cheetal gathering to rest during night in Periyar National park (Personal Communication). In the present study group size of cheetal were observed 3.78± 0.13 in winter and 5.15±0.12 in summers which can be comparable with various studies. Mishra (1982) reported that mean group size of cheetal was 7.5 in Chitawan National Park where as in Sariska, chital group size was 7.8 ± 8.3 (Sankar, 1994). Barrette (1991) reported that chital group in Wilpattu is 6. In sambar, group size was small, numbering fewer than 6 individuals (Jerdon, 1874; Schaller, 1967). The characteristic social unit in sambar is one hind and one fawn or one hind, one yearling and one fawn (Schaller, 1967; Kelton, 1981; Downes, 1983). Family groups usually travel in a single file led by the adult female (Kelton, 1981). In the present study the smaller

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group size of 1-5 individuals was recorded throughout the year also reported in Mudumalai (Ramesh, 2010). Eisenberg and Lockhart, (1972) commented that water holes are places where sambar populations come together in late evenings to form temporary aggregations before dispersing for food. Johnsingh (1983) also recorded large association of sambar near water holes and feeding sites in Bandipur. In case of gaur group size the findings of present study is comparable with Mudumalai (Ramesh, 2010) (group size was 1-42), an earlier study in Pench (Sankar et al. 2001) (group size 2 – 19), with Kanha (Schaller, 1967) (groups size 2 – 40) and Jaldapara (Bhattacharyya et al. 1997) (group size 1 – 70).

Population estimation was done for cheetal, sambar, nilgai and gaur in Pench Tiger Reserve. The study suggested that PTR in harbour very good population of ungulates. Cheetal was most abundant ungulate of the reserve. During the study chowsingha was also reported, however, there was no direct sighting. Chowsingha were recorded only from the mixed forest of sanctuary area. To estimate the population trends of the species under observation, pooled density was calculated and it seems like that cheetal, nilgai and gaur shows increasing trend, whereas sambar shows decreasing trends but this require long term monitoring and investigation. It was also recorded that sex ratio of these ungulate species found in PTR is female biased. During the study it was observed that the general condition of macro and micro habitat of the

PTR is better to support the good number of ungulates, however it is suggested to investigate the complete absence of chowsingha from the national park area despite of availability of suitable habitat.

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Table 5.1: Densities (Individuals Per km2) of Cheetal in Pench Tiger Reserve, Madhya Pradesh, during winter and summer (2013 to 2015)

Cheetal Winter Cheetal Summer Years/Seasons 2013 2014 2015 Pooled 2013 2014 2015 Pooled

Total effort NA 240 240 960 240 240 240 2160

Total Observations NA 155 231 386 242 197 191 630

Truncated at 45 40

Observation after Truncation NA 153 231 380 230 197 181 601

Density ± SE/ km² NA 50.21 ± 8.03 63.50 ± 11.19 31.48 ± 3.47 96.25 ± 10.34 76.28 ± 11.19 79.19 ± 9.68 39.99 ± 2.73

Group Density ± SE/ km² NA 15.18 ± 2.27 15.42 ± 2.62 8.33 ± 0.87 25.34 ± 2.51 19.12 ± 2.65 22.13 ± 2.39 7.75 ± 0.49

Mean Group Size ± SE 3.78 ± 0.13 5.15 ± 0.12

Effective Strip Width± SE 23.75 ± 1.02 17.94 ± 0.57 (m)

AIC value 872.35 1531.95

Model+ Adjustment term Half-normal Simple Polynomial Half-normal Hermite Polynomial

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Table 5.2: Densities (Individuals Per km2) of Sambar in Pench Tiger Reserve, Madhya Pradesh, during winter and summer (2013 to 2015) Sambar Winter Sambar Summer Years/Seasons 2013 2014 2015 Pooled 2013 2014 2015 Pooled

Total effort NA 272 240 1024 240 240 240 2160

Total Observations NA 48 32 80 27 41 45 113

Truncated at 50 45

Observation after Truncation NA 48 32 78 27 41 40 104

Density ± SE/ km² NA 6.93 ± 1.69 4.27 ± 1.05 3.36 ± 0.71 10.21 ± 2.58 15.73 ± 4.88 8.53 ± 2.48 4.06 ± 0.74

Group Density ± SE/ km² NA 2.46 ± 0.57 2.17 ± 0.51 1.60 ± 0.32 2.17 ± 0.48 3.43 ± 0.97 2.99 ± 0.82 1.28 ± 0.22

Mean Group Size ± SE 2.08 ± 0.11 3.15 ± 0.18

Effective Strip Width± SE (m) 23.66 ± 3.45 18.67 ± 2.34

AIC value 201.68 213.34

Model+ Adjustment term Half-normal Cosine Half-normal Cosine

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Table 5.3: Densities (Individuals Per km2) of Nilgai in Pench Tiger Reserve, Madhya Pradesh, during winter and summer (2013 to 2015) Nilgai Winter Nilgai Summer Years/Seasons 2013 2014 2015 Pooled 2013 2014 2015 Pooled

Total effort NA 240 240 960 224 240 240 2112

Total Observations NA 31 50 81 21 39 31 91

Truncated at 45 60

Observation after Truncation NA 31 50 76 21 39 31 91

Density ± SE/ km² NA 2.90 ± 0.75 3.49 ± 0.63 1.75 ± 0.62 1.78 ± 0.66 2.02 ± 0.46 2.82 ± 0.84 0.83 ± 0.12

Group Density ± SE/ km² NA 1.83 ± 0.45 2.46 ± 0.42 1.23 ± 0.43 1.19 ± 0.42 1.59 ± 0.35 1.56 ± 0.44 0.57 ± 0.08

Mean Group Size ± SE 1.42 ± 0.07 1.46 ± 0.07

Effective Strip Width± SE (m) 31.97 ± 10.66 37.63 ± 3.06

AIC value 193.25 181.09

Model+ Adjustment term Hazard rate Simple polynomial Uniform Cosine

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Table 5.4: Densities (Individuals Per km2) of Gaur in Pench Tiger Reserve, Madhya Pradesh, during winter and summer (2013 to 2015) Gaur Winter Gaur Summer Years/Seasons 2013 2014 2015 Pooled 2013 2014 2015 Pooled

Total effort NA 240 240 960 240 240 240 2160

Total Observations NA 39 36 75 32 32 44 108

Truncated at 50 55

Observation after Truncation NA 39 36 68 32 32 44 107

Density ± SE/ km² NA 4.51 ± 1.18 3.34 ± 0.69 1.82 ± 0.28 4.01 ± 1.16 2.16 ± 0.57 6.02 ± 2.27 1.54 ± 0.30

Group Density ± SE/ km² NA 2.81 ± 0.70 2.24 ± 0.43 1.21 ± 0.17 2.13 ± 0.55 1.59 ± 0.40 2.55 ± 0.92 0.77 ± 0.14

Mean Group Size ± SE 1.50 ± 0.08 1.98 ± 0.15

Effective Strip Width± SE (m) 29.14 ± 3.06 31.77 ± 2.92

AIC value 203.33 261.26

Model+ Adjustment term Half-normal Simple Polynomial Half-normal Hermite Polynomial

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Table 5.5: Density and Group density of different ungulate species in Pench Tiger Reserve, Madhya Pradesh, (2013 to 2015)

Habitat Cheetal (/ km²) Sambar (/ km²) Nilgai (/ km²) Gaur (/ km²)

Gp Density Density ± Gp Density ± Density ± Gp Density ± Density ± Gp Density ± Density ± SE ± SE SE SE SE SE SE SE

Bamboo Forest 6.31 ± 0.79 2.22 ± 0.23 1.06 ± 0.27 0.27 ± 0.06 0.24 ± 0.07 0.18 ± 0.05 0.27 ± 0.07 0.19 ± 0.04

Grassland 18.22 ± 2.34 3.82 ± 0.45 2.08 ± 0.54 0.50 ± 0.12 0.38 ± 0.10 0.26 ± 0.06 1.47 ± 0.35 0.97 ± 0.23

Miscellaneous Forest 13.64 ± 1.92 2.82 ± 0.36 1.61 ± 0.47 0.54 ± 0.14 0.43 ± 0.09 0.33 ± 0.07 0.22 ± 0.06 0.19 ± 0.05

Teak Forest 13.06 ± 1.44 3.92 ± 0.39 4.05 ± 0.85 1.22 ± 0.24 1.25 ± 0.27 0.77 ± 0.16 0.62 ± 0.10 0.48 ± 0.07

Teak Miscellaneous Forest 24.01 ± 2.58 5.72 ± 0.55 0.84 ± 0.22 0.33 ± 0.07 0.55 ± 0.13 0.30 ± 0.07 1.70 ± 0.50 0.72 ± 0.19

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Table 5.6: Seasonal variation in Density ± Standard deviation of different ungulate species in different habitats of Pench Tiger Reserve, Madhya Pradesh, (2013 to 2015)

Cheetal Habitat Post Monsoon Summer Winter Bamboo Forest (n=180) 91.29± 89.58 117.12±101.25 157.11±106.17 Grassland 167.72±112.49 278.13±206.05 192.67±128.52 Mixed (n = 180 in PNP & n = 144 in PMS 94.96±106.94 114.36±132.24 115.80±143.31 Teak Forest (n=180) 124.20±102.89 117.48±97.79 159.23±94.36 Teak Mixed Forest (n=180) 152.86±121.88 163.12±119.43 157.11±115.03 Sambar Post Monsoon Summer Winter Bamboo Forest (n=180) 45.64±51.34 69.70±83.63 98.19±94.59 Grassland 41.40±45.15 50.24±62.78 61.04±78.29 Mixed (n = 180 in PNP & n = 144 in PMS 64.85±66.56 65.89±69.94 82.51±82.67 Teak Forest (n=180) 100.84±101.77 72.54±65.28 80.67±78.55 Teak Mixed Forest (n=180) 30.78±37.85 89.87±88.36 53.07±65.20 Nilgai Post Monsoon Summer Winter Bamboo Forest (n=180) 46.70±49.99 52.72±65.83 68.47±72.48 Grassland 72.18±61.98 73.60±76.18 95.54±81.03 Mixed (n = 180 in PNP & n = 144 in PMS 39.37±39.22 66.09±74.43 72.95±69.73 Teak Forest (n=180) 64.75±66.63 62.98±68.84 16.98±32.89 Teak Mixed Forest (n=180) 56.26±59.54 68.64±68.83 47.23±54.53 Gaur Post Monsoon Summer Winter Bamboo Forest (n=180) 18.04±26.02 14.15±21.40 12.73±25.01 Grassland 37.15±38.40 23.35±33.69 13.26±25.75 Mixed (n = 180 in PNP & n = 144 in PMS 21.42±22.97 11.61±20.71 10.71±18.94 Teak Forest (n=180) 19.10±21.48 15.21±24.91 7.96±19.99 Teak Mixed Forest (n=180) 25.47±30.61 16.98±21.98 18.04±33.35 Chowsingha Post Monsoon Summer Winter Bamboo Forest (n=180) 0 0 0 Grassland 0 0 0 Mixed (n = 180 in PNP & n = 144 in PMS 18.52±27.19 14.02±26.71 14.76±26.12 Teak Forest (n=180) 0 0 0 Teak Mixed Forest (n=180) 0 0 0 PNP=Pench national Park; PMS=Pench Mowgli Sanctuary

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Table 5.7: Proportions of different age and sex classes among different ungulate species in Pench Tiger Reserve, Madhya Pradesh (2013 to 2015)

Winter Summer Species Obs AM AF SADM SADF Y Obs AM AF SADM SADF Y Cheetal 1472 18.75 37.64 14.13 10.6 18.88 3282 21.12 35.31 10.94 9.9 22.73 Sambar 165 30.91 52.73 2.42 4.24 9.7 341 26.68 47.8 4.11 6.16 15.25 Nilgai 113 28.32 59.29 2.65 3.54 6.19 133 36.09 59.4 0 1.5 3.01 Gaur 114 24.56 59.65 0 0 15.79 213 20.66 53.05 0 0 26.29 Obs= Total number of observations of particular species, AM= Adult Male, AF= Adult Female SADM= Sub-adult male, SADF= Sub adult female, Y= Yearlings

Table 5.8: Seasonal variation in age-sex ratios among different ungulate species in Pench Tiger Reserve, Madhya Pradesh (2013 to 2015).

Winter Summer Species Observations AF:AM AF:Y Observations AF:AM AF:Y Cheetal 1472 100:50 100:50 3282 100:60 100:64 Sambar 165 100:59 100:18 341 100:56 100:32 Nilgai 113 100:48 100:10 133 100:60 100:5 Gaur 114 100:41 100:26 213 100:39 100:50

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Figure 5.1: Overall Individual and Group density of ungulates of PTR

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Figure 5.2: Probability of cheetal detections from line transect sampling (winter)

Figure 5.3: Probability of cheetal detections from line transect sampling (summer)

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Figure 5.4: Probability of sambar detections from line transect sampling (winter)

Figure 5.5: Probability of sambar detections from line transect sampling (summer)

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Figure 5.6: Probability of Nilgai detections from line transect sampling (winter)

Figure 5.7: Probability of Nilgai detections from line transect sampling (sum

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Figure 5.8: Probability of Gaur detections from line transect sampling (winter)

Figure 5.9: Probability of Gaur detections from line transect sampling (summer)

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Figure 5.10: Mean size of pellets of different ungulate species in PTR

Figure 5.11: Seasonal variation in mean group size of different ungulate species in Pench Tiger Reserve (2013 to 2015).

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Chapter 6-Habitat Utilization and Habitat Suitability Index Modelling

Chapter – 6

CHAPTER 6 HABITAT UTILIZATION AND HABITAT SUITABILITY INDEX MODELLING

6.1 Habitat Utilization Pattern

6.1.1 Introduction

Habitat is a natural home of animals and supports animal populations primarily for space, food and shelter. Habitats are usually described in terms of salient physical and chemical features of the environment. Association of species within an environment is generally considered as communities and the status of an organism in its community, in terms of its relationships to food and enemies is referred to as its niche (Mc Farland, 1981). Plant communities depending upon the physical features of the environment provide a variety of possible habitats which animals are able to exploit for their survival. The potential of habitat utilization by animals, however also depends upon the intraspecific and interspecific competitive abilities. Ungulates being ground dwelling herbivores , depend entirely upon a major part of physical environment of vegetation which provide them not only cover or shade but also food including water and minerals (Jarman, 1974). The above features of vegetation vary in time and space and therefore the importance of these vegetational variations considerably affects the ungulate species. Consequently the ungulate responds to these features in the form of habitat preferences.

All the habitats of the world harbour variety of potential food resources. Each animal finds its own food resources by means of experience and/or adaptations in course of time and selects foods that allow its kind to mature and reproduce. On the basis of this selectivity each animal can be described and characterized in terms of its basic food selection (Shukla, 1990). The availability of desired food varies from place to place as well as season to season. Therefore, the utilization of habitat supporting wild animal population is greatly dependent upon seasonal availability of food, phenology of plants, seasonal movement of ungulates and consequently their distribution (Dinerstein, 1979 and Shukla, 1990).

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The peculiar arrangement of food, water and shelter for a particular species is referred to as habitat niche (Hanson, 1962). Thus each type of stand structure provides a particular set of species adapted to exploit the niche provided by the habitat for the purpose of feeding and reproduction (Shukla, 1990). The concept of edge effect was introduced by Leopold (1933), who pointed out that the wild animal may prefer to live on the edge of two habitat types utilizing both as and when crucial. Hirst (1975) studied the description of ungulates to structural gradients within the vegetational types. Hirst (1975) pointed out differences existing in the social structure of animal populations related to differences in cover density.

The information on specific habitat requirements of mammals is crucial for their conservation and management. Some of the major factors which govern the habitat use by mammals include aspect, slope, food availability, vegetation cover, terrain and cover against extremes of weather and other biotic pressures. This chapter deals and discusses with the habitat utilization pattern of different ungulate species of the PTR.

6.1.2 Methodology

6.1.2.1 Data collection .Vegetation ecology includes the investigation of species composition and sociological interaction of species in communities (Mueller-Dornbois and Ellenberg, 1974). The structural property of a community is the quantitative relationship between the species growing around. The quantitative study of vegetation is called phytosociology and its principal aim is to describe the vegetation, explain its pattern and classify it in a meaningful way (Ilorkar and Khatri, 2003). Vegetation composition generally indicates species diversity which governs the distribution of individuals among the species in a particular habitat (Sahu et al. 2007). For assessing habitat utilization pattern of different ungulate species, data was also collected on the vegetation composition (habitat parameters) such and density of tree, regenerating trees, shrubs, herbs and grasses in different seasons between 2013 to 2015.

Tree species density was determined by circular plot method. In this method, after selecting a point randomly a 10m radius circular plot was laid. The distance between two points was 200 meter. At each and every point all the tree species with their individuals were counted in 10 meter radius circular plot. On each sampling plot canopy cover/ tree cover was measured at four different points, using a mirror of

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25x25 c.m. which is divided into 100 equal grids squire. The mirror was kept horizontally at 1.25m above the ground level, and grid squares which covered more than 50% by tree foliage were counted. Percentage canopy cover for each sampling plot was calculated from these counts.

Along with these data regenerating tree species and shrub species with their individuals were also counted in 5m radius circular plots. .

The ground cover composition was assessed by laying four sampling quadrates of 0.50 x 0.50 m dimension in four different directions and counting grass and herbs species with their individuals. Shrub cover, grass cover and herb cover was also measured for each and every plot in different seasons by using ocular estimation.

6.1.2.2 Data Analysis The data analysis of indirect evidences such as faecal matters of different ungulate species was organized in simple habitat parameter matrix in order to investigate the habitat selection at macro level. Densities of all these variables were calculated by following formula:

Density/ ha= (Number of individual/ Area) × 10000

The species diversity and richness of trees, shrubs, herbs and grasses were calculated by using Shanon Weiner Index (H) and Margleef Richness Index (RI) for richness following the formula:

(H) = -∑(Pi*ln Pi)

Where, Pi is the proportion of ith species in the sample.

And (RI) = S-1/ ln (n).

Where, S is the no. of species in sample and n is the no. of total individuals.

Species diversity and richness were calculated by using modified version of “SPECDIVER BAS” (Ludwing and Reynolds, 1988), a module of software STASTICAL ECOLOGY written in BASIC.

To understand the habitat selection at micro level, Principal Component Analysis (PCA) was used to confounding of highly correlated variables. All the quantitative

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data in the data matrix were transformed using Log and Arcsine transformation to improve the normalcy in the data and then transformed data were standardized by calculating the mean and standard deviation of each column of data matrix (Zar, 1984).

mean s = standard deviation 푎 − Where a is the transformed value of each cell of data matrix

Factor analysis was used to reduce the dimensionality of different habitat variables. The first two factors were used for interpretation as these explained maximum variations in the data set. Before using PCA most of the auto-correlated variables were dropped. As habitat selection analysis concentrated on 30 variables around different sampling plots in different season, were recorded out of which different variables for different ungulate species in different seasons were used for PCA, and factor scores were saved. Utilized and available plots were plotted in two dimension space defined by PCI, and PCII. All the extracted factors with eigen values of more than one were saved and used for logistic regression analysis. In logistic regression, the principal component was then used as candidate variables in logistic regression model with forward stepwise entry.

6.1.3 Results

6.1.3.1- Factors affecting the selection of habitats by cheetal in different seasons In post monsoon season, PCA was performed on available and used plot for cheetal. During post monsoon season, 13 variables from 175 sampling plots of 30 variables were selected (Table 6.1.1). The first two principal components accounted for 30.22% of the variation on data set. The first factor was highly positively correlated with herb diversity (r = 0.83), herb density(r = 0.80) and tree diversity (r = 0.71).The second factor was highly positively correlated with shrub density(r = 0.79), shrub diversity(r = 0.75) and shrub cover(r = 0.68).The logistic regression model had an efficiency of 69.14% correct classification of available and used plots by cheetal during post monsoon season. According to this model sapling richness and tree diversity are most important component for the selection of habitat (Table 6.1.16). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization for cheetal during post monsoon season the distribution of

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available and utilized plots in relation to first and second component (Fig 6.1.1). The graph shows that during post monsoon season cheetal preferred the area with low to medium shrub density, shrub diversity and shrub richness and medium herb diversity, herb density and tree diversity.

During summer season, 519 sampling plots consisting 30 variables were monitored and from these 30 variables, 13 variables for further analysis were considered (Table 6.1.2). The first two principal components accounted for 35.06% of the variation on data set. The first factor was highly positively correlated with grass density(r = 0.85), grass diversity(r = 0.69), herb density(r = 0.61) and negatively correlated with litter(r = -0.79). The second factor was highly positively correlated with herb cover(r = 0.81), herb % (r = 0.72) herb diversity(r = 0.50) and sapling diversity(r = 0.41).The logistic regression model had an efficiency of 77.26% correct classification of available and used plots by cheetal during post monsoon season. According to this model grass density, herb cover, herb diversity, sapling diversity, seedling density, shrub diversity and tree diversity are most important component for the selection of habitat (Table 6.1.17). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For cheetal during summer season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.2). The graph shows that during summer season cheetal preferred the area with low to medium herb cover, herb %, herb diversity sapling diversity, grass density, grass diversity herb density with medium to high litter.

During winter season, out of 350 sampling plots of 30 variables, 10 variables were selected for further analysis (Table 6.1.3). The first two principal components accounted for 31.59% of the variation on data set. The first factor was highly positively correlated with shrub diversity (r = 0.84) and shrub density (r = 0.79). The second factor was highly positively correlated with herb diversity(r = 0.65), seedling diversity(r = 0.62) and herb density(r = 0.65).The logistic regression model had an efficiency of 79.71% correct classification of available and used plots by cheetal during post monsoon season. According to this model sapling density, seedling diversity, tree diversity and herb diversity are three most important components for the selection of habitat (Table 6.1.18). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For cheetal during winter season the distribution of available and utilized plots in

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relation to first and second component (Fig 6.1.3). The graph shows that during winter season cheetal preferred the area with medium herb diversity, seedling diversity and herb density and low to high shrub diversity and shrub density.

6.1.3.2 Factors affecting the selection of habitats by Chowsingha in different seasons For chowsingha during post monsoon season, 16 variables from 175 sampling plots of 30 variables were selected (Table 6.1.4). The first two principal components accounted for 30.22% of the variation on data set. The first factor was highly positively correlated with herb density (r = 0.78), herb diversity (r = 0.82), tree diversity (r = 0.63) and sapling density (r = 0.52). The second factor was highly positively correlated with tree density (r = 0.81), and tree cover (r = 0.54) and negatively correlated with distance from nearest human habitation (r = -0.78) and distance from nearest water hole (r = -0.62). The logistic regression model had an efficiency of 97.71% correct classification of available and used plots by cheetal during post monsoon season. According to this model tree density is most important components for the selection of habitat (Table 6.1.19). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During post monsoon season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.4). The graph shows that during post monsoon season chowsingha preferred the area with low tree density and tree cover and avoiding human habitations and water holes and on the other hand they also prefer the area with medium herb density, herb diversity, tree diversity and sapling density.

During summer season, 11 variables from 519 sampling plots of 30 variables were selected (Table 6.1.5). The first two principal components accounted for 38.89% of the variation on data set. The first factor was highly positively correlated with herb density (r = 0.81), grass density (r = 0.70), herb diversity (r = 0.69) and herb cover (r = 0.68). The second factor was highly positively correlated with bear ground (r= 0.82) and negatively correlated with litter (r = -0.73) The logistic regression model had an efficiency of 93.26% correct classification of available and used plots by cheetal during post monsoon season. According to this model grass density herb density and distance from nearest human habitation are the most important components for the selection of habitat (Table 6.1.20). The scored were saved to correlate these two PCs

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to find out the relation of habitat parameters with the availability and utilization. During summer season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.5). The graph shows that during summer season chowsingha preferred the area with medium herb density, grass density, herb diversity, herb cover and bear ground and on the other hand avoiding litters.

During winter season, 12 variables from 350 sampling plots of 30 variables were selected (Table 6.1.6). The first two principal components accounted for 31.05% of the variation on data set. The first factor was highly positively correlated with shrub density (r = 0.82) and shrub diversity (r = 0.73) and shrub cover (r = 0.72). The second factor was highly positively correlated with seedling density (r = 0.84), seedling diversity(r = 0.86) and distance from nearest human habitation (r = 0.50).The logistic regression model had an efficiency of 94.29% correct classification of available and used plots by cheetal during post monsoon season. According to this model sapling density, shrub cover, seedling diversity, tree density, herb density and distance from nearest human habitation are the most important components for the selection of habitat (Table 6.1.21). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For chowsingha during winter season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.6). The graph shows that during winter season chowsingha preferred the area with medium to high seedling density, seedling diversity and human habitation and low to medium shrub density, shrub diversity and shrub cover.

6.1.3.3- Factors affecting the selection of habitats by Sambar in different seasons For sambar during post monsoon season, 15 variables from 175 sampling plots of 30 variables were selected (Table 6.1.7). The first two principal components accounted for 26.52% of the variation on data set. The first factor was highly positively correlated with herb diversity (r = 0.84), herb density (r = 0.79) and tree diversity (r = 0.70). The second factor was highly positively correlated with grass diversity (r = 0.88), and grass density (r = 0.86). The logistic regression model had an efficiency of 97.40% correct classification of available and used plots by sambar during post monsoon season. According to this model tree density is most important components for the selection of habitat (Table 6.1.22). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and

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utilization. During post monsoon season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.7). The graph shows that during post monsoon season sambar preferred the area with low to medium grass diversity and grass density and medium to high herb diversity, herb density and tree diversity.

During summer season, 11 variables from 519 sampling plots of 30 variables were selected (Table 6.1.8). The first two principal components accounted for 41.31% of the variation on data set. The first factor was highly positively correlated with grass density (r = 0.83), herb density (r = 0.74), weathered stone (r = 0.54) and negatively correlated with litter (r = -0.82). The second factor was highly positively correlated with Herb % (r = 0.76) and herb cover (r = 0.69) and negatively correlated with rocks (r = -0.61).The logistic regression model had an efficiency of 66.28% correct classification of available and used plots by sambar during summer season. According to this model herb diversity is the most important components for the selection of habitat (table 6.1.23). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During summer season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.8). The graph shows that during summer season sambar preferred the area with low to high herb % and herb cover and high to low rocks and medium to high grass density, herb density and weathered stone and on the other hand avoiding litters.

During winter season, 12 variables from 350 sampling plots of 30 variables were selected (Table 6.1.9). The first two principal components accounted for 33.32% of the variation on data set. The first factor was positively correlated with herb density (r = 0.67), herb cover (r = 0.65), herb diversity (r = 0.62) and grass density (r = 0.61) and negatively correlated with rocks (r = -0.53). The second factor was highly positively correlated with seedling density (r = 0.89), seedling diversity(r = 0.87). The logistic regression model had an efficiency of 66.57% correct classification of available and used plots by sambar during winter season. The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For sambar during winter season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.9). The graph shows that during winter season sambar preferred the area with low to medium

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seedling density and seedling diversity and medium herb density, herb cover, herb diversity, grass density and avoiding rocks.

6.1.3.4 Factors affecting the selection of habitats by Nilgai in different seasons For Nilgai during post monsoon season, 13 variables from 175 sampling plots of 30 variables were selected (Table 6.1.10). The first two principal components accounted for 30.42% of the variation on data set. The first factor was highly positively correlated with herb density (r = 0.81), herb diversity (r = 0.80), tree diversity (r = 0.71). The second factor was highly positively correlated with grass density (r = 0.85), and grass diversity (r = 0.84). The logistic regression model had an efficiency of 66.86% correct classification of available and used plots by nilgai during post monsoon season. The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During post monsoon season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.10). The graph shows that during post monsoon season nilgai preferred the area with low to medium grass density and grass diversity and medium herb density, herb diversity and tree diversity.

During summer season, 11 variables from 519 sampling plots of 30 variables were selected (Table 6.1.11). The first two principal components accounted for 39.31% of the variation on data set. The first factor was highly positively correlated with Herb % (r = 0.79), herb density (r = 0.77), herb diversity(r = 0.71) and grass density (r = 0.54). The second factor was highly positively correlated with grass cover (r = 0.78) and grass diversity (r = 0.64) and negatively correlated with litter (r = -0.61) and shrub cover (r = -0.54). The logistic regression model had an efficiency of 63.78% correct classification of available and used plots by nilgai during summer season. According to this model grass density and weathered stones are the most important components for the selection of habitat (Table 6.1.24). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During summer season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.11). The graph shows that during summer season nilgai preferred the area with medium herb %, herb density, herb diversity, and grass density and low to high grass cover and grass diversity and avoids litter and shrub cover.

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During winter season, 13 variables from 350 sampling plots of 30 variables were selected (Table 6.1.12). The first two principal components accounted for 29.09% of the variation on data set. The first factor was highly positively correlated with seedling density (r = 0.90) and seedling diversity (r = 0.87). The second factor was also highly positively correlated with sapling density (r = 0.85) and sapling diversity(r = 0.80). The logistic regression model had an efficiency of 62.57% correct classification of available and used plots by nilgai during winter season. According to this model tree diversity and weathered stones are two most important factors for the selection of habitat (Table 6.1.25). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For nilgai during winter season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.12). The graph shows that during winter season nilgai preferred the area with low to medium sapling density and sapling diversity and low to high seedling density and seedling diversity.

6.1.3.5 Factors affecting the selection of habitats by Gaur in different seasons For Gaur during post monsoon season, 11 variables from 175 sampling plots of 30 variables were selected (Table 6.1.13). The first two principal components accounted for 35.35% of the variation on data set. The first factor was highly positively correlated with herb diversity (r = 0.83), herb density (r = 0.82) and tree richness (r = 0.69) .The second factor was highly positively correlated with grass density (r = 0.91), and grass diversity (r = 0.88). The logistic regression model had an efficiency of 52.57% correct classification of available and used plots by gaur during post monsoon season. The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During post monsoon season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.13). The graph shows that during post monsoon season gaur preferred the area with low to medium grass density and grass diversity and medium herb diversity, herb density and tree richness.

During summer season, 11 variables from 519 sampling plots of 30 variables were selected (Table 6.1.14). The first two principal components accounted for 37.58% of the variation on data set. The first factor was highly positively correlated with Herb density (r = 0.76), herb cover (r = 0.74), herb diversity (r = 0.71) and grass density (r = 0.53). The second factor was positively correlated with grass diversity (r = 0.66)

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and negatively correlated with shrub cover (r = -0.71) and litter (r = -0.67). The logistic regression model had an efficiency of 68.40% correct classification of available and used plots by gaur during summer season. According to this model grass density herb density and shrub cover are the most important components for the selection of habitat (Table 6.1.26). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. During summer season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.14). The graph shows that during summer season gaur preferred the area with low to medium herb density, herb cover, herb diversity, grass density and grass diversity and avoiding shrub cover as well as litter.

During winter season, 12 variables from 350 sampling plots of 30 variables were selected (Table 6.1.15). The first two principal components accounted for 31.19% of the variation on data set. The first factor was highly positively correlated with herb density (r = 0.74), herb cover (r = 0.71) and herb diversity (r = 0.57) and negatively correlated with rock (r = 0.48). The second factor was positively correlated with grass cover (r = 0.77) and grass density (r = 0.54) and negatively correlated with tree cover (r = 0.72). The logistic regression model had an efficiency of 74.50% correct classification of available and used plots by gaur during winter season. According to this model grass cover, herb diversity and seedling diversity are three most important factors for the selection of habitat (Table 6.1.27). The scored were saved to correlate these two PCs to find out the relation of habitat parameters with the availability and utilization. For gaur during winter season the distribution of available and utilized plots in relation to first and second component (Fig 6.1.15). The graph shows that during winter season gaur preferred the area with medium herb density, herb cover and herb diversity and low to high grass cover and grass density and avoiding rocks and tree cover.

6.1.3.6- Relationship between habitat parameters with fecal matter densities of different ungulate species during different season 6.1.3.6.1- Relationship during post monsoon season During post monsoon season pellet group density of cheetal was found significantly positively correlated with grass density (r = 0.1580, P < 0.05), sapling diversity (r = 0.1604, P < 0.05), tree cover (r = 0.2571, P < 0.01) and tree density (r = 0.4548, P < 0.01) and negatively correlated with DNHH (r = - 0.2625, P < 0.05). Chowsingha was

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Chapter – 6 found positively correlated with DNHH (r = 0.4607, P < 0.01), DNWH (r = 0.2871, P < 0.01), Grass % (r = 0.2253, P < 0.01), seedling density (r = 0.2335, P < 0.01), tree diversity (r = 0.2518, P < 0.01) and tree richness (r = 0.2489, P < 0.01) and negatively correlated with bear ground (r = -02287, P < 0.01), shrub cover (r = 0.2230, P < 0.01), shrub diversity (r = - 0.1813, P < 0.05), tree cover (r = - 0.3392, P < 0.01) and tree density (r = - 0.7935, P < 0.01), showing significant results. Gaur and nilgai were positively correlated with seedling richness (r = 0.2077, P < 0.01) and tree density (r = 0.2565, P < 0.01) respectively with a significant result. Similarly sambar was found positively correlated with tree cover (r = 0.2194, P < 0.01) and tree density (r = 0.2323, P < 0.01) and the results were significant (Table 6.1.28).

6.1.3.6.2- Relationship during summer season During summer season mean pellet group density of cheetal was found significantly positively correlated with grass cover (r = 0.2646, P < 0.01), grass % (r = 0.3207, P < 0.01), grass richness (r = 0.1162, P < 0.01) and shrub diversity (r = 0.1196, P < 0.01) and negatively correlated with bear ground (r = - 0.1258, P < 0.01), DNHH (r = - 0.2926, P < 0.01), DNWH (r = - 0.1203, P < 0.01), grass density (r = - 0.1784, P < 0.01), herb % (r = - 0.1140, P < 0.01), herb density (r = - 0.2763, P < 0.01), rock (r = - 0.1842, P < 0.01), shrub cover (r = - 0.0884, P < 0.05) and weathered stones (r = - 0.1850, P < 0.01). In the same way chowsingha was positively correlated with DNHH (r = 0.4481, P < 0.01), DNWH (r = 0.2157, P < 0.01), grass density (r = 0.1267, P < 0.01), seedling density (r = 0.1130, P < 0.05), seedling diversity (r = 0.0975, P < 0.05), tree density (r = 0.1392, P < 0.01), tree diversity (r = 0.1955, P < 0.01), tree richness ((r = 0.1788, P < 0.01) and weathered stones ((r = 0.0979, P < 0.05) and negatively correlated with sapling density (r = - 0.1349, P < 0.01), shrub cover (r = - 0.1161, P < 0.01), shrub diversity ((r = - 0.1238, P < 0.01) and shrub density (r = - 0.1227, P < 0.01) and the results were found to be significant. Gaur was found positively correlated with grass density (r = 0.2846, P < 0.01), grass diversity (r = 0.1387, P < 0.01), herb cover (r = 0.1326, P < 0.01), herb % (r = 0.1502, P < 0.01), herb density (r = 0.2897, P < 0.01), shrub richness (r = 0.0966, P < 0.05) and tree cover (r = 0.0897, P < 0.05) and negatively correlated with the litter (r = - 0.1690, P < 0.01), showing significant results. Nilgai was significantly positively correlated with grass density (r = 0.1735, P < 0.01), grass diversity (r = 0.1387, P < 0.01), grass richness (r = 0.1157, P < 0.01), herb cover (r = 0.1682, P < 0.01), herb % (r = 0.1720,

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P < 0.01), herb density (r = 0.1844, P < 0.01), sapling diversity (r = 0.1361, P < 0.01) and sapling richness (r = 0.1277, P < 0.01) and negatively correlated with litters ((r = - 0.1047, P < 0.05). Sambar was found positively correlated with herb diversity (r = 0.1346, P < 0.01), litter (r = 0.1707, P < 0.01), seedling diversity (r = 0.1289, P < 0.01), seedling richness (r = 0.1122, P < 0.05) and negatively correlated with DNWH (r = - 0.1419, P < 0.01), grass cover (r = - 0.1337, P < 0.01), grass % (r = - 0.0962, P < 0.05), grass density (r = - 0.1289, P < 0.01), herb density (r = - 0.920, P < 0.05) and weathered stones (r = - 0.0934, P < 0.05)(Table 6.1.29).

