Forests and Forestry of West Bengal: Survey and Analysis 3030807053, 9783030807054

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Gautam Kumar Das

Forests and Forestry of West Bengal Survey and Analysis

Forests and Forestry of West Bengal

Gautam Kumar Das

Forests and Forestry of West Bengal Survey and Analysis

Gautam Kumar Das Kolkata, West Bengal, India

ISBN 978-3-030-80705-4    ISBN 978-3-030-80706-1 (eBook) https://doi.org/10.1007/978-3-030-80706-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

“I know…I know self-ego will get washed away Life will bust into silent pain Even then the flute will play song Stone will melt down into tears” – Rabindranath Tagore

Dedicated to the forest-living tribal people who love trees, save trees, and worship trees

Foreword

Forest of West Bengal is home to a rich and bewildering variety of wildlife and floral diversity including flowering plants. The current forest cover of the state is about 19.04% of the total geographical area and lies chiefly in almost all the districts of West Bengal. The principal forest products are timber, firewood, and charcoal. Forests in West Bengal belong to eight forest type groups, which are further divided into 30 forest types. Considering such different aspects of forests and forestry, forest cover mapping has been exercised for all the districts of West Bengal that reflects the district-wise status of forests and its present trends and provides inputs for monitoring forests through the implementation of different forest models and strategies for forest restoration. Forest models are properly identified and recognized during the forest survey in the concerned districts. Along with the forest cover mapping, district-wise forest statistics have been assessed from the biennial reports of the Forest Survey of India that revealed the yearly changing scenario of the forest cover for the districts of West Bengal. Specifications of relevant forest-related information like forest types, myths and history, current forestry practices, joint forest management, and eco-development committee are included for the districts. District-wise forest cover change matrix revealed the dwindling of the forest cover alarmingly in the 1970s and 1980s; yet a shift to joint forest conservation by making stakeholders in forestry initiatives and social forestry creation managed by the forest department has yielded the present results. Social forestry work in almost all the districts of West Bengal has started showing results. District-wise break-up revealed the decrease of forest cover of South 24 Parganas, Uttar Dinajpur, Murshidabad, and Howrah districts, while Bankura, Paschim Medinipur, Purulia, and Birbhum have recorded a rise. District-wise statistics and classification of forest types, forest models, and forest strategies will provide a scientific basis for forest research, management of wildlife and biodiversity, identification and classification of floral and faunal assemblages, assessment of biomass and carbon stock at forest floors of diverse applications. Application of statistical analysis on the survey and analyzed data using statistical methods is an attempt for the interpretation of different aspects of the forests and forestry studies of West Bengal. The data pool obtained applying such statistical ix

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Foreword

methods in the field of forest research will be useful for statistical estimation, and specific statistical techniques will be helpful in the determination of species diversity, vegetation pattern, examination of similarity indices, wildlife conservation, man-wildlife conflict, interpretation of physicochemical parameters of forest soils, or even for the occupational pattern of the tribal community living in the vicinity of the forest stands of West Bengal. Availability of such statistically analyzed data pools has immense use for the researchers in the field of forests and forestry, foresters for integrity monitoring of the forest stands and afforestation program, and for the overall management by the government sector for regeneration and restoration of the forests of West Bengal. The book Forests and Forestry of West Bengal is composed of such ideas providing a new look and a new inspiration for forest researchers with a thought well expressed for the researchers in the field of forests and forestry. All its chapters contain uncommon content of new focuses above the conventional thoughts related to the forests and forestry studies for the state of West Bengal. The most identical chapter of the book is the statistical analysis concerned with forest matters applying statistical methods that will provide the immensity of space encouraging forests and forestry-related study and research. Different forest types, forest models, and strategies for forest restoration, identified and worked out during the decade-long survey and analysis, depict their unprecedented, glorious presence along the tree lines in the forest of the state. Strategies for afforestation and restoration of forests, vastly discussed with different forest models, will help preceding the forest researchers, managers, and officials to work out different plans and projects for forest regeneration. The book is dedicated to the purposes of forest studies and devoted to the research for the forests and forestry in West Bengal. Kolkata, West Bengal, India  Gautam Kumar Das 10 June 2021

Contents

1 Forest Status of West Bengal����������������������������������������������������������������������   1 Physiography: At a Glance����������������������������������������������������������������������������   1 Forest Covers of West Bengal������������������������������������������������������������������������   3 Forests in the Past������������������������������������������������������������������������������������������   7 Joint Forest Management, Eco-Development Committee, and Self Help Groups������������������������������������������������������������������������������������  11 National Parks and Wildlife Sanctuaries��������������������������������������������������������  12 Current Forest Status��������������������������������������������������������������������������������������  12 Summary��������������������������������������������������������������������������������������������������������  15 References������������������������������������������������������������������������������������������������������  17 2 District-Wise Forest Matrix, Forest Models and Strategies��������������������  19 District-Wise Forest Matrix ��������������������������������������������������������������������������  19 Forest Cover of Bankura District ������������������������������������������������������������������  20 Forest Cover Change Matrix��������������������������������������������������������������������  21 Forest Cover of Birbhum District������������������������������������������������������������������  22 Past Presence of the Forest����������������������������������������������������������������������  23 Forest Cover Change Matrix��������������������������������������������������������������������  25 Forest Cover of Cooch Behar District������������������������������������������������������������  25 Green Infrastructure of Trees ������������������������������������������������������������������  26 Forest Cover Change Matrix��������������������������������������������������������������������  27 Forest Cover of Darjeeling District����������������������������������������������������������������  28 Forest Bathing������������������������������������������������������������������������������������������  29 Forest Cover Change Matrix��������������������������������������������������������������������  31 Forest Cover of Howrah Districts������������������������������������������������������������������  32 Rescue Forest ������������������������������������������������������������������������������������������  33 Forest Cover Change Matrix��������������������������������������������������������������������  33 Forest Cover of Kolkata District��������������������������������������������������������������������  34 Tree Equity and Urban Heat Island Effects ��������������������������������������������  35 Forest Cover Change Matrix��������������������������������������������������������������������  36

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Forest Cover of Hugli District�����������������������������������������������������������������������  37 Tree Islands����������������������������������������������������������������������������������������������  37 Forest Cover Change Matrix��������������������������������������������������������������������  38 Forest Cover of Jalpaiguri District����������������������������������������������������������������  39 Forgotten Forest ��������������������������������������������������������������������������������������  40 Forest Cover Change Matrix��������������������������������������������������������������������  42 Forest Cover of Malda District����������������������������������������������������������������������  42 Women’s Participation for Forest Restoration ����������������������������������������  44 Forest Cover Change Matrix��������������������������������������������������������������������  45 Forest Cover of Murshidabad District�����������������������������������������������������������  46 Solitary Tree��������������������������������������������������������������������������������������������  47 Forest Cover Change Matrix��������������������������������������������������������������������  48 Forest Cover of Nadia District ����������������������������������������������������������������������  49 Social Forestry ����������������������������������������������������������������������������������������  50 Forest Cover Change Matrix��������������������������������������������������������������������  52 Forest Cover of North 24 Parganas District ��������������������������������������������������  53 Green Infrastructure ��������������������������������������������������������������������������������  53 Blue Carbon ��������������������������������������������������������������������������������������������  54 Reclamation History of Forest Lands������������������������������������������������������  55 Forest Cover Change Matrix��������������������������������������������������������������������  56 Forest Cover of Paschim Medinipur District ������������������������������������������������  57 Community Forests����������������������������������������������������������������������������������  57 Forests and the Tribal Community����������������������������������������������������������  58 Forest Cover Change Matrix��������������������������������������������������������������������  61 Forest Cover of Purba Bardhaman and Paschim Bardhaman Districts����������  62 Myths-History������������������������������������������������������������������������������������������  63 Divisional Forest Scenario ����������������������������������������������������������������������  63 Forest Cover Change Matrix��������������������������������������������������������������������  63 Forest Cover of Purba Medinipur District ����������������������������������������������������  65 Historical Perspectives����������������������������������������������������������������������������  65 Social Distancing of Trees ����������������������������������������������������������������������  66 Blue Carbon Storage��������������������������������������������������������������������������������  66 Forest Cover Change Matrix��������������������������������������������������������������������  67 Forest Cover of Purulia District ��������������������������������������������������������������������  68 Jungle Mahal��������������������������������������������������������������������������������������������  69 Forest Cover Change Matrix��������������������������������������������������������������������  71 Forest Cover of South 24 Parganas District ��������������������������������������������������  72 Mangroves and Marsh Vegetation������������������������������������������������������������  73 Faunal Assemblages��������������������������������������������������������������������������������  73 Pristine Forests����������������������������������������������������������������������������������������  74 Forest Cover Change Matrix��������������������������������������������������������������������  75 Forest Cover of Uttar Dinajpur District ��������������������������������������������������������  76 Pocket Forest��������������������������������������������������������������������������������������������  76 Forest Cover Change Matrix��������������������������������������������������������������������  78