6.1.3.6.3- Relationship during winter season During winter season cheetal was found significantly positively correlated with grass % (r = 0.1857, P < 0.01), herb % (r = 0.1968, P < 0.01) and herb richness (r = 0.1071, P < 0.05) and negatively correlated with DNHH (r = - 0.3060, P < 0.01), DNWH (r = - 0.1404, P < 0.01), grass diversity (r = - 0.1528, P < 0.01), grass richness (r = - 0.1087, P < 0.05), rocks (r = - 0.1141, P < 0.05), seedling density (r = - 0.2414, P < 0.01), seedling diversity (r = - 0.2546, P < 0.01), seedling richness (r = - 0.2558, P < 0.01), shrub density (r = - 0.1403, P < 0.01), tree density (r = - 0.1231, P < 0.05), tree diversity (r = - 0.1974, P < 0.01) and tree richness (r = - 0.2053, P < 0.01). In the same way chowsingha was found positively correlated with DNHH (r = 0.5726, P < 0.01), DNWH (r = 0.2759, P < 0.01), grass density (r = 0.2201, P < 0.01), grass diversity (r = 0.1627, P < 0.01), herb density (r = 0.2505, P < 0.01), seedling density (r = 0.1957, P < 0.01), seedling diversity (r = 0.2520, P < 0.01), seedling richness (r = 0.2655, P < 0.01), tree density (r = 0.1194, P < 0.05), tree diversity (r = 0.2363, P < 0.01) and tree richness (r = 0.2422, P < 0.01) and negatively correlated with herb % (r = - 0.1183, P < 0.01), herb richness (r = - 0.1121, P < 0.05), shrub cover (r = - 0.1867, P < 0.01), shrub diversity (r = - 0.1527, P < 0.01) and shrub richness (r = - 0.1300, P < 0.05) with a significant result. Gaur was found positively correlated with grass % (r = 0.1206, P < 0.05), grass diversity (r = 0.1553, P < 0.01), herb density (r = 0.0023, P < 0.01), seedling density (r = 0.1536, P < 0.01), seedling diversity (r = 0.1205, P < 0.05) and seedling richness (r = 0.1412, P < 0.01) and the results were found to be significant. Nilgai was positively correlated with grass density ((r = 0.1545, P < 0.01), sapling density (r = 0.1500, P < 0.01), tree diversity (r = 0.1058, P < 0.05) and tree richness (r = 0.1288, P < 0.05) and negatively correlated with weathered stones (r = - 0.1360, P < 0.05), showing significant result. In the same way mean pellet group

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Chapter – 6 density of sambar was found significantly positively correlated with herb cover (r = 0.1099, P < 0.05), herb density (r = 0.1263, P < 0.05), litter (r = 0.1460, P < 0.01) and tree cover (r = 0.1067, P < 0.01) and negatively correlated with weathered stones (r = - 0.1627, P < 0.05) (Table 6.1.30). Based on finding it was found that different factors are responsible for habitat utilization of different ungulate species. So the null hypothesis was rejected. 6.1.4 Discussion Habitat studies provide crucial information about the ecological requirements of a species or community. Habitats of animals have been studied for long. From the initial days of Aristotle (344 B.C.) where man learnt about habitat use by animals due to innate curiosity to today’s times when understanding ecological relationships (Merriam, 1890; Adams, 1908; Svardson, 1949; Morisson et al., 1992), conservation of natural resources (e.g. Soule, 1986) and management of areas with specific requirements (e.g. Fox et al. 1988; Rahmani, 1989) have made it mandatory to understand habitat requirements of different species. Increasing habitat loss causes a significant increase in extinction risk among many species, especially habitat specialists (Rahmani, 1989; Birdlife International, 2001; Norris and Harper, 2003; Mallon, 2003). While it is important to assess the habitat usage, it is equally important to conduct studies addressing the pattern of usage. It is assumed that high quality resources will be selected more than low quality ones and use may change with availability when the latter is not uniform (Manly et al., 1993). Garshelis (2000) discussed fatal flaws in habitat selection models based on use versus availability. These models rely significantly on the assumption that an animal is more likely to use a particular habitat type if more of it is available. This hypothesis may get violated if habitat type offers abundant or non limiting resources (e.g. small area may be enough the requirement of habitat for resting and hiding). Another theory is that highly selected habitat provides high fitness potential or carrying capacity. There are various problems foreseen in this assumption: Habitat types used only for short times or in small amounts may still be critical to fitness, whereas it is also possible that none of the observed habitats provide a sustainable level of fitness. If resources are plentiful or non- limiting, their apparent selection is likely to be arbitrary as some animals may necessitate a mix of habitat types. Furthermore, competition can exclude all but dominant individuals from the best habitat. Multivariate analysis provided useful information for habitat usage of different ungulate species. 133

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Unoccupied habitat with little selection cannot be assumed to provide low fitness potential. Although effect of habitat cover, landscape structure and spatial variables on abundance of birds has been reported (Heikkinen et al., 2004), fitness potential of habitat cannot be assumed to vary with habitat selection and a gradient in observed density does not necessarily indicate a gradient in habitat quality (Hobbs & Hanley, 1990). Similarly, population response cannot be assumed to contrast positively with availability of selected habitat for animals that are subject to strong competition for habitat. It is important that habitat selection studies be supplemented with mechanistic approaches to understanding the fitness value of habitat (Hobbs & Hanley, 1990). Learning how key fitness elements like growth, survival and reproductive success depend on habitat characteristics seems more likely to produce general and reliable results than is habitat selection modelling alone (Railsback, et al. 2003). When using direct sightings or evidence of presence of animals in a particular area, one has to remember that they might be using a particular habitat just for transit between two optimal habitat types (Boitani & Fuller, 2000). It is equally important for habitat studies to evade the problem of obtaining spurious inferences due to inadequate information about the pattern of use. The approaches used in this study for collecting data on habitat use reduced chances of collecting insufficient or biased data (except chowsingha). While direct sightings (e.g. homing in telemetry studies) have their own limitations of being biased by detectability and accessibility, they provide data on presence as well as animal activity. This information can be further used for assessment of habitat-use pattern in an area. Animals, especially ungulates defecate at a particular rate, which varies between species, but is usually constant within species (Marques et al., 2001; Laing et al., 2003). Using them as indirect evidences of presence have their understandable strengths, but at the same time there are some fatal caveats in the method. Although the issue of detectability is reduced to a great extent when areas were combed thoroughly for faecal matter, the issues of disintegration rate and site selection pose a serious concern of unclear results (Marques et al., 2001; Laing et al., 2003).

In the present study in case of cheetal their presence were found almost everywhere throughout different seasons. Ordination graph also supports different factors which were important for their distribution in different seasons and the results of Multivariate analysis is further supported by Logistic regression model. For cheetal

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Chapter – 6 this model suggested that out of 30 different habitat variables a total 10 variables (sapling richness, tree diversity, grass density, herb cover, herb diversity, sapling diversity, seedling density, shrub diversity, sapling density and seedling diversity), were most important factors and significantly influencing preference for the selection of habitat. The study suggests that herb diversity, tree diversity, tree density, tree cover, grass density, grass richness, grass cover and shrub diversity were most important factors which influence cheetals, in habitat selection. These findings are supported by Abrahmson (1989) who suggest that plants make up much of the physical and biological environment for the everyday necessities of animals. This is why plant diversity greatly affects patterns of animal distribution and abundance. Findings of this study also suggest that cheetal preference was near to human habitations and water holes means avoiding distance from humans and water hole. These results are supported by Sharatchandra & Gadgil (1975) who suggested that cheetal shows strong affinity for human habitation. Water is a basic need for all wild animals including cheetal therefore its presence near to water bodies is obvious.

For chowsingha it was observed that their distribution restricted to mixed forest in sanctuary area. Logistic regression model supported findings of factorial analysis. In case of chowsingha, this model suggests that vegetation densities as well as distance from human habitation are two factors which largely govern their distribution. Logistic model suggests seven different habitat variables (Tree density, DNHH, Grass density, Herb density, sapling density, shrub cover and seedling diversity), which are most important and significantly influence their presence in different forest type. Tree canopy cover is an important feature that may influence an animal’s habitat preference as it has direct relationship with the energy costs incurred by thermal radiation (DeVos & Mosby, 1971). In general the findings of the study suggest that they prefer the area with medium vegetation cover and these findings support Sharma et al. (2007) who suggested that shaded canopy was used more in comparison to open. The result also shows that most of the pellet group were found from those forest types which are richer in browse food item and this result is also supported by findings of their food and feeding habit in next chapter (Chapter 7). The result reveals that chowsingha usually avoided water holes and probably it may due to anti predatory strategies for protecting themselves and their young ones (Leuthold, 1977; Caro et al., 2004; Broom and Ruxton, 2005). Chowsingha is habitat generalist (Berwick, 1974

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and Sharma, 2006). However it mostly lives in area with less human disturbances (Prater, 1980, Sharma et al. 2005). The findings of my study also show that chowsingha avoided human habitations.

In the present study most of the pellet groups of sambar were recorded from hilly terrain of the study area with teak dominating forest type. In general, present study shows that sambar avoids dense forest which is also supported by Imam (2014). However this is contrary to the studies conducted by Ramesh et al., (2012) and Khushwaha et al., (2014). Findings of factorial analysis state that density and diversity of tree and herbs were the most important factors for their habitat preference which is significantly supported by logistic regression analysis. These findings are similar to the study conducted by Khushwaha et al., (2004). Water is an important component for wild animals particularly when temperature is hot. Sambar, being an animal of hilly terrain do not want to spent much energy in search of water, therefore they restrict their home ranges and confined themselves around the water resources in summer. In certain occasion they rush to water body to avoid predation (Yahya, 2014), often unsuccessfully. Finding of our study also shows similar trend. It is also supported by studies conducted by Imam (2014), Jhonsingh, (1983) and Eisenberg and Lockhart (1972). The study area consists tropical dry and tropical moist deciduous forests, so that covering of grounds with litter due to leaves fall is obvious during summer. Sambar avoids those habitats which are covered with high amount of litter as such habitat contains very few plant materials to be utilized as food. In the present study similar results were recorded, where sambar avoids litters in summer. Among other habitat variables, rocks were playing very poor role in the distribution of animals; therefore in the present study presence of sambar is not having any relationship with rocks. However during winter they show avoidance with rocks. In a similar manner, Imam et al. (2009) suggested that rocks are not preferred by herbivores.

Nilgai are found in open area with undulating terrain or flat terrain (Berwick, 1974). In the present study pellets group of nilgai was reported form flat area as well as hilly terrain of different forest type. The study also shows that nilgai generally avoids dense forest that is supported by findings of Chakraborty (1991), Daniel (1994), Sankar (1994) and Khan (1996). Our result shows that Nilgai prefer area having medium grass as well as medium herb density, followed by fairly good grass cover. These

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results are supported by findings of (Berwick, 1974), Sheffield et al. (1983), Leslie (2008), in which they all show similar trend. In the present study it was also found that nilgai avoids litter and probably this may be due to the same as in case of sambar.

Ordination graph for gaur suggests different factors which were important for distribution of gaur in different seasons and the results of factorial analysis are more or less strongly supported by correlation analysis. In general our study shows that grasses and herbs are the most important variables for gaur which determined their habitat selection. The gaur prefer green grasses when available but even feeds on coarse, dry grasses (Schaller, 1967) and probably this could be the reason for utilizing those area which have fairly good density, cover and diversity of grasses. Findings of the study suggested that gaur also uses those areas where herb density and diversity are moderately dense. Gaur is generalist feeder due to its large body size and more or less similar finding has been already reported from different part of India (Brander, 1923, Schaller, 1967, Krisnan, 1972 and Sankar et al. 2013). Avoidance of shrubs by guar was recorded in summer as during this period very few palatable material was available with shrubs. A part from this, shrubs were also not providing much shelter from predator as well environmental condition. The results also reveal that gaur avoids litter in summer season and probably reason for this could be as in case of nilgai and sambar.

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6.2: Habitat Suitability Index Modelling

6.2.1 Introduction

An understanding of the relationship between spatial distribution of animals and their habitats plays an important role in conservation and management of threatened species (Lecis and Norris, 2003). Wildlife management involves preservation of wildlife species, as well as management of a complete ecosystem (Wulf et al. 1988). Until recently many conventional methods have been applied for collection of natural resources data for better management, therefore large numbers of ground-based studies have been carried out on wildlife species and their habitats (Rodgers, 1990, Bhat and Rawat, 1995). Ground survey methods and mapping of habitats are always useful, however, these are tedious and have limitations as whole area cannot be accessed in one go in many of the cases and the information collected may not have high accuracy level (Panwar, 1986). The geospatial technology, including remote sensing (RS) and geographic information system (GIS), is a very effective measurement for the assessment of natural. Remotely sensed data provides capabilities for frequent, real-time assessment, monitoring and management of large areas (Kushwaha et al. 2004). The U.S. Fish and Wildlife Service has developed habitat suitability index (HSI) models for the number of species, and these models plays very important role for the management of wildlife and their habitats (Davis et al. 1990, Kushwaha and Roy, 2002 and Ryul Park and Lee, 2003). The outputs of such models are usually simple, easily understandable and can be used for the assessment of environmental impacts or prioritisation of conservation efforts in a timely and cost-effective manner (Kushwaha et al. 2004; Zarri et al. 2008).The aim of habitat suitability model is to evaluate an area on the basis of the sustainability of important habitat factors for the given species. In other words it is to assess the detailed ecological information about the species and with help of that the characteristics of the habitat can be evaluated (Kushwaha et. al 2000, Kushwaha and Roy, 2002). As evident from the earlier review, a large number of studies have been carried out to evaluate the habitat of different species in various parts of the world as well as Indian subcontinent (Hizrel et al., 2001, Park et al., 2003, Roy et al., 1995, Chadwick et al., Kushwaha et al. 2004, 2006, and Imam et al., 2009).

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Pench Tiger Reserve (PTR) represents tropical dry deciduous and tropical moist deciduous ecosystem in Central India. Earlier studies which concentrated on the quantitative and qualitative description of vegetation structure in PTR included Sankar et al. (2001), Areendran (2007) and Basu (2012). Basu (2012) studied Evaluation of impact of landscape changes on large Mammal Habitats in Pench Tiger Reserve, Madhya Pradesh, India, in which vegetation types of PTR were evaluated and developed habitat suitability model for all the large mammals including spotted deer (Axis axis), Sambar (Rusa unicolor), wild boar (Sus scrofa) and carnivores like tiger (Panthera tigris), leopard (Panthera pardus), and dhole (Cuon alpines).

Considering the magnitude and effectiveness of geospatial technology, it is used in present chapter to model the suitable habitats available for cheetal, sambar, nilgai, gaur and chowsingha in Pench Tiger Reserve.

6.2.2 Materials

Satellite Data Landsat-8 OLI Data (April 4, 2015)

ASTER DEM (digital elevation model)

Ancillary Data Topographic maps

Digital boundary of Pench Tiger Reserve obtained from Forest Dept.

Software’s ERDAS IMAGINE 2013

ArcGIS 10

6.2.3 Methodology

The study was conducted to evaluate the habitat suitability index for different ungulate species present in PTR. Detailed information on these ungulates has already been discussed in Chapter 1. In the beginning the satellite data was collected and processed. The field survey was done to collect on presence of different ungulates. Ground truthing was also performed. The post field work included a database creation

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and habitat suitability index modeling. In the present study ERDAS IMAGINE 13 and ArcGIS 10 software’s were used for data processing and GIS analysis.

6.2.3.1 Factors influencing the habitat suitability

Identification of factors that influence the spatial distribution of animal species is important for developing effective method of conservation planning and habitat suitability evaluation (Heinanen and Von Numers, 2009). Therefore, factors related to distribution of ungulates related literatures, statistical data from field surveys and suggestions from conservation experts were taken into consideration as input data for modelling. These factors are representative, operational and indicative to the analysis and can provide most information requisite in the evaluation. Based on some previous analysis of habitat evaluation (Store and Jokimaki, 2003, Ranganathan, 2008), variables like vegetation types slope, distance from water resource, and distance from road were prepared using topographic sheet. These variables were selected as these are basically representing main features of suitable habitats of ungulate species to provide variables for habitat suitability model. The above mention factors were obtained after analysis of remote sensing imagery in GIS domain (Imam et al., 2009).

6.2.3.2 Data collection and data processing

The satellite data of LANDSAT-8 of dated 4th April 2015, Path- 144, Row- 45 with ground resolution of 30m was acquired from website USGS. Then satellite image was exported to ERDAS IMAGINE 2013 in image format for further analysis. From the satellite data an area of interest (AOI) was made and FCC was created. Advanced spacebourne thermal emission and reflection (ASTER) was downloaded from the USGS website and from this Digital elevation model (DEM) was developed in ERDAS domain. DEM was used to prepare slope layer.

The topographic maps; (55 O/2, 55 O/5, 55 O/6, 55 O/9, 55 K /13 and 55 K /14) of scale 1:50,000 of study area were collected from Survey of India (SOI), Jabalpur and forest boundary map was collected from Pench Tiger Reserve, Seoni, Madhya Pradesh. These topographic maps were scanned and exported to ERDAS IMAGINE and then these maps were geo-referenced with root mean square error of one-third of a pixel and the image was re-sampled using the nearest neighborhood method. This data was then re-projected in to universe transverse Mercator geodetic system-84 (UTM-WGS 84) projection for further analysis.

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6.2.3.3 Field Survey

The field survey was carried out throughout the year (except monsoon) from 2013 to 2015. In Pench Tiger Reserve there are three ranges (Kurai, Karmajhiri and Gumtara). Kurai range forms Pench Mowgli Sanctuary whereas Karmajhiri and Gumtara range to form Pench National Park. It was not feasible to cover all the three ranges of PTR, Karmajhiri range was selected as intensive study area for the collection of data and within that different habitat were identified for long term study. Fifteen line transects were laid in different identified habitats and on each transect 10 circular plots at every 200m interval were laid. So overall 150 sampling plots were laid and from these plots species presence were collected in different seasons (from 2013-2015) and their GPS locations were recorded. Information on some other variables such as tree cover, shrub cover, distance from nearest waterhole, distance from nearest human habitation were also collected. Slope and distances to roads were not recorded, as they would be more accurately derived from remotely sensed data and processed in a GIS framework (Imam et al. 2009).

6.2.3.4 Post-field analysis Land use/Land cover

The geo-coded FCC of LANDSAT 8 dated 4th April 2015 was digitally analyzed and the forest vegetation map (Land use and land cover) of the study area was prepared through digital analysis of satellite data using unsupervised classification. The classification uses ISODATA algorithm for differentiating spectral reflectance of various objects. Multi-spectral imagery was used to identify the spectral signature of the spectral classes present in the image. Unsupervised clustering was used for classifying land use/cover types. Classification was performed using maximum likelihood as it has been proven very efficient for land use/cover mapping. The unsupervised classification was then followed by the accuracy assessment. For assessing the accuracy of the maps the accuracy assessment is the most important aspect to assess the reliability of maps. During this study the accuracy assessment method were used and random points were generated by the software. Accuracy was tested using Cohen’s Kappa Statistics (Lillesand and Kiefer, 2000).

The accuracy of the map was done using 100 random selected points. The land cover information of these locations was compared to classified maps. Accuracy was tested using Cohen’s Kappa Statistics (Khat coefficient) (Lillesand and Kiefer, 2000).

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6.2.3.5 Database preparation for Habitat Suitability Analysis

Vector layers of roads and water body present in the study area were generated and their distance map has been generated. Land use Land cover map was prepared by using unsupervised classification and categorized in 12 different classes. Slope and elevation maps were prepared by using ASTER DEM data of 30 m resolution. These layers were then ranked according to their ecological importance with reference to study species. After categorizing all layers, a linear additive model was adopted to evaluate suitable habitat in the Pench Tiger Reserve for different ungulates. Weight assigned to different base layers were evaluated by using analytical hierarchy process (AHP) developed by SAATY (1980, 1991).

For the advance of providing methodology frame and reducing uncertainty, AHP is widely used in environmental evaluation and regional sustainable management (Xiong et al., 2007). In this numerical values are assigned to judge relative importance of each factor (Saaty and Vargas, 1991). In the construction of pair-wise comparison matrix, each factor is rated against every other factor by assigning a relative dominant value between 1 and 9 to the intersecting cell (Table 6.2.2).Different pair-wise comparison matrix for ungulates was prepared by using the relative scale measure shown in the table 6.2.3 to 6.2.7. In the process of AHP, the prime task of calculation is the eigenvector. Each element in the eigenvector indicates the relative priority of corresponding factor (Saaty, 1977, Saaty and Vargas, 1991), i.e. if a factor is preferred to another; its eigenvector component is larger than that of the other. A sum/product method is used to obtain the eigen value and the subsequent eigenvector. The weights finally derived by AHP are used for developing the HSI model. To examine the rationality of AHP, it is necessary to determine the degree of consistency that has been used in developing the judgments. In AHP, an index of consistency, known as the consistency ratio (CR), is used to indicate the probability that the matrix judgments were randomly generated (Saaty, 1977, Dai et al., 2001).

CR = 퐶퐼 Where푅퐼 RI is the average of the resulting consistency index depending on the order of the matrix given by Saaty (1977), and consistency index (CI) is defined as:

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CI = −푛 휆푚푎푥 Where푛 −1 is the principal eigen value of the matrix, n is the order of matrix In the present−푛 study four factors (Forest types, distance from road, distance from 휆푚푎푥 water and Slope) were considered for assessing the habitat suitability. These factors were compared with each other according to experts‟ judgments and allotting them priority weight. The initial scale allotted to each variable is given in Table 6.2.1. After that computer program Excel was used for developing pair-wise comparison matrix to calculate final weight (Table 6.2.14, 6.2.16, 6.2.18, 6.2.20 and 6.2.22). Then final weight [Consistency Index (CI)] derived for each variable are used with HSI equation in GIS domain.

HSI was calculated as the sum of habitat suitability factors multiplied by corresponding weights determined by AHP (Eastman et al., 1995, Wang et al., 2008)

= 푛

Where 퐻푆퐼 �푖=1 푊푖 � 푖

Wi= weight of factor and is the rating factor of i.

The final weights of above∫ 풊said variables were obtained from the above analysis and HSI were calculated for different ungulates as follows:

HSI for Chowsingha = (2.20 x LULCI) + (0.90 x DFWI) + (- 0.53 x SI) + (-0.36 x DFRI)

HSI for Cheetal = (2.44 x LULCI) + (0.90 x DFWI) + (- 0.41 x SI) + (-0.25 x DFRI)

HSI for Sambar = (2.28 x LULCI) + (0.91 x DFWI) + (0.44 x SI) + (-0.37 x DFRI)

HSI for Gaur = (2.16 x LULCI) + (0.94 x DFWI) + (- 0.53 x SI) + (-0.37 x DFRI)

HSI for Nilgai = (2.33 x LULCI) + (0.97 x DFWI) + (- 0.45 x SI) + (0.25 x DFRI)

Where,

HSI = Habitat Suitability Index,

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LULCI= Land use/Land cover Index,

DFWI= Distance from water-body Index

SI= Slope Index,

DFRI= Distance from road Index

6.2.4 Results and Discussion Habitat Suitability Index for different ungulates in Pench Tiger Reserve was developed using the linear additive model. On the basis of assigned suitability weightage, calculated output layers were reclassified. Reclassified raster layer shows areas as per their suitability categories for different species of ungulates. Area of each category for different ungulates were also evaluated which are shown in the table 6.2.25 to 6.2.29. 6.2.4.1 Habitat Suitability Index for Chowsingha The habitat suitability index map (Fig 6.2.7) depicts that out of 821km2, 237 km2 (28.87%) of forest of PTR was found highly suitable for chowsingha, whereas 219 km2 (26.67%) was moderately suitable, 217 km2 (26.43%) was suitable and 102 km2 (12.42%) least suitable. On the other hand 46 km2 (5.60%) of PTR was completely avoided by Chowsingha. The model revealed that 81.97% of study area was suitable for chowsingha and major portion of suitable area is confined to both sides of Pench river where as very few patches of suitable habitat is located towards far eastern portion of PTR. It is observed that chowsingha prefers mixed forest and probably due to this there major suitable habitat are found in the sanctuary area where ample amount of food/shelter resources are available and least inter-specific completion with other sympatric species is existing. A part from this, sanctuary area has minimal anthropogenic pressure, which disturbed least to their shyness behaviour. Furthermore this area has major continuous intact forest cover; therefore it provides major percentage of suitable habitat to chowsingha. The far western part of PTR is connected with narrow corridor to western part is having dense as well as open forest cover therefore very few portion of suitable habitat is available for chowsingha. On the other hand western portion of corridor is completely avoided by chowsingha. This may be due to anthropogenic pressure created by villages located on both sides of corridor.

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About 148 km2 (18.02%) of PTR falls under the categories of least suitable and completely avoidable by the species. The least suitable areas are located around those habitats which are suitable for chowsingha so it can be said that it is working as buffer for habitats suitable for the species. The model revealed that chowsingha usually avoids those areas which are near to villages, probably due to human and livestock activities. It is also recorded that chowsingha avoids those area which are found along the reservoir. The reason behind this may be frequent visit of predators. 6.2.4.2 Habitat Suitability Index for Cheetal The habitat suitability index map (Fig 6.2.8) states that 233 km2 (28.38%) of forest of PTR was found highly suitable for cheetal, however 196 km2 (23.87%) was moderately suitable, 212 km2 (25.82%) was suitable, 130 km2 (15.83%) least suitable and about 50 km2 (6.09%) of PTR was found completely avoided by Cheetal. The model revealed that 78.08% of study area was suitable for cheetal and its major portion is confined towards eastern part of Pench river. However two large continuous patch of suitable habitat are also available on the western part of the river. The LULC map illustrate that these suitable areas are covered with mixed and teak mixed forest. The literatures suggest that cheetal prefer this type of habitat due to availability of different type of food resources as well as shelter, as mixed forest support varieties of food items. The model map shows a large continuous chunk of suitable habitat on western tail of PTR. This portion is connected with Pench-Kanha corridor and provides larger space for animal movement. Probably this may be the reason that this area has suitable habitat for cheetal. The HSI model shows that cheetal generally avoids areas which are lying along the reservoir. It is also observed that some of the areas lying along the reservoir are least preferred by the species. Inspire of the availability of water cheetal avoids these areas because its approach is difficult. Availability of predator in large number may be another reason for avoidance of this area by the cheetal. However suitable habitats for cheetal are available along major part of the river. Cheetal do not prefer hilly terrain (Schaller, 1967, Khan, 1996) therefore some of the north western parts of reserve are found not suitable consequently avoided by the species. The corridor which links west and east part of PTR is also avoided by cheetal and the possible reason is availability of human habitation.

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6.2.4.3 Habitat Suitability Index for Sambar In case of sambar the results of habitat suitability index map (Fig 6.2.9) reveals that out of 821km2 of PTR, 226 km2 (27.53%) of forest of PTR was highly suitable for sambar, whereas 213 km2 (25.94%) was moderately suitable, 222 km2 (27.04%) was suitable and 118 km2 (14.37%) least suitable. On the other hand 42 km2 (5.12%) of PTR was completely avoided by sambar. The model revealed that 80.51% of study area was found suitable for sambar. Similar to cheetal and chowsingha, major portion of suitable habitat are lined on both side of river which almost bifurcate the PTR. Rest of the suitable habitat for sambar is present in fragmented form in western part of the reserve. The major intact portion of suitable habitats are lying sanctuary area. It was observed during filed visits that this part of reserve is well protected and having corridor connectivity with Pench- Kanha Corridor. A part from this the sanctuary area is having more undulating terrain comparable to other part of the PA. Therefore it is preferred by sambar (Biswas and Sankar, 2002, Kushwaha et. al. 2004) In addition to these moderately suitable habitats are present in a very fragmented manner towards far western part of the reserve. These moderately suitable habitats are under continuous threat from all sides due to presence of large number of human habitation and other anthropogenic activities. The future of extreme western part of PTR is also under threat because the corridor which connects it with eastern part is having human habitation throughout its range. Along the western side of reservoir the area which are adjacent to it are found least suitable and some of the southern portion of reservoir is completely avoidable but as we move away from the reservoir towards west the habitat found here are suitable for the sambar. The LULC map revealed that the area along the western bank of reservoir is either submerged area or having least vegetation so that they are avoided. Contrary to this the area which are away from the reservoir (in sudden western part) are having mixed forest that is generally preferred by sambar. 6.2.4.4 Habitat Suitability Index for Gaur The habitat suitability index map for gaur (Fig 6.2.10) shows that 241 km2 (29.35%) of forest of PTR was highly suitable for gaur, however 220 km2 (26.80%) was moderately suitable, 213 km2 (25.94%) was suitable, 107 km2 (13.03%) least suitable and about 40 km2 (4.87%) of PTR was found completely avoided by Gaur. The model revealed that 82.10% of study area was found to be suitable for gaur and major portion of them are confined to eastern side of river. However the sanctuary 146

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area provides more intact and continuous patch of suitable habitat, similarly the area lying towards western part of sanctuary is also having suitable habitat but they are in patchy form. When we move towards western part of Pench river, most of the area lying in the middle portion are suitable for gaur. In these areas small patches of palatable grasslands are present and probably providing suitable habitat for species (Anonymous, 2008). Far western tail of PA is having scattered distribution of suitable habitat. Contrary to this the corridor linking western and eastern part of the reserve is completely avoided by the species. The reason may be human habitation and their activities. The model map shows that area lying in sanctuary is having mostly suitable habitat for gaur. The LULC map demonstrates that these suitable areas are enclosed with mixed and teak mixed forest. The studies conducted by Paliwal and Mathur, (2012), Areendran, (2007) state that species prefer this type of habitat due to availability of different type of foraging items due to variety and variability among species in mixed forest. The HSI map shows a large continuous chunk of suitable habitat on western tail of PTR. This portion is connected with Pench-Kanha corridor and provides larger space for animal movement and this may be the reason that area has suitable habitat for Gaur. The HSI model shows that gaur generally avoids areas which are very closer to the reservoir. On the other hand as we move away from the reservoir towards west the habitat found here are suitable for the Gaur. Like sambar it is also found that some of the areas lying along the reservoir are least preferred by the gaur because approach to water is difficult. Availability of predator in large number may be another reason for avoidance of this area by the gaur. However suitable habitats for gaur are available along major part of the both side of the river. It may be due to a well known fact that water is one the most immediate necessity for the survival of animal species (Rosenstock et al., 1999, Gedir et al., 2016). 6.2.4.5 Habitat Suitability Index for Nilgai For Nilgai the habitat suitability index map (Fig 6.2.11) depicts that out of 821km2, 242 km2 (29.48%) of forest of PTR was found highly suitable for nilgai, whereas 196 km2 (23.87%) was moderately suitable, 231 km2 (28.14%) was suitable and 109 km2 (13.28%) least suitable. On the other hand 43 km2 (5.24%) of PTR was found to be completely avoided by the nilgai.

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The HSI model develops for Nilgai shows that 81.49% forest area of PTR was found suitable for the species. It is recorded that major part of north eastern portion of tiger reserve encompasses highly suitable habitats for the nilgai. It is also observed that in most of the cases highly suitable habitats are surrounded by either moderately suitable or suitable habitat, thus creating buffer zone for highly suitable area. North eastern part of PTR was also connected with other PAs and probably this may be the reason that its supports suitable for nilgai. Similarly three large and sum of the small patches of suitable areas are also found on western side of river. However the areas lying along the reservoir are either least suitable or completely avoided by the species. The HSI map also depicts that north central portion of tiger reserve also having least suitable and completely avoided by the species. The reason behind this could be presence of human habitations and their activities. The far western portion of area which is connected by a corridor is comparatively less suitable than the eastern part of PTR and the reason may be its irregular shape and exposer to human habitation and agricultural land. If we see the narrow corridor it was found that nilgai completely avoid this area and probably it is due to anthropogenic activities. The HSI model which was used in the present study developed very good models and clearly indicating the actual suitable habitat for the study species. Pench Tiger Reserve is one of the best manage national park with negligible disturbance. The models show that most of the area in national park is suitable for all the study species i.e. Cheetal, sambar, nilgai, gaur and chowsingha. However the population data, as well as habitat use data shows that population of all the ungulate species is abundant in the national park, specially the spotted deer population. However the chowsingha is completely absent in the national park though the habitat is very suitable for the chowsingha, on the other hand it is present in the sanctuary area. Now this is the objective of the further investigation for the complete absence of chowsingha from national park. Is there any threat for the chowsingha in national park area??? Or is it the over abundant population of Spotted dear which is forcing the chowsingha to leave the national park.

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Table 6.1.1: Selection of 13 variables for cheetal during post monsoon season, on the first five components

Variables PC I PC II PC III PC IV PC V

Grass Density 0.01134 -0.113 0.909253 -0.01387 0.010692

Grass Richness 0.198453 -0.05437 0.862491 0.182965 0.053895

Herb % -0.02167 0.108066 0.11368 -0.17192 0.70132

Herb Density 0.800077 0.161477 -0.12381 0.165948 -0.09202

Herb Diversity 0.834769 0.007297 0.213284 0.106519 -0.09308

Sapling Density 0.232386 0.164858 0.053602 0.69654 -0.07064

Sapling Richness -0.02938 -0.03849 0.069131 0.833047 -0.13963

Shrub Cover -0.38346 0.681967 0.024556 -0.12986 -0.12446

Seedling Richness 0.170973 0.028527 0.043857 0.545397 0.276046

Shrub Diversity 0.124941 0.757954 -0.07709 0.204318 0.031136

Shrub Density 0.169103 0.799746 -0.12138 0.030978 0.113098

Tree Diversity 0.712578 -0.04926 0.131184 0.073238 0.118702

Grass Cover 0.021888 0.059519 0.059112 -0.13859 -0.76027

% of Variance by each component 16.6195 13.60108 13.04564 12.7911 9.492599

Cumulative Variance 16.6195 30.22058 43.26622 56.05732 65.54992

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Table 6.1.2: Selection of 13 variables for cheetal during summer season, on the first four components

Variables PC I PC II PC III PC IV

Grass Density 0.852318 0.329496 -0.00407 0.026206

Grass Diversity 0.69666 -0.06872 0.212774 0.15385

Herb Cover 0.03165 0.816703 0.007898 0.004728

Herb % 0.213533 0.729786 -0.02144 0.227197

Herb Density 0.612743 0.614052 0.015576 -0.03993

Herb Diversity 0.252365 0.503524 0.349906 0.208849

Litter -0.79072 -0.11462 0.06348 0.018978

Sapling Diversity 0.01162 0.416012 0.132256 -0.23178

Seedling Density -0.0173 0.003414 0.869068 0.065749

Seedling Diversity -0.03595 0.045859 0.825149 0.158227

Shrub Diversity 0.235634 0.18077 0.443214 -0.1879

Tree Density -0.0059 0.056601 0.158133 0.79367

Tree Diversity 0.103672 0.021007 -0.02082 0.808526

% of Variance by each component 18.39073 16.67784 14.21566 11.71986

Cumulative Variance 18.39073 35.06856 49.28423 61.00409

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Table 6.1.3: Selection of 10 variables for cheetal during winter season, on the first four components

Variables PC I PC II PC III PC IV

Bear Ground -0.20642 0.127675 -0.07243 0.863453

Grass Cover -0.41827 0.062451 -0.25698 -0.59888

Sapling Density 0.107479 -0.18074 0.654178 0.13548

Seedling Diversity 0.044978 0.627898 -0.19994 0.032906

Shrub Diversity 0.844615 -0.00141 0.051616 0.00549

Shrub Density 0.79525 0.199471 -0.08932 -0.05165

Tree Density -0.06382 0.272643 0.738826 -0.06798

Herb Diversity 0.103806 0.651703 0.170063 0.171239

Herb Density 0.062302 0.592898 0.196403 -0.09072

Tree Diversity -0.09451 0.466001 0.556445 0.011171

% of Variance by each component 16.04591 15.5465 14.72846 11.68647

Cumulative Variance 16.04591 31.59242 46.32087 58.00734

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Table 6.1.4: Selection of 16 variables for chowsingha during post monsoon season on the first five components

Variables PC I PC II PC III PC IV PC V

DNHH 0.180818 -0.78095 -0.10566 0.038065 0.134322

DNWH 0.113128 -0.6265 -0.24333 -0.05745 -0.09246

Grass Density 0.03864 0.095658 0.870989 -0.10958 -0.08056

Grass Diversity 0.175931 0.117058 0.869954 -0.05387 0.180096

Herb Density 0.788249 -0.12004 -0.09465 0.114407 0.082898

Herb Diversity 0.828631 0.019006 0.26604 -0.04591 -0.04655

Seedling Richness 0.144472 0.01137 0.096048 0.031604 0.823127

Shrub Diversity 0.22383 0.144072 -0.08888 0.737124 0.150883

Shrub Density 0.194959 0.00112 -0.12106 0.779117 0.057986

Tree Diversity 0.639863 -0.18812 0.157906 -0.09285 0.150888

Seedling Density 0.079965 -0.15306 -0.03604 0.048283 0.863732

Tree Cover 0.305532 0.546895 -0.36321 0.135679 0.150006

Tree Density 0.114957 0.815847 0.040759 0.149316 -0.20085

Litter 0.156896 0.010502 0.020433 -0.05042 0.105913

Shrub Cover -0.34038 0.062521 0.019556 0.718462 -0.13179

Sapling Density 0.524425 0.130411 -0.00561 0.145778 0.165057

% of Variance by each component 15.20478 13.15893 11.61238 11.11165 10.4106

Cumulative Variance 15.20478 28.36371 39.97609 51.08775 61.49835

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Table 6.1.5: Selection of 11 variables for chowsingha during summer season on the first four components

Variables PC I PC II PC III PC IV

Bear Ground -0.14924 0.823994 -0.19017 0.010268

DNHH -0.14974 -0.01262 0.504382 0.394114

Grass Density 0.703184 0.504863 0.209085 0.017934

Grass Diversity 0.413004 0.345828 -0.0468 0.360777

Herb Cover 0.682397 -0.12511 -0.20269 -0.10771

Herb Density 0.81947 0.276781 0.201186 -0.04619

Herb Diversity 0.690942 -0.09108 -0.08277 0.392645

Litter -0.36428 -0.7381 -0.23163 0.067612

Rock -0.06934 -0.19402 0.766668 -0.04558

Weathered Stone 0.156425 0.229378 0.624678 -0.10626

Seedling Density 0.046147 -0.03226 -0.03477 0.839328

% of Variance by each component 22.62219 16.27501 13.25225 10.693

Cumulative Variance 22.62219 38.8972 52.14945 62.84245

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Table 6.1.6: Selection of 12 variables for chowsingha during winter season on the first four components

Variables PC I PC II PC III PC IV

DNHH -0.08744 0.503098 0.103436 0.153993

Rock 0.198387 0.083908 0.036005 -0.6635

Sapling Density -0.01071 0.050715 0.815287 -0.04029

Sapling Diversity 0.047561 -0.01081 0.788388 0.141778

Shrub Cover 0.726393 -0.03526 -0.10733 -0.11413

Seedling Density 0.123614 0.844303 -0.01675 -0.10605

Seedling Diversity 0.046785 0.860174 -0.05828 0.008612

Shrub Diversity 0.73486 -0.10067 0.172541 0.016358

Shrub Density 0.827885 0.217726 0.063781 -0.01136

Tree Cover 0.143365 -0.06637 0.544416 -0.14552

Tree density -0.13708 0.218795 0.397622 0.254496

Herb Densiy 0.135526 0.148513 0.044727 0.803067

% of Variance by each component 15.64684 15.40582 15.03144 10.34394

Cumulative Variance 15.64684 31.05266 46.0841 56.42804

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Table 6.1.7: Selection of 15 variables for sambar during post monsoon season on the first five components

Variables PC I PC II PC III PC IV PC V

Bear Ground 0.0331 0.107829 0.105539 -0.02115 -0.05286

Grass Density 0.027686 0.868578 -0.136 -0.08949 -0.01271

Grass Diversity 0.136071 0.883648 -0.06249 0.155549 0.10511

Herb Cover -0.03474 0.262041 0.084252 -0.09036 -0.05554

Herb Density 0.799433 -0.12273 0.133806 0.125296 0.135612

Herb Diversity 0.840358 0.278337 0.011688 -0.03738 0.108396

Sapling Density 0.248571 0.018811 0.109222 0.053603 0.786884

Sapling Diversity 0.005983 0.06579 -0.0031 0.067754 0.88574

Shrub Cover -0.36659 -0.01112 0.660385 -0.09221 -0.10445

Seedling Density 0.095794 -0.0226 0.079414 0.871813 0.02822

Seedling Diversity 0.101087 0.134554 0.063229 0.809678 0.165089

Shrub Diversity 0.12455 -0.04253 0.77041 0.092093 0.17558

Shrub Density 0.186455 -0.15938 0.777779 0.067677 0.012508

Tree Density -0.02252 0.295619 0.346735 -0.47999 0.22211

Tree Diversity 0.701757 0.086988 -0.04878 0.192243 0.04821

% of Variance by each component 14.17153 12.35785 12.25404 11.7995 10.48367

Cumulative Variance 14.17153 26.52938 38.78343 50.58293 61.0666

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Table 6.1.8: Selection of 11 variables for sambar during summer season on the first four components

Variables PC I PC II PC III PC IV

Grass Density 0.83167 0.275057 0.010902 0.057624

Herb Cover 0.207257 0.698108 0.041515 0.145543

Herb % 0.317452 0.768572 0.048339 -0.04386

Herb Density 0.747446 0.401299 0.053116 0.283604

Herb Diversity 0.25406 0.449724 0.400123 0.371092

Litter -0.82924 -0.04759 0.018185 0.200196

Rock 0.25652 -0.61443 0.028021 0.254696

Seedling Density -0.00611 -0.04101 0.892497 0.025786

Seedling Diversity -0.0352 0.089607 0.872159 -0.12956

Tree Cover -0.0003 -0.05004 -0.09414 0.876582

Weathered Stone 0.542355 -0.35789 -0.03183 0.233333

% of Variance by each component 22.79538 18.51902 15.77547 10.81643

Cumulative Variance 22.79538 41.31441 57.08987 67.9063

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Table 6.1.9: Selection of 12 variables for sambar during winter season on the first four components

Variables PC I PC II PC III PC IV

Grass Density 0.617863 -0.04698 -0.27626 -0.24029

Grass Diversity 0.525874 0.300114 -0.19871 -0.29665

Herb Cover 0.651132 -0.01108 0.147166 -0.02512

Herb Density 0.674319 0.013231 0.116658 0.080885

Herb Diversity 0.62023 0.166813 0.171585 0.121907

Rock -0.53704 0.072539 0.282335 -0.02778

Sapling Density 0.090378 0.061656 -0.08938 0.774791

Seedling Density -0.01843 0.890214 0.110038 0.020309

Seedling Diversity 0.090306 0.878828 0.016763 -0.00997

Shrub Diversity 0.054001 -0.10344 0.854835 0.072936

Shrub Density 0.005772 0.232585 0.779192 0.025355

Tree Cover -0.08705 -0.05413 0.171437 0.734017

% of Variance by each component 18.64389 14.68341 13.73232 10.95089

Cumulative Variance 18.64389 33.3273 47.05962 58.01051

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Table 6.1.10: Selection of 13 variables for nilgai during post monsoon season on the first five components

Variables PC I PC II PC III PC IV PC V

Bear Ground 0.033174 0.087197 0.145117 -0.18004 0.77344

Grass Density 0.029686 0.8533 -0.1575 -0.01247 -0.0681

Grass Diversity 0.192529 0.842057 -0.09295 0.092512 -0.02599

Herb Cover -0.1104 0.342522 0.191584 -0.16618 -0.664

Herb Density 0.812999 -0.12732 0.1381 0.159142 0.155004

Herb Diversity 0.809097 0.308874 0.076198 0.049183 0.190658

Sapling Density 0.272129 0.097576 0.205385 0.712376 0.071758

Sapling Diversity 0.036877 0.175811 0.152545 0.729696 0.042664

Shrub Diversity 0.077692 -0.05813 0.822329 0.10419 0.066816

Shrub Density 0.149088 -0.19062 0.780919 -0.00352 -0.04857

Tree density -0.21671 0.351259 0.382679 0.19499 0.383455

Tree Diversity 0.719011 0.106245 0.022408 0.067615 -0.38626

Weathered Stone 0.007137 0.177989 0.183061 -0.62183 0.15007

% of Variance by each component 15.6501 14.77087 12.67736 12.1274 11.05648

Cumulative Variance 15.6501 30.42097 43.09832 55.22572 66.2822

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Table 6.1.11: Selection of 11 variables for nilgai during summer season on the first four components

Variables PC I PC II PC III PC IV

Grass Cover -0.21032 0.784389 0.098162 -0.24644

Grass Density 0.541971 0.584057 0.136077 0.398907

Grass Diversity 0.266494 0.642017 0.095017 0.007217

Herb Density 0.772069 0.185781 0.147996 0.40693

Herb Diversity 0.712008 0.077332 0.138668 -0.0729

Litter -0.27313 -0.61032 -0.10247 -0.42719

Shrub Cover -0.06223 -0.54188 0.220273 -0.06898

Shrub Diversity 0.119719 0.00058 0.964583 0.043496

Shrub Richness 0.107547 0.025136 0.960039 0.078209

Weathered Stone -0.05477 -0.02919 0.058663 0.916929

Herb % 0.791789 0.041077 -0.01417 -0.06718

% of Variance by each component 20.42124 18.89421 18.11881 13.01229

Cumulative Variance 20.42124 39.31545 57.43427 70.44655

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Table 6.1.12: Selection of 12 variables for nilgai during winter season on the first four components

Variables PC I PC II PC III PC IV

Grass Diversity 0.184193 -0.11114 0.704165 -0.01403

Sapling Density 0.034442 0.858866 -0.05121 -0.04525

Sapling Diversity -0.04178 0.805131 0.048232 0.109625

Seedling Density 0.903729 0.024702 -0.0129 0.082457

Seedling Diversity 0.872191 -0.03975 0.087385 0.030668

Shrub Density 0.243303 0.058401 -0.02429 0.823154

Shrub Diversity -0.09748 0.044668 -0.11302 0.866866

Tree Cover -0.04496 0.40482 -0.40175 0.206145

Tree Density 0.093143 0.16943 0.040496 -0.07937

Tree Diversity 0.046809 0.120994 0.208774 0.057519

Weathered Stone -0.15184 -0.26567 -0.43257 0.108789

Grass Density -0.12767 0.022666 0.833204 -0.05608

% of Variance by each component 14.46036 14.0346 13.41612 12.65104

Cumulative Variance 14.46036 28.49496 41.91107 54.56212

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Table 6.1.13: Selection of 11 variables for gaur during post monsoon season on the first five components

Variables PC I PC II PC III PC IV PC V

Grass Cover 0.01937 0.01773 0.040881 -0.00275 0.927732

Grass Density -0.00017 0.910932 -0.00757 -0.06274 -0.01006

Grass Diversity 0.158197 0.885537 0.111142 0.007303 0.010927

Herb Density 0.82843 -0.14969 0.149968 0.11631 -0.00231

Herb Diversity 0.837916 0.271011 0.099781 0.084341 -0.11565

Sapling Density 0.244657 0.018346 0.798777 0.096397 -0.0079

Sapling Diversity -0.01282 0.079367 0.891016 0.021165 0.032265

Shrub Cover -0.26007 -0.0918 -0.16237 0.692012 0.283543

Shrub Diversity 0.221953 -0.16602 0.174326 0.672859 0.01614

Tree Density -0.04543 0.225018 0.11457 0.672762 -0.29173

Tree Richness 0.690624 0.127515 0.021371 -0.32286 0.15281

% Variance 18.81586 16.54234 14.08016 13.84407 9.677326

Cumulative Variance 18.81586 35.35819 49.43835 63.28242 72.95975

161

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Table 6.1.14: Selection of 11 variables for gaur during summer season on the first four components

Variables PC I PC II PC III PC IV

Grass Density 0.532323 0.668331 0.006519 0.281585

Grass Diversity 0.202897 0.66028 0.107513 -0.07009

Herb Cover 0.748557 -0.00422 -0.08403 -0.18049

Herb Density 0.769326 0.341655 -0.02896 0.323692

Herb Diversity 0.710792 0.095936 0.263814 -0.06004

Litter -0.22538 -0.67352 0.100465 -0.39162

Rock -0.13825 -0.17574 -0.15573 0.720156

Weathered Stone 0.059176 0.135748 0.140154 0.716734

Tree Density 0.082369 -0.03453 0.846187 0.037967

Tree Diversity 0.001513 0.172781 0.793592 -0.04323

Shrub Cover 0.146912 -0.71251 -0.14299 0.203141

% Variance 18.94311 18.63852 13.72139 13.2312

Cumulative Variance 18.94311 37.58163 51.30303 64.53423

162

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Table 6.1.15: Selection of 12 variables for gaur during winter season on the first four components

Variables PC I PC II PC III PC IV

Grass Cover -0.01733 0.779207 -0.21886 0.086678

Grass Density 0.387903 0.542842 -0.23612 0.319827

Grass Diversity 0.240756 0.399004 -0.0976 0.582634

Herb Cover 0.718925 0.223949 0.150146 -0.07686

Herb Diversity 0.578493 -0.17434 0.071945 0.386832

Rock -0.48926 -0.08613 0.380166 -0.12165

Shrub Diversity 0.17191 -0.21118 0.750229 -0.09076

Shrub Density 0.000786 -0.05286 0.817325 0.173796

Tree Cover -0.00677 -0.72495 -0.00587 0.118555

Tree Diversity 0.211236 -0.25999 -0.13709 0.644751

Herb Density 0.743051 -0.02958 0.060074 0.071128

Seedling Diversity -0.10909 0.122692 0.272341 0.649888

% of Variance by each component 16.14885 15.04564 13.44078 12.62686

Cumulative Variance 16.14885 31.19449 44.63526 57.26212

163

Chapter – 6

Table 6.1.16: Logistic Regression Model for cheetal during post monsoon season

Variable B S.E. Wald Df Sig R Exp (B) Sapling Richness -0.3514 0.1745 4.0575 1 0.044 -0.0972 0.7037 Tree Diversity 0.4974 0.1977 6.3282 1 0.0119 0.1409 1.6444 Constant -0.8402 0.172 23.8581 1 0

Table 6.1.17: Logistic Regression Model for cheetal during summer season

Variable B S.E. Wald Df Sig R Exp (B) Grass Density 0.398 0.1213 10.7584 1 0.001 0.1213 1.4888 Herb Cover -0.2851 0.121 5.5491 1 0.0185 -0.0772 0.7519 Herb Diversity -0.3442 0.1184 8.4563 1 0.0036 -0.1042 0.7088 Sapling Diversity -0.342 0.1137 9.0535 1 0.0026 -0.1089 0.7103 Seedling Density 0.4783 0.1235 15.0038 1 0.0001 0.1478 1.6133 Shrub Diversity -0.4088 0.1169 12.2333 1 0.0005 -0.1311 0.6644 Tree Diversity 0.3381 0.1182 8.1842 1 0.0042 0.102 1.4023 Constant -1.2057 0.1141 111.5995 1 0

Table 6.1.18: Logistic Regression Model for cheetal during winter season

Variable B S.E. Wald Df Sig R Exp (B) Sapling Density -0.3296 0.1456 5.1284 1 0.0235 -0.0931 0.7192 Seedling Diversity 0.8275 0.1475 31.456 1 0 0.2856 2.2875 Tree Diversity 1.2119 0.2316 27.3816 1 0 0.2651 3.3597 Herb Diversity -0.3219 0.1606 4.0162 1 0.0451 0.0747 0.7248 Constant -1.8135 0.1902 90.8927 1 0

164

Chapter – 6

Table 6.1.19: Logistic Regression Model for chowsingha during post monsoon season

Variable B S.E. Wald Df Sig R Exp (B) Tree Density 3.8117 2.0164 3.5736 1 0.0587 0.1107 45.2285 Constant 7.3602 4.7295 2.4218 1 0.1197

Table 6.1.20: Logistic Regression Model for chowsingha during summer season

Variable B S.E. Wald Df Sig R Exp (B) DNHH -0.9955 0.1201 68.759 1 0 -0.4568 0.3695 Grass Density -1.4099 0.3443 16.7701 1 0 -0.2149 0.2442 Herb Density 0.9063 0.2609 12.0715 1 0.0005 0.1774 2.4752 Constant 3.0781 0.2494 152.3654 1 0

Table 6.1.21: Logistic Regression Model for chowsingha during winter season

Variable B S.E. Wald Df Sig R Exp (B) DNHH -1.0961 0.1858 34.8027 1 0 -0.3834 0.3342 Sapling Density 0.5391 0.2613 4.2566 1 0.0391 0.1006 1.7145 Shrub Cover 1.2062 0.4116 8.5875 1 0.0034 0.1718 3.3407 Seedling Diversity -0.9146 0.269 11.5621 1 0.0007 -0.207 0.4007 Tree Density -1.0848 0.4394 6.0953 1 0.0136 -0.1355 0.338 Herb Density -1.0601 0.3478 9.2878 1 0.0023 -0.1807 0.3464 Constant 4.3297 0.5685 58.0141 1 0

165

Chapter – 6

Table 6.1.22: Logistic Regression Model for sambar during post monsoon season Variable B S.E. Wald Df Sig R Exp (B) Tree Density 3.8117 2.0164 3.5736 1 0.0587 0.1107 45.2285 Constant 7.3602 4.7295 2.4218 1 0.1197

Table 6.1.23: Logistic Regression Model for sambar during summer season Variable B S.E. Wald Df Sig R Exp (B)

Herb Diversity -0.1759 0.0905 3.7794 1 0.0519 -0.0518 0.8387

Constant -0.6802 0.0933 53.1627 1 0

Table 6.1.24: Logistic Regression Model for nilgai during summer season Variable B S.E. Wald Df Sig R Exp (B)

Grass Density -0.4051 0.0956 17.9714 1 0 -0.1524 0.6669

Weathered Stone 0.1956 0.0933 4.3938 1 0.0361 0.059 1.2161

Constant -0.516 0.0926 31.077 1 0

Table 6.1.25: Logistic Regression Model for nilgai during winter season Variable B S.E. Wald Df Sig R Exp (B)

Tree Diversity -0.2194 0.1114 3.8797 1 0.0489 -0.0635 0.803

Weathered Stone 0.3274 0.1124 8.4884 1 0.0036 0.118 1.3874

Constant -0.4895 0.1122 19.0396 1 0

166

Chapter – 6

Table 6.1.26: Logistic Regression Model for gaur during summer season Variable B S.E. Wald Df Sig R Exp (B)

Grass Density -0.701 0.1988 12.4266 1 0.0004 -0.1274 0.4961

Herb Density -0.4183 0.1862 5.045 1 0.0247 -0.0688 0.6582

Shrub Cover -0.2547 0.115 4.9048 1 0.0268 -0.0672 0.7751

Constant 1.0081 0.1154 76.3415 1 0

Table 6.1.27: Logistic Regression Model for gaur during winter season Variable B S.E. Wald Df Sig R Exp (B)

Grass Cover -0.2418 0.1233 3.844 1 0.0499 -0.0679 0.7852

Herb Diversity -0.3821 0.1637 5.4487 1 0.0196 -0.0928 0.6824

Seedling Diversity -0.2635 0.1204 4.7876 1 0.0287 -0.0834 0.7683

Constant 1.108 0.1294 73.485 1 0

167

Chapter – 6

Table 6.1.28: Correlation between faecal matter densities of different ungulate species with habitat variables during post monsoon season

Variables Cheetal Chowsingha Gaur Nilgai Sambar Bear Ground 0.0928 -0.2287** -0.0385 0.0507 0.1437 DNHH -0.2625** 0.4607** -0.0667 -0.0969 -0.0841 DNWH -0.1259 0.2871** 0.0159 -0.1327 -0.0410 Grass Cover 0.0516 0.0477 -0.1072 0.0156 -0.0058 Grass % -0.0948 0.2253** -0.0526 -0.0139 -0.0677 Grass Density 0.1580* -0.0212 0.1203 0.1245 -0.0253 Grass Diversity 0.1178 0.0221 0.0391 0.0698 0.0200 Grass Richness 0.1130 0.0497 0.0776 0.0755 0.0376 Herb Cover 0.0607 -0.0295 0.1052 0.0623 -0.0761 Herb % 0.0540 -0.0497 0.0514 0.0287 -0.0608 Herb Density 0.0959 0.0264 0.0289 -0.0498 0.0838 Herb Diversity 0.1370 -0.0638 0.0194 0.0306 0.1069 Herb Richness 0.1148 -0.0249 -0.0043 0.0567 0.0833 Litter -0.0773 -0.0232 0.0842 0.0075 0.0327 Rock -0.0404 0.0376 -0.0038 -0.1136 -0.1144 Sapling Density 0.1087 -0.0415 -0.0023 -0.0235 0.0186 Sapling Diversity 0.1604* -0.1050 0.0196 -0.0310 0.0091 Sapling Richness 0.1215 -0.0595 0.0235 -0.0419 -0.0421 Shrub Cover 0.0517 -0.2230** -0.1148 0.1080 0.0718 Seedling Density -0.1341 0.2335** -0.0419 -0.0234 -0.0653 Seedling Diversity -0.0996 0.1213 0.1444 0.0057 0.0090 Seedling Richness -0.0596 0.0405 0.2077** 0.0111 0.0176 Shrub Diversity 0.0996 -0.1813* 0.0150 -0.0161 0.0925 Shrub Richness 0.1015 -0.1403 0.0325 -0.0052 0.1121 Shrub Density -0.0099 -0.0788 0.0386 -0.0250 0.0221 Tree Cover 0.2571** -0.3392** 0.0573 0.0603 0.2194** Tree Density 0.4548** -0.7935** 0.0533 0.2565** 0.2323** Tree Diversity -0.0827 0.2518** -0.0879 -0.0458 0.0172 Tree Richness -0.0986 0.2489** -0.0804 -0.0402 0.0280 Weathered Stones 0.1486 -0.0190 -0.0007 -0.0200 0.0106 *0.05;**0.01

168

Chapter – 6

Table 6.1.29: Correlation between faecal matter densities of different ungulate species with habitat variables during summer season

Variables Cheetal Chowsingha Gaur Nilgai Sambar Bear Ground -0.1258** 0.0273 0.0624 0.0389 -0.0774 DNHH -0.2926** 0.4481** -0.0480 -0.0636 -0.0807 DNWH -0.1203** 0.2157** -0.0304 0.0314 -0.1419** Grass Cover 0.2646** 0.0361 0.0529 0.0380 -0.1337** Grass % 0.3207** -0.0213 0.0490 0.0414 -0.0962* Grass Density -0.1784** 0.1267** 0.2846** 0.1735** -0.1289** Grass Diversity 0.0825 0.0370 0.1387** 0.1270** 0.0395 Grass Richness 0.1162** -0.0417 0.0803 0.1157** 0.0663 Herb Cover -0.0413 -0.0370 0.1326** 0.1682** 0.0161 Herb % -0.1140** -0.0104 0.1502** 0.1720** 0.0089 Herb Density -0.2763** 0.0051 0.2897** 0.1844** -0.0920* Herb Diversity 0.0136 -0.0165 0.0708 0.0810 0.1346** Herb Richness 0.0736 -0.0381 -0.0112 0.0444 0.1580 Litter 0.0672 -0.0421 -0.1690** -0.1047* 0.1707** Rock -0.1842** 0.0089 -0.0404 -0.0290 -0.0387 Sapling Density 0.0609 -0.1349** -0.0498 0.0652 -0.0116 Sapling Diversity 0.0572 -0.0760 0.0466 0.1361** -0.0210 Sapling Richness 0.0165 -0.0324 0.0529 0.1277** -0.0354 Shrub Cover -0.0884* -0.1161** 0.0171 -0.0740 0.0534 Seedling Density 0.0042 0.1130* 0.0019 -0.0367 0.0528 Seedling Diversity 0.0138 0.0975* -0.0643 -0.0509 0.1289** Seedling Richness -0.0007 0.0807 -0.0376 -0.0473 0.1122* Shrub Diversity 0.1196** -0.1238** 0.0850 0.0232 0.0257 Shrub Richness 0.0743 -0.0814 0.0966* 0.0029 0.0097 Shrub Density 0.0647 -0.1227** 0.0506 -0.0183 0.0404 Tree Cover -0.0479 -0.0044 0.0897* -0.0058 -0.0051 Tree Density -0.0715 0.1392** -0.0026 0.0583 0.0507 Tree Diversity -0.0168 0.1955** 0.0004 0.0371 -0.0110 Tree Richness 0.0194 0.1788** -0.0116 0.0285 -0.0074 Weathered Stones -0.1850** 0.0979* 0.0413 -0.0557 -0.0934* *0.05;**0.01

169

Chapter – 6

Table 6.1.30: Correlation between faecal matter densities of different ungulate species with habitat variables during winter season

Variables Cheetal Chowsingha Gaur Nilgai Sambar Bear Ground -0.0641 0.0634 0.0110 -0.0324 -0.0782 DNHH -0.3060** 0.5276** -0.0083 0.0549 -0.0653 DNWH -0.1404** 0.2759** 0.0661 -0.0514 0.0006 Grass Cover 0.0913 0.1361 0.0925 0.0777 -0.0431 Grass % 0.1857** 0.0372 0.1206* 0.1022 -0.0152 Grass Density 0.0369 0.2201** 0.0667 0.1545** 0.0409 Grass Diversity -0.1528** 0.1627** 0.1553** -0.0037 -0.0896 Grass Richness -0.1087* 0.0400 0.1505** -0.0100 -0.0968 Herb Cover 0.0924 0.0247 0.0643 0.0306 0.1099* Herb % 0.1968** -0.1183* -0.0148 0.0233 0.0595 Herb Density 0.0813 0.2505** 0.0023** 0.0173 0.1263* Herb Diversity -0.0247 0.0342 0.1421 0.0383 0.0254 Herb Richness 0.1071* -0.1121* 0.0815 0.0001 0.0260 Litter -0.0964 0.0347 -0.0031 0.0422 0.1460** Rock -0.1141* -0.0198 -0.0815 -0.0802 -0.0183 Sapling Density 0.0879 -0.0055 -0.0324 0.1500** 0.0934 Sapling Diversity 0.0452 -0.0099 -0.0050 0.0281 0.0393 Sapling Richness 0.0096 -0.0133 0.0175 -0.0136 -0.0048 Shrub Cover 0.0437 -0.1867** 0.0080 0.0354 0.0955 Seedling Density -0.2414** 0.1957** 0.1536** -0.0613 -0.0745 Seedling Diversity -0.2546** 0.2520** 0.1205* 0.0034 -0.0698 Seedling Richness -0.2558** 0.2655** 0.1412** 0.0014 -0.0644 Shrub Diversity 0.0132 -0.1527** 0.0742 -0.0352 0.0585 Shrub Richness 0.0259 -0.1300* 0.0639 -0.0499 0.0312 Shrub Density -0.1403** -0.0030 0.0498 0.0209 -0.0106 Tree Cover 0.0256 -0.0539 -0.0444 0.0484 0.1067* Tree Density -0.1231* 0.1194* -0.0109 -0.0007 0.0103 Tree Diversity -0.1974** 0.2363** -0.0175 0.1058* -0.0520 Tree Richness -0.2053** 0.2422** 0.0035 0.1288* -0.0756 Weathered Stones -0.1011 -0.0521 -0.0757 -0.1360* -0.1065* *0.05;**0.01

170

Chapter – 6

Table 6.2.1: Rank allocated to different layers

S.No. Layers Assigned Rank

1 Land use/Land cover 1

2 Distance from water body 2

3 Slope map 3

4 Distance from road 4

Table 6.2.2: Pair-wise comparison scale for Analytical Hierarchy Process preferences (Saaty 1980)

Weightage Preferences

9 Extremely Preferred

8 Very Strongly to Extremely Preferred

7 Very Strongly preferred

6 Strongly to Very Strongly

5 Strongly Preferred

4 Moderately to Strongly Preferred

3 Moderately Preferred

2 Equally to Moderately preferred

1 Equally Preferred

171

Chapter – 6

Table 6.2.3: Pair-wise comparison matrix of LULC map for Chowsingha

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and water body 1 1.00 9 3 3 7 3 3 5 3 3 3 3

2.Mixed forest 2 0.11 1.00 3 5 7 5 3 3 3 5 3 7

3.Bamboo mixed 3 0.33 0.33 1.00 3 5 3 3 3 3 3 3 3

4.Teak mixed forest 4 0.33 0.20 0.33 1.00 5 3 3 5 3 3 3 5

5.Open area with grassland 5 0.14 0.14 0.20 0.20 1.00 5 3 3 3 3 3 7

6.Argemon Sps 6 0.33 0.20 0.33 0.33 0.20 1.00 3 3 3 3 3 5

7.Lantana 7 0.33 0.33 0.33 0.33 0.33 0.33 1.00 5 3 3 3 3

8.Scrub 8 0.20 0.33 0.33 0.20 0.33 0.33 0.20 1.00 3 3 3 5

9.Teak dominated 9 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3 3 3

10.Teak 10 0.33 0.20 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3 3

11.Human_habitation 11 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3

12.Dry river bed 12 0.33 0.14 0.33 0.20 0.14 0.20 0.33 0.20 0.33 0.33 0.33 1.00

4.12 12.55 9.87 14.27 27.01 21.87 20.53 29.20 26.00 30.67 31.33 48.00

172

Chapter – 6

Table 6.2.4: Pair-wise comparison matrix of LULC map for Cheetal

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 1.00 5 3 7 9 3 3 3 3 5 5 5

2.Mixed forest 2 0.20 1.00 5 5 7 3 3 3 3 5 3 5

3.Bamboo mixed 3 0.33 0.20 1.00 5 5 3 3 3 3 3 3 5

4.Teak mixed forest 4 0.14 0.20 0.20 1.00 7 3 5 3 5 5 5 7

5.Open area with grassland 5 0.11 0.14 0.20 0.14 1.00 5 3 9 5 7 5 9

6.Argemon Sps 6 0.33 0.33 0.33 0.33 0.20 1.00 3 5 3 5 3 7

7.Lantana 7 0.33 0.33 0.33 0.20 0.33 0.33 1.00 3 3 5 3 7

8.Scrub 8 0.33 0.33 0.33 0.33 0.11 0.20 0.33 1.00 3 5 3 7

9.Teak dominated 9 0.33 0.33 0.33 0.20 0.20 0.33 0.33 0.33 1.00 3 3 3

10.Teak 10 0.20 0.20 0.33 0.20 0.14 0.20 0.20 0.20 0.33 1.00 3 5

11.Human_habitation 11 0.20 0.33 0.33 0.20 0.20 0.33 0.33 0.33 0.33 0.33 1.00 3

12.Dry river bed 12 0.20 0.20 0.20 0.14 0.11 0.14 0.14 0.14 0.33 0.20 0.33 1.00

3.72 8.61 11.60 19.75 30.30 19.54 22.34 31.01 30.00 44.53 37.33 64.00

173

Chapter – 6

Table 6.2.5: Pair-wise comparison matrix of LULC map for Sambar

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 1.00 5 3 5 3 3 3 3 9 7 3 3

2.Mixed forest 2 0.20 1.00 3 5 3 3 3 3 7 5 3 5

3.Bamboo mixed 3 0.33 0.33 1.00 3 3 3 3 3 5 5 3 3

4.Teak mixed forest 4 0.20 0.20 0.33 1.00 3 5 3 3 7 7 3 3

5.Open area with grassland 5 0.33 0.33 0.33 0.33 1.00 3 3 3 3 5 3 3

6.Argemon Sps 6 0.33 0.33 0.33 0.20 0.33 1.00 3 3 5 5 3 3

7.Lantana 7 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3 5 5 3 3

8.Scrub 8 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 7 5 3 3

9.Teak dominated 9 0.11 0.14 0.20 0.14 0.33 0.20 0.20 0.14 1.00 7 3 7

10.Teak 10 0.14 0.20 0.20 0.14 0.20 0.20 0.20 0.20 0.14 1.00 5 7

11.Human_habitation 11 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.20 1.00 3

12.Dry river bed 12 0.33 0.20 0.33 0.33 0.33 0.33 0.33 0.33 0.14 0.14 0.33 1.00

3.99 8.74 9.73 16.15 15.20 19.73 20.40 23.01 49.62 52.34 33.33 44.00

174

Chapter – 6

Table 6.2.6: Pair-wise comparison matrix of LULC map for Gaur

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 1.00 5 5 7 9 3 3 3 3 5 3 3

2.Mixed forest 2 0.20 1.00 5 5 5 3 5 3 3 5 3 5

3.Bamboo mixed 3 0.20 0.20 1.00 3 5 3 3 3 5 3 3 3

4.Teak mixed forest 4 0.14 0.20 0.33 1.00 7 3 3 3 3 3 3 7

5.Open area with grassland 5 0.11 0.20 0.20 0.14 1.00 3 3 5 3 5 3 7

6.Argemon Sps 6 0.33 0.33 0.33 0.33 0.33 1.00 3 3 3 5 3 5

7.Lantana 7 0.33 0.20 0.33 0.33 0.33 0.33 1.00 3 5 3 3 3

8.Scrub 8 0.33 0.33 0.33 0.33 0.20 0.33 0.33 1.00 3 5 3 3

9.Teak dominated 9 0.33 0.33 0.20 0.33 0.33 0.33 0.20 0.33 1.00 5 3 5

10.Teak 10 0.20 0.20 0.33 0.33 0.20 0.20 0.33 0.20 0.20 1.00 3 5

11.Human_habitation 11 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3

12.Dry river bed 12 0.33 0.20 0.33 0.14 0.14 0.20 0.33 0.33 0.20 0.20 0.33 1.00

3.85 8.53 13.73 18.29 28.88 17.73 22.53 25.20 29.73 40.53 31.33 50.00

175

Chapter – 6

Table 6.2.7: Pair-wise comparison matrix of LULC map for Nilgai

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 1.00 5 3 7 9 3 3 3 3 5 7 5

2.Mixed forest 2 0.20 1.00 5 5 5 3 3 5 3 5 7 5

3.Bamboo mixed 3 0.33 0.20 1.00 3 3 3 3 3 3 3 5 3

4.Teak mixed forest 4 0.14 0.20 0.33 1.00 7 3 3 5 3 5 5 5

5.Open area with grassland 5 0.11 0.20 0.33 0.14 1.00 3 3 3 3 5 5 7

6.Argemon Sps 6 0.33 0.33 0.33 0.33 0.33 1.00 3 3 3 3 5 3

7.Lantana 7 0.33 0.33 0.33 0.33 0.33 0.33 1.00 3 3 5 3 3

8.Scrub 8 0.33 0.20 0.33 0.20 0.33 0.33 0.33 1.00 3 3 5 3

9.Teak dominated 9 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 5 3 3

10.Teak 10 0.20 0.20 0.33 0.20 0.20 0.33 0.20 0.33 0.20 1.00 5 5

11.Human_habitation 11 0.14 0.14 0.20 0.20 0.20 0.20 0.33 0.20 0.33 0.20 1.00 5

12.Dry river bed 12 0.20 0.20 0.33 0.20 0.14 0.33 0.33 0.33 0.33 0.20 0.20 1.00

3.66 8.34 11.87 17.94 26.88 17.87 20.53 27.20 25.87 40.40 51.20 48.00

176

Chapter – 6

Table 6.2.8: Synthesis matrix of LULC map Class for Chowsingha

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 0.24 0.72 0.30 0.21 0.26 0.14 0.15 0.17 0.12 0.10 0.10 0.06 2.56