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Forest Cover of Dakshin Dinajpur District��������������������������������������������������   79 Myths-History����������������������������������������������������������������������������������������   80 Bioeconomy Model��������������������������������������������������������������������������������   80 Forest Cover Change Matrix������������������������������������������������������������������   81 Summary������������������������������������������������������������������������������������������������������   82 References����������������������������������������������������������������������������������������������������   82 3 Forest Stands – Case Studies��������������������������������������������������������������������   85 Forest Management of Bankura District������������������������������������������������������   86 Joypur and Beliatore Forests, Bankura District ������������������������������������������   86 Ground Water Table ������������������������������������������������������������������������������   89 Forest Scenario��������������������������������������������������������������������������������������   89 Floral Diversity��������������������������������������������������������������������������������������   91 Elephant Corridor����������������������������������������������������������������������������������   91 Soil Characteristics��������������������������������������������������������������������������������   93 Soil Analysis and Results����������������������������������������������������������������������   95 Discussion����������������������������������������������������������������������������������������������   97 Remarks ������������������������������������������������������������������������������������������������������   98 Garh Jangal and Aduria Forest of Bardhaman Forest Division ������������������   98 Ground Water Table ������������������������������������������������������������������������������   99 Divisional Forest Scenario ��������������������������������������������������������������������  100 Soil Characteristics��������������������������������������������������������������������������������  102 Soil Analysis and Results����������������������������������������������������������������������  103 Discussion����������������������������������������������������������������������������������������������  104 Impact of Potassium on Forest Vegetation��������������������������������������������  104 Results and Discussion��������������������������������������������������������������������������������  107 Remarks ������������������������������������������������������������������������������������������������������  108 Summary������������������������������������������������������������������������������������������������������  109 References����������������������������������������������������������������������������������������������������  110 4 Statistical Analysis of Forest Soil Properties ������������������������������������������  113 Statistical Analysis ��������������������������������������������������������������������������������������  114 Forest Floor Substrate Soils ������������������������������������������������������������������������  114 Relationships of the Soil Nutrients��������������������������������������������������������  114 Analysis and Results������������������������������������������������������������������������������������  115 Discussion����������������������������������������������������������������������������������������������������  116 Statistical Prediction of Nitrogen Availability ��������������������������������������  117 pH, Salinity, and Organic Carbon Relationships of the Marsh Sediments����������������������������������������������������������������������������������������������  120 Precision Test for Sediment Sampling ��������������������������������������������������  122 Results and Discussion��������������������������������������������������������������������������������  124 Random Sampling Without Replacement����������������������������������������������������  127 Remarks ������������������������������������������������������������������������������������������������������  129 Bulk Density of Forest Soils������������������������������������������������������������������  129 Remarks ������������������������������������������������������������������������������������������������  132

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Paired T-Test for Organic Carbon and Nitrogen Stocks������������������������������  133 Distribution Pattern of Organic Carbon and Nitrogen Stocks within the Soil Profile����������������������������������������������������������������������������  133 Paired T-Test������������������������������������������������������������������������������������������  134 Paired T-Test of Organic Carbon Stocks������������������������������������������������  135 Paired T-Test for Nitrogen Stocks����������������������������������������������������������  136 Remarks ������������������������������������������������������������������������������������������������  137 Summary������������������������������������������������������������������������������������������������������  138 References����������������������������������������������������������������������������������������������������  138 5 Forest Vegetation Sampling and Analysis������������������������������������������������  141 Random Sampling and Analysis������������������������������������������������������������������  141 Data Sampling in Quadrats for Garh Jangal and Aduria Forests ����������  142 Data Analysis and Results����������������������������������������������������������������������������  143 Data Sampling in Quadrats for the 14 Sample Spots in 11 Districts��������������������������������������������������������������������������������������������������  144 Data Analysis and Results����������������������������������������������������������������������������  145 Data Sampling in Quadrats for the 27 Sample Spots from 19 Districts of West Bengal������������������������������������������������������������������������  147 Remarks ������������������������������������������������������������������������������������������������������  149 Timber and Non-timber Plant Species of Garh Jangal��������������������������  150 Remarks ������������������������������������������������������������������������������������������������  152 Pocket Forest Tree Inventory ����������������������������������������������������������������������  152 Tree Inventory of a Pocket Forest in Urban Zone����������������������������������  152 Remarks ������������������������������������������������������������������������������������������������  155 Measurements of Similarity Index��������������������������������������������������������������  155 Similarity index Determination of Tree Species of Chilapata and Mendabari Forests of Dooars����������������������������������������������������������  156 Remarks ������������������������������������������������������������������������������������������������������  160 Similarity Determination Using Methods of Correlation Coefficient����������������������������������������������������������������������������������������������  160 Remarks ������������������������������������������������������������������������������������������������  162 Percentage Similarity ����������������������������������������������������������������������������  162 Morisita Index of Similarity������������������������������������������������������������������  163 Morisita-Horn Index of Similarity ��������������������������������������������������������  164 Horn’s Index of Similarity ��������������������������������������������������������������������  165 Preference of Choices Similarity indices Measures������������������������������������  166 Biomass stock and Carbon Content Estimation������������������������������������������  166 Estimation of Biomass stock and Carbon Content of Dead Wood��������  166 Estimation of Root Biomass of Dead Wood������������������������������������������  172 Estimation of Biomass of Branches and Foliage of the Dead Wood ����  173 Remarks ������������������������������������������������������������������������������������������������������  176 Wood Volume Determination����������������������������������������������������������������������  176 Remarks ������������������������������������������������������������������������������������������������  179 Summary������������������������������������������������������������������������������������������������������  180 References����������������������������������������������������������������������������������������������������  180

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6 Estimation of Biodiversity Indices and Species Richness����������������������  183 Estimation of Biodiversity and Species Diversity Indices ��������������������������  184 Diversity Index and Species Richness Estimation ��������������������������������  184 Determination of Species Diversity ������������������������������������������������������������  186 Results����������������������������������������������������������������������������������������������������������  187 Results����������������������������������������������������������������������������������������������������������  189 Relative Abundance of Species Diversity����������������������������������������������  189 Results����������������������������������������������������������������������������������������������������������  189 Estimation of Variance of Diversity ������������������������������������������������������  190 Species Richness Index��������������������������������������������������������������������������  192 Remarks ������������������������������������������������������������������������������������������������������  193 Estimation of Diversity Index in Sunderbans����������������������������������������  194 Remarks ������������������������������������������������������������������������������������������������  197 Assessing Diversity Indices for the Macroinvertebrates������������������������  197 Depositional Environment ��������������������������������������������������������������������������  199 Materials and Methods��������������������������������������������������������������������������������  199 Determination of Species Diversity Indices������������������������������������������������  200 Results and Discussion��������������������������������������������������������������������������������  202 Shannon-Wiener Index��������������������������������������������������������������������������������  203 Margalef Diversity Index ����������������������������������������������������������������������������  203 Magurran Diversity Index����������������������������������������������������������������������������  204 Reformulating Margalef and Magurran Diversity Indices��������������������������  204 Remarks ������������������������������������������������������������������������������������������������������  206 Relative Abundance Determination of Elephants in Elephant Corridors������������������������������������������������������������������������������������������������  206 Remarks ������������������������������������������������������������������������������������������������  209 Percentage Similarity Estimation at Kulik Bird Sanctuary��������������������  209 Remarks ������������������������������������������������������������������������������������������������  211 Distance Coefficients Estimation of Wildlife in Dooars������������������������  211 Remarks ������������������������������������������������������������������������������������������������  214 Summary������������������������������������������������������������������������������������������������������  214 References����������������������������������������������������������������������������������������������������  215 7 Statistical Measures of Human-Wildlife Conflict and Anthropogenic Interferences ������������������������������������������������������������  219 Human-Wildlife Conflict ����������������������������������������������������������������������������  220 Human-Elephant Conflict����������������������������������������������������������������������  220 Results and Discussion��������������������������������������������������������������������������������  221 Remarks ������������������������������������������������������������������������������������������������������  225 Man-Tiger Conflict��������������������������������������������������������������������������������  225 Remarks ������������������������������������������������������������������������������������������������  227 Natural Habitat of Wildlife��������������������������������������������������������������������������  228 Correlation of the Number of Tigers in Two Tiger Zones ��������������������  228 Estimation of Number of Deer (Axis axis) for Accommodation����������  230 Goodness of Fit Tests for Deer in Deer Hubs����������������������������������������  232