2.Mixed forest 2 0.03 0.08 0.30 0.35 0.26 0.23 0.15 0.10 0.12 0.16 0.10 0.15 2.02

3.Bamboo mixed 3 0.08 0.03 0.10 0.21 0.19 0.14 0.15 0.10 0.12 0.10 0.10 0.06 1.36

4.Teak mixed forest 4 0.08 0.02 0.03 0.07 0.19 0.14 0.15 0.17 0.12 0.10 0.10 0.10 1.25

5.Open area with grassland 5 0.03 0.01 0.02 0.01 0.04 0.23 0.15 0.10 0.12 0.10 0.10 0.15 1.05

6.Argemon Sps 6 0.08 0.02 0.03 0.02 0.01 0.05 0.15 0.10 0.12 0.10 0.10 0.10 0.87

7.Lantana 7 0.08 0.03 0.03 0.02 0.01 0.02 0.05 0.17 0.12 0.10 0.10 0.06 0.78

8.Scrub 8 0.05 0.03 0.03 0.01 0.01 0.02 0.01 0.03 0.12 0.10 0.10 0.10 0.61

9.Teak dominated 9 0.08 0.03 0.03 0.02 0.01 0.02 0.02 0.01 0.04 0.10 0.10 0.06 0.51

10.Teak 10 0.08 0.02 0.03 0.02 0.01 0.02 0.02 0.01 0.01 0.03 0.10 0.06 0.41

11.Human_habitation 11 0.08 0.03 0.03 0.02 0.01 0.02 0.02 0.01 0.01 0.01 0.03 0.06 0.34

12.Dry river bed 12 0.08 0.01 0.03 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.02 0.23

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Table 6.2.9: Synthesis matrix of LULC map Class for Cheetal

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 0.27 0.58 0.26 0.35 0.30 0.15 0.13 0.10 0.10 0.11 0.13 0.08 2.57

2.Mixed forest 2 0.05 0.12 0.43 0.25 0.23 0.15 0.13 0.10 0.10 0.11 0.08 0.08 1.84

3.Bamboo mixed 3 0.09 0.02 0.09 0.25 0.17 0.15 0.13 0.10 0.10 0.07 0.08 0.08 1.33

4.Teak mixed forest 4 0.04 0.02 0.02 0.05 0.23 0.15 0.22 0.10 0.17 0.11 0.13 0.11 1.36

5.Open area with grassland 5 0.03 0.02 0.02 0.01 0.03 0.26 0.13 0.29 0.17 0.16 0.13 0.14 1.38

6.Argemon Sps 6 0.09 0.04 0.03 0.02 0.01 0.05 0.13 0.16 0.10 0.11 0.08 0.11 0.93

7.Lantana 7 0.09 0.04 0.03 0.01 0.01 0.02 0.04 0.10 0.10 0.11 0.08 0.11 0.74

8.Scrub 8 0.09 0.04 0.03 0.02 0.00 0.01 0.01 0.03 0.10 0.11 0.08 0.11 0.64

9.Teak dominated 9 0.09 0.04 0.03 0.01 0.01 0.02 0.01 0.01 0.03 0.07 0.08 0.05 0.44

10.Teak 10 0.05 0.02 0.03 0.01 0.00 0.01 0.01 0.01 0.01 0.02 0.08 0.08 0.34

11.Human_habitation 11 0.05 0.04 0.03 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.03 0.05 0.27

12.Dry river bed 12 0.05 0.02 0.02 0.01 0.00 0.01 0.01 0.00 0.01 0.00 0.01 0.02 0.16

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Table 6.2.10: Synthesis matrix of LULC map Class for Sambar

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 0.25 0.57 0.31 0.31 0.20 0.15 0.15 0.13 0.18 0.13 0.09 0.07 2.54

2.Mixed forest 2 0.05 0.11 0.31 0.31 0.20 0.15 0.15 0.13 0.14 0.10 0.09 0.11 1.85

3.Bamboo mixed 3 0.08 0.04 0.10 0.19 0.20 0.15 0.15 0.13 0.10 0.10 0.09 0.07 1.39

4.Teak mixed forest 4 0.05 0.02 0.03 0.06 0.20 0.25 0.15 0.13 0.14 0.13 0.09 0.07 1.33

5.Open area with grassland 5 0.08 0.04 0.03 0.02 0.07 0.15 0.15 0.13 0.06 0.10 0.09 0.07 0.99

6.Argemon Sps 6 0.08 0.04 0.03 0.01 0.02 0.05 0.15 0.13 0.10 0.10 0.09 0.07 0.87

7.Lantana 7 0.08 0.04 0.03 0.02 0.02 0.02 0.05 0.13 0.10 0.10 0.09 0.07 0.75

8.Scrub 8 0.08 0.04 0.03 0.02 0.02 0.02 0.02 0.04 0.14 0.10 0.09 0.07 0.67

9.Teak dominated 9 0.03 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.13 0.09 0.16 0.52

10.Teak 10 0.04 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.00 0.02 0.15 0.16 0.46

11.Human_habitation 11 0.08 0.04 0.03 0.02 0.02 0.02 0.02 0.01 0.01 0.00 0.03 0.07 0.35

12.Dry river bed 12 0.08 0.02 0.03 0.02 0.02 0.02 0.02 0.01 0.00 0.00 0.01 0.02 0.27

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Table 6.2.11: Synthesis matrix of LULC map Class for Gaur

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 0.26 0.59 0.36 0.38 0.31 0.17 0.13 0.12 0.10 0.12 0.10 0.06 2.71

2.Mixed forest 2 0.05 0.12 0.36 0.27 0.17 0.17 0.22 0.12 0.10 0.12 0.10 0.10 1.91

3.Bamboo mixed 3 0.05 0.02 0.07 0.16 0.17 0.17 0.13 0.12 0.17 0.07 0.10 0.06 1.30

4.Teak mixed forest 4 0.04 0.02 0.02 0.05 0.24 0.17 0.13 0.12 0.10 0.07 0.10 0.14 1.21

5.Open area with grassland 5 0.03 0.02 0.01 0.01 0.03 0.17 0.13 0.20 0.10 0.12 0.10 0.14 1.07

6.Argemon Sps 6 0.09 0.04 0.02 0.02 0.01 0.06 0.13 0.12 0.10 0.12 0.10 0.10 0.91

7.Lantana 7 0.09 0.02 0.02 0.02 0.01 0.02 0.04 0.12 0.17 0.07 0.10 0.06 0.74

8.Scrub 8 0.09 0.04 0.02 0.02 0.01 0.02 0.01 0.04 0.10 0.12 0.10 0.06 0.63

9.Teak dominated 9 0.09 0.04 0.01 0.02 0.01 0.02 0.01 0.01 0.03 0.12 0.10 0.10 0.56

10.Teak 10 0.05 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.02 0.10 0.10 0.39

11.Human_habitation 11 0.09 0.04 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.01 0.03 0.06 0.34

12.Dry river bed 12 0.09 0.02 0.02 0.01 0.00 0.01 0.01 0.01 0.01 0.00 0.01 0.02 0.23

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Table 6.2.12: Synthesis matrix of LULC map Class for Nilgai

Class Name 1 2 3 4 5 6 7 8 9 10 11 12

1.River and waterbody 1 0.27 0.60 0.25 0.39 0.33 0.17 0.15 0.11 0.12 0.12 0.14 0.10 2.76

2.Mixed forest 2 0.05 0.12 0.42 0.28 0.19 0.17 0.15 0.18 0.12 0.12 0.14 0.10 2.04

3.Bamboo mixed 3 0.09 0.02 0.08 0.17 0.11 0.17 0.15 0.11 0.12 0.07 0.10 0.06 1.25

4.Teak mixed forest 4 0.04 0.02 0.03 0.06 0.26 0.17 0.15 0.18 0.12 0.12 0.10 0.10 1.35

5.Open area with grassland 5 0.03 0.02 0.03 0.01 0.04 0.17 0.15 0.11 0.12 0.12 0.10 0.15 1.04

6.Argemon Sps 6 0.09 0.04 0.03 0.02 0.01 0.06 0.15 0.11 0.12 0.07 0.10 0.06 0.85

7.Lantana 7 0.09 0.04 0.03 0.02 0.01 0.02 0.05 0.11 0.12 0.12 0.06 0.06 0.73

8.Scrub 8 0.09 0.02 0.03 0.01 0.01 0.02 0.02 0.04 0.12 0.07 0.10 0.06 0.59

9.Teak dominated 9 0.09 0.04 0.03 0.02 0.01 0.02 0.02 0.01 0.04 0.12 0.06 0.06 0.52

10.Teak 10 0.05 0.02 0.03 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.10 0.10 0.40

11.Human_habitation 11 0.04 0.02 0.02 0.01 0.01 0.01 0.02 0.01 0.01 0.00 0.02 0.10 0.27

12.Dry river bed 12 0.05 0.02 0.03 0.01 0.01 0.02 0.02 0.01 0.01 0.00 0.00 0.02 0.21

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Table 6.2.13: Pair-wise comparison matrix of different layers for Chowsingha

Class Name 1 2 3 4

Vegetation 1 1.00 9 5 3

Distance from Water Bodies 2 0.11 1.00 5 3

Slope 3 0.20 0.20 1.00 3

Distance from Road 4 0.33 0.33 0.33 1.00

1.64 10.53 11.33 10.00

Table 6.2.14: Synthesized Matrix of different layers for Chowsingha

Class Name 1 2 3 4 Consistency Index (CI)

Vegetation 1 0.61 0.85 0.44 0.30 2.20

Distance from Water Bodies 2 0.07 0.09 0.44 0.30 0.90

Slope 3 0.12 0.02 0.09 0.30 0.53

Distance from Road 4 0.20 0.03 0.03 0.10 0.36

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Table 6.2.15: Pair-wise comparison matrix of different layers for Cheetal

Class Name 1 2 3 4

Vegetation 1 1.00 9.00 7.00 5.00

Distance from Water Bodies 2 0.11 1.00 5.00 5.00

Slope 3 0.14 0.20 1.00 3.00

Distance from Road 4 0.20 0.20 0.33 1.00

1.45 10.40 13.33 14.00

Table 6.2.16: Synthesized Matrix of different layers for Cheetal

Class Name 1 2 3 4 Consistency Index (CI

Vegetation 1 0.69 0.87 0.53 0.36 2.44

Distance from Water Bodies 2 0.08 0.10 0.38 0.36 0.90

Slope 3 0.10 0.02 0.08 0.21 0.41

Distance from Road 4 0.14 0.02 0.03 0.07 0.25

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Table 6.2.17: Pair-wise comparison matrix of different layers for Sambar

Class Name 1 2 3 4

Vegetation 1 1.00 7 9 3

Distance from Water Bodies 2 0.14 1.00 7 3

Slope 3 0.11 0.14 1.00 3

Distance from Road 4 0.33 0.33 0.33 1.00

1.59 8.48 17.33 10.00

Table 6.2.18: Synthesized Matrix of different layers for Sambar

Class Name 1 2 3 4 Consistency Index (CI

Vegetation 1 0.63 0.83 0.52 0.30 2.28

Distance from Water Bodies 2 0.09 0.12 0.40 0.30 0.91

Slope 3 0.07 0.02 0.06 0.30 0.44

Distance from Road 4 0.21 0.04 0.02 0.10 0.37

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Table 6.2.19: Pair-wise comparison matrix of different layers for Gaur

Class Name 1 2 3 4

Vegetation 1 1.00 7 5 3

Distance from Water Bodies 2 0.14 1.00 5 3

Slope 3 0.20 0.20 1.00 3

Distance from Road 4 0.33 0.33 0.33 1.00

1.68 8.53 11.33 10.00

Table 6.2.20: Synthesized Matrix of different layers for Gaur

Class Name 1 2 3 4 Consistency Index (CI

Vegetation 1 0.60 0.82 0.44 0.30 2.16

Distance from Water Bodies 2 0.09 0.12 0.44 0.30 0.94

Slope 3 0.12 0.02 0.09 0.30 0.53

Distance from Road 4 0.20 0.04 0.03 0.10 0.37

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Table 6.2.21: Pair-wise comparison matrix of different layers for Nilgai

Class Name 1 2 3 4

Vegetation 1 1.00 9 5 5

Distance from Water Bodies 2 0.11 1.00 5 5

Slope 3 0.20 0.20 1.00 3

Distance from Road 4 0.20 0.20 0.33 1.00

1.51 10.40 11.33 14.00

Table 6.2.22: Synthesized Matrix of different layers for Nilgai

Class Name 1 2 3 4 Consistency Index (CI

Vegetation 1 0.66 0.87 0.44 0.36 2.33

Distance from Water Bodies 2 0.07 0.10 0.44 0.36 0.97

Slope 3 0.13 0.02 0.09 0.21 0.45

Distance from Road 4 0.13 0.02 0.03 0.07 0.25

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Table 6.2.23: Area occupied by each land use/cover classes

Classes Area in Km2 Area (%)

River and water body 27.95 3.37

Mixed forest 222.67 26.87

Bamboo mixed 9.47 1.14

Teak mixed forest 196.23 23.68

Open area with grassland patch 22.85 2.76

Argemon Sps 2.86 0.35

Lantana Patch 9.33 1.13

Scrub 32.13 3.88

Teak dominated 84.84 10.24

Teak 135.11 16.30

Human habitation 80.3 9.69

Dry river bed 4.98 0.60

Total 828.72 100

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Table 6.2.24: Accuracy assessment for vegetation and land cover classes in Pench tiger Reserve, Madhya Pradesh

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Chapter – 6

Table 6.2.25: Area wise Habitat Suitability status for Chowsingha in PTR

Percentage S. No. Category Area (Km2) (% Area)

1 Highly Suitable 237 28.87

2 Moderately Suitable 219 26.67

3 Suitable 217 26.43

4 Least Suitable 102 12.42

5 Completely Avoidable 46 5.60

Total Area in Km2 821 100

Table 6.2.26: Area wise Habitat Suitability status for Cheetal in PTR

Percentage S. No. Category Area (Km2) (% Area)

1 Highly Suitable 233 28.38

2 Moderately Suitable 196 23.87

3 Suitable 212 25.82

4 Least Suitable 130 15.83

5 Completely Avoidable 50 6.09

Total Area in Km2 821 100

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Chapter – 6

Table 6.2.27: Area wise Habitat Suitability status for Sambar in PTR

Percentage S. No. Category Area (Km2) (% Area)

1 Highly Suitable 226 27.53

2 Moderately Suitable 213 25.94

3 Suitable 222 27.04

4 Least Suitable 118 14.37

5 Completely Avoidable 42 5.12

Total Area in Km2 821 100

Table 6.2.28: Area wise Habitat Suitability status for Gaur in PTR

Percentage S. No. Category Area (Km2) (% Area)

1 Highly Suitable 241 29.35

2 Moderately Suitable 220 26.80

3 Suitable 213 25.94

4 Least Suitable 107 13.03

5 Completely Avoidable 40 4.87

Total Area in Km2 821 100

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Chapter – 6

Table 6.2.29: Area wise Habitat Suitability status for Nilgai in PTR

Percentage S. No. Category Area (Km2) (% Area)

1 Highly Suitable 242 29.48

2 Moderately Suitable 196 23.87

3 Suitable 231 28.14

4 Least Suitable 109 13.28

5 Completely Avoidable 43 5.24

Total Area in Km2 821 100

191

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4

2

0

-2

-4 PLOTS

Available Plot

Herb diversity, Herb density, Tree diversity, density, Herb diversity, Herb -6 Utilized Plot -3 -2 -1 0 1 2

Shrub density, Shrub diversity, Shrub Cover

Figure 6.1.1 Ordination of available and utilized plots for cheetal during post monsoon season in Pench Tiger Reserve

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Chapter – 6

2

1

0

-1

-2

PLOTS -3 Available pl ot

Grass density,Grass Diversity, Grass Density,Herb -Litter -4 Utilized plot -4 -3 -2 -1 0 1 2 3

Herb cover, Herb %, Herb diversity, Sapling Diversity

Figure 6.1.2 Ordination of available and utilized plots for cheetal during summer season in Pench Tiger Reserve

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Chapter – 6

2

1

0

-1

-2 PLOTS

Available plot

-3 Utilized plot Shrub Diversity, Shrub Density Shrub Diversity, Shrub -6 -4 -2 0 2 4

Herb Diversity, Seedling Diversity, Herb Density

Figure 6.1.3 Ordination of available and utilized plots for cheetal during winter season in Pench Tiger Reserve

194

Chapter – 6

4

2

0

-2

-4 PLOTS

Available Plot

Herb density, heb diversity, tree diversity, sapling density sapling diversity, tree heb diversity, density, Herb -6 Utilized Plot -3 -2 -1 0 1 2

-DNHH, -DNWH, tree density, tree cover

Figure 6.1.4 Ordination of available and utilized plots for chowsingha during post monsoon season in Pench Tiger Reserve

195

Chapter – 6

3

2

1

0

-1

-2

-3 PLOTS

-4 Available plot

Herb Density, Grass Density, Herb Diversity, Herb Cover Herb Diversity, Herb Density, Grass Density, Herb -5 Utilized plot -4 -2 0 2 4 6

Bear Ground, -Litter

Figure 6.1.5 Ordination of available and utilized plots for chowsingha during summer season in Pench Tiger Reserve

196

Chapter – 6

3

2

1

0

-1

PLOTS -2 Available plot

-3 Utilized plot Shrub Density, Shrub Diversity, Shrub Cover Shrub Diversity, Shrub Density, Shrub -2 -1 0 1 2 3 4

Seedling Density, Seedling Diversity, DNHH

Figure 6.1.6 Ordination of available and utilized plots for chowsingha during winter season in Pench Tiger Reserve

197

Chapter – 6

2

0

-2

-4 PLOT

Available plots

-6 Utilized plots Herb diversity, herb density, tree herb density, diversity diversity, Herb -4 -3 -2 -1 0 1 2

Grass diversity, grass density

Figure 6.1.7 Ordination of available and utilized plots for sambar during post monsoon season in Pench Tiger Reserve

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Chapter – 6

4

3

2

1

0

-1

-2 PLOTS -3 Available plot

Grass Density, Weathered Herb Density, -Litter Stone, -4 Utilized plot -3 -2 -1 0 1 2 3 4

Herb %, Herb Cover, -Rock

Figure 6.1.8 Ordination of available and utilized plots for sambar during summer season in Pench Tiger Reserve

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Chapter – 6

4

2

0

-2

-4

PLOTS -6 Available plot

Herb Density, Herb Cover, Herb Diversity , Grass Density, -Rock Density, , Grass Diversity Herb Cover, Herb Density, Herb -8 Utilized plot -2 -1 0 1 2 3

Seedling Density, Seedling Diversity

Figure 6.1.9 Ordination of available and utilized plots for sambar during winter season in Pench Tiger Reserve

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Chapter – 6

4

2

0

-2

-4 PLOT

Available plots

-6 Utilized plots Herb Density, Herb Diversity, Tree Diversity -3 -2 -1 0 1 2 3

Grass Density, Grass Diversity

Figure 6.1.10 Ordination of available and utilized plots for nilgai during post monsoon season in Pench Tiger Reserve

201

Chapter – 6

4

2

0

-2

-4 PLOTS

Available plot

Herb %, Herb G Herb Diversity, Density, Density rass -6 Utilized plot -4 -3 -2 -1 0 1 2 3 4

Grass Cover , Grass Diversity, -Litter, -Shrub Cover

Figure 6.1.11 Ordination of available and utilized plots for nilgai during summer season in Pench Tiger Reserve

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Chapter – 6

3

2

1

0

-1 PLOTS

Available plot

-2 Utilized plot Seedling density, Seedling Diversity density, Seedling Seedling -4 -3 -2 -1 0 1 2 3

Sapling Denisty, Sapling Diversity

Figure 6.1.12 Ordination of available and utilized plots for nilgai during winter season in Pench Tiger Reserve

203

Chapter – 6

2

1

0

-1

-2

-3

-4 PLOTS

-5 Available plot

-6 Utilized plot Herb diversity, herb density, tree herb density, richness diversity, Herb -4 -3 -2 -1 0 1 2

Grass density, grass diversity

Figure 6.1.13 Ordination of available and utilized plots for gaur during post monsoon season in Pench Tiger Reserve

204

Chapter – 6

3

2

1

0

-1

-2

-3 PLOTS -4 Available plot

Herb Density, Herb Cover, Herb Diversity, Grass Density -5 Utilized plot -5 -4 -3 -2 -1 0 1 2 3

Grass Divers ity, -Shrub Cover, -Litter

Figure 6.1.14 Ordination of available and utilized plots for gaur during summer season in Pench Tiger Reserve

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Chapter – 6

4

2

0

-2

-4

PLOTS -6 Available plot

-8 Utilized plot Herb Density, Herb Cover, Herb Diveristy, -Rock Herb Cover, Herb Diveristy, Density, Herb -4 -3 -2 -1 0 1 2 3 4

Grass Cover, Grass Density, -Tree Cover

Figure 6.1.15 Ordination of available and utilized plots for gaur during winter season in Pench Tiger Reserve

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Chapter – 6

Figure 6.2.1: Slope Map of Pench Tiger Reserve

207

Chapter – 6

Figure 6.2.2: Map of Euclidean distance from Water body

208

Chapter – 6

Figure 6.2.3: Map of Euclidean distance from Road

209

Chapter – 6

Figure 6.2.4: Detailed methodology (Schematic)

210

Chapter -6

Figure 6.2.5: False Color Composite (FCC) of the study area

211

Chapter -6

Figure 6.2.6: Land Use/Land Cover of Pench Tiger Reserve

212

Chapter -6

Fig 6.2.7: Chowsingha Habitat Suitability Map

213

Chapter -6

Fig 6.2.8: Cheetal Habitat Suitability Map 214

Chapter -6

Fig 6.2.9: Sambar Habitat Suitability Map 215

Chapter -6

Fig 6.2.10: Gaur Habitat Suitability Map

216

Chapter -6

Fig 6.2.11: Nilgai Habitat Suitability Map

217

Chapter 7- Food and Feeding Habit

Chapter -7

CHAPTER 7- FOOD AND FEEDING HABIT

7.1 Introduction

The most important and consistent activity determining the survival, health and power of movement in the life of animals are feeding. It dominates the other activities in the lives of most of the animals resulting in their ever-lasting quest for food (Shukla, 1990). The nature of foods and the ways in which these are obtained play a very important role in shaping the structure and behaviour of animals. Almost all important features and behavioural mechanism are also linked with the need in locate and obtain proper nourishment. The problem of food selection is common to all species and plays a pivotal role in the evolutionary divergence of many groups of animals (Mc Farland, 1981).

Sub-order rumanntia of artiodactylya are the most dominant order in the study area. It iswell known fact that apart from having microbial fermentation within a compartmentalized stomach and chewing the cud, the ruminants have other features like possession of selenodont molars and the tendency to replace the upper incisors by the horny pad (Green, 1985). Living ruminants comprise the primitive tragulids () and the more advanced pecorans, which was then defined to comprise Antilocaparinae, , Cervidae and (Flower, 1883; Green, 1985). Thus, the smaller boy size ungulates have higher energy requirement than larger ungulates, and hence, the requisite can only be met by selecting higher quality food from the available food resources of the area.

Hoffman (1973) classified the digestive system of the ruminants into three main categories, as follows: i) browsers or concentrate selectors (feeds mainly on the foliage of trees, shrubs or forbs); ii) bulk and roughage feeders (feeds mainly on grasses); and iii) intermediate or adaptable mixed feeders (either browse or graze) (Green, 1987b). Most of the cervids (deer) are either concentrate selectors or mixed feeders Green (1985). Small bovids forage in dense and closed habitat and the larger species feed upon high-fiber vegetation in open grasslands (Gomez et al. 2014).

The energy requirement of the ruminants depends majorly on its body size (Bell, 1970; Jarman, 1974; Green, 1985). Energy requirements are proportional to the body weight of the species raised to the power of 0.75 (Kleiber, 1961; Green, 1985).

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Chapter -7

For the conservation and management purpose of the species, it is therefore, important to know wether species competes for forage with the much larger extant to other sympatric species and is there any seasonal variation in the diet of the species or not. Food habits studies using the microhistological technique of identifying diet constituents have appeared in the literature since Baumgartner and Martin (1939) first described the technique. After that Denham (1965) and Sparks and Malechek (1968) verified the technique by hand compounding mixtures of grasses and forbs. Several researchers have used the techniques for the feeding ecology estimation for e.g. Vavra and Holechek (1980), Holechek, Vavra and Pieper (1982), Alipayo et al. (1992), Ilyas and Khan (2004), Ilyas and Syed (2014) and Haleem et al. (2014b).

Examining faecal samples by a microhistological technique is the most commonly used method for determining the vegetational composition of range of herbivore diets (Holechek, 1982; Alipayo, 1992). Moreover the advantage of faecal analysis over ingestal or ruminal content analysis is that the material can be obtained without animals’ being killed or even disturbed. Such decisive factors are crucial for the studies of rare or endangered species also. Consequently, in the present study, the technique was used to understand the feeding habits and the preferred food material of gaur, chowsingha and sambaralong with seasonal variation in the preference of their diet.

In the present chapter an account of feeding habits and mechanism of feedings of above ungulates has been discussed.

7.2 Methodology

7.2.1 Data collection

Allthough direct observation on the feeding of these ungulates was made. For detail analysis of feeding ecology of ungulates data were collected by indirect method only. Permanent slides of all available potential food plants were prepared for using them as reference slides. Pellet groups of ungulates were collected separately for micro- histological studies. Reference slides for food plants of the study area and pellet/dung samples were prepared according to Sparks and Malechek (1968) and another modified method (Holechek, 1982, Ilyas and Khan, 2004, Syed and Ilyas, 2012, Haleem et al. 2014b). For preparation of reference slides a few bits of leaves were taken from available plants. These were shredded coarsely and placed in a test tube.

219

Chapter -7

Concentrate nitric acid (HNO3) and distilled water was added in the test tube in the ratio of 1:3. The test tube was allowed to heat in a water bath for a minute or two till the material of the test tube become transparent (Chlorophyll free). Highly coloured

and tough material was boiled for a second time with fresh quantities of HNO3 and distilled water. The tube was allowed to cool down and the liquid drained off. The sample was repeatedly washed in distilled water. The sample was stained in saffarenin for two minutes to stain properly. After staining, the sample was washed repeatedly in distilled water and dehydrated by passing it through a mixture of alcohol and distilled water in the ratio of 1:3, 1:1 and 3:1 respectively. The sample was finally placed into absolute alcohol. The mounting was done in Canada balsam and the photograph of each sample was captured using Digital microscope (Barska, AY11374).

Fifteen permanent transects (three in each habitat), each of two km were established and permanent plots of 10m radius were marked at every 200 m intervals. These permanent plots were cleared at the start of different seasons. Pellets/dungs of chowsingha, sambar and gaur were collected separately from each plot in different identified habitat at summer, post monsoon and winter season. For chowsingha random search were also done in mixed forest of the sanctuary. These pellets/dungs were used for micro-histological studies. Pellets/dungs of each species from different plots in different habitat were mixed thoroughly for each season. A total of nine major samples were prepared, three for chowsingha, three for sambar and three for gaur, each representing summer, post-monsoon and winter season.

The major samples were put in a tray, shaken and tossed about several times before distributing them on tray. The entire sample was first halved and then quartered. Two opposite quarters were combined and such combined portion was rejected. The remaining combine portion was toss again in the same manner and spread over a tray. The sample was first halved and the quartered again. The opposite quarters were combined and one combined portion was again rejected. This procedure was repeated for six times and the sample retained at the end served as starting sample for micro- histological work.

The starting sample of pellets/dung was oven dried and then grinded in coarse powder form. The grinding did not fractionate the particles but merely separated the agglomerates into single particles. Two successive sieves of 30 & 60mm were placed

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on above the other and grounded sample was sieved. The portion of sample left on top sieve was rejected. The sieved sample was passed through the second sieve and the portion remaining on the second sieve was retained for analysis. The entire sample was first halved and then quartered. One quarter was selected as final sample and three quarters were kept as reserve in case of loss of sample in subsequent processing. A small portion of the final sample was then transferred into test tube with a ratio of

1:3 of concentrated Nitric Acid (HNO3) and Water (H2O) that was heated in a water bath for few minutes. After settling down fresh nitric acid and water were added to the precipitate and the sample was boiled again in order to obtain a fairly transparent or clear powder. The material was then washed with water repeatedly till the nitric acid was washed off completely to preparing the slides for analysis. The washed samples were used for preparing slides for analysis.

Out of starting samples, 32 random samples were taken out for chowsingha and gaur respectively, whereas for sambar it was 35. From these random samples, four slides were preparedfor each ungulates giving a total of 128 (32samples x 4slides) for chowsingha and gaur, whereas for sambar it was 140 (35samples x 4slides). While identifying the plant fragmentsfrom a slide, five field of view (FOV) were considered. Plant fragments from pellet groups were identified with the help of reference slides of the plant on the basis of micro-anatomical characters such as, cell wall, cell shapes, silica bodies, trichomes, stomata and companion cells, following Satkopan, 1972. Frequency of occurrence was based on the presence or absence of a species in each of 100 fields for each pellet sample. Because pellet may relatively uniform in size, the average relative frequency of occurrence represents the relative abundance of different species in the pellet sample.

7.2.2 Data analysis

The information on identified browse and grass particles was analyzed to calculate the proportion of different tree, shrub, herb and grass at the species level in the diet of different ungulate species. Diet composition of different ungulate were also studied by analyzing their pellet groups/dung piles collected from different sampling plots using micro-histological technique. Permanent reference slides of available plants were prepared and used for plant fragment identification from the different pellet groups. The proportion of each species in the diet composition of ungulates was compared

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with the food plants available in Pench Tiger Reserve using Bonferroni 95% confidence interval following Neu et al. (1974). The formula for calculating 95% Bonferroni confidence interval is as follows:

/ (1 )/ + / (1 )/

푃푖푒 − 푍∝ 2푘�푃푖푒 − 푃푖푒 푛 ≤ 푃푖푒 ≤ 푃푖푒 푍∝ 2푘�푃푖푒 − 푃푖푒 푛 Where is the observed proportional utilization of each species and / is the

upper standard푃푖푒 normal table value corresponding to a probability tail area푍∝ 2of푘 /2 and n is the total number of species recorded in sampling. For each species∝ its푘 proportional availability (Pio) in the sample was calculated by dividing its total number (n) from that of total number of all individuals (N) sampled for all species

(Pio= n/ΣN). Utilization was significantly greater or less than expected when Pio lay outside the 95% confidence limits constructed for proportional utilization (Pie) which was calculated by dividing number of plant fragments identified for each species (b)

by the total number of identified browse fragment (B) of all species (Pie=b/ΣB). The statistical tests were conducted following Zar (1999) and Fowler and Cohen (1986).

7.3- Results

7.3.1- Chowsingha diet During summer season total of 1048 plant fragment were evaluated, out of which 23 species (18 browse and 6 grass species) of plant were identified from different samples of pellet group of chowsingha. Out of all 18 browse species identified from the pellet group of chowsingha proportion of Albizia odoratissima was found maximum (9.160305) and minimum (0.572519) for Terminalia tomentosa. Similarly for grasses it was found that the proportion of Eragrostis tenella was maximum (24.5229) and minimum (0.19084) was recorded for Imperata cylindrical (Table 7.1).In the diet of chowsingha percentage of grass was found maximum (45.42) and minimum (3.34) for climber (Figure 7.1).During summers the diet of chowsingha constitutes 54.58% browse food items and 45.41% grass food items (Figure 7.2). Table (7.4) shows the values of 95% Bonferroni confidence interval values for different plant species utilised by chowsingha during summer season. During summer season, 54 tree, 7 shrub, 3 climber, 14 herb and 10 grass species were recorded with 17795 individual of these species. This value is used for calculating proportional availability. The results reveals that chowsingha preferred 10 tree, 1 shrub, 2 climber,

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2 herb and 3 grass species and completely avoided other species. Buchanania lanzan, Cassia fistula, Bahunia barigata, Gardinia latifolia, Albizia odoratissima, Grewia latifolia, Milosa tomentosa, Ixora arborea, Madhuca indica, Lenia coromandalica, Hilectrux izora, Bauhinia vahlii, Ziziphus oenoplia, Guizotia abyssinica, Ventilago maderaspatana, Eragrostis tenella, Heteropogon contortus and Dicanthium spp. are the plants which were utilised more than their availability. The species which were utilised less than their availability were Lagerstroemia parviflora and Imperata cylindrical. Ziziphus xylopyra, Terminalia tomentosa and Eulaliopsis binata were utilised in proportion to their availability. Except these, 65 species were sampled but not recorded from the diet, which suggests total avoidance by the chowsingha.

A total of 2635 plants fragments were examined to determine feeding habit of chowsingha during post monsoon season. During post monsoon,43 plant species (37 browse and 6 grass species) were identified from the pellet group of chowsingha. Out of 37 identified browse species proportion of Guizotia abyssinica was found maximum (10.967) and minimum (0.0759) for Gardinia latifolia and Gymnosporia spinosa. For grasses during post monsoon season proportion of Eragrostis tenella was maximum (12.827) and minimum (1.555) for Eulaliopsis binata (Table 7.1).The result also reveals that, in the diet of chowsingha during post monsoon season percentage of herb was found maximum (34.88) and minimum (2.24) for shrubs (Figure 7.1).The results show that during post monsoon season diet of chowsingha constitutes 72.98% browse items and 27.02% grass items(Figure 7.2).Table (7.5) shows the values of 95% Bonferroni confidence interval values for different plant species utilised by chowsingha during in post monsoon season. During post monsoon, 52 tree, 7 shrub, 3 climber, 16 herb and 11 grass species were recorded with 28126 individual of these species. This value is used for calculating proportional availability. The results shows that during post monsoon chowsingha preferred 19 tree, 3 climber, 3 herb and 1 grass species and completely avoided other species. Buchanania lanzan, Cassia fistula, Bahunia barigata, Emblica offcinalis, Aegle marmelos, Chloroxylon swietenia, Albizia odoratissima, Grewia latifolia, Anogeissus latifolia, Ziziphus xylopyra, Syzygium cumini, Flacourtia indica, Milosa tomentosa, Ixora arborea, Madhuca indica, Randia dumetorum, Lenia coromandalica, Mitragyna parvifolia, Butea monosperma, Bauhinia vahlii, Butea parviflora, Ziziphus oenoplia, Marsilea quadrifolia, Guizotia abyssinica, Desmodium triflorum and Cynodon dactylon are the

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plants which were utilised more than their availability. The species which were utilised less than their availability were Tectona grandis, Diospyros melanoxylon, Bamboo spp., Lantana camara, Elephantopus scaber, Ocimum canum and Dicanthium spp., Gardinia latifolia, Gymnosporia spinosa, Lagerstroemia parviflora, Terminalia tomentosa, Hilectrux izora, Sida spp., Eragrostis tenella, Apluda mutica, Eulaliopsis binata and Heteropogon contortus were utilised in proportion to their availability. Except these, 46 species were also sampledbut not recorded from the diet, which suggests total avoidance by the chowsingha during post monsoon season.