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Anthropogenic Interferences������������������������������������������������������������������������  233 Human Entries in the Forests of the Sunderbans ����������������������������������  233 Tiger Attack Cases in the Sunderbans����������������������������������������������������  234 Remarks ������������������������������������������������������������������������������������������������  236 Occupational Hazards����������������������������������������������������������������������������������  236 Leaf Puckers of Mayur Jharna Elephant Reserve����������������������������������  236 Results and Discussion��������������������������������������������������������������������������������  237 Proportion of Shared Leaves������������������������������������������������������������������������  242 Ratio Data����������������������������������������������������������������������������������������������������  243 Remarks ������������������������������������������������������������������������������������������������  243 Summary������������������������������������������������������������������������������������������������������  244 References����������������������������������������������������������������������������������������������������  245 Index������������������������������������������������������������������������������������������������������������������  247

Abbreviations and Units

AGB Above Ground Biomass BGB Below Ground Biomass BD Bulk Density cft cubic feet DBH Diameter at breast height DF Dense Forest DIP Digital Image Processing EC Electrical Conductivity EDC Eco-development Committee ESP Exchangeable Sodium Percentage FAO The Food and Agriculture Organization of United Nations FCS Forest Carbon Stock FPC Forest Protection Committee FSI Forest Survey of India FRA Global Forest Resources Assessment GA Geographical Area GBD Ganga-Brahmaputra Delta GBM Ganga-Brahmaputra-Meghana GIS Geographical Information System GPS Global Positioning System GW Ground Water ha hectare ham hectare meter IFS India Forest Service ISFR India State of Forest Report IPCC Intergovernmental Panel on Climate Change JFM Joint Forest Management LISS Linear Imaging and Self-Scanning Sensor MDF Moderately Dense Forest MPP Mobile Patrolling Party NASA National Aeronautics and Space Administration xvii

xviii

Abbreviations and Units

NOAA National Oceanic and Atmospheric Administration NP National Park NPK Nitrogen Phosphorus Potassium NTFP Non-timber Forest Products OF Open Forest PF Protected Forest ppt parts per thousand (‰) RF Reserved Forest RFA Recorded Forest Area SHG Self Help Group SOC Soil Organic Carbon STR Sunderbans Tiger Reserve TC Tree Cover TOF Trees Outside Forest UNFCCC United Nation Framework Convention on Climate Change UN-REDD  United Nation Reduced Emissions from Degradation and Deforestation VDF Very Dense Forest w.r.t with respect to 1 dS/m 1000 EC = 1000 ppm

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3

Forest map of West Bengal showing locations of forest patches�����   4 Butea monosperma in the forest patches of Purulia district������������   5 Forest cover change matrix of West Bengal������������������������������������  11