During winters a total of 3658 plant fragments were examined to investigate food and feeding habit of chowsingha in winter season.In winters 48 species (41 browse and 7 grass species) of plants were indentified from their diet. Out of all identified browse species from the pellet group of chowsingha proportion of Ocimum canum was maximum (8.911) and minimum (0.191) for Annona squamosa. Similarly for grasses, proportion of Dicanthium spp. was maximum (6.943) and minimum (0.628) for Pennisetum pedicellatum (Table 7.1).In the diet of chowsingha during winters percentage of trees was maximum (39.61) and minimum (3.81) for shrubs (Figure 7.1). During winters diet of chowsingha constitute 75.62% of browse items and 24.38% grass items (Figure 7.2). Table (7.6) represents the values of 95% Bonferroni confidence interval values for different plant species utilised by chowsingha in winters. During winter, 54 tree, 6 shrub, 4 climber, 12 herb and 10 grass species were recorded with 23692 individual of these species. This value is used for calculating proportional availability. The results suggest that in winters chowsingha preferred 21 tree, 3 climber, 1 shrub, 4 herb and 5 grass species and completely avoided other species. Cassia fistula, Bahunia barigata, Emblica offcinalis, Gardinia latifolia, Aegle marmelos, Kydia calycina, Albizia odoratissima, Grewia latifolia, Cleistanthus collinus, Ziziphus xylopyra, Syzygium cumini, Milosa tomentosa, Bridelia retusa, Acacia catechu, Ixora arborea, Madhuca indica, Mitragyna parvifolia, Butea monosperma, Soymida febrifuga, Terminalia tomentosa, Annona squamosa , Bauhinia vahlii, Ziziphus oenoplia, Butea parviflora, Hilectrux izora, Phyllanthus amarus, Elephantopus scaber, Sida spp., Ocimum canum, Pennisetum pedicellatum, Cynodon dactylon, Themeda quadrivalvis, Chloris barbataand Dicanthium spp. are the plants which were utilized more than their availability. The species which were utilized less than their availability wereLagerstroemia parviflora, Tectona grandis,

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Diospyros melanoxylon, Bamboo spp., Lantana camara, Marsilea quadrifolia, Eragrostis tenella and Heteropogon contortus. Chloroxylon swietenia, Lenia coromandalica, Pheonix aquilis, and Vallaris solanacea, were utilised in proportion to their availability. Except these, another 40 species were sampled but not recorded from the diet, which suggests that they were totally avoided by the chowsingha during winter season.

A part from identified plants fragments 1524, 1143 and 467 unidentified fragments of monocot and dicot was found in summer, post-monsoon and winter season respectively and there proportion was 30.12% and 69.88% in summers, 28.60% and 71.39% in post monsoon and 76.01% and 23.98% in winters respectively (Figure 7.7).

7.3.2- Gaur diet While investigating food and feeding habit of gaur in summer season, 2016 plant fragments were identified. These 2016 plant fragments were belongs to 34 species (26 browse and 8 grass species) of plants. Among browse species proportion of Bamboo spp. was maximum (12.19029) and minimum (0.099108) for three species namely Aegle marmelos, Ficus hispida and Sida acuta. Out of all grasses identified from the dung pile of gaur the proportion of Heteropogon contortus was found maximum (22.49752) and minimum (0.297324) for Pennisetum pedicellatum (Table 7.2).In the diet of gaur percentage of grass was found maximum (62.09) and minimum (3.32) for climber (Figure 7.3). During summer gaur diet constitutes37.90% browse items and 62.09% grass items (Figure 7.4).Table (7.7) shows the values of 95% Bonferroni confidence interval values for different plant species utilized by gaur during summer season. During summer season, 54 tree, 7 shrub, 3 climber, 14 herb and 10 grass species were recorded with 17795 individual of these species. This value is used for calculating proportional availability. The results reveals that in summer gaur preferred 6 tree, 1 shrub, 2 climber, 3 herb and 5 grass species and totally avoided other species. Cassia fistula, Milosa tomentosa, Ficus hispida, Schleichera oleosa, Madhuca indica, Ficus glomerata, Bauhinia vahlii, Butea parviflora, Hilectrux izora, Phyllanthus amarus, Desmodium spp, Spirodela polyrhiza, Imperata cylindrical, Pennisetum pedicellatum, Cynodon dactylon, Dicanthium spp, and Eulaliopsis binata are the plants which were utilized more than their availability. The species which were utilised less than their availability wereBuchanania lanzan, Aegle marmelos, Syzygium cumini, Lenia coromandalica, Tectona grandis, Diospyros melanoxylon,

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Grewia spp, Lantana camara, Guizotia abyssinica, Sida acuta, Eragrostis tenella, Themeda quadrivalvis and Heteropogon contortus. In the same way the species which were utilized in proportion to their availability were Casearia tomentosa, Bamboo spp., Grewia hirsute and Sida spp.Except these, another 55 species were sampled during summers but not recorded from the diet, which suggests total avoidance by the gaur.

In post monsoon season a total of 6960 fragments of different plant species were indentified while investigating post monsoon feeding habit of gaur. During post monsoon season 48 species (38 browse and 10 grass species) were identified from different dung piles of gaur. Out of 38 browse species, proportion of Desmodium triflorumwas maximum (8.14) and minimum (0.043) for Cleistanthus collinus. Among grasses proportion of Heteropogon contortus was maximum (10.431) and minimum (0.445) for Imperata cylindrical (Table 7.2). In the diet grass percentage was maximum (39.08) and minimum (2.74) for climbers(Figure 7.3). The result also reveals that during post monsoon season gaur diet constitute 60.92% browse items and 39.08% grass items (Figure 7.4). Table (7.8) shows the values of 95% Bonferroni confidence interval values for different plant species utilized by gaur in post monsoon season. During post monsoon, 52 tree, 7 shrub, 3 climber, 16 herb and 11 grass species were recorded with 28126 individual of these species. This value is used for calculating proportional availability. The results reveals that during post monsoon gaur preferred 13 tree, 3 climber, 3 shrub, 7 herb and 5 grass species and completely avoided other species.Cassia fistula, Bahunia barigata, Emblica offcinalis, Aegle marmelos, Chloroxylon Swietenia, Syzygium cumini, Acacia catechu, Ixora arborea, Madhuca indica, Mitragyna parvifolia, Butea monosperma, Terminalia tomentosa, Ficus glomerata, Bauhinia vahlii, Butea parviflora, Ziziphus oenoplia, Hilectrux izora, Bamboo spp., Grewia hirsute, Phyllanthus amarus, Marsilea quadrifolia, Guizotia abyssinica, Desmodium spp., Ocimum basilicum, Desmodium triflorum, Ocimum canum, Pennisetum pedicellatum, Cynodon dactylon, Apluda mutica, Chloris barbata and Heteropogon contortus are the species which were utilized more than their availability.The species which were utilized less than their availability were Gardinia latifolia, Cleistanthus collinus, Tectona grandis, Diospyros melanoxylon, Lantana camara, Spirodela polyrhiza, Eragrostis tenella, Imperata cylindrical, Themeda quadrivalvis and Dicanthium spp., Anogeissus latifolia, Milosa tomentosa,

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Pheonix aquilis, Vitex negundo, Tribulus terrestris, Sida spp. and Eulaliopsis binata are the species which were utilized in proportion to their availability. Except these, there were 41plant species were sampled but not recorded from the diet during post monsoon which suggests total avoidance by the gaur.

While investigating winter food and feeding habit of gaur 5076 plant fragments were evaluated. A total of 50 species (40 browse and 10 grass species) of plants were identified during winter season from the different dung piles of gaur. Among identified browse species proportion of Marsilea quadrifolia was maximum (8.983) and minimum (0.078) for Diospyros Montana. For grasses, proportion of Eragrostis tenella was maximum (11.13) and minimum (0.216) for Cyperus scariosus (Table 7.2). It was also found that grass percentage was maximum (32.37) in the diet and minimum (2.97) for climbers(Figure 7.3). The result also shows that during winters the diet of gaur constitutes 67.63% browse items and 32.37% grass items (Figure 7.4). Table (7.9) shows the values of 95% Bonferroni confidence interval values for different plant species utilized by gaur in winters. During winter, 54 tree, 6 shrub, 4 climber, 12 herb and 10 grass species were recorded with 23692 individual of these species. This value is used for calculating proportional availability. The results shows that in winters gaur preferred 15 tree, 2 climber, 4 shrub, 4 herb and 7 grass species and completely avoided other species. Cassia fistula, Bahunia barigata, Emblica offcinalis, Gymnosporia spinosa, Aegle marmelos, Albizia odoratissima, Grewia latifolia, Anogeissus latifolia, Cleistanthus collinus, Syzygium cumini, Acacia catechu, Ixora arborea, Madhuca indica, Butea monosperma, Casearia tomentosa, Bauhinia vahlii, Ziziphus oenoplia, Hilectrux izora, Bamboo spp., Pheonix aquilis, Grewia hirsute, Phyllanthus amarus, Elephantopus scaber, Guizotia abyssinica, Sida spp., Pennisetum pedicellatum, Cynodon dactylon, Cyperus scariosus, Themeda quadrivalvis, Chloris barbata, Dicanthium spp. and Eulaliopsis binata are the plant species which were utilized more than their availability. The species which were utilized less than their availability were Lenia coromandalica, Tectona grandis, Lantana camara, Marsilea quadrifolia, Casia tora, Sida acuta and Eragrostis tenella. Chloroxylon swietenia, Terminalia tomentosa, Diospyros melanoxylon, Diospyros montana, Vallaris solanacea, Tribulus terrestris, Parthenium hysterophorus, Ocimum canum, Imperata cylindrical and Heteropogon contortus are the species which were utilized in proportion to their availability. Except these 37 species were also sampled

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A part from identified plants fragments 956, 1155 and 757 unidentified fragments of monocot and dicot was found in summer, post-monsoon and winter season respectively and there proportion was 32.74% and 67.25% in summers, 65.19% and 34.81% in post monsoon and 77.81% and 22.19% in winters respectively (Figure 7.7).

7.3.3- Sambar diet A total of 2272 plant fragments were identified during investigating summer food habit of sambar. For sambar total 57 plant species (49 browse and 8 grass species) was identified from the samples of pellet group. Out of all browse recorded from the diet of sambar, the proportion of Butea parviflora was maximum (10.83333) and minimum (0.04386) for Gymnosporia spinosa. Similarly among grasses proportion of Eragrostis tenella was maximum (18.72807) followed by Thysanolaena maxima (16.18421) and minimum (0.131579) for Chloris barbata (Table 7.3). In the diet of sambar percentage of grass was found maximum (41.63), followed by trees (23.73), climbers (12.54), herb (11.84) and minimum (10.26) for shrubs(Figure 7.5). During summers sambar diet constitutes 58.37% browse items and 41.62% grass items (Figure 7.6).Table (7.10) shows the values of 95% Bonferroni confidence interval values for different plant species utilized by sambar in summer season. During summer season, 54 tree, 7 shrub, 3 climber, 14 herb and 10 grass species were recorded with 17795 individual of these species. This value is used for calculating proportional availability. The result shows that in summer sambar preferred 14 tree, 3 shrub, 3 climber, 3 herb and 3 grass species and totally avoided other species. Cassia fistula, Aegle marmelos, Grewia latifolia, Ziziphus xylopyra, Flacourtia indica, Bridelia retusa, Garuga pinnata, Ixora arborea, Madhuca indica, Schrebera swietenioides, Ficus infectoria, Butea monosperma, Bombax ceiba, Annona squamosa, Bauhinia vahlii, Butea parviflora, Asparagus racemosum, Hilectrux izora, Grewia hirsute, Holarrhena antidysenterica, Phyllanthus amarus, Desmodium Spp., Spirodela polyrhiza, Pennisetum pedicellatum, Cynodon dactylon and Thysanolaena maxima are the plants which were utilized more than their availability. The species which were utilised less than their availability wereBuchanania lanzan, Gymnosporia spinosa, Tectona grandis, Diospyros Montana, Lantana camara, Elephantopus scaber, Sida spp, Ocimum canum, Eragrostis tenella, Chloris barbata and

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Heteropogon contortus. In the same way the species which were utilized in proportion to their availability were Bahunia barigata, Semecarpus anacardium, Albizia odoratissima, Syzygium cumini, Acacia catechu, Ougeinia oojeinensis, Casearia tomentosa, Bamboo spp., Pheonix aquilis, Grewia spp, Sida acuta, Xanthium strumaxium, Imperata cylindrical and Cyperus scariosus. Except these, another 38 species were sampled during summers but not recorded from the diet, which suggests total avoidance by the sambar.

During post monsoon 4515 plant fragments were examined for investigating post monsoon food and feeding habit. In post monsoon season 57 plant species (50 browse and 7 grass species) were identified from the different pellet group of sambar. Out of 50 browse species proportion of Marsilea quadrifolia was maximum (10.077) and minimum (0.066) for Ficus hispida. For grasses proportion of Eragrostis tenella was maximum (15.26) and minimum (0.753) for Pennisetum pedicellatum (Table 7.3). During post monsoon season percentage of herb was maximum (32.53) and minimum (4.61) for climbers (Figure 7.5). The result also reveals that during post monsoon season diet of sambar constitutes 70.61% browse items and 29.39% grass items(Figure 7.6). Table (7.11) shows the values of 95% Bonferroni confidence interval values for different plant species utilized by sambar during post monsoon season. During post monsoon, 52 tree, 7 shrub, 3 climber, 16 herb and 11 grass species were recorded with 28126 individual of these species. This value is used for calculating proportional availability. The results shows that during post monsoon sambar preferred 22 tree, 4 climber, 4 shrub, 3 herb and 3 grass species and completely avoided other species. Cassia fistula, Bahunia barigata, Emblica offcinalis, Aegle marmelos, Chloroxylon Swietenia, Albizia odoratissima, Grewia latifolia, Anogeissus latifolia, Cleistanthus collinus, Ziziphus xylopyra, Syzygium cumini, Milosa tomentosa, Acacia catechu, Schleichera oleosa, Ixora arborea, Madhuca indica, Butea monosperma, Terminalia tomentosa, Bombax ceiba, Ougeinia oojeinensis, Ficus glomerata, Diospyros montana, Bauhinia vahlii, Butea parviflora, Ziziphus oenoplia, Hilectrux izora, Pheonix aquilis, Grewia hirsute, Holarrhena antidysenterica, Marsilea quadrifolia, Ocimum basilicum, Desmodium triflorum, Pennisetum pedicellatum, Cynodon dactylon and Chloris barbata are the plants which were utilized more than their availability. The species which were utilized less than their availability were Lagerstroemia parviflora, Tectona grandis, Diospyros

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melanoxylon, Lantana camara, Vitex negundo, Spirodela polyrhiza, Casia tora, Vallaris solanacea, Sida spp., Guizotia abyssinica, Sida acuta, Dicanthium spp. and Heteropogon contortus. Buchanania lanzan, Ficus hispida, Bamboo spp., Phyllanthus amarus, Elephantopus scaber, Ocimum canum, Eragrostis tenella and Apluda mutica were utilized in proportion to their availability. Except these, 32 species were also sampled but not recorded from the diet, which suggests that they were completely avoided by sambar during post monsoon season.

While investigating winter feeding habit of sambar 3431 plant fragments were evaluated. During winters season a total of 51 species (44 browse and 7 grass species) of plants were identified from the different samples of pellet group of sambar. Out of 44 identified browse species in the diet of sambar, proportion of Marsilea quadrifolia was maximum (13.144) and minimum (0.058) for Alangium salviifolium. In the same way for grasses, proportion of Eragrostis tenella was maximum (3.759) and minimum (0.466) for Pennisetum pedicellatum (Table 7.3). In the diet percentage of herbs were maximum (38.5) and minimum (6.79) for climbers (Figure 7.5). The result also suggests that during winter’s sambar diet constitutes 88.95% browse food items and 11.05% grass food items(Figure 7.6).Table (7.12) represents the values of 95% Bonferroni confidence interval values for different plant species utilized by sambar in winters. During winter, 54 tree, 6 shrub, 4 climber, 12 herb and 10 grass species were recorded with 23692 individual of these species. This value is used for calculating proportional availability. The results suggest that in winters sambar preferred 20 tree, 4 climber, 3 shrub, 6 herb and 4 grass species and completely avoided other species. The plant species which were utilized more than their availability were Cassia fistula, Bahunia barigata, Emblica offcinalis, Gardinia latifolia, Pterocarpus marsupium, Aegle marmelos, Chloroxylon swietenia, Albizia odoratissima, Grewia latifolia, Anogeissus latifolia, Syzygium cumini, Flacourtia indica, Bridelia retusa, Acacia catechu, Ixora arborea, Madhuca indica, Randia dumetorum, Butea monosperma, Terminalia tomentosa, Diospyros melanoxylon, Bauhinia vahlii, Ziziphus oenoplia, Asparagus racemosum, Butea parviflora, Hilectrux izora, Pheonix aquilis, Grewia hirsute, Marsilea quadrifolia, Vallaris solanacea, Elephantopus scaber, Guizotia abyssinica, Parthenium hysterophorus, Sida spp., Pennisetum pedicellatum, Cynodon dactylon, Chloris barbata and Eulaliopsis binata. The species which were utilized less than their availability were Alangium salviifolium, Lagerstroemia parviflora,

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Tectona grandis, Bamboo spp., Lantana camara, Casia tora, Sida spp., Eragrostis tenella, Dicanthium spp. and Heteropogon contortus.Buchanania lanzan, Lenia coromandalica, Casearia tomentosa, and Ocimum canum were utilized in proportion to their availability. Except these, another 35 species were sampled but not recorded from the diet, which suggests that they were totally avoided by the sambar during winter season. A part from identified plants fragments 1536, 1150 and 553 unidentified fragments of monocot and dicot was found in summer, post-monsoon and winter season respectively and there proportion was 52.21% and 47.78% in summers, 54.69% and 45.31% in post monsoon and 23.86% and 76.14% in winters respectively (Figure 7.7). 7.3.4- Feeding on Lantana by different ungulates: Gaur and sambar were found to feed on Lantana camera during summer season and the proportion in the diet was 0.54% and 2.80% respectively, however chowsingha was not found to feed on lantana during summer. In the same way in post monsoon season chowsingha, gaur and sambar were reorded to feed on lanatana and their proportion in the diet were 1.10%, 0.79% and 1.50% respectively. In winter season the parpotion of Lantana camera in the diet of chowsingha, gaur and samabr were recorded 1.39%, 1.31% and 3.75% respectively (Figure 7.8) Based on finding it was concluded that there is difference in food and feeding habits of different ungulate species and based on this the null hypothesis was rejected. 7.4 Discussion Conservation of the species requires a good understanding of its ecology (especially habitat and feeding ecology). Hanley (1984) and Ilyas & Khan (2004) suggested that the herbivore ungulates are the best indicator of health of the forest and understanding their food preferences and changes resulting from biotic influences, are important in interpreting relationships between the environment and the consumer (Leopold and Krausman, 1987). Consumer-resource interactions is the core motif of ecological food chains or food webs (Bascompte, 2009) and are an umbrella term for a variety of more specialized types of biological species interactions including plant-herbivore. The study and modeling of these kinds of interactions is comparatively new in population ecology (Murdoch et al. 2003; Turchin, 2003). According to Gotelli (2001), the relationship between herbivores and plants is cyclic. When palatable species of plants are 231

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abundant, herbivores increase in numbers, reducing the plant density and causes decline in the number of herbivores which eventually resulted into the recovery of plants and start the same cycle again. This suggests that the population of the herbivore fluctuates around the carrying capacity of the food source. Thus it is important to study the feeding ecology of herbivores; also keystone herbivores keep vegetation populations in check and allow for a greater diversity of both herbivores and plants (Smith and Smith, 2001).

Because of cryptic nature of ungulates, it is difficult to have direct observations of animal to study its feeding ecology. Moreover animal cannot be sacrificed for applying the oesophageal and rumen fistula technique for collecting rumen content. Therefore it is most relevant to rely on the indirect evidences (pellet group) to study the feeding ecology of the species. Microhistology technique was used to understand the feeding ecology of the musk deer. The technique was first described by Baumgartner and Martin (1939) and reviewed by Holechek et al. (1982). It is un- doubtly the only technique to study the feeding ecology of small secretive species such as musk deer. However this method for determining food habits of small herbivores can be biased by differential digestibility of ingested plant species (Holechek et al. 1982). Forbs are usually highly digestible and as a result, underestimated by feacal analyses (Vavra et. al., 1978; Vavra and Holechek, 1980; McInnis .et al. 1983). Some grass and browse species are overestimated by feacal analyses, while others are underestimated (Dearden et al. 1975; Leslie et al.1983). Research on food habits of sympatric ungulates has not much progressed in India as it has in Africa (e.g. Talbot and Talbot, 1962, Gwynne and Bell, 1968, Leuthold, 1970, Bell, 1971, Jarman and Sinclair, 1979). In India Schaller (1967) has listed the food plant of wild ungulates in Kanha and Berwick (1976) made cafeteria and field feeding traits in the hottest and driest season in Gir. Feeding patterns of different mountain ungulates was first reported by Schaller (1977) but the study did not provide their detailed dietary profile, where as Mishra and Johnsingh (1996), Ilyas and Khan (2004), and Ilyas and Syed (2014) studied the diet of temperate and sub-alpine ungulates. Furthermore Haleem et al. (2014b) studied food and feeding habit of chowsingha in Pench Tiger Reserve. Different ungulate species was found to be having varying food and feeding habits. Some may be primarily grazer and

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occasionally browser and on the other hand others may be primarily browser but occasionally graze also.

Our result suggested that chowsingha preferred dicot over monocot in general but proportion of grasses was found maximum in the diet which is similar to the study of Baskaran et al. (2011). Among all identified plants species from the diet of chowsingha proportion of Eragrostis tenella was found maximum throughout different season, which suggested that the animal is largely grazing and it may be due to the high availability of Eragrostis tenella grass. However, the result of Bonferroni shows that chowsingha mostly prefer Albizia odoratissima in summers, Guizotia abyssinica in post monsoon and Ocimum canum in winter seasons, which may be due to availability of leaves at the accessible height of tree seedling and sapling in summers. It was also observed that Guizotia abyssinica and Ocimum canum sprout in large numbers after rainy season and available throughout the year for chowsingha. The outcome of the study reveals that inspite of availability of variety of plant food items, chowsingha prefers to feed on some of the selected plant material and avoids rest of the plant species, and therefore they are considered to be specialist that is also supported by Levine’s Index for niche breath (Haleem et al. 2014b).

Gaur has been described basically as a grazer (Brander, 1923, Krishnan, 1972). Similarly, it has been reported by Kumar et al. (2004) that grasses are major component of gaur diet. It is well studied that there as in association of body mass with diet of lower quality (Clauss et al. 2013). Since gaur is having largest body mass among ungulates but the available food in the form of grasses having low nutritive value. Therefore intakes of these grasses were found high in comprasion to other food resources. This is also supported by result obtained in micro-histological investigation where content of grass fragments were present more in numbers. Gaur feeds on variety of plants food items and this habit enables them to colonize wider range of vegetation types (from dry tropical forest to moist tropical forest) of the study area. The results reveal that on average grasses (44.5%) were most preferred food items by gaur in Pench Tiger Reserve where as climbers were the least one (3.01%). These findings are supported by Krishnan (1972), Peden et al. (1974), Reynolds & Hawley (1987), Sathyanarayana & Murthy (1995) and Nayak & Patra (2015). However our results were contrary to the findings of Shukla & Khare (1998) and Gad & Shyama

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(2009). Gad and Sharma (2009) observed that gaur diet consists of herbs, trees, shrubs, and grases with high preference for trees. Whereas these observations are in agreement with the findings of Shukla and Khare (1998), who recorded that gaur grazed and browsed on a much wider variety of plants than any other ungulates of India with a preference for the upper portions of plants such as leaf blades, stems, seed and flower of grass species. When seasonal comparison was done, not much difference were found in their diet during post monsoon and winter where as in summer the dietary proportion of herbs were least. It was observed that during summer herbs were available in small amount than to post monsoon and winter, therefore gaur switched over towards other food items. Another reason may be overgrazing of herbs by other sympatric species such as cheetal, sambar and nilgai, whose population are more abundant than gaur. Among all Eragrostis tenella was found more or less most preferred food item in winter and post monsoon season however in summer season their preference was recorded for Heteropogon contortus. We need to further investigate the reason for the preference of that particular species in summer season.

Sambars occur in tropical and temperate regions at high and low altitudes, and are able to utilise a wide variety of food plants (Schaller, 1967; Downes, 1983; Ngampongsai, 1987; Bentley, 1998). Sambars are known to feed on tree seedlings, shrubs, grasses, forbs and ferns (Dinerstein, 1987; Ngampongsai, 1987; Padmalal et al. 2003, Geist, 1998 and Leslie, 2011). Our study also shows that sambar diet constitute different species of trees, climbers, shrubs, herbs as well as grasses and this is again supported by King (1990), Varman and Sukumar (1993) Stafford (1997) who variously described sambar as intermediate feeders utilising a mixture of browse and herbs. Among all contribution of grasses in the diet were found more or less maximum which is contrary to the findings of Jhonsingh and Sankar (1991) who found that contribution of grasses in the diet of sambar did not very much. The study also reveals that sambar in general primarily prefer browse food items throughout different seasons which suggest that they are primarily browser. This is supported by findings of Ismail and Jiwan (2014) who suggested that Sambar deer preferred woody species more than non-woody species and they are browser animals. These are also supported by findings of Santiapillai et al. (1981), Burke (1982), Ngampongsai (1987), Shea et al. (1990), and Semiadi et al. (1995). Sambar was found to prefer

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Eragrostis tenella during post monsoon and summer season. However during winter season apart from the Eragrostis tenella the sambar give more preference to a particular herb called Marsilea quadrifolia. The contribution of Marsilea quadrifolia was 13.14% while Eragrostis tenella was 3.75% in winters. Again we need to further investigate the cause for the preference of that particular species in winter season.

Lantana camera primarily considered to be an exotic weed species, is largely distributed in the tiger reserve due to its fast growing nature. The LULC map also revealed that 1.13% of forest area is covered with Lantana camera. It is reported that Lantana is having toxic substances of Lantadene A and B, Pentacyclic Triterpene acids, Dihydrolantadene A, and Icterogenin (Anonymous, 2016), therefore it should be avoided by ungulates as their food, but in PTR it has been observed that ungulates are also feeding on Lantana. The finding of micro-histological analysis also suggest that inspite of feeding on other food resources, they were found to feed on Lantana camera throughout different season; however proportion of Lantana camera in their diet was in small fragments of about 1.5%,. The present study suggests that PTR has enough food resources and capable of supporting ungulate population, therefore feeding of Lantana may be considered as an opportunistic or stress feeding behaviour. Furthermore it could be suggested that consumption of Lantana by ungulates is a matter of surprise and needs further detailed investigation.

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Table-7.1 Chowsingha diet in different season: occurrence of fragments of tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season.

Season S.No Plant Species Summer Post Monsoon Winter N n P N n P N n P Trees 1 Buchanania lanzan 82 15 1.43 99 29 1.1005693 96 15 0.4100601 2 Alangium salviifolium 8 0 0 4 0 0 4 0 0 3 Cassia fistula 130 74 7.06 152 89 3.3776091 184 126 3.4445052 4 Terminalia arjuna 4 0 0 1 0 0 1 0 0 5 Bahunia barigata 86 9 0.86 48 12 0.455408 77 49 1.3395298 6 Emblica offcinalis 52 0 0 77 29 1.1005693 63 55 1.5035539 7 Gardinia latifolia 17 9 0.86 12 2 0.0759013 13 11 0.3007108 8 Ficus benghalensis 1 0 0 1 0 0 1 0 0 9 Pterocarpus marsupium 1 0 0 1 0 0 1 0 0 10 Gymnosporia spinosa 12 0 0 18 2 0.0759013 13 0 0 11 Aegle marmelos 37 0 0 16 6 0.227704 34 46 1.2575178 12 Kydia calycina 34 0 0 27 0 0 26 18 0.4920722 13 Semecarpus anacardium 27 0 0 27 0 0 27 0 0 14 Chloroxylon Swietenia 122 0 0 111 39 1.4800759 185 32 0.874795 15 Hymenodictyon excelsum 2 0 0 5 0 0 2 0 0 16 Delbergia sissoo 6 0 0 0 0 0 0 0 0 17 Albizia odoratissima 5 96 9.16 5 73 2.7703985 5 99 2.7063969 18 Grewia latifolia 70 10 0.95 72 16 0.6072106 62 36 0.9841443 19 Anogeissus latifolia 63 0 0 74 24 0.9108159 77 0 0 20 Delbergia peniculata 9 0 0 9 0 0 9 0 0

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21 Gardinia gummifera 0 0 0 0 0 0 4 0 0 22 Cleistanthus collinus 2 0 0 4 0 0 2 48 1.3121925 23 Ziziphus xylopyra 31 22 2.10 42 39 1.4800759 42 36 0.9841443 24 Adina cordifolia 3 0 0 3 0 0 3 0 0 25 Acacia leucophloca 5 0 0 5 0 0 5 0 0 26 Syzygium cumini 271 0 0 167 69 2.6185958 193 185 5.0574084 27 Flacourtia indica 45 0 0 10 35 1.3282732 20 0 0 28 Milosa tomentosa 192 19 1.81 240 41 1.5559772 208 74 2.0229634 29 Bridelia retusa 26 0 0 29 0 0 29 21 0.5740842 30 Ficus hispida 7 0 0 4 0 0 25 0 0 31 Garuga pinnata 1 0 0 1 0 0 1 0 0 32 Acacia catechu 23 0 0 24 0 0 24 18 0.4920722 33 Schleichera oleosa 20 0 0 16 0 0 17 0 0 34 Careya arborea 2 0 0 1 0 0 1 0 0 35 Sterculia urens 1 0 0 1 0 0 3 0 0 36 Lagerstroemia parviflora 650 16 1.53 609 87 3.3017078 727 42 1.1481684 37 Ixora arborea 78 26 2.48 121 79 2.9981025 78 93 2.5423729 38 Madhuca indica 25 33 3.15 36 24 0.9108159 30 54 1.4762165 39 Randia dumetorum 71 0 0 2 4 0.1518027 22 0 0 40 Schrebera swietenioides 1 0 0 1 0 0 1 0 0 41 Lenia coromandalica 117 19 1.81 148 21 0.7969639 132 29 0.7927829 42 Mitragyna parvifolia 40 0 0 24 9 0.341556 44 41 1.1208311 43 Ficus infectoria 2 0 0 2 0 0 2 0 0 44 Butea monosperma 27 0 0 47 34 1.2903226 41 59 1.6129032 45 Soymida febrifuga 21 0 0 21 0 0 21 43 1.1755057 46 Terminalia tomentosa 104 6 0.57 107 19 0.7210626 117 94 2.5697102 47 Boswellia serrata 5 0 0 5 0 0 5 0 0 48 Bombax ceiba 2 0 0 2 0 0 2 0 0

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49 Annona squamosa 1 0 0 0 0 0 5 7 0.1913614 50 Tectona grandis 1035 0 0 890 16 0.6072106 1091 67 1.831602 51 Diospyros melanoxylon 872 0 0 428 0 0 657 51 1.3942045 52 Ougeinia oojeinensis 44 0 0 60 0 0 51 0 0 53 Casearia tomentosa 59 0 0 25 0 0 26 0 0 54 Ficus glomerata 1 0 0 1 0 0 1 0 0 55 Diospyros Montana 28 0 0 10 0 0 10 0 0 Climber 56 Bauhinia vahlii 5 35 3.34 5 48 1.8216319 9 87 2.3783488 57 Ziziphus oenoplia 120 53 5.06 147 81 3.0740038 112 94 2.5697102 58 Butea parviflora 1 0 0 7 5 0.1897533 27 17 0.4647348 59 Asparagus racemosum 0 0 0 0 0 0 21 0 0 Shrub 60 Hilectrux izora 140 51 4.87 175 23 0.8728653 115 39 1.0661564 61 Bamboo spp. 353 0 0 508 7 0.2656546 419 28 0.7654456 62 Pheonix aquilis 129 0 0 262 0 0 170 21 0.5740842 63 Grewia hirsute 81 0 0 259 0 0 234 0 0 64 Grewia spp 58 0 0 20 0 0 3 0 0 65 Holarrhena antidysenterica 10 0 0 0 0 0 0 0 0 66 Lantana camara 1155 0 0 1160 29 1.1005693 999 51 1.3942045 67 Vitex negundo 0 0 0 58 0 0 0 0 0 Herb 68 Phyllanthus amarus 127 0 0 787 0 0 208 45 1.2301804 69 Marsilea quadrifolia 0 0 0 1588 209 7.9316888 2768 89 2.4330235 70 Casia tora 1441 0 0 1463 0 0 1165 0 0 71 Vallaris solanacea 158 0 0 314 0 0 219 32 0.874795 72 Tribulus terrestris 258 0 0 59 0 0 28 0 0 73 Elephantopus scaber 402 0 0 635 8 0.3036053 370 128 3.4991799

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74 Guizotia abyssinica 949 70 6.68 1040 289 10.967742 1178 289 7.9004921 75 Sida acuta 367 0 0 1121 0 0 980 0 0 76 Desmodium spp. 3 0 0 49 0 0 0 0 0 77 Xanthium strumaxium 57 0 0 0 0 0 0 0 0 78 Sida spp. 9 0 0 324 59 2.2390892 63 71 1.9409513 79 Ventilago maderaspatana 8 9 0.86 0 0 0 0 0 0 80 Spirodela polyrhiza 7 0 0 148 0 0 0 0 0 81 Sida spp. 777 0 0 865 0 0 1079 0 0 82 Ocimum canum 158 0 0 453 99 3.7571157 386 326 8.9119738 83 Parthenium hysterophorus 0 0 0 372 0 0 36 0 0 84 Ocimum basilicum 0 0 0 156 0 0 0 0 0 85 Desmodium triflorum 0 0 0 842 255 9.6774194 0 0 0 Grasses 86 Eragrostis tenella 3798 257 24.52 5477 338 12.827324 4871 247 6.7523237 87 Imperata cylindrical 51 2 0.19 184 0 0 103 0 0 88 Pennisetum pedicellatum 3 0 0 94 0 0 46 23 0.6287589 89 Cynodon dactylon 18 0 0 504 87 3.3017078 55 89 2.4330235 90 Themeda quadrivalvis 218 0 0 334 0 0 98 39 1.0661564 91 Chloris barbata 208 0 0 277 0 0 182 51 1.3942045 92 Cyperus scariosus 74 0 0 12 0 0 20 0 0 93 Dicanthium spp. 512 64 6.11 1989 86 3.2637571 1311 254 6.9436851 94 Eulaliopsis binata 254 12 1.15 387 41 1.5559772 55 0 0 95 Heteropogon contortus 1306 141 13.45 1811 112 4.2504744 1842 189 5.1667578 96 Apluda mutica 0 0 0 395 48 1.8216319 0 0 0 Sub-Total 17795 1048 100 28126 2635 100 23692 3658 100 Browse Unidentified 0 1065 0 816 0 355 Grass Unidenstified 0 459 0 327 0 112 N = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence

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Table-7.2 Gaur diet in different season: occurrence of fragments of tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season.

Season S.No Plant Species Summer Post Monsoon Winter N n P N n P N n P Trees 1 Buchanania lanzan 82 3 0.14 99 0 0 96 17 0.33 2 Alangium salviifolium 8 0 0 4 0 0 4 0 0 3 Cassia fistula 130 58 2.87 152 85 1.22 184 139 2.73 4 Terminalia arjuna 4 0 0 1 0 0 1 0 0 5 Bahunia barigata 86 0 0 48 28 0.4 77 59 1.16 6 Emblica offcinalis 52 0 0 77 38 0.54 63 62 1.22 7 Gardinia latifolia 17 0 0 12 4 0.05 13 0 0 8 Ficus benghalensis 1 0 0 1 0 0 1 0 0 9 Pterocarpus marsupium 1 0 0 1 0 0 1 0 0 10 Gymnosporia spinosa 12 0 0 18 0 0 13 9 0.17 11 Aegle marmelos 37 2 0.09 16 29 0.41 34 31 0.61 12 Kydia calycina 34 0 0 27 0 0 26 0 0 13 Semecarpus anacardium 27 0 0 27 0 0 27 0 0 14 Chloroxylon Swietenia 122 0 0 111 48 0.68 185 49 0.96 15 Hymenodictyon excelsum 2 0 0 5 0 0 2 0 0 16 Delbergia sissoo 6 0 0 0 0 0 0 0 0 17 Albizia odoratissima 5 0 0 5 0 0 5 13 0.25 18 Grewia latifolia 70 0 0 72 0 0 62 29 0.57 19 Anogeissus latifolia 63 0 0 74 18 0.25 77 32 0.63 20 Delbergia peniculata 9 0 0 9 0 0 9 0 0

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21 Gardinia gummifera 0 0 0 0 0 0 4 0 0 22 Cleistanthus collinus 2 0 0 4 3 0.04 2 8 0.15 23 Ziziphus xylopyra 31 0 0 42 0 0 42 0 0 24 Adina cordifolia 3 0 0 3 0 0 3 0 0 25 Acacia leucophloca 5 0 0 5 0 0 5 0 0 26 Syzygium cumini 271 19 0.94 167 154 2.21 193 235 4.62 27 Flacourtia indica 45 0 0 10 0 0 20 0 0 28 Milosa tomentosa 192 47 2.33 240 67 0.96 208 0 0 29 Bridelia retusa 26 0 0 29 0 0 29 0 0 30 Ficus hispida 7 2 0.09 4 0 0 25 0 0 31 Garuga pinnata 1 0 0 1 0 0 1 0 0 32 Acacia catechu 23 0 0 24 13 0.18 24 18 0.35 33 Schleichera oleosa 20 7 0.34 16 0 0 17 0 0 34 Careya arborea 2 0 0 1 0 0 1 0 0 35 Sterculia urens 1 0 0 1 0 0 3 0 0 36 Lagerstroemia parviflora 650 0 0 609 0 0 727 0 0 37 Ixora arborea 78 0 0 121 69 0.99 78 68 1.33 38 Madhuca indica 25 41 2.03 36 64 0.919 30 46 0.9 39 Randia dumetorum 71 0 0 2 0 0 22 0 0 40 Schrebera swietenioides 1 0 0 1 0 0 1 0 0 41 Lenia coromandalica 117 3 0.14 148 0 0 132 17 0.33 42 Mitragyna parvifolia 40 0 0 24 16 0.22 44 0 0 43 Ficus infectoria 2 0 0 2 0 0 2 0 0 44 Butea monosperma 27 0 0 47 39 0.56 41 39 0.76 45 Soymida febrifuga 21 0 0 21 0 0 21 0 0 46 Terminalia tomentosa 104 0 0 107 49 0.7 117 32 0.63 47 Boswellia serrata 5 0 0 5 0 0 5 0 0 48 Bombax ceiba 2 0 0 2 0 0 2 0 0

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49 Annona squamosa 1 0 0 0 0 0 5 0 0 50 Tectona grandis 1035 6 0.29 890 28 0.4 1091 97 1.91 51 Diospyros melanoxylon 872 20 0.99 428 52 0.74 657 127 2.5 52 Ougeinia oojeinensis 44 0 0 60 0 0 51 0 0 53 Casearia tomentosa 59 12 0.59 25 0 0 26 18 0.35 54 Ficus glomerata 1 19 0.94 1 27 0.38 1 0 0 55 Diospyros montana 28 0 0 10 0 0 10 4 0.07 Climber 56 Bauhinia vahlii 5 36 1.78 5 62 0.89 9 87 1.71 57 Ziziphus oenoplia 120 0 0 147 98 1.4 112 64 1.26 58 Butea parviflora 1 31 1.53 7 31 0.44 27 0 0 59 Asparagus racemosum 0 0 0 0 0 0 21 0 0 Shrub 60 Hilectrux izora 140 34 1.68 175 93 1.33 115 88 1.73 61 Bamboo spp. 353 246 12.2 508 393 5.64 419 387 7.62 62 Pheonix aquilis 129 0 0 262 55 0.79 170 69 1.35 63 Grewia hirsuta 81 19 0.94 259 88 1.26 234 79 1.55 64 Grewia spp 58 5 0.24 20 0 0 3 0 0 65 Holarrhena antidysenterica 10 0 0 0 0 0 0 0 0 66 Lantana camara 1155 11 0.54 1160 55 0.79 999 67 1.31 67 Vitex negundo 0 0 0 58 15 0.21 0 0 0 Herb 68 Phyllanthus amarus 127 39 1.93 787 326 4.68 208 127 2.5 69 Marsilea quadrifolia 0 0 0 1588 459 6.59 2768 456 8.98 70 Casia tora 1441 0 0 1463 0 0 1165 68 1.33 71 Vallaris solanacea 158 0 0 314 0 0 219 38 0.74 72 Tribulus terrestris 258 0 0 59 23 0.33 28 11 0.21 73 Elephantopus scaber 402 0 0 635 0 0 370 121 2.38

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74 Guizotia abyssinica 949 60 2.97 1040 543 7.8 1178 455 8.96 75 Sida acuta 367 22 1.09 1121 0 0 980 34 0.66 76 Desmodium spp. 3 11 0.54 49 59 0.84 0 0 0 77 Xanthium strumaxium 57 0 0 0 0 0 0 0 0 78 Sida spp. 9 3 0.14 324 88 1.26 63 31 0.61 79 Ventilago maderaspatana 8 0 0 0 0 0 0 0 0 80 Spirodela polyrhiza 7 7 0.34 148 21 0.3 0 0 0 81 Sida spp. 777 0 0 865 0 0 1079 0 0 82 Ocimum canum 158 0 0 453 281 4.03 386 88 1.73 83 Parthenium hysterophorus 0 0 0 372 0 0 36 14 0.27 84 Ocimum basilicum 0 0 0 156 152 2.18 0 0 0 85 Desmodium triflorum 0 0 0 842 567 8.14 0 0 0 Grasses 86 Eragrostis tenella 3798 366 18.15 5477 539 7.74 4871 565 11.13 87 Imperata cylindrica 51 51 2.52 184 31 0.44 103 32 0.63 88 Pennisetum pedicellatum 3 6 0.29 94 47 0.67 46 35 0.68 89 Cynodon dactylon 18 143 7.09 504 439 6.3 55 68 1.33 90 Themeda quadrivalvis 218 38 1.88 334 67 0.96 98 55 1.08 91 Chloris barbata 208 0 0 277 145 2.08 182 72 1.14 92 Cyperus scariosus 74 0 0 12 0 0 20 11 0.21 93 Dicanthium spp. 512 113 5.6 1989 403 5.79 1311 345 6.79 94 Eulaliopsis binata 254 82 4.06 387 92 1.32 55 28 0.55 95 Heteropogon contortus 1306 454 22.51 1811 726 10.43 1842 432 8.51 96 Apluda mutica 0 0 0 395 231 3.31 0 0 0 Sub-Total 17795 2016 100 28126 6960 100 23692 5076 100 Browse Unidentified 0 643 0 402 0 168 Grass Unidenstified 0 313 0 753 0 589 N = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence

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Table-7.3 Sambar diet in different season: occurrence of fragments of tree, shrub, herb and grasses within the identified browse and grass fragments in faecal pellets during summer, post-monsoon and winter season. Season S.No Plant Species Summer Post Monsoon Winter N n P N n P N n P Trees 1 Buchanania lanzan 82 8 0.35 99 23 0.5 96 11 0.32 2 Alangium salviifolium 8 0 0 4 0 0 4 2 0.58 3 Cassia fistula 130 37 1.62 152 57 1.26 184 87 2.53 4 Terminalia arjuna 4 0 0 1 0 0 1 0 0 5 Bahunia barigata 86 18 0.78 48 26 0.57 77 39 1.13 6 Emblica offcinalis 52 0 0 77 32 0.7 63 54 1.57 7 Gardinia latifolia 17 0 0 12 0 0 13 8 0.23 8 Ficus benghalensis 1 0 0 1 0 0 1 0 0 9 Pterocarpus marsupium 1 0 0 1 0 0 1 5 0.14 10 Gymnosporia spinosa 12 1 0.04 18 0 0 13 0 0 11 Aegle marmelos 37 14 0.61 16 95 2.1 34 43 1.25 12 Kydia calycina 34 0 0 27 0 0 26 0 0 13 Semecarpus anacardium 27 8 0.35 27 0 0 27 0 0 14 Chloroxylon Swietenia 122 0 0 111 48 1.06 185 97 2.82 15 Hymenodictyon excelsum 2 0 0 5 0 0 2 0 0 16 Delbergia sissoo 6 0 0 0 0 0 0 0 0 17 Albizia odoratissima 5 3 0.13 5 12 0.26 5 32 0.93 18 Grewia latifolia 70 26 1.14 72 40 0.88 62 39 1.13 19 Anogeissus latifolia 63 0 0 74 35 0.77 77 32 0.93 20 Delbergia peniculata 9 0 0 9 0 0 9 0 0

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21 Gardinia gummifera 0 0 0 0 0 0 4 0 0 22 Cleistanthus collinus 2 0 0 4 8 0.17 2 0 0 23 Ziziphus xylopyra 31 21 0.92 42 29 0.64 42 0 0 24 Adina cordifolia 3 0 0 3 0 0 3 0 0 25 Acacia leucophloca 5 0 0 5 0 0 5 0 0 26 Syzygium cumini 271 50 2.19 167 131 2.9 193 169 4.92 27 Flacourtia indica 45 29 1.27 10 0 0 20 7 0.2 28 Milosa tomentosa 192 0 0 240 85 1.88 208 0 0 29 Bridelia retusa 26 47 2.06 29 0 0 29 14 0.4 30 Ficus hispida 7 0 0 4 3 0.06 25 0 0 31 Garuga pinnata 1 2 0.87 1 0 0 1 0 0 32 Acacia catechu 23 8 0.35 24 17 0.37 24 38 1.1 33 Schleichera oleosa 20 0 0 16 12 0.26 17 0 0 34 Careya arborea 2 0 0 1 0 0 1 0 0 35 Sterculia urens 1 0 0 1 0 0 3 0 0 36 Lagerstroemia parviflora 650 0 0 609 18 0.39 727 38 1.1 37 Ixora arborea 78 40 1.75 121 80 1.77 78 85 2.47 38 Madhuca indica 25 31 1.35 36 32 0.7 30 45 1.31 39 Randia dumetorum 71 0 0 2 0 0 22 8 0.23 40 Schrebera swietenioides 1 14 0.61 1 0 0 1 0 0 41 Lenia coromandalica 117 0 0 148 0 0 132 18 0.52 42 Mitragyna parvifolia 40 0 0 24 0 0 44 0 0 43 Ficus infectoria 2 42 1.84 2 0 0 2 0 0 44 Butea monosperma 27 29 1.27 47 55 1.21 41 67 1.95 45 Soymida febrifuga 21 0 0 21 0 0 21 0 0 46 Terminalia tomentosa 104 0 0 107 45 0.99 117 26 0.75 47 Boswellia serrata 5 0 0 5 0 0 5 0 0 48 Bombax ceiba 2 8 0.35 2 16 0.35 2 0 0

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49 Annona squamosa 1 59 2.58 0 0 0 5 0 0 50 Tectona grandis 1035 20 0.87 890 58 1.28 1091 39 1.13 51 Diospyros melanoxylon 872 0 0 428 46 1.01 657 121 3.52 52 Ougeinia oojeinensis 44 8 0.35 60 29 0.64 51 0 0 53 Casearia tomentosa 59 7 0.3 25 0 0 26 8 0.23 54 Ficus glomerata 1 0 0 1 4 0.08 1 0 0 55 Diospyros montana 28 3 0.13 10 7 0.15 10 0 0 Climber 56 Bauhinia vahlii 5 21 0.92 5 67 1.48 9 48 1.39 57 Ziziphus oenoplia 120 0 0 147 135 2.99 112 138 4.02 58 Butea parviflora 1 247 10.83 7 6 0.13 27 29 0.84 59 Asparagus racemosum 0 18 0.78 0 0 0 21 18 0.52 Shrub 60 Hilectrux izora 140 48 2.1 175 125 2.76 115 95 2.76 61 Bamboo spp. 353 64 2.8 508 95 2.1 419 39 1.13 62 Pheonix aquilis 129 14 0.61 262 57 1.26 170 41 1.19 63 Grewia hirsuta 81 26 1.14 259 101 2.23 234 62 1.8 64 Grewia spp 58 8 0.35 20 14 0.31 3 0 0 65 Holarrhena antidysenterica 10 10 0.43 0 0 0 0 0 0 66 Lantana camara 1155 64 2.8 1160 68 1.5 999 129 3.75 67 Vitex negundo 0 0 0 58 8 0.17 0 0 0 Herb 68 Phyllanthus amarus 127 40 1.75 787 132 2.92 208 0 0 69 Marsilea quadrifolia 0 0 0 1588 455 10.07 2768 451 13.14 70 Casia tora 1441 0 0 1463 39 0.86 1165 148 4.31 71 Vallaris solanacea 158 0 0 314 22 0.48 219 68 1.98 72 Tribulus terrestris 258 0 0 59 0 0 28 0 0 73 Elephantopus scaber 402 2 0.87 635 89 1.97 370 101 2.94

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74 Guizotia abyssinica 949 0 0 1040 59 1.3 1178 431 12.56 75 Sida acuta 367 93 4.07 1121 38 0.84 980 0 0 76 Desmodium spp. 3 40 1.75 49 0 0 0 0 0 77 Xanthium strumaxium 57 14 0.61 0 0 0 0 0 0 78 Sida spp. 9 0 0 324 49 1.08 63 23 0.67 79 Ventilago maderaspatana 8 0 0 0 0 0 0 0 0 80 Spirodela polyrhiza 7 19 0.83 148 21 0.46 0 0 0 81 Sida spp. 777 57 2.5 865 45 0.99 1079 44 1.28 82 Ocimum canum 158 5 0.21 453 67 1.48 386 43 1.25 83 Parthenium hysterophorus 0 0 0 372 0 0 36 12 0.34 84 Ocimum basilicum 0 0 0 156 111 2.45 0 0 0 85 Desmodium triflorum 0 0 0 842 342 7.57 0 0 0 Grasses 86 Eragrostis tenella 3798 427 18.72 5477 689 15.26 4871 129 3.75 87 Imperata cylindrica 51 8 0.35 184 0 0 103 0 0 88 Pennisetum pedicellatum 3 51 2.23 94 34 0.75 46 16 0.46 89 Cynodon dactylon 18 70 3.07 504 225 4.98 55 29 0.84 90 Themeda quadrivalvis 218 0 0 334 0 0 98 0 0 91 Chloris barbata 208 3 0.13 277 67 1.48 182 48 1.39 92 Cyperus scariosus 74 8 0.35 12 0 0 20 0 0 93 Dicanthium spp. 512 0 0 1989 123 2.72 1311 39 1.13 94 Eulaliopsis binata 254 369 16.18 387 0 0 55 29 0.84 95 Heteropogon contortus 1306 13 0.57 1811 88 1.94 1842 89 2.59 96 Apluda mutica 0 0 0 395 101 2.23 0 0 0 Sub-Total 17795 2272 100 28126 4515 100 23692 3431 100 Browse Unidentified 0 734 0 629 0 421 Grass Unidenstified 0 802 0 521 0 132 N = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence

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Table-7.4 Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during summer season.

n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance)

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00461 0.014312977 0.011≤Pi≤0.017 a 2 Alangium salviifolium 0.00045 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.00731 0.070610687 0.064≤Pi≤0.077 a 4 Terminalia arjuna 0.00022 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00483 0.008587786 0.006≤Pi≤0.011 a 6 Emblica offcinalis 0.00292 0 0.00≤Pi≤0.00 d 7 Gardinia latifolia 0.00096 0.008587786 0.006≤Pi≤0.011 a 8 Ficus benghalensis 5.62E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 5.62E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00067 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00208 0 0.00≤Pi≤0.00 d 12 Kydia calycina 0.00191 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00152 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00686 0 0.00≤Pi≤0.00 d

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15 Hymenodictyon excelsum 0.00011 0 0.00≤Pi≤0.00 d 16 Delbergia sissoo 0.00034 0 0.00≤Pi≤0.00 a 17 Albizia odoratissima 0.00028 0.091603053 0.084≤Pi≤0.098 a 18 Grewia latifolia 0.00393 0.009541985 0.007≤Pi≤0.011 d 19 Anogeissus latifolia 0.00354 0 0.00≤Pi≤0.00 d 20 Delbergia peniculata 0.00051 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 0.00011 0 0.00≤Pi≤0.00 c 22 Ziziphus xylopyra 0.00174 0.020992366 0.017≤Pi≤0.024 d 23 Adina cordifolia 0.00017 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00028 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.01523 0 0.00≤Pi≤0.00 d 26 Flacourtia indica 0.00253 0 0.00≤Pi≤0.00 a 27 Milosa tomentosa 0.01079 0.018129771 0.014≤Pi≤0.021 d 28 Bridelia retusa 0.00146 0 0.00≤Pi≤0.00 d 29 Ficus hispida 0.00039 0 0.00≤Pi≤0.00 d 30 Garuga pinnata 5.62E-05 0 0.00≤Pi≤0.00 d 31 Acacia catechu 0.00129 0 0.00≤Pi≤0.00 d 32 Schleichera oleosa 0.00112 0 0.00≤Pi≤0.00 d 33 Careya arborea 0.00011 0 0.00≤Pi≤0.00 d 34 Sterculia urens 5.62E-05 0 0.00≤Pi≤0.00 b 35 Lagerstroemia parviflora 0.03653 0.015267176 0.012≤Pi≤0.018 a 36 Ixora arborea 0.00438 0.02480916 0.020≤Pi≤0.028 a 37 Madhuca indica 0.0014 0.03148855 0.027≤Pi≤0.035 d 38 Randia dumetorum 0.00399 0 0.00≤Pi≤0.00 d 39 Schrebera swietenioides 5.62E-05 0 0.00≤Pi≤0.00 a 40 Lenia coromandalica 0.00657 0.018129771 0.014≤Pi≤0.021 d 41 Mitragyna parvifolia 0.00225 0 0.00≤Pi≤0.00 d

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42 Ficus infectoria 0.00011 0 0.00≤Pi≤0.00 d 43 Butea monosperma 0.00152 0 0.00≤Pi≤0.00 d 44 Soymida febrifuga 0.00118 0 0.00≤Pi≤0.00 c 45 Terminalia tomentosa 0.00584 0.005725191 0.003≤Pi≤0.007 d 46 Boswellia serrata 0.00028 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 0.00011 0 0.00≤Pi≤0.00 d 48 Annona squamosa 5.62E-05 0 0.00≤Pi≤0.00 d 49 Tectona grandis 0.05816 0 0.00≤Pi≤0.00 d 50 Diospyros melanoxylon 0.049 0 0.00≤Pi≤0.00 d 51 Ougeinia oojeinensis 0.00247 0 0.00≤Pi≤0.00 d 52 Casearia tomentosa 0.00332 0 0.00≤Pi≤0.00 d 53 Ficus glomerata 5.62E-05 0 0.00≤Pi≤0.00 d 54 Diospyros montana 0.00157 0 0.00≤Pi≤0.00 d Shrub 55 Hilectrux izora 0.00787 0.048664122 0.00≤Pi≤0.00 a 56 Bamboo spp. 0.01984 0 0.043≤Pi≤0.054 d 57 Pheonix aquilis 0.00725 0 0.00≤Pi≤0.00 d 58 Grewia hirsute 0.00455 0 0.00≤Pi≤0.00 d 59 Grewia spp 0.00326 0 0.00≤Pi≤0.00 d 60 Holarrhena antidysenterica 0.00056 0 0.00≤Pi≤0.00 d 61 Lantana camara 0.06491 0 0.00≤Pi≤0.00 d Climber 62 Bauhinia vahlii 0.00028 0.033396947 0.028≤Pi≤0.037 a 63 Ziziphus oenoplia 0.00674 0.050572519 0.045≤Pi≤0.056 a 64 Butea parviflora 5.62E-05 0 0.00≤Pi≤0.00 d Herb 65 Phyllanthus amarus 0.00714 0 0.00≤Pi≤0.00 d

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66 Casia tora 0.08098 0 0.00≤Pi≤0.00 d 67 Vallaris solanacea 0.00888 0 0.00≤Pi≤0.00 d 68 Tribulus terrestris 0.0145 0 0.00≤Pi≤0.00 d 69 Elephantopus scaber 0.02259 0 0.00≤Pi≤0.00 d 70 Guizotia abyssinica 0.05333 0.066793893 0.060≤Pi≤0.072 a 71 Sida acuta 0.02062 0 0.00≤Pi≤0.00 d 72 Desmodium spp. 0.00017 0 0.00≤Pi≤0.00 d 73 Xanthium strumaxium 0.0032 0 0.00≤Pi≤0.00 d 74 Sida spp. 0.00051 0 0.00≤Pi≤0.00 d 75 Ventilago maderaspatana 0.00045 0.008587786 0.006≤Pi≤0.011 a 76 Spirodela polyrhiza 0.00039 0 0.00≤Pi≤0.00 d 77 Sida spp. 0.04366 0 0.00≤Pi≤0.00 d 78 Ocimum canum 0.00888 0 0.00≤Pi≤0.00 d Grasses 79 Eragrostis tenella 0.21343 0.245229008 0.234≤Pi≤0.255 a 80 Imperata cylindrical 0.00287 0.001908397 0.0008≤Pi≤0.002 b 81 Pennisetum pedicellatum 0.00017 0 0.00≤Pi≤0.00 d 82 Cynodon dactylon 0.00101 0 0.00≤Pi≤0.00 d 83 Themeda quadrivalvis 0.01225 0 0.00≤Pi≤0.00 d 84 Chloris barbata 0.01169 0 0.00≤Pi≤0.00 d 85 Cyperus scariosus 0.00416 0 0.00≤Pi≤0.00 d 86 Dicanthium spp. 0.02877 0.061068702 0.055≤Pi≤0.067 a 87 Eulaliopsis binata 0.01427 0.011450382 0.008≤Pi≤0.014 c 88 Heteropogon contortus 0.07339 0.134541985 0.126≤Pi≤0.143 a

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Table-7.5Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during post monsoon season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00352 0.01101 0.0088≤Pi≤0.0131 a 2 Alangium salviifolium 0.00014 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.0054 0.03378 0.0300≤Pi≤0.0374 a 4 Terminalia arjuna 3.56E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00171 0.00455 0.0031≤Pi≤0.0059 a 6 Emblica offcinalis 0.00274 0.01101 0.0088≤Pi≤0.0131 a 7 Gardinia latifolia 0.00043 0.00076 0.0001≤Pi≤0.0013 c 8 Ficus benghalensis 3.56E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 3.56E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00064 0.00076 0.0001≤Pi≤0.0013 c 11 Aegle marmelos 0.00057 0.00228 0.0012≤Pi≤0.0032 a 12 Kydia calycina 0.00096 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00096 0 0.00≤Pi≤0.00 d 14 Chloroxylon swietenia 0.00395 0.0148 0.0123≤Pi≤0.0172 a 15 Hymenodictyon excelsum 0.00018 0 0.00≤Pi≤0.00 d 16 Albizia odoratissima 0.00018 0.0277 0.0243≤Pi≤0.0310 a 17 Grewia latifolia 0.00256 0.00607 0.0044≤Pi≤0.0076 a

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18 Anogeissus latifolia 0.00263 0.00911 0.0071≤Pi≤0.0110 a 19 Delbergia peniculata 0.00032 0 0.00≤Pi≤0.00 d 20 Cleistanthus collinus 0.00014 0 0.00≤Pi≤0.00 d 21 Ziziphus xylopyra 0.00149 0.0148 0.0123≤Pi≤0.0172 a 22 Adina cordifolia 0.00011 0 0.00≤Pi≤0.00 d 23 Acacia leucophloca 0.00018 0 0.00≤Pi≤0.00 d 24 Syzygium cumini 0.00594 0.02619 0.0229≤Pi≤0.0294 a 25 Flacourtia indica 0.00036 0.01328 0.0109≤Pi≤0.0156 a 26 Milosa tomentosa 0.00853 0.01556 0.0130≤Pi≤0.0181 a 27 Bridelia retusa 0.00103 0 0.00≤Pi≤0.00 d 28 Ficus hispida 0.00014 0 0.00≤Pi≤0.00 d 29 Garuga pinnata 3.56E-05 0 0.00≤Pi≤0.00 d 30 Acacia catechu 0.00085 0 0.00≤Pi≤0.00 d 31 Schleichera oleosa 0.00057 0 0.00≤Pi≤0.00 d 32 Careya arborea 3.56E-05 0 0.00≤Pi≤0.00 d 33 Sterculia urens 3.56E-05 0 0.00≤Pi≤0.00 d 34 Lagerstroemia parviflora 0.02165 0.03302 0.0293≤Pi≤0.0366 c 35 Ixora arborea 0.0043 0.02998 0.0264≤Pi≤0.0334 a 36 Madhuca indica 0.00128 0.00911 0.0071≤Pi≤0.0110 a 37 Randia dumetorum 7.11E-05 0.00152 0.0007≤Pi≤0.0023 a 38 Schrebera swietenioides 3.56E-05 0 0.00≤Pi≤0.00 d 39 Lenia coromandalica 0.00526 0.00797 0.0061≤Pi≤0.0097 a 40 Mitragyna parvifolia 0.00085 0.00342 0.0022≤Pi≤0.0046 a 41 Ficus infectoria 7.11E-05 0 0.00≤Pi≤0.00 d 42 Butea monosperma 0.00167 0.0129 0.0105≤Pi≤0.0152 a 43 Soymida febrifuga 0.00075 0 0.00≤Pi≤0.00 d 44 Terminalia tomentosa 0.0038 0.00721 0.0054≤Pi≤0.0089 c 45 Boswellia serrata 0.00018 0 0.00≤Pi≤0.00 d

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46 Bombax ceiba 7.11E-05 0 0.00≤Pi≤0.00 d 47 Tectona grandis 0.03164 0.00493 0.0034≤Pi≤0.0063 b 48 Diospyros melanoxylon 0.01522 0.00607 0.0044≤Pi≤0.0076 b 49 Ougeinia oojeinensis 0.00213 0 0.00≤Pi≤0.00 d 50 Casearia tomentosa 0.00089 0 0.00≤Pi≤0.00 d 51 Ficus glomerata 3.56E-05 0 0.00≤Pi≤0.00 d 52 Diospyros Montana 0.00036 0 0.00≤Pi≤0.00 d Climber 53 Bauhinia vahlii 0.00018 0.01822 0.0154≤Pi≤0.0209 a 54 Butea parviflora 0.00025 0.0019 0.0010≤Pi≤0.0027 a 55 Ziziphus oenoplia 0.00523 0.03074 0.0271≤Pi≤0.0342 a Shrub 56 Hilectrux izora 0.00622 0.00873 0.0068≤Pi≤0.016 c 57 Bamboo spp. 0.01806 0.00266 0.0015≤Pi≤0.0037 b 58 Pheonix aquilis 0.00932 0 0.00≤Pi≤0.00 d 59 Grewia hirsuta 0.00921 0 0.00≤Pi≤0.00 d 60 Holarrhena antidysenterica 0.00071 0 0.00≤Pi≤0.00 d 61 Lantana camara 0.04124 0.01101 0.0088≤Pi≤0.0131 b 62 Vitex negundo 0.00206 0 0.00≤Pi≤0.00 d Herb 63 Phyllanthus amarus 0.02798 0 0.00≤Pi≤0.00 d 64 Marsilea quadrifolia 0.05646 0.07932 0.07375≤Pi≤0.0848 a 65 Spirodela polyrhiza 0.00526 0 0.00≤Pi≤0.00 d 66 Casia tora 0.05202 0 0.00≤Pi≤0.00 d 67 Vallaris solanacea 0.01116 0 0.00≤Pi≤0.00 d 68 Parthenium hysterophorus 0.01323 0 0.00≤Pi≤0.00 d 69 Tribulus terrestris 0.0021 0 0.00≤Pi≤0.00 d 70 Sida spp. 0.03075 0 0.00≤Pi≤0.00 d

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71 Elephantopus scaber 0.02258 0.00304 0.0019≤Pi≤0.0041 b 72 Guizotia abyssinica 0.03698 0.10968 0.1032≤Pi≤0.1161 a 73 Sida acuta 0.03986 0 0.00≤Pi≤0.00 d 74 Desmodium spp. 0.00174 0 0.00≤Pi≤0.00 d 75 Ocimum basilicum 0.00555 0 0.00≤Pi≤0.00 d 76 Sida spp. 0.01152 0.02239 0.0193≤Pi≤0.0254 c 77 Desmodium triflorum 0.02994 0.09677 0.0906≤Pi≤0.1028 a 78 Ocimum canum 0.01611 0.03757 0.00336≤Pi≤0.0414 b Grasses 79 Eragrostis tenella 0.19473 0.12827 0.1213≤Pi≤0.1351 c 80 Imperata cylindrica 0.00654 0 0.00≤Pi≤0.00 d 81 Pennisetum pedicellatum 0.00334 0 0.00≤Pi≤0.00 d 82 Cynodon dactylon 0.01792 0.03302 0.0293≤Pi≤0.0366 a 83 Apluda mutica 0.01404 0.01822 0.0154≤Pi≤0.0209 c 84 Cyperus scariosus 0.00043 0 0.00≤Pi≤0.00 d 85 Themeda quadrivalvis 0.01188 0 0.00≤Pi≤0.00 d 86 Chloris barbata 0.00985 0 0.00≤Pi≤0.00 d 87 Dicanthium spp. 0.07072 0.03264 0.0289≤Pi≤0.0362 b 88 Eulaliopsis binata 0.01376 0.01556 0.0130≤Pi≤0.0181 c 89 Heteropogon contortus 0.06439 0.0425 0.0383≤Pi≤0.0466 c

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Table-7.6Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the chowsingha during winter season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00405 0.0041 0.00≤Pi≤0.00 d 2 Alangium salviifolium 0.00017 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.00777 0.03445 0.0303≤Pi≤0.0385 a 4 Terminalia arjuna 4.22E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00325 0.0134 0.0108≤Pi≤0.0159 a 6 Emblica offcinalis 0.00266 0.01504 0.0123≤Pi≤0.0177 a 7 Gardinia latifolia 0.00055 0.00301 0.0017≤Pi≤0.0042 a 8 Ficus benghalensis 4.22E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 4.22E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00055 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00144 0.01258 0.0100≤Pi≤0.0150 a 12 Kydia calycina 0.0011 0.00492 0.0033≤Pi≤0.0064 a 13 Semecarpus anacardium 0.00114 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00781 0.00875 0.0066≤Pi≤0.0108 c 15 Hymenodictyon excelsum 8.44E-05 0 0.00≤Pi≤0.00 d

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16 Albizia odoratissima 0.00021 0.02706 0.0234≤Pi≤0.0306 a 17 Grewia latifolia 0.00262 0.00984 0.0076≤Pi≤0.0120 a 18 Anogeissus latifolia 0.00325 0 0.00≤Pi≤0.00 d 19 Delbergia peniculata 0.00038 0 0.00≤Pi≤0.00 d 20 Gardinia gummifera 0.00017 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 8.44E-05 0.01312 0.0105≤Pi≤0.0156 a 22 Ziziphus xylopyra 0.00177 0.00984 0.0076≤Pi≤0.0120 a 23 Adina cordifolia 0.00013 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00021 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.00815 0.05057 0.0456≤Pi≤0.0554 a 26 Flacourtia indica 0.00084 0 0.00≤Pi≤0.00 d 27 Milosa tomentosa 0.00878 0.02023 0.0170≤Pi≤0.0233 a 28 Bridelia retusa 0.00122 0.00574 0.0040≤Pi≤0.0074 a 29 Ficus hispida 0.00106 0 0.00≤Pi≤0.00 d 30 Garuga pinnata 4.22E-05 0 0.00≤Pi≤0.00 d 31 Acacia catechu 0.00101 0.00492 0.0033≤Pi≤0.0064 a 32 Schleichera oleosa 0.00072 0 0.00≤Pi≤0.00 d 33 Careya arborea 4.22E-05 0 0.00≤Pi≤0.00 d 34 Sterculia urens 0.00013 0 0.00≤Pi≤0.00 d 35 Lagerstroemia parviflora 0.03069 0.01148 0.0091≤Pi≤0.0138 b 36 Ixora arborea 0.00329 0.02542 0.0219≤Pi≤0.0289 a 37 Madhuca indica 0.00127 0.01476 0.0120≤Pi≤0.0174 a 38 Randia dumetorum 0.00093 0 0.00≤Pi≤0.00 d 39 Schrebera swietenioides 4.22E-05 0 0.00≤Pi≤0.00 d 40 Lenia coromandalica 0.00557 0.00793 0.0059≤Pi≤0.0099 c 41 Mitragyna parvifolia 0.00186 0.01121 0.0088≤Pi≤0.0135 a

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42 Ficus infectoria 8.44E-05 0 0.00≤Pi≤0.00 d 43 Butea monosperma 0.00173 0.01613 0.0133≤Pi≤0.0189 a 44 Soymida febrifuga 0.00089 0.01176 0.0093≤Pi≤0.0141 a 45 Terminalia tomentosa 0.00494 0.0257 0.0221≤Pi≤0.0292 a 46 Boswellia serrata 0.00021 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 8.44E-05 0 0.00≤Pi≤0.00 d 48 Annona squamosa 0.00021 0.00191 0.0009≤Pi≤0.0028 a 49 Tectona grandis 0.04605 0.01832 0.0153≤Pi≤0.0213 b 50 Diospyros melanoxylon 0.02773 0.01394 0.0113≤Pi≤0.0165 b 51 Ougeinia oojeinensis 0.00215 0 0.00≤Pi≤0.00 d 52 Casearia tomentosa 0.0011 0 0.00≤Pi≤0.00 d 53 Ficus glomerata 4.22E-05 0 0.00≤Pi≤0.00 d 54 Diospyros montana 0.00042 0 0.00≤Pi≤0.00 d Climber 55 Bauhinia vahlii 0.00038 0.02378 0.0203≤Pi≤0.0271 a 56 Ziziphus oenoplia 0.00473 0.0257 0.0221≤Pi≤0.0292 a 57 Asparagus racemosum 0.00089 0 0.00≤Pi≤0.00 d 58 Butea parviflora 0.00114 0.00465 0.0031≤Pi≤0.0061 a Shrub 59 Hilectrux izora 0.00485 0.01066 0.0083≤Pi≤0.0129 a 60 Bamboo spp. 0.01769 0.00765 0.0057≤Pi≤0.0096 b 61 Pheonix aquilis 0.00718 0.00574 0.0040≤Pi≤0.0074 c 62 Grewia hirsuta 0.00988 0 0.00≤Pi≤0.00 d 63 Holarrhena antidysenterica 0.00013 0 0.00≤Pi≤0.00 d 64 Lantana camara 0.04217 0.01394 0.0113≤Pi≤0.0165 b Herb

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65 Phyllanthus amarus 0.00878 0.0123 0.0098≤Pi≤0.0147 a 66 Marsilea quadrifolia 0.11683 0.02433 0.0208≤Pi≤0.0277 b 67 Casia tora 0.04917 0 0.00≤Pi≤0.00 d 68 Vallaris solanacea 0.00924 0.00875 0.0066≤Pi≤0.0108 c 69 Sida spp. 0.04554 0 0.00≤Pi≤0.00 d 70 Tribulus terrestris 0.00118 0 0.00≤Pi≤0.00 d 71 Elephantopus scaber 0.01562 0.03499 0.0308≤Pi≤0.0390 a 72 Guizotia abyssinica 0.04972 0.07901 0.00≤Pi≤0.00 d 73 Sida acuta 0.04136 0 0.00≤Pi≤0.00 d 74 Parthenium hysterophorus 0.00152 0 0.00≤Pi≤0.00 d 75 Sida spp. 0.00266 0.01941 0.0163≤Pi≤0.0224 a 76 Ocimum canum 0.01629 0.08912 0.0827≤Pi≤0.0954 a Grasses 77 Eragrostis tenella 0.2056 0.06752 0.0619≤Pi≤0.0731 b 78 Imperata cylindrica 0.00435 0 0.00≤Pi≤0.00 d 79 Pennisetum pedicellatum 0.00194 0.00629 0.0045≤Pi≤0.0080 a 80 Cynodon dactylon 0.00232 0.02433 0.0208≤Pi≤0.0277 a 81 Cyperus scariosus 0.00084 0 0.00≤Pi≤0.00 d 82 Themeda quadrivalvis 0.00414 0.01066 0.0083≤Pi≤0.0129 a 83 Chloris barbata 0.00768 0.01394 0.0113≤Pi≤0.0165 a 84 Dicanthium spp. 0.05534 0.06944 0.0637≤Pi≤0.0751 a 85 Eulaliopsis binata 0.00232 0 0.00≤Pi≤0.00 d 86 Heteropogon contortus 0.07775 0.05167 0.0467≤Pi≤0.0566 b