Fig. 2.1 Forest cover change matrix of Bankura district, West Bengal���������  23 Fig. 2.2 Forest cover change matrix of Birbhum district, West Bengal��������  25 Fig. 2.3 Forest cover change matrix of Cooch Behar district, West Bengal�������������������������������������������������������������������������������������  28 Fig. 2.4 Pine tree lines (Pinus roxburghii) in the forest cover of Darjeeling������������������������������������������������������������������������������������  30 Fig. 2.5 Forest cover change matrix of Darjeeling district, West Bengal�������������������������������������������������������������������������������������  32 Fig. 2.6 Forest cover change matrix of Howrah district, West Bengal���������  34 Fig. 2.7 Forest cover change matrix of Kolkata district, West Bengal����������  37 Fig. 2.8 Forest cover change matrix of Hugli district, West Bengal�������������  39 Fig. 2.9 Forest cover change matrix of Jalpaiguri district, West Bengal������  43 Fig. 2.10 Forest cover change matrix of Malda district, West Bengal������������  46 Fig. 2.11 Forest cover change matrix of Murshidabad district, West Bengal�������������������������������������������������������������������������������������  49 Fig. 2.12 Forest cover change matrix of Nadia district, West Bengal�������������  53 Fig. 2.13 Forest cover change matrix of North 24 Parganas, West Bengal�������������������������������������������������������������������������������������  56 Fig. 2.14 Forest cover change matrix of Paschim Medinipur district, West Bengal�������������������������������������������������������������������������������������  61 Fig. 2.15 Forest cover change matrix of Bardhaman district��������������������������  64 Fig. 2.16 Forest cover change matrix of Purba Medinipur district, West Bengal����������������������������������������������������������������������������������������������  68 Fig. 2.17 Location map of Jungle Mahal in the south-west part of West Bengal����������������������������������������������������������������������������������������������  70 Fig. 2.18 Forest cover change matrix of Purulia district, West Bengal�����������  71

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List of Figures

Fig. 2.19 Forest cover change matrix of South 24 Parganas, West Bengal�����������������������������������������������������������������������������������   75 Fig. 2.20 Fisherman engaged in capturing fishes from the river waters of Kulik encompassing the Kulik Bird Sanctuary�������������������������   78 Fig. 2.21 Forest cover change matrix of Uttar Dinajpur district, West Bengal�����������������������������������������������������������������������������������   79 Fig. 2.22 Forest cover change matrix of Dakshin Dinajpur district, West Bengal�����������������������������������������������������������������������������������   81 Fig. 3.1 Elephant corridor at Joypur Forest������������������������������������������������   93 Fig. 3.2 Soil chemical parameters of the forest soil samples of West Bengal��������������������������������������������������������������������������������������������  105 Fig. 3.3 Soil substrate characteristics of classified vegetation in the forest floors of West Bengal������������������������������������������������  106 Fig. 5.1 A data sampling quadrat covered with timber tree species at Garh Jangal��������������������������������������������������������������������������������  151 Fig. 6.1 Formation of dome-shaped bioturbation structures by the Thalassina anomala scattered around the mudflat of Hana Char of the Sunderbans����������������������������������������������������  200

List of Tables

Table 1.1 Shannon-Wiener Index of tree, shrub, and herb species in different type groups of West Bengal and their location, area, and major species���������������������������������������������������������������������   6 Table 1.2 Percentage area under different forest types of West Bengal�����������   7 Table 1.3 Recorded forest area of West Bengal�����������������������������������������������   8 Table 1.4 Actual forest cover of West Bengal��������������������������������������������������   8 Table 1.5 District-wise forest cover in West Bengal����������������������������������������   9 Table 1.6 Status of Joint Forest Management Committees (JFMC), Eco-Development Committees (EDC), and Self Help Groups (SHG) at the protected forest areas of West Bengal������������  13 Table 1.7 National parks, Wildlife Sanctuaries, and Conservation Reserves of West Bengal����������������������������������������������������������������������������������  14 Table 1.8 Wildlife census operations in the forests of West Bengal����������������  15 Table 1.9 The inventory of accommodation of different ecotourism centers�����������������������������������������������������������������������������������������������  16 Table 2.1 Name of the forest division and forest ranges of Birbhum district�����������������������������������������������������������������������������������������������  23 Table 2.2 Forest ranges and forest beats of Kalimpong forest division�����������  29 Table 3.1 Name of the forest divisions, forest ranges and beats of Bankura district����������������������������������������������������������������������������  87 Table 3.2 Stage of groundwater resources in the district of Bankura���������������  90 Table 3.3 Common timber trees and their associated species identified at Joypur and Beliatore forest ranges�����������������������������������������������  92 Table 3.4 Soil physico-chemical properties for the samples collected at selected pedons of the forest patches of West Bengal������������������  95 Table 3.5 Micronutrient contents of forest soil samples collected at the forest patches of West Bengal�������������������������������������������������  96 Table 3.6 Name of forest ranges and beats under the Bardhaman forest division�����������������������������������������������������������������������������������  99