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Table-7.7 Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the gaur during summer season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence.

a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00461 0.00149 0.0004≤Pi≤0.0024 b 2 Alangium salviifolium 0.00045 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.00731 0.02877 0.0244≤Pi≤0.0330 a 4 Terminalia arjuna 0.00022 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00483 0 0.00≤Pi≤0.00 d 6 Emblica offcinalis 0.00292 0 0.00≤Pi≤0.00 d 7 Gardinia latifolia 0.00096 0 0.00≤Pi≤0.00 d 8 Ficus benghalensis 5.62E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 5.62E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00067 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00208 0.00099 0.0001≤Pi≤0.0018 b 12 Kydia calycina 0.00191 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00152 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00686 0 0.00≤Pi≤0.00 d

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15 Hymenodictyon excelsum 0.00011 0 0.00≤Pi≤0.00 d 16 Delbergia sissoo 0.00034 0 0.00≤Pi≤0.00 d 17 Albizia odoratissima 0.00028 0 0.00≤Pi≤0.00 d 18 Grewia latifolia 0.00393 0 0.00≤Pi≤0.00 d 19 Anogeissus latifolia 0.00354 0 0.00≤Pi≤0.00 d 20 Delbergia peniculata 0.00051 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 0.00011 0 0.00≤Pi≤0.00 d 22 Ziziphus xylopyra 0.00174 0 0.00≤Pi≤0.00 d 23 Adina cordifolia 0.00017 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00028 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.01523 0.00942 0.0069≤Pi≤0.0119 b 26 Flacourtia indica 0.00253 0 0.00≤Pi≤0.00 d 27 Milosa tomentosa 0.01079 0.02331 0.0194≤Pi≤0.0272 a 28 Bridelia retusa 0.00146 0 0.00≤Pi≤0.00 d 29 Ficus hispida 0.00039 0.00099 0.0001≤Pi≤0.0018 a 30 Garuga pinnata 5.62E-05 0 0.00≤Pi≤0.00 d 31 Acacia catechu 0.00129 0 0.00≤Pi≤0.00 d 32 Schleichera oleosa 0.00112 0.00347 0.0019≤Pi≤0.0049 a 33 Careya arborea 0.00011 0 0.00≤Pi≤0.00 d 34 Sterculia urens 5.62E-05 0 0.00≤Pi≤0.00 d 35 Lagerstroemia parviflora 0.03653 0 0.00≤Pi≤0.00 d 36 Ixora arborea 0.00438 0 0.00≤Pi≤0.00 d 37 Madhuca indica 0.0014 0.02034 0.0166≤Pi≤0.0239 a 38 Randia dumetorum 0.00399 0 0.00≤Pi≤0.00 d 39 Schrebera swietenioides 5.62E-05 0 0.00≤Pi≤0.00 d 40 Lenia coromandalica 0.00657 0.00149 0.0004≤Pi≤0.0024 b 41 Mitragyna parvifolia 0.00225 0 0.00≤Pi≤0.00 d

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42 Ficus infectoria 0.00011 0 0.00≤Pi≤0.00 d 43 Butea monosperma 0.00152 0 0.00≤Pi≤0.00 d 44 Soymida febrifuga 0.00118 0 0.00≤Pi≤0.00 d 45 Terminalia tomentosa 0.00584 0 0.00≤Pi≤0.00 d 46 Boswellia serrata 0.00028 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 0.00011 0 0.00≤Pi≤0.00 d 48 Annona squamosa 5.62E-05 0 0.00≤Pi≤0.00 d 49 Tectona grandis 0.05816 0.00298 0.0015≤Pi≤0.0043 b 50 Diospyros melanoxylon 0.049 0.00992 0.0073≤Pi≤0.012 b 51 Ougeinia oojeinensis 0.00247 0 0.00≤Pi≤0.00 d 52 Casearia tomentosa 0.00332 0.00595 0.0039≤Pi≤0.0079 c 53 Ficus glomerata 5.62E-05 0.00942 0.0069≤Pi≤0.0119 a 54 Diospyros montana 0.00157 0 0.00≤Pi≤0.00 d Climber 55 Bauhinia vahlii 0.00028 0.01786 0.0144≤Pi≤0.0212 a 56 Ziziphus oenoplia 0.00674 0 0.00≤Pi≤0.00 d 57 Butea parviflora 5.62E-05 0.01538 0.0122≤Pi≤0.0185 a Shrub 58 Hilectrux izora 0.00787 0.01687 0.0135≤Pi≤0.0201 a 59 Bamboo spp. 0.01984 0.12202 0.01135≤Pi≤0.1304 c 60 Pheonix aquilis 0.00725 0 0.00≤Pi≤0.00 d 61 Grewia hirsute 0.00455 0.00942 0.0069≤Pi≤0.0119 c 62 Grewia spp 0.00326 0.00248 0.0011≤Pi≤0.0037 b 63 Holarrhena antidysenterica 0.00056 0 0.00≤Pi≤0.00 d 64 Lantana camara 0.06491 0.00546 0.0035≤Pi≤0.0073 b Herb 65 Phyllanthus amarus 0.00714 0.01935 0.0157≤Pi≤0.0228 a

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66 Casia tora 0.08098 0 0.00≤Pi≤0.00 d 67 Vallaris solanacea 0.00888 0 0.00≤Pi≤0.00 d 68 Tribulus terrestris 0.0145 0 0.00≤Pi≤0.00 d 69 Elephantopus scaber 0.02259 0 0.00≤Pi≤0.00 d 70 Guizotia abyssinica 0.05333 0.02976 0.0253≤Pi≤0.0341 b 71 Sida acuta 0.02062 0.01091 0.0082≤Pi≤0.0135 b 72 Desmodium spp 0.00017 0.00546 0.0035≤Pi≤0.0073 a 73 Xanthium strumaxium 0.0032 0 0.00≤Pi≤0.00 d 74 Sida spp. 0.00051 0.00149 0.0004≤Pi≤0.0024 c 75 Ventilago maderaspatana 0.00045 0 0.00≤Pi≤0.00 d 76 Spirodela polyrhiza 0.00039 0.00347 0.0019≤Pi≤0.0049 a 77 Sida spp 0.04366 0 0.00≤Pi≤0.00 d 78 Ocimum canum 0.00888 0 0.00≤Pi≤0.00 d Grasses 79 Eragrostis tenella 0.21343 0.18155 0.1716≤Pi≤0.1914 b 80 Imperata cylindrical 0.00287 0.0253 0.0212≤Pi≤0.0293 a 81 Pennisetum pedicellatum 0.00017 0.00298 0.0015≤Pi≤0.0043 a 82 Cynodon dactylon 0.00101 0.07093 0.0643≤Pi≤0.0775 a 83 Themeda quadrivalvis 0.01225 0.01885 0.00153≤Pi≤0.00223 b 84 Chloris barbata 0.01169 0 0.00≤Pi≤0.00 d 85 Cyperus scariosus 0.00416 0 0.00≤Pi≤0.00 d 86 Dicanthium spp. 0.02877 0.05605 0.0501≤Pi≤0.0619 a 87 Eulaliopsis binata 0.01427 0.04067 0.0355≤Pi≤0.0457 a 88 Heteropogon contortus 0.07339 0.2252 0.2144≤Pi≤0.2359 b

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Table-7.8 Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the gaur during post monsoon season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00352 0 0.00≤Pi≤0.00 d 2 Alangium salviifolium 0.00014 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.0054 0.01221 0.0099≤Pi≤0.0144 a 4 Terminalia arjuna 3.56E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00171 0.00402 0.0027≤Pi≤0.0053 a 6 Emblica offcinalis 0.00274 0.00546 0.0039≤Pi≤0.0069 a 7 Gardinia latifolia 0.00043 0.00057 8.169≤Pi≤0.0010 b 8 Ficus benghalensis 3.56E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 3.56E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00064 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00057 0.00417 0.0028≤Pi≤0.0054 a 12 Kydia calycina 0.00096 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00096 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00395 0.0069 0.0051≤Pi≤0.0085 a 15 Hymenodictyon excelsum 0.00018 0 0.00≤Pi≤0.00 d 16 Albizia odoratissima 0.00018 0 0.00≤Pi≤0.00 d

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17 Grewia latifolia 0.00256 0 0.00≤Pi≤0.00 d 18 Anogeissus latifolia 0.00263 0.00259 0.0015≤Pi≤0.0036 c 19 Delbergia peniculata 0.00032 0 0.00≤Pi≤0.00 d 20 Cleistanthus collinus 0.00014 0.00043 4.034≤Pi≤0.0008 b 21 Ziziphus xylopyra 0.00149 0 0.00≤Pi≤0.00 d 22 Adina cordifolia 0.00011 0 0.00≤Pi≤0.00 d 23 Acacia leucophloca 0.00018 0 0.00≤Pi≤0.00 d 24 Syzygium cumini 0.00594 0.02213 0.0191≤Pi≤0.0251 a 25 Flacourtia indica 0.00036 0 0.00≤Pi≤0.00 d 26 Milosa tomentosa 0.00853 0.00963 0.0076≤Pi≤0.0116 c 27 Bridelia retusa 0.00103 0 0.00≤Pi≤0.00 d 28 Ficus hispida 0.00014 0 0.00≤Pi≤0.00 d 29 Garuga pinnata 3.56E-05 0 0.00≤Pi≤0.00 d 30 Acacia catechu 0.00085 0.00187 0.0009≤Pi≤0.0027 a 31 Schleichera oleosa 0.00057 0 0.00≤Pi≤0.00 d 32 Careya arborea 3.56E-05 0 0.00≤Pi≤0.00 d 33 Sterculia urens 3.56E-05 0 0.00≤Pi≤0.00 d 34 Lagerstroemia parviflora 0.02165 0 0.00≤Pi≤0.00 d 35 Ixora arborea 0.0043 0.00991 0.0078≤Pi≤0.0119 a 36 Madhuca indica 0.00128 0.0092 0.0072≤Pi≤0.0111 a 37 Randia dumetorum 7.11E-05 0 0.00≤Pi≤0.00 d 38 Schrebera swietenioides 3.56E-05 0 0.00≤Pi≤0.00 d 39 Lenia coromandalica 0.00526 0 0.00≤Pi≤0.00 d 40 Mitragyna parvifolia 0.00085 0.0023 0.0013≤Pi≤0.0032 a 41 Ficus infectoria 7.11E-05 0 0.00≤Pi≤0.00 d 42 Butea monosperma 0.00167 0.0056 0.0040≤Pi≤0.0071 a 43 Soymida febrifuga 0.00075 0 0.00≤Pi≤0.00 d 44 Terminalia tomentosa 0.0038 0.00704 0.0053≤Pi≤0.0087 a

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45 Boswellia serrata 0.00018 0 0.00≤Pi≤0.00 d 46 Bombax ceiba 7.11E-05 0 0.00≤Pi≤0.00 d 47 Tectona grandis 0.03164 0.00402 0.0027≤Pi≤0.0053 b 48 Diospyros melanoxylon 0.01522 0.00747 0.0056≤Pi≤0.0092 b 49 Ougeinia oojeinensis 0.00213 0 0.00≤Pi≤0.00 d 50 Casearia tomentosa 0.00089 0 0.00≤Pi≤0.00 d 51 Ficus glomerata 3.56E-05 0.00388 0.0026≤Pi≤0.0051 a 52 Diospyros montana 0.00036 0 0.00≤Pi≤0.00 d Climber 53 Bauhinia vahlii 0.00018 0.00891 0.0069≤Pi≤0.0108 a 54 Butea parviflora 0.00025 0.00445 0.0030≤Pi≤0.0058 a 55 Ziziphus oenoplia 0.00523 0.01408 0.0116≤Pi≤0.0165 a Shrub 56 Hilectrux izora 0.00622 0.01336 0.0110≤Pi≤0.0157 a 57 Bamboo spp. 0.01806 0.05647 0.0517≤Pi≤0.0612 a 58 Pheonix aquilis 0.00932 0.0079 0.0060≤Pi≤0.0097 c 59 Grewia hirsute 0.00921 0.01264 0.0103≤Pi≤0.0149 a 60 Holarrhena antidysenterica 0.00071 0 0.00≤Pi≤0.00 d 61 Lantana camara 0.04124 0.0079 0.0060≤Pi≤0.0097 b 62 Vitex negundo 0.00206 0.00216 0.0012≤Pi≤0.0031 c Herb 63 Phyllanthus amarus 0.02798 0.04684 0.0424≤Pi≤0.0511 a 64 Marsilea quadrifolia 0.05646 0.06595 0.0608≤Pi≤0.0710 a 65 Spirodela polyrhiza 0.00526 0.00302 0.0018≤Pi≤0.0041 b 66 Casia tora 0.05202 0 0.00≤Pi≤0.00 d 67 Vallaris solanacea 0.01116 0 0.00≤Pi≤0.00 d 68 Parthenium hysterophorus 0.01323 0 0.00≤Pi≤0.00 d 69 Tribulus terrestris 0.0021 0.0033 0.0021≤Pi≤0.0044 c

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70 Sida spp. 0.03075 0 0.00≤Pi≤0.00 d 71 Elephantopus scaber 0.02258 0 0.00≤Pi≤0.00 d 72 Guizotia abyssinica 0.03698 0.07802 0.0725≤Pi≤0.0835 a 73 Sida acuta 0.03986 0 0.00≤Pi≤0.00 d 74 Desmodium spp. 0.00174 0.00848 0.0065≤Pi≤0.0103 a 75 Ocimum basilicum 0.00555 0.02184 0.0188≤Pi≤0.0248 a 76 Sida spp. 0.01152 0.01264 0.0103≤Pi≤0.0149 c 77 Desmodium triflorum 0.02994 0.08147 0.0758≤Pi≤0.0870 a 78 Ocimum canum 0.01611 0.04037 0.0363≤Pi≤0.0444 a Grasses 79 Eragrostis tenella 0.19473 0.07744 0.0719≤Pi≤0.0829 b 80 Imperata cylindrical 0.00654 0.00445 0.0030≤Pi≤0.0058 b 81 Pennisetum pedicellatum 0.00334 0.00675 0.0050≤Pi≤0.0084 a 82 Cynodon dactylon 0.01792 0.06307 0.0580≤Pi≤0.0680 a 83 Apluda mutica 0.01404 0.03319 0.0295≤Pi≤0.0368 a 84 Cyperus scariosus 0.00043 0 0.00≤Pi≤0.00 d 85 Themeda quadrivalvis 0.01188 0.00963 0.0076≤Pi≤0.0116 b 86 Chloris barbata 0.00985 0.02083 0.0178≤Pi≤0.0237 a 87 Dicanthium spp. 0.07072 0.0579 0.0530≤Pi≤0.0627 b 88 Eulaliopsis binata 0.01376 0.01322 0.0108≤Pi≤0.0155 c 89 Heteropogon contortus 0.06439 0.10431 0.0980≤Pi≤0.1105 a

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Table-7.9Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the gaur during winter season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00405 0.00335 0.00≤Pi≤0.00 d 2 Alangium salviifolium 0.00017 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.00777 0.02738 0.0237≤Pi≤0.0310 a 4 Terminalia arjuna 4.20E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00325 0.01162 0.0092≤Pi≤0.0140 a 6 Emblica offcinalis 0.00266 0.01221 0.0097≤Pi≤0.0146 a 7 Gardinia latifolia 0.00055 0 0.00≤Pi≤0.00 d 8 Ficus benghalensis 4.20E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 4.20E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00055 0.00177 0.0008≤Pi≤0.0027 a 11 Aegle marmelos 0.00144 0.00611 0.0043≤Pi≤0.0078 a 12 Kydia calycina 0.0011 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00114 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00781 0.00965 0.0074≤Pi≤0.0118 c 15 Hymenodictyon excelsum 8.40E-05 0 0.00≤Pi≤0.00 d 16 Albizia odoratissima 0.00021 0.00256 0.0014≤Pi≤0.0036 a 268

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17 Grewia latifolia 0.00262 0.00571 0.0040≤Pi≤0.0073 a 18 Anogeissus latifolia 0.00325 0.0063 0.0045≤Pi≤0.0080 a 19 Delbergia peniculata 0.00038 0 0.00≤Pi≤0.00 d 20 Gardinia gummifera 0.00017 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 8.40E-05 0.00158 0.0006≤Pi≤0.0024 a 22 Ziziphus xylopyra 0.00177 0 0.00≤Pi≤0.00 d 23 Adina cordifolia 0.00013 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00021 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.00815 0.0463 0.0416≤Pi≤0.0509 a 26 Flacourtia indica 0.00084 0 0.00≤Pi≤0.00 d 27 Milosa tomentosa 0.00878 0 0.00≤Pi≤0.00 d 28 Bridelia retusa 0.00122 0 0.00≤Pi≤0.00 d 29 Ficus hispida 0.00106 0 0.00≤Pi≤0.00 d 30 Garuga pinnata 4.20E-05 0 0.00≤Pi≤0.00 d 31 Acacia catechu 0.00101 0.00355 0.0022≤Pi≤0.0048 a 32 Schleichera oleosa 0.00072 0 0.00≤Pi≤0.00 d 33 Careya arborea 4.20E-05 0 0.00≤Pi≤0.00 d 34 Sterculia urens 0.00013 0 0.00≤Pi≤0.00 d 35 Lagerstroemia parviflora 0.03069 0 0.00≤Pi≤0.00 d 36 Ixora arborea 0.00329 0.0134 0.0108≤Pi≤0.0159 a 37 Madhuca indica 0.00127 0.00906 0.0069≤Pi≤0.0111 a 38 Randia dumetorum 0.00093 0 0.00≤Pi≤0.00 d 39 Schrebera swietenioides 4.20E-05 0 0.00≤Pi≤0.00 d 40 Lenia coromandalica 0.00557 0.00335 0.0020≤Pi≤0.0046 b 41 Mitragyna parvifolia 0.00186 0 0.00≤Pi≤0.00 d 42 Ficus infectoria 8.40E-05 0 0.00≤Pi≤0.00 d 43 Butea monosperma 0.00173 0.00768 0.0057≤Pi≤0.0096 a

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44 Soymida febrifuga 0.00089 0 0.00≤Pi≤0.00 d 45 Terminalia tomentosa 0.00494 0.0063 0.0045≤Pi≤0.0080 c 46 Boswellia serrata 0.00021 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 8.40E-05 0 0.00≤Pi≤0.00 d 48 Annona squamosa 0.00021 0 0.00≤Pi≤0.00 d 49 Tectona grandis 0.04605 0.01911 0.0160≤Pi≤0.0221 b 50 Diospyros melanoxylon 0.02773 0.02502 0.0215≤Pi≤0.0285 c 51 Ougeinia oojeinensis 0.00215 0 0.00≤Pi≤0.00 d 52 Casearia tomentosa 0.0011 0.00355 0.0022≤Pi≤0.0048 a 53 Ficus glomerata 4.20E-05 0 0.00≤Pi≤0.00 d 54 Diospyros Montana 0.00042 0.00079 0.0001≤Pi≤0.0014 c Climber 55 Bauhinia vahlii 0.00038 0.01714 0.0143≤Pi≤0.0200 a 56 Ziziphus oenoplia 0.00473 0.01261 0.0101≤Pi≤0.0151 a 57 Asparagus racemosum 0.00089 0 0.00≤Pi≤0.00 d 58 Butea parviflora 0.00114 0 0.00≤Pi≤0.00 d Shrub 59 Hilectrux izora 0.00485 0.01734 0.0144≤Pi≤0.0202 a 60 Bamboo spp. 0.01769 0.07624 0.0703≤Pi≤0.0821 a 61 Pheonix aquilis 0.00718 0.01359 0.0110≤Pi≤0.0161 a 62 Grewia hirsute 0.00988 0.01556 0.0127≤Pi≤0.0183 a 63 Holarrhena antidysenterica 0.00013 0 0.00≤Pi≤0.00 d 64 Lantana camara 0.04217 0.0132 0.0106≤Pi≤0.0157 b Herb 65 Phyllanthus amarus 0.00878 0.02502 0.0215≤Pi≤0.0285 a 66 Marsilea quadrifolia 0.11683 0.08983 0.0834≤Pi≤0.0962 b 67 Casia tora 0.04917 0.0134 0.0108≤Pi≤0.0159 b

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68 Vallaris solanacea 0.00924 0.00749 0.0055≤Pi≤0.0094 c 69 Sida spp. 0.04554 0 0.00≤Pi≤0.00 d 70 Tribulus terrestris 0.00118 0.00217 0.0011≤Pi≤0.0032 c 71 Elephantopus scaber 0.01562 0.02384 0.0204≤Pi≤0.0272 a 72 Guizotia abyssinica 0.04972 0.08964 0.0832≤Pi≤0.0960 a 73 Sida acuta 0.04136 0.0067 0.0048≤Pi≤0.0085 b 74 Parthenium hysterophorus 0.00152 0.00276 0.0015≤Pi≤0.0039 c 75 Sida spp. 0.00266 0.00611 0.0043≤Pi≤0.0078 a 76 Ocimum canum 0.01629 0.01734 0.0144≤Pi≤0.0202 c Grasses 77 Eragrostis tenella 0.2056 0.11131 0.0104≤Pi≤0.1183 b 78 Imperata cylindrical 0.00435 0.0063 0.0045≤Pi≤0.0080 c 79 Pennisetum pedicellatum 0.00194 0.0069 0.0050≤Pi≤0.0087 a 80 Cynodon dactylon 0.00232 0.0134 0.0108≤Pi≤0.0159 a 81 Cyperus scariosus 0.00084 0.00217 0.0011≤Pi≤0.0032 a 82 Themeda quadrivalvis 0.00414 0.01084 0.0085≤Pi≤0.0131 a 83 Chloris barbata 0.00768 0.01418 0.0115≤Pi≤0.0168 a 84 Dicanthium spp. 0.05534 0.06797 0.0623≤Pi≤0.0735 a 85 Eulaliopsis binata 0.00232 0.00552 0.0038≤Pi≤0.0071 a 86 Heteropogon contortus 0.07775 0.08511 0.0788≤Pi≤0.0913 c

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Table-7.10Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the sambar during summer season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00461 0.00351 0.0019≤Pi≤0.0050 b 2 Alangium salviifolium 0.00045 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.00731 0.01623 0.0129≤Pi≤0.0194 a 4 Terminalia arjuna 0.00022 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00483 0.00789 0.0056≤Pi≤0.0101 c 6 Emblica offcinalis 0.00292 0 0.00≤Pi≤0.00 d 7 Gardinia latifolia 0.00096 0 0.00≤Pi≤0.00 d 8 Ficus benghalensis 5.62E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 5.62E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00067 0.00044 0.0001≤Pi≤0.0009 b 11 Aegle marmelos 0.00208 0.00614 0.0041≤Pi≤0.0081 a 12 Kydia calycina 0.00191 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00152 0.00351 0.0019≤Pi≤0.0050 c 14 Chloroxylon Swietenia 0.00686 0 0.00≤Pi≤0.00 d 15 Hymenodictyon excelsum 0.00011 0 0.00≤Pi≤0.00 d 16 Delbergia sissoo 0.00034 0 0.00≤Pi≤0.00 d

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17 Albizia odoratissima 0.00028 0.00132 0.0003≤Pi≤0.0022 c 18 Grewia latifolia 0.00393 0.0114 0.0086≤Pi≤0.0141 a 19 Anogeissus latifolia 0.00354 0 0.00≤Pi≤0.00 d 20 Delbergia peniculata 0.00051 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 0.00011 0 0.00≤Pi≤0.00 d 22 Ziziphus xylopyra 0.00174 0.00921 0.0067≤Pi≤0.0116 a 23 Adina cordifolia 0.00017 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00028 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.01523 0.02193 0.0181≤Pi≤0.0257 c 26 Flacourtia indica 0.00253 0.01272 0.0098≤Pi≤0.0156 a 27 Milosa tomentosa 0.01079 0 0.00≤Pi≤0.00 d 28 Bridelia retusa 0.00146 0.02061 0.0169≤Pi≤0.0242 a 29 Ficus hispida 0.00039 0 0.00≤Pi≤0.00 d 30 Garuga pinnata 5.62E-05 0.00088 0.0001≤Pi≤0.0016 a 31 Acacia catechu 0.00129 0.00351 0.0019≤Pi≤0.0050 c 32 Schleichera oleosa 0.00112 0 0.00≤Pi≤0.00 d 33 Careya arborea 0.00011 0 0.00≤Pi≤0.00 d 34 Sterculia urens 5.62E-05 0 0.00≤Pi≤0.00 d 35 Lagerstroemia parviflora 0.03653 0 0.00≤Pi≤0.00 d 36 Ixora arborea 0.00438 0.01754 0.0141≤Pi≤0.0209 a 37 Madhuca indica 0.0014 0.0136 0.0106≤Pi≤0.0165 a 38 Randia dumetorum 0.00399 0 0.00≤Pi≤0.00 d 39 Schrebera swietenioides 5.62E-05 0.00614 0.0041≤Pi≤0.0081 a 40 Lenia coromandalica 0.00657 0 0.00≤Pi≤0.00 d 41 Mitragyna parvifolia 0.00225 0 0.00≤Pi≤0.00 d 42 Ficus infectoria 0.00011 0.01842 0.0149≤Pi≤0.0218 a 43 Butea monosperma 0.00152 0.01272 0.0098≤Pi≤0.0156 a 44 Soymida febrifuga 0.00118 0 0.00≤Pi≤0.00 d

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45 Terminalia tomentosa 0.00584 0 0.00≤Pi≤0.00 d 46 Boswellia serrata 0.00028 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 0.00011 0.00351 0.0019≤Pi≤0.0050 a 48 Annona squamosa 5.62E-05 0.02588 0.0217≤Pi≤0.0299 a 49 Tectona grandis 0.05816 0.00877 0.0063≤Pi≤0.0111 b 50 Diospyros melanoxylon 0.049 0 0.00≤Pi≤0.00 d 51 Ougeinia oojeinensis 0.00247 0.00351 0.0019≤Pi≤0.0050 c 52 Casearia tomentosa 0.00332 0.00307 0.0016≤Pi≤0.0044 c 53 Ficus glomerata 5.62E-05 0 0.00≤Pi≤0.00 d 54 Diospyros montana 0.00157 0.00132 0.0003≤Pi≤0.0022 b Climber 55 Bauhinia vahlii 0.00028 0.00921 0.0067≤Pi≤0.0116 a 56 Ziziphus oenoplia 0.00674 0 0.00≤Pi≤0.00 d 57 Butea parviflora 5.62E-05 0.10833 0.1003≤Pi≤0.1163 a 58 Asparagus racemosum 0 0.00789 0.0056≤Pi≤0.0101 a Shrub 59 Hilectrux izora 0.00787 0.02105 0.0173≤Pi≤0.0247 a 60 Bamboo spp. 0.01984 0.02807 0.0238≤Pi≤0.0323 c 61 Pheonix aquilis 0.00725 0.00614 0.0041≤Pi≤0.0081 c 62 Grewia hirsuta 0.00455 0.0114 0.0086≤Pi≤0.0141 a 63 Grewia spp 0.00326 0.00351 0.0019≤Pi≤0.0050 c 64 Holarrhena antidysenterica 0.00056 0.00439 0.0026≤Pi≤0.0060 a 65 Lantana camara 0.06491 0.02807 0.0238≤Pi≤0.0323 b Herb 66 Phyllanthus amarus 0.00714 0.01754 0.0141≤Pi≤0.0209 a 67 Casia tora 0.08098 0 0.00≤Pi≤0.00 d 68 Vallaris solanacea 0.00888 0 0.00≤Pi≤0.00 d 69 Tribulus terrestris 0.0145 0 0.00≤Pi≤0.00 d

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70 Elephantopus scaber 0.02259 0.00088 0.0001≤Pi≤0.0016 b 71 Guizotia abyssinica 0.05333 0 0.00≤Pi≤0.00 d 72 Sida acuta 0.02062 0.04079 0.0356≤Pi≤0.0458 c 73 Desmodium Spp. 0.00017 0.01754 0.0141≤Pi≤0.0209 a 74 Xanthium strumaxium 0.0032 0.00614 0.0041≤Pi≤0.0081 c 75 Sida spp. 0.00051 0 0.00≤Pi≤0.00 d 76 Ventilago maderaspatana 0.00045 0 0.00≤Pi≤0.00 d 77 Spirodela polyrhiza 0.00039 0.00833 0.0059≤Pi≤0.0106 a 78 Sida spp 0.04366 0.025 0.0209≤Pi≤0.0290 b 79 Ocimum canum 0.00888 0.00219 0.0009≤Pi≤0.0033 b Grasses 80 Eragrostis tenella 0.21343 0.18728 0.1772≤Pi≤0.1973 b 81 Imperata cylindrica 0.00287 0.00351 0.0019≤Pi≤0.0050 c 82 Pennisetum pedicellatum 0.00017 0.02237 0.0185≤Pi≤0.0261 a 83 Cynodon dactylon 0.00101 0.0307 0.0262≤Pi≤0.0351 a 84 Themeda quadrivalvis 0.01225 0 0.00≤Pi≤0.00 d 85 Chloris barbata 0.01169 0.00132 0.0003≤Pi≤0.0022 b 86 Cyperus scariosus 0.00416 0.00351 0.0019≤Pi≤0.0050 c 87 Dicanthium spp. 0.02877 0 0.00≤Pi≤0.00 d 88 Thysanolaena maxima 0.01427 0.16184 0.1523≤Pi≤0.1713 a 89 Heteropogon contortus 0.07339 0.0057 0.0037≤Pi≤0.0076 b

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Table-7.11Bonferroni confidence limits for for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the sambar during post monsoon summer season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00352 0.00509 0.0036≤Pi≤0.0065 c 2 Alangium salviifolium 0.00014 0 0.00≤Pi≤0.00 d 3 Cassia fistula 0.0054 0.01262 0.0103≤Pi≤0.0149 a 4 Terminalia arjuna 3.56E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00171 0.00576 0.0042≤Pi≤0.0073 a 6 Emblica offcinalis 0.00274 0.00709 0.0053≤Pi≤0.0088 a 7 Gardinia latifolia 0.00043 0 0.00≤Pi≤0.00 d 8 Ficus benghalensis 3.56E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 3.56E-05 0 0.00≤Pi≤0.00 d 10 Gymnosporia spinosa 0.00064 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00057 0.02104 0.0180≤Pi≤0.0239 a 12 Kydia calycina 0.00096 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00096 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00395 0.01063 0.0085≤Pi≤0.0127 a 15 Hymenodictyon excelsum 0.00018 0 0.00≤Pi≤0.00 d 16 Albizia odoratissima 0.00018 0.00266 0.0015≤Pi≤0.0037 a

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17 Grewia latifolia 0.00256 0.00886 0.0069≤Pi≤0.0107 a 18 Anogeissus latifolia 0.00263 0.00775 0.0059≤Pi≤0.0095 a 19 Delbergia peniculata 0.00032 0 0.00≤Pi≤0.00 d 20 Cleistanthus collinus 0.00014 0.00177 0.0009≤Pi≤0.0026 a 21 Ziziphus xylopyra 0.00149 0.00642 0.0047≤Pi≤0.0080 a 22 Adina cordifolia 0.00011 0 0.00≤Pi≤0.00 d 23 Acacia leucophloca 0.00018 0 0.00≤Pi≤0.00 d 24 Syzygium cumini 0.00594 0.02901 0.0255≤Pi≤0.0324 a 25 Flacourtia indica 0.00036 0 0.00≤Pi≤0.00 d 26 Milosa tomentosa 0.00853 0.01883 0.0160≤Pi≤0.0216 a 27 Bridelia retusa 0.00103 0 0.00≤Pi≤0.00 d 28 Ficus hispida 0.00014 0.00066 0.0001≤Pi≤0.0011 c 29 Garuga pinnata 3.56E-05 0 0.00≤Pi≤0.00 d 30 Acacia catechu 0.00085 0.00377 0.0025≤Pi≤0.0050 a 31 Schleichera oleosa 0.00057 0.00266 0.0015≤Pi≤0.0037 a 32 Careya arborea 3.56E-05 0 0.00≤Pi≤0.00 d 33 Sterculia urens 3.56E-05 0 0.00≤Pi≤0.00 d 34 Lagerstroemia parviflora 0.02165 0.00399 0.0026≤Pi≤0.0052 b 35 Ixora arborea 0.0043 0.01772 0.0150≤Pi≤0.0204 a 36 Madhuca indica 0.00128 0.00709 0.0053≤Pi≤0.0088 a 37 Randia dumetorum 7.11E-05 0 0.00≤Pi≤0.00 d 38 Schrebera swietenioides 3.56E-05 0 0.00≤Pi≤0.00 d 39 Lenia coromandalica 0.00526 0 0.00≤Pi≤0.00 d 40 Mitragyna parvifolia 0.00085 0 0.00≤Pi≤0.00 d 41 Ficus infectoria 7.11E-05 0 0.00≤Pi≤0.00 d 42 Butea monosperma 0.00167 0.01218 0.0099≤Pi≤0.0144 a 43 Soymida febrifuga 0.00075 0 0.00≤Pi≤0.00 d

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44 Terminalia tomentosa 0.0038 0.00997 0.0079≤Pi≤0.0120 a 45 Boswellia serrata 0.00018 0 0.00≤Pi≤0.00 d 46 Bombax ceiba 7.11E-05 0.00354 0.0023≤Pi≤0.0047 a 47 Tectona grandis 0.03164 0.01285 0.0105≤Pi≤0.0151 b 48 Diospyros melanoxylon 0.01522 0.01019 0.0081≤Pi≤0.0122 b 49 Ougeinia oojeinensis 0.00213 0.00642 0.0047≤Pi≤0.0080 a 50 Casearia tomentosa 0.00089 0 0.00≤Pi≤0.00 d 51 Ficus glomerata 3.56E-05 0.00089 0.0002≤Pi≤0.0014 a 52 Diospyros montana 0.00036 0.00155 0.0007≤Pi≤0.0023 a Climber 53 Bauhinia vahlii 0.00018 0.01484 0.0123≤Pi≤0.0173 a 54 Butea parviflora 0.00025 0.00133 0.0005≤Pi≤0.0020 a 55 Ziziphus oenoplia 0.00523 0.0299 0.0263≤Pi≤0.0334 a Shrub 56 Hilectrux izora 0.00622 0.02769 0.0243≤Pi≤0.0310 a 57 Bamboo spp. 0.01806 0.02104 0.0180≤Pi≤0.0239 c 58 Pheonix aquilis 0.00932 0.01262 0.0180≤Pi≤0.0149 a 59 Grewia hirsuta 0.00921 0.02237 0.0193≤Pi≤0.0254 a 60 Holarrhena antidysenterica 0.00071 0.0031 0.0019≤Pi≤0.0042 a 61 Lantana camara 0.04124 0.01506 0.0125≤Pi≤0.0175 b 62 Vitex negundo 0.00206 0.00177 0.0009≤Pi≤0.0026 b Herb 63 Phyllanthus amarus 0.02798 0.02924 0.0257≤Pi≤0.0327 c 64 Marsilea quadrifolia 0.05646 0.10078 0.0945≤Pi≤0.1069 a 65 Spirodela polyrhiza 0.00526 0.00465 0.0032≤Pi≤0.0060 b 66 Casia tora 0.05202 0.00864 0.0067≤Pi≤0.0105 b 67 Vallaris solanacea 0.01116 0.00487 0.0034≤Pi≤0.0063 b

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68 Parthenium hysterophorus 0.01323 0 0.00≤Pi≤0.00 d 69 Tribulus terrestris 0.0021 0 0.00≤Pi≤0.00 d 70 Sida spp. 0.03075 0.00997 0.0079≤Pi≤0.0120 b 71 Elephantopus scaber 0.02258 0.01971 0.0168≤Pi≤0.0225 c 72 Guizotia abyssinica 0.03698 0.01307 0.0107≤Pi≤0.0154 b 73 Sida acuta 0.03986 0.00842 0.0065≤Pi≤0.0102 b 74 Desmodium spp. 0.00174 0 0.00≤Pi≤0.00 d 75 Ocimum basilicum 0.00555 0.02458 0.0213≤Pi≤0.0277 a 76 Sida spp. 0.01152 0.01085 0.0087≤Pi≤0.0129 b 77 Desmodium triflorum 0.02994 0.07575 0.0703≤Pi≤0.0811 a 78 Ocimum canum 0.01611 0.01484 0.0123≤Pi≤0.0173 c Grasses 79 Eragrostis tenella 0.19473 0.1526 0.1452≤Pi≤0.1600 c 80 Imperata cylindrica 0.00654 0 0.00≤Pi≤0.00 d 81 Pennisetum pedicellatum 0.00334 0.00753 0.0057≤Pi≤0.0093 a 82 Cynodon dactylon 0.01792 0.04983 0.0453≤Pi≤0.0543 a 83 Apluda mutica 0.01404 0.02237 0.0193≤Pi≤0.0254 c 84 Cyperus scariosus 0.00043 0 0.00≤Pi≤0.00 d 85 Themeda quadrivalvis 0.01188 0 0.00≤Pi≤0.00 d 86 Chloris barbata 0.00985 0.01484 0.0123≤Pi≤0.0173 a 87 Dicanthium spp. 0.07072 0.02724 0.0238≤Pi≤0.0305 b 88 Eulaliopsis binata 0.01376 0 0.00≤Pi≤0.00 d 89 Heteropogon contortus 0.06439 0.01949 0.0166≤Pi≤0.0223 b

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Table-7.12Bonferroni confidence limits for available (Pi0) and utilised proportion of different plants species (Pie), 95% bonferroni

confidence limits for Pie, and rating of preferences or avoidances of the sambar during winter summer season. n = number of individuals per sampled species, n = number of plant fragment recorded in diet, p = percent of occurrence. a = utilised more than availability, b = utilised less than availability, c = utilisation in proportion to availability, d =species available but not recorded in diet composition (totally avoidance

S.No Plant Species Pio Pie 95% Bonferroni Rating Tree 1 Buchanania lanzan 0.00405 0.0032061 0.0019≤Pi≤0.0044 c 2 Alangium salviifolium 0.00017 0.0005829 4.348≤Pi≤0.0011 b 3 Cassia fistula 0.00777 0.025357 0.0218≤Pi≤0.0288 a 4 Terminalia arjuna 4.20E-05 0 0.00≤Pi≤0.00 d 5 Bahunia barigata 0.00325 0.0113669 0.0088≤Pi≤0.0137 a 6 Emblica offcinalis 0.00266 0.0157389 0.0129≤Pi≤0.0185 a 7 Gardinia latifolia 0.00055 0.0023317 0.0012≤Pi≤0.0034 a 8 Ficus benghalensis 4.20E-05 0 0.00≤Pi≤0.00 d 9 Pterocarpus marsupium 4.20E-05 0.0014573 0.0006≤Pi≤0.0023 a 10 Gymnosporia spinosa 0.00055 0 0.00≤Pi≤0.00 d 11 Aegle marmelos 0.00144 0.0125328 0.0100≤Pi≤0.0150 a 12 Kydia calycina 0.0011 0 0.00≤Pi≤0.00 d 13 Semecarpus anacardium 0.00114 0 0.00≤Pi≤0.00 d 14 Chloroxylon Swietenia 0.00781 0.0282716 0.0245≤Pi≤0.0319 a 15 Hymenodictyon excelsum 8.40E-05 0 0.00≤Pi≤0.00 d

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16 Albizia odoratissima 0.00021 0.0093267 0.0071≤Pi≤0.0114 a 17 Grewia latifolia 0.00262 0.0113669 0.0089≤Pi≤0.0137 a 18 Anogeissus latifolia 0.00325 0.0093267 0.0071≤Pi≤0.0114 a 19 Delbergia peniculata 0.00038 0 0.00≤Pi≤0.00 d 20 Gardinia gummifera 0.00017 0 0.00≤Pi≤0.00 d 21 Cleistanthus collinus 8.40E-05 0 0.00≤Pi≤0.00 d 22 Ziziphus xylopyra 0.00177 0 0.00≤Pi≤0.00 d 23 Adina cordifolia 0.00013 0 0.00≤Pi≤0.00 d 24 Acacia leucophloca 0.00021 0 0.00≤Pi≤0.00 d 25 Syzygium cumini 0.00815 0.0492568 0.0444≤Pi≤0.0540 a 26 Flacourtia indica 0.00084 0.0020402 0.0010≤Pi≤0.0030 a 27 Milosa tomentosa 0.00878 0 0.00≤Pi≤0.00 d 28 Bridelia retusa 0.00122 0.0040804 0.0026≤Pi≤0.0055 a 29 Ficus hispida 0.00106 0 0.00≤Pi≤0.00 d 30 Garuga pinnata 4.20E-05 0 0.00≤Pi≤0.00 d 31 Acacia catechu 0.00101 0.0110755 0.0087≤Pi≤0.0134 a 32 Schleichera oleosa 0.00072 0 0.00≤Pi≤0.00 d 33 Careya arborea 4.20E-05 0 0.00≤Pi≤0.00 d 34 Sterculia urens 0.00013 0 0.00≤Pi≤0.00 d 35 Lagerstroemia parviflora 0.03069 0.0110755 0.0087≤Pi≤0.0134 b 36 Ixora arborea 0.00329 0.0247741 0.0213≤Pi≤0.0282 a 37 Madhuca indica 0.00127 0.0131157 0.0105≤Pi≤0.0156 a 38 Randia dumetorum 0.00093 0.0023317 0.0012≤Pi≤0.0034 a 39 Schrebera swietenioides 4.20E-05 0 0.00≤Pi≤0.00 d 40 Lenia coromandalica 0.00557 0.0052463 0.0036≤Pi≤0.0068 c 41 Mitragyna parvifolia 0.00186 0 0.00≤Pi≤0.00 d

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42 Ficus infectoria 8.40E-05 0 0.00≤Pi≤0.00 d 43 Butea monosperma 0.00173 0.0195278 0.0164≤Pi≤0.0226 a 44 Soymida febrifuga 0.00089 0 0.00≤Pi≤0.00 d 45 Terminalia tomentosa 0.00494 0.007578 0.0056≤Pi≤0.0095 a 46 Boswellia serrata 0.00021 0 0.00≤Pi≤0.00 d 47 Bombax ceiba 8.40E-05 0 0.00≤Pi≤0.00 d 48 Annona squamosa 0.00021 0 0.00≤Pi≤0.00 d 49 Tectona grandis 0.04605 0.0113669 0.0089≤Pi≤0.0137 b 50 Diospyros melanoxylon 0.02773 0.0352667 0.0311≤Pi≤0.0393 a 51 Ougeinia oojeinensis 0.00215 0 0.00≤Pi≤0.00 d 52 Casearia tomentosa 0.0011 0.0023317 0.0012≤Pi≤0.0034 c 53 Ficus glomerata 4.20E-05 0 0.00≤Pi≤0.00 d 54 Diospyros Montana 0.00042 0 0.00≤Pi≤0.00 d Climber 55 Bauhinia vahlii 0.00038 0.0139901 0.0113≤Pi≤0.0166 a 56 Ziziphus oenoplia 0.00473 0.0402215 0.0358≤Pi≤0.0446 a 57 Asparagus racemosum 0.00089 0.0052463 0.0036≤Pi≤0.0068 a 58 Butea parviflora 0.00114 0.0084523 0.0064≤Pi≤0.0104 a Shrub 59 Hilectrux izora 0.00485 0.0276887 0.0240≤Pi≤0.0313 a 60 Bamboo spp. 0.01769 0.0113669 0.0089≤Pi≤0.0137 b 61 Pheonix aquilis 0.00718 0.0119499 0.0095≤Pi≤0.0143 a 62 Grewia hirsute 0.00988 0.0180705 0.0150≤Pi≤0.0210 a 63 Holarrhena antidysenterica 0.00013 0 0.00≤Pi≤0.00 d 64 Lantana camara 0.04217 0.0375984 0.0333≤Pi≤0.0418 b Herb

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65 Phyllanthus amarus 0.00878 0 0.00≤Pi≤0.00 d 66 Marsilea quadrifolia 0.11683 0.1314486 0.1238≤Pi≤1390 a 67 Casia tora 0.04917 0.0431361 0.0038≤Pi≤0.0476 b 68 Vallaris solanacea 0.00924 0.0198193 0.0167≤Pi≤0.0229 a 69 Sida spp. 0.04554 0.0128242 0.0103≤Pi≤0.0153 b 70 Tribulus terrestris 0.00118 0 0.00≤Pi≤0.00 d 71 Elephantopus scaber 0.01562 0.0294375 0.0256≤Pi≤0.0332 a 72 Guizotia abyssinica 0.04972 0.1256194 0.1182≤Pi≤0.1330 a 73 Sida acuta 0.04136 0 0.00≤Pi≤0.00 d 74 Parthenium hysterophorus 0.00152 0.0034975 0.0021≤Pi≤0.0048 a 75 Sida spp. 0.00266 0.0067036 0.0048≤Pi≤0.0085 a 76 Ocimum canum 0.01629 0.0125328 0.0100≤Pi≤0.0150 c Grasses 77 Eragrostis tenella 0.2056 0.0375984 0.0334≤Pi≤0.0418 b 78 Imperata cylindrical 0.00435 0 0.00≤Pi≤0.00 d 79 Pennisetum pedicellatum 0.00194 0.0046634 0.0031≤Pi≤0.0061 a 80 Cynodon dactylon 0.00232 0.0084523 0.0064≤Pi≤0.0104 a 81 Cyperus scariosus 0.00084 0 0.00≤Pi≤0.00 d 82 Themeda quadrivalvis 0.00414 0 0.00≤Pi≤0.00 d 83 Chloris barbata 0.00768 0.0139901 0.0113≤Pi≤0.0166 a 84 Dicanthium spp. 0.05534 0.0113669 0.0089≤Pi≤0.0137 b 85 Eulaliopsis binata 0.00232 0.0084523 0.0064≤Pi≤0.0104 a 86 Heteropogon contortus 0.07775 0.02594 0.0223≤Pi≤0.0294 b

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50

45

40

35

30 Summer 25 Post Monsoon 20 Winter 15

10

5

0 Tree Climber Shrub Herb Grass

Figure 7.1: Percentage of occurrence of tree, climber, shrubs, herbs and grasses in the diet of Chowsingha in different seasons

80

70

60

50

40 Browse Grass 30

20

10

0 Summer Post Monsoon Winter

Figure 7.2: Percentage of occurrence of identified browse and grass in the diet of Chowsingha in different seasons

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70 62.09 60

50

40 Summer Post Monsoon 30 Winter

20 15.61 11.84 10 7.14 3.32

0 Tree Climber Shrub Herb Grass

Figure 7.3: Percentage of occurrence of tree, climber, shrubs, herbs and grasses in the diet of Gaur in different seasons

80

70

60

50

40 Browse Grass 30

20

10

0 Summer Post Monsoon Winter

Figure 7.4: Percentage of occurrence of identified browse and grass in the diet of Gaur in different seasons

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45

40

35

30

25 Summer

20 Post Monsoon Winter 15

10

5

0 Tree Climber Shrub Herb Grass

Figure 7.5: Percentage of occurrence of tree, climber, shrubs, herbs and grasses in the diet of Sambar in different seasons

100

90

80

70

60

50 Browse Grass 40

30

20

10

0 Summer Post Monsoon Winter

Figure 7.6: Percentage of occurrence of identified browse and grass in the diet of Sambar in different seasons

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90

80

70

60

50 Summer 40 Post Monsson 30 Winter 20

10

0 Monocot Dicot Monocot Dicot Monocot Dicot Chowsingha Gaur Sambar

Figure 7.7: Proportion of unidentified monocot and dicot in the diet of chowsingha, Gaur and Sambar in different seasons

Chowsingha Gaur Sambar

3.75

1.5 2.8 1.31 0.79

1.1 1.39 0.54

Summer Post Monsoon Winter

Figure 7.8: Proportion of Lantana camera in the diet of chowsingha, Gaur and Sambar in different seasons

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Chapter 8- Resource Partitioning

Chapter -8

CHAPTER 8- RESOURCE PARTITIONING

8.1 Introduction

Among potential competitors resource partitioning with reference to habitat, takes a central place for successful existence. As hypothesis by Gause (1934), ecological isolation in temporal and spatial scales governs the co-existence of species and the sympatric species co-occur at one place by showing isolation in terms of use of various resources which govern habitat use pattern (Dar et al. 2012). A number of resource niches exists that could be partitioned by the species and if there is complete overlap between two species at one niche axis there must be partitioning along another unmeasured axis (Gordon, 2000). The use of multiple resources allows for partitioning, resulting in niche differentiation and coexistence (Schoener, 1974; Bagchi et al. 2003). Connell (1980) stated that it is not enough to show that species differ in their use of resources in a way that reduce niche overlap, even in the absence of competition, species will differ in their utilization of resources. Thus, studies of habitat selection of individuals or populations continue to play an important role in the efforts to generate sufficient knowledge for effective wildlife management (Otis, 1997; Dar et al. 2012). The present chapter deals with resource partitioning among different ungulates species of Pench Tiger Reserve in terms of habitat as well as food.

8.2- Methodology

8.2.1- Data Collection Habitat overlap among the ungulate species of the study area was analyzed on the basis of the distribution of pellets/dung of different species at different habitat types Stratified random sampling was carried out in different habitat and ungulates pellet or dung were searched inside 10 m radius of every plot. After the collection of pellet samples, every plot was cleared for the next season data. Along with the pellet group/dung count, vegetation sampling was also done. The method gives an idea about the resource competition between different ungulate species of the area.

8.2.2- Data Analysis For assessing resource partitioning in terms of habitat overlap, between different ungulate species of the intensive study area, the Pianka’s niche overlap index was used (Pianka, 1973).

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. Pianka Index Ojk = ∑ 푃푖푗.푃푖푘 2 2 �∑ 푃푖푗 ∑푃푖푘 Pij = Proportion resource i is of the total resources used by species j

Pik = Proportion resource i is of the total resources used by species k

This is a symmetric measure of overlap so that overlap between species A and species B is identical to overlap between species B and species A. This measure of overlap ranges from 0 (no resources used in common) to 1.0 (complete overlap) (Pianka (1986).

Niche Breadth was calculated following Levin’s (1968) Index.

B= 1 2 �∑ 푃푖

Where B is the niche breadth and Pi is the proportion of plant taxon or food category i in the diet (Levin’s, 1968). Furthermore this index was standardized to a scale of 0−1 following Hurlbert (1978). Bs = (B–1) / (n–1) where n is the total number of taxa or food categories consumed by at least one ungulate.

8.3- Results

Table 8.1 represents resource overlap indices (Pianka’s Index) with reference to habitat among different ungulates species in different seasons. The result revealed that cheetal was almost largely overlap with sambar, nilgai and gaur throughout different seasons. However the overlapping with chowsingha was least during all three different seasons. Similarly, sambar and nilgai, also largely overlapped with other ungulates except chowsingha. Contrary to these, were found leastly overlapped with chowsingha during all three different seasons. When we compare the overlapping indices in accordance with three different seasons (summer, post monsoon and winter), it was found that during summers, overlapping of chowsingha with gaur, cheetal, nilgai and sambar was least that ranged from 0.3 – 0.45 (Pianka Index). However, other ungulates largely overlapped with each other, ranging from

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0.87- 0.99 (Painka Index). In post monsoon maximum overlapping was recorded between cheetal and nilgai (0.99,), whereas minimum overlapping was found between nilgai and chowsingha (0.31,). In winter season maximum overlapping was found between cheetal and gaur (0.96,) and minimum between cheetal and chowsingha (0.33,).

The result of seasonal dietary overlap among different food items (Trees, Climbers, Shrubs, Herbs and Grasses) for chowsingha, sambar and gaur is shown in Table 8.2. The study revealed that there is complete dietary overlap between tree and climbers during post monsoon and winter season whereas in summer a little deviation was recorded. In case of trees and shrubs, dietary overlapping may be considered as fairly good in winter as well as in summer season; however, during post monsoon, food overlap between trees and shrubs was comparatively low. The results also suggest that dietary overlap among trees and herbs shows predominantly overlap in post monsoon; winter and summer season whereas among trees and grasses it was comparative low. It is also found that there was high level of diet overlap between climber and herb; however it is comparatively lower than to climbers and shrubs. During winter and summer season it was found that dietary overlap between climber and grasses was predominantly higher and in post monsoon it was found to be comparatively low. The dilatory overlap among shrub and herb, shrub and grass and herb and grasses was fairly high and almost similar trend was recorded throughout post monsoon, winter and summer season.

Niche breadth of different ungulate species is summarized in Figure 8.1. For chowsingha and gaur niche breadth was maximum in summer (B= 0.24, B= 0.22) and minimum in winter (B= 0.15, B= 0.16) respectively. In case of sambar it was again maximum in summer (B= 0.17) and minimum in post-monsoon (B= 0.15).

The results state that, there was more or less no habitat overlap among cheetal, sambar nilgai and gaur whereas chowsingha shows habitat overlap with other ungulate species. Based on these findings the null hypothesis was accepted for cheetal, sambar, nilgai and gaur, however it was rejected for chowsingha.

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8.4- Discussion

Resources found in a particular habitat are shared by sympatric species inhabiting in the same area and it has been well documented (Jarman, 1971, Schoener. 1974, 1982, 1986). Competition is considered to be a major selective force causing the differential use of resources and the consequent morphological and behavioural differences between species (Cody, 1974, Schoener, 1974, 1982, 1986). The best evidence supporting this view is those studies which have experimentally manipulated on potentially competing population either in the laboratory or in the field (Schoener, 1983). Experimental manipulations of ungulate populations are difficult to achieve. One alternative approach is to compare species overlap in periods of high and low resources availability (Gordon and Illius, 1989). Each species is adapted to utilize a unique niche like shady or sunny, dry or wet etc., and thus allowing their coexistence (Whitefield, 2002; Bagchi et al. 2003).

Analysis of niche differences reveals the evolutionary adjustments in a community, designed to facilitate coexistence of ecologically similar species (Dueser and Shugart, 1979; Reinert, 1984; Bagchi et al. 2003). To facilitate the coexistence, the niche differentiation occurs along several dimensions and the number of these dimensions increases with the species richness (Schoener, 1974 & 1983; Bagchi et al. 2003). According to Schoener (1974 & 1983) and Toft (1985), the coexisting species segregate primarily by their habitats and subsequently by the dietary and temporal specializations.

Variety of indices can be used for measuring the ecological niche such as dietary components, microhabitat, or temporal or spatial activity (Dar et al. 2012). Pianka’s overlap index is a measure used to estimate the extant of overlap, the indices range from 0 (no overlap between the two species) to 1 (complete overlap) based on the usage of particular resource (Pianka, 1986). The index was also used by several researchers in calculating resource overlap like habitat and diet overlap. De-Pin et al. (2013) used the indices to calculate the dietary overlap between adults and juveniles of dwarf blue sheep in Baima snow mountain national nature reserve, Yunnan. The similar index for measuring diet overlap between predators was used by Andheria (2007) in Bandipur tiger reserve, India. Dar (2012) used the same indices to calculate

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Chapter -8 the utilization of habitat micro-components of four sympatric ungulate species in Shivalik ecosystem, Uttarakhand, India.

Our study suggested that habitat of chowsingha was least overlapped with other ungulates namely cheetal, sambar, nilgai and gaur. It was recorded that chowsingha mostly confined in the sanctuary area where availability of their food items (Eragrostis tenella, Albizia odoratissima, Ocimum canum and Guizotia abyssinica) was in ample amount. The terrain of sanctuary was much and more undulating and it is found in various studies that chowsingha prefers this particular type of terrain (Sharma et al. 2007, Haleem et al. 2014b). The body size of chowsingha is comparatively smaller than other ungulates found in the study area so that they don’t want to compete with them and probably this may be one of the reasons of least overlap.

It is suggested that if body size of different species found in a particular area is similar then they may avoid habitat overlapping (Bowers and Brown, 1982, Brown and Nicoletto, 1991). In PTR due to availability of plentiful food items, chowsingha and cheetal are having more or less similar body size (Personal Observation), therefore they prohibit their co-existence, hence majority of chowsingha confined to sanctuary where cheetal population is very less and they face least inter specific competition.

It is suggested that herbivore species whose density is low can coexist with those species which are abundant (Macandza, et al. 2012). However, our study is contrary to this finding where chowsingha (whose numbers are low) avoids other abundant herbivores and due to this they stay away from national park where other ungulates were more abundant and consequently concentrate more on sanctuary resources.

The present study revealed that chowsingha are selective feeder and usually prefer Albizia odoratissima, Guizotia abyssinica, Marsilea quadrifolia and Ocimum canum. Furthermore, the floral composition of study area exhibit that Albizia odoratissima, Guizotia abyssinica, Marsilea quadrifolia and Ocimum canum are widely distributed in sanctuary area throughout the year. Probably due to their selective feeding nature and preference for above mentioned floral species, chowsingha are more confined to sanctuary than other ungulates. The reason could be over abundant population of cheetal and due to this they shifted from National park towards sanctuary area.

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Findings of the present study suggest that the overlap in diet between chowsingha, sambar and gaur was much higher than what would be expected on the basis of chance. A high degree of dietary overlap can be interpreted in two different, and exactly opposite, ways (Gotelli and Graves, 1996, Mysterud, 2000). First, it can mean that competition is intense because species use the same resources. Alternatively, it may indicate that competition is absent, permitting species to share abundant resources (Gordon & Illius, 1989, Gotelli & Graves, 1996, Putman, 1996, Mysterud, 2000). The crucial difference is whether or not the shared resources are limited. Although we did not specifically test this hypothesis, the generally low animal densities, (chowsingha, gaur and sambar) the apparently abundant supply of grass and the good physical condition (Personal Observation) of herbivores even at the end of the dry season showed no apparent indication of limited resources. In the present study it was also observed that niche breadth value of chowsingha, sambar and gaur was about the same (low value) which suggest that they are specialist and selective feeder and ultimately it leads them to high level of overlap.

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Table-8.1 Habitat overlaps between different ungulate species of Pench Tiger Reserve in different seasons

Seasons S.No. Overlapping Species

Summer Post Monsoon Winter

1 Cheetal and Sambar 0.878083 0.8594415 0.952942

2 Cheetal and Nilgai 0.952786 0.9915951 0.916563

3 Cheetal and Gaur 0.986659 0.9885305 0.964055

4 Cheetal and Chowsingha 0.300779 0.3271118 0.327201

5 Sambar and Nilgai 0.973599 0.9026467 0.888218

6 Sambar and Gaur 0.937607 0.8288272 0.911794

7 Sambar and Chowsingha 0.416207 0.4690097 0.480399

8 Nilgai and Gaur 0.982052 0.9777711 0.915262

9 Nilgai and Chowsingha 0.453457 0.308383 0.495711

10 Gaur and Chowsingha 0.310301 0.3799694 0.369225

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Table-8.2 Dietary overlaps among different food items between Chowsingha, Sambar and Gaur in Pench Tiger Reserve throughout different seasons

Season S.No. Overlapping Food Items Post-Monsoon Winter Summer Tree Climber 0.9930253 0.97857 0.78173 1 Tree Shrub 0.7278321 0.81372 0.84042 2 Tree Herb 0.9355948 0.96339 0.91699 3 Tree Grass 0.878334 0.87239 0.8671 4 Climber Shrub 0.8034846 0.82184 0.754 5 Climber Herb 0.9639096 0.97521 0.93557 6 Climber Grass 0.9203914 0.78634 0.75095 7 Shrub Herb 0.8905897 0.92663 0.92386 8 Shrub Grass 0.9284732 0.87556 0.9983 9 Herb Grass 0.990456 0.86764 0.92824 10

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0.3

0.25

0.2

Summer 0.15 Post Monsoon Winter 0.1

0.05

0 Chowsingha Sambar Gaur

Figure-8.1 Niche Breadth of different ungulate species of Pench Tiger Reserve in different seasons

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Chapter 9- Management and Conservation Stratigies

Chapter - 9

CHAPTER 9- MANAGEMENT AND CONSERVATION STRATIGIES

Conservation threats and management mitigation:

The conservation status and aspects of ecology of large herbivore ungulates was not well studied in Pench tiger Reserve. This report serves to show for the first time the results of detailed study on ecology of ungulate in Central India specially the Pench Tiger Reserve, though it was challenging to work of the shy species in Tiger Reserve. Nevertheless we gained valuable and unique data from transect walk, sighting, micro histology and questioner surveys. We have documented the occurrence of the ungulate species in and around the protected area, providing valuable insight into its abundance, food habit as well as conflicting issues that need to be addressed in order to conserve.

The large ungulates in the Pench tiger reserve is not equally distributed throughout the Pench tiger reserve. The Chital (Axis deer) is doing very well in Pench National Park and there population has increased a lot. The population has increase so much that it is not only raising conflict between the man and animal but also replacing some of the important species. For the better management it is suggested that some of the population of spotted deer should be translocated from National park to Sanctuary area. However the population trend of spotted deer shows that the mortality is either high in old aged animal or young individuals, therefore we may also predict that the population will be ecologically reduced after a given time interval as there is a mark difference in the mortality of adult and the sub adult. It is recommended that some of the population of spotted deer should be translocated in the sanctuary area and the buffer zone of Pench tiger Reserve to reduce the pressure on the National Park and will also reduce the conflict.

Chowsingha listed in schedule I species of Indian Wildlife Protection Act 1972, and however it is not present in the Pench National Park, while interview with the locals shows the confirms the distribution of chowsingha in past in National park area. However the direct sighting as well as indirect evidence of Chowsingha was recorded from the sanctuary area, though the National park is also having the habitat for the Chowsingha. There could be various reasons for the species not present in National park area, over abundant population of spotted deer could be one of the reason.

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Therefore it is recommended to investigate the reasons and causes of absence of Chowsingha from National park.

Effort should be enhanced and expedite to map the distribution and abundance of ungulate species in Pench tiger reserve using geo spatial tools. Efforts should also be investigate the ranging pattern of ungulates, the combine study will provide detail look into the area of preference and avoidance of the ungulate species. Special emphasis should be given to Chowsingha species as it is present only in the sanctuary area.

The results of micro histology show the presence of Lantana camera in the diet of most of the ungulate species. Lantana camera is one of the exotic weed species which has spread all over the park, and most of the ungulate species are feeding on the Lantana camera, though it is not a preferred diet but still micro-histological studies shows its presence in the diet of ungulates. It will be an interesting study to assess the impact of Lantana camera on the diet of ungulates. Since ungulates are the important prey base of the thriving tiger population and other carnivore species in Pench hence it is strongly recommended to quantify the status and distribution of Lantana camera in Pench tiger reserve and its impact on the health of ungulate species for their long term survival.

During the vegetation study in Pench tiger reserve it was noticed that underground vegetation cover is very low near the boundaries of the Karmajhiri range. When the area was investigated it was found no tree species seedling or saplings were present more over the shrub, herb and grass cover were also found negligible. It is serious concern and this should be investigated in detail to find out the reason and the alternative to restore the ground cover of the areas, mean while plantation of native species should be done in those areas.

During the study it was notice that lopping, tree cutting and illegal sand mining is going on in and around the buffer zone of the Pench tiger reserve, though the forest staff is very alert and doing its best to control all the illegal activities but due to less staff in buffer area forest dept is not as successful to control the same in buffer zone as it should be. Illegal rampant fishing is going on in and around the Totla dam (transitional area between Pench Tiger Reserve MP and Pench Tiger Reserve Maharashtra). Though the Forest dept of MP and Maharashtra both are working very

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hard, but unfortunately they are not well equipped and armed also. There are constrain about the proper patrolling vehicle, since area is quit big it is getting difficult to manage with limited no of facilities. Therefore it is strongly recommended to provide some more manpower, patrolling vehicle (boat) and the gauds should be well equipped and well armed.

Though we tried to focus the issue to understand the status, distribution and aspects of ecology of ungulate species, but due to shortage of time, and more over studying too many species simultaneously in such a less time has not allow us to actually do the ecological work. We strongly recommend that there should be a detailed study on all the ungulate species present in the Pench tiger reserve. Investigations should be initiated to understand the detailed ecology specially food preference, ranging pattern, conflict issues, its Geospatial study to see the distribution at spatial level to better understand the ecology of ungulates using direct as well indirect method. Their population structure should be assessed on temporal and spatial level for each species. Over all the Pench tiger reserve is one of the best manage tiger Reserve and having a very good prey base for thriving tiger population. We need to make some above said efforts for proper conservation and management of ungulates as they are the best prey base for the Tiger.

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Chapter 10- Conclusion Chapter -10

CHAPTER 10- CONCLUSION Implications of the Present study

Protected areas are the government initiation for in-situ conservation of biodiversity and environment. Declaration of Pench as Tiger reserve in 1992 was a step towards the conservation of biodiversity specially Tiger, its associated species and prey base. Studying different aspects of fauna and flora is an important way of monitoring long term conservation and other aspects of an ecosystem. Considering this, the present study on “Conservation status and ecology of ungulates in Pench Tiger Reserve with special reference to resource partitioning’ was conducted and which mainly focussed on prey base of the PTR. The study also investigated relationship of ungulates with forage items which is an important aspect of resource partitioning among ungulates in an area and ultimately governs the management of predator and prey relationship.

It has been considered that ungulate management must have three major components: research to increase its knowledge base, a choice of management objectives with the inclusion of local people welfare, and use of scientific knowledge to achieve those objectives (Bianchet, 2007). In addition to being valuable for management, research on ungulates has made major contributions to the development of ecological theory. The study of ungulates is particularly important, because their longevity, strong iteroparity, and overlapping generations produce unique patterns of population dynamics and life- history evolution (Gaillard et al., 2000).

Forests are dynamic in nature. Regeneration of seedlings, its growth and competition with each other and with large trees and their survival are the governing factors for its dynamic nature. Which species would be dominant in the forest in coming years, depends not only on climate and soils, but also on management decisions made today for conservation of native species. Our findings are in accordance with these predictions. The study implies that Teak have higher regeneration rate than their associated species and this may be the reason for maintaining its dominance in terms of trees. Although Tendu have regenerated more seedling than other species but due to collection of its leave by locals for their livelihood the regeneration are supressed. The early phase of regeneration of these floral species are providing underground cover which is beneficial for small ground dwelling mammalian species as well as avifauna and also helps in recruitment of new trees in the upcoming future.

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Feeding of an animal determines its survival and movement pattern in their life cycle. Therefore, it is important to know floral composition and availability of food resources in the area while studying food and feeding behaviour of a particular animal. The floral diversity of the study area is rich, which lies in the tropical dry deciduous and tropical moist deciduous forest, with variety of the trees, shrubs, herbs and grass species and has potential to support wild animals in terms of their basic requirement. The present study revealed that the reserve is having Tectona grandis as a dominant species whereas, Diospyros melanoxylon, Lagerstroemia parviflora and Syzygium cumini, were their associated species.

For chowsingha diet, among browse food items, proportion of Albizia odoratissima, Guizotia abyssinica and Ocimum canum were found maximum for summer, post monsoon and winter seasons in their diet however for grasses it was maximum for Eragrostis tenella throughout seasons. Whereas the availability of Albizia odoratissima and Ocimum canum is not enough in the study sites, hence the park managers should work in a way that the preferred vegetation species should be available enough to sustain the chowsingha population in the reserve. The availability of Eragrostis tenella is sufficient in the reserve and it may supplement the diet of chowsingha.

For diet of gaur, proportion of bamboos, Desmodium triflorum and Marsilea quadrifolia were maximum among browse food material in summer, post monsoon and winter seasons but the availability of Desmodium triflorum which is less abundant as compared to Marsilea quadrifolia requires attention so that its availability in the site becomes fairly good. Gaur is primarily a grazer and it was recorded that among grasses it feed on Heteropogon contortus in summer and post monsoon and in winter it switches to Eragrostis tenella. The findings also show that the reserve has enough Heteropogon contortus as well as Eragrostis tenella to support and sustain populations of gaur.

In case of sambar, Marsilea quadrifoli and Eragrostis tenella, which were generally preferred were recorded in high proportions in all three seasons and availability of both the species in the reserve is good enough to sustain its population.

The overall diet spectrum of ungulates (chowsingha, gaur and sambar) indicate that Albizia odoratissima, Guizotia abyssinica, Ocimum canum, Desmodium triflorum and

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Marsilea quadrifolia, Heteropogon contortus as well as Eragrostis tenella constituted maximum proportion of their diet. Out of seven preferred food items, the availability of Guizotia abyssinica, Marsilea quadrifolia, Heteropogon contortus and Eragrostis tenella are according to utilization, whereas Albizia odoratissima, Ocimum canum and Desmodium triflorum are available less than their utilisation and hence needs management interventions to sustain ungulate population.

Besides above mentioned preferred food species, the density of other food resources available in the study area are fairly good in the form of varieties of trees, shrubs, herbs and grasses. It implies that if an area has fulfilled the feeding requirements of the animals, then it may sustain them as healthy population. The most of the preferred food species of ungulates are more or less distributed in all habitat, and some preferred food species need to be managed and developed by the park authorities to provide food availability to the ungulates. Since the preferred food species are having patchy distribution throughout the PTR, the results of HSI models developed for different ungulate species also show that 80% of habitat is suitable for them. The entire area is thus suitable for the studied ungulate species and it could be the reason that population of prey base (ungulates) in PTR is good in number and ultimately supporting the predators of the area.

Due to the availability of preferred food throughout the PTR, the area support good number of ungulate population. The biomass of ungulates is enough to sustain the predators in good number in Pench. Findings of the present study reveals that the current total biomass of ungulate population in PTR is 24,83,391.6 kg and Majumder (2011) stated that the PTR has 9244kg biomass of three main predators namely tiger, leopard and dhole. According to predictive model given by Carbone, and Gittlaman (2002), 10000 kg of prey population is necessary to sustain 45 kg of a predator, and in case of present study the ungulates biomass can easily sustain the predator population of the PTR. It concludes that the PTR has enough prey population to sustain good number of Tiger and other predator species. Though the national park as well as sanctuary both are having good population of ungulates and their predator species as well, there is no or zero conflict in and around the national park area. However at the periphery of the sanctuary a national highway (NH7) is passing through the buffer zone and also the area is surrounded by villages, where some conflict (man animal) has been recorded, which needs to be further investigated.

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The results of population structure shows that population of ungulates yield a healthy Adult: Juvenile ratio in case of cheetal (100:64) and gaur (100:50), which is a sign of healthy recruitment, where as in case of sambar (100:32) and nilgai (100:5) it is less, as compared to above said prey base. Since sambar constitutes important diet of tiger and dhole (Majumder, 2011), and if recruitment of sambar decreases then it can force predators to feed on other prey species. Therefore, long term monitoring of these populations are needed to keep watch on their population trend and mitigation measure should be taken to increase recruitment of the young one of sambar in the population.

The study finds that ungulate community of PTR segregate mainly in terms of space as well as food. Cheetal is predominantly a grazer, remain in the flat areas and is opportunistic feeder. Sambar segregates from gaur in terms of food habit as it is predominantly a browser, whereas gaur predominately being a grazer. Sambar segregates from nilgai in terms of space as sambar prefers hilly terrain but the nilgai prefers open areas. In case of nilgai and gaur their food preferences segregates them as gaur is grazer while nilgai is predominantly a browser. These predictions are in accordance with the findings of the present study, which implies that there is distinct ecological segregation among different ungulates of the PTR.

Despite of overlap between chowsingha and other ungulate species (cheetal, sambar, nilgai and gaur), the species partition their resources at a very fine scale. Chowsingha being smaller in size is a qualitative and selective feeder while sambar and gaur being larger in size are coarse feeders. Both the predictions are in accordance with findings of the study where sambar and gaur dietary niche breath is larger than the chowsingha. Based on the findings, study implies that although ungulates of PTR are using similar habitat to sustain but within that they further partition their resource base either in terms of space or food.

This suggests that the current management practices in PTR are efficient and maintaining overall health of the park in terms of flora and fauna. Under the present study if suggested management recommendation can be apply the PTR would be a better habitat for not only prey population but also for their predators in upcoming future.

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