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List of Tables

Table 3.7 Stage of groundwater resources in the district of Bardhaman��������  101 Table 4.1 Soil nutrients parameters of mangrove sediment samples of the Sunderbans���������������������������������������������������������������������������  115 Table 4.2 Data of computed pairwise correlation coefficients for the various variables using Excel’s correlation data analysis tool������������������������������������������������������������������������������������  116 Table 4.3 Organic carbon and available nitrogen of the sediment samples collected from the terrestrial forest patches of West Bengal����������  118 Table 4.4 Organic carbon and available nitrogen of the sediment samples collected from the mangroves swamp of the Sunderbans��������������  119 Table 4.5 Physico-chemical parameters of the marsh sediments collected from the marginal bars of the Sunderbans����������������������  121 Table 4.6 Data of computed pairwise correlation coefficients for the various variables using Excel’s correlation data analysis tool������������������������������������������������������������������������������������  121 Table 4.7 Soil chemical parameters of the sediment samples collected in the Sunderbans���������������������������������������������������������������������������  123 Table 4.8 Bulk density (BD) of soil samples collected from the forest floors of Nadia district��������������������������������������������������������������������  130 Table 4.9 Bulk density and soil organic carbon obtained for the soil samples of the forest patches of Nadia district�������������������������������  131 Table 4.10 Mean organic carbon stock estimated from five soil layers of a set of soil pits from terrestrial natural forests of Joypur and Beliatore�����������������������������������������������������������������������������������  135 Table 4.11 Mean available nitrogen stock estimated from five soil layers of a set of soil pits from terrestrial natural forests of Joypur and Beliatore�����������������������������������������������������������������������������������  136 Table 5.1 Collected data used for sample size determination from Garh Jangal and Aduria forests���������������������������������������������������������������  143 Table 5.2 Collected data used for sample size determination from 14 forest patches of West Bengal��������������������������������������������������������  145 Table 5.3 Data collected for computation of optimum sample size from 27 forest patches of West Bengal�������������������������������������������  148 Table 5.4 Inventory of trees of a pocket forest in the urban areas of Kolkata metropolis���������������������������������������������������������������������  154 Table 5.5 Computation of mean for basal diameters of the tree inventory from grouped data����������������������������������������������������������  154 Table 5.6 Computation of mean for DBH of the tree inventory from grouped data��������������������������������������������������������������������������  155 Table 5.7 Computation of mean for heights of the tree inventory from grouped data��������������������������������������������������������������������������  155 Table 5.8 List of identified tree species and tree density per hectare area at Chilapata and Mendabari forests����������������������������������������  158

List of Tables

xxiii

Table 5.9 List of tree species identified and sampled at Chilapata and Mendabari forests��������������������������������������������������������������������  161 Table 5.10 Percentage composition of tree species sampled at Chilapata and Mendabari forests��������������������������������������������������������������������  163 Table 5.11 Comparison of data obtained from the different measures of similarity indices of the tree species samples of Chilapata and Mendabari��������������������������������������������������������������������������������  166 Table 5.12 Volume and biomass of dead wood of three tree samples from outside the forest��������������������������������������������������������������������  167 Table 5.13 Amount of stem biomass stock calculated after estimation of dead wood volume using the data of average DBH and stem top diameter, and height of the dead trees collected from Bethuadahari wildlife sanctuary�����������������������������  169 Table 5.14 Data collected for dead wood biomass estimation at Bethuadahari wildlife sanctuary�������������������������������������������������  171 Table 5.15 Stem biomass and root biomass of dead wood of three tree samples from outside the forest�����������������������������������������������  172 Table 5.16 Root biomass and branches & foliage biomass of dead wood of three tree samples from outside the forest������������������������  174 Table 5.17 Estimation of biomass stock of stem, branches & foliage, and roots of dead wood of Bethuadahari sanctuary using regression fit line of statistical methods�����������������������������������������  175 Table 5.18 Wood volumes in the forest ranges of Bankura district������������������  177 Table 6.1 Computation of diversity of species using Excel’s data analysis tools of the floral communities identified at Raidak forest areas��������������������������������������������������������������������������������������  188 Table 6.2 Computation of diversity of species using Excel’s data analysis tools of the floral communities identified at Murti forest areas��������������������������������������������������������������������������������������  188 Table 6.3 List of identified tree species and tree density per hectare area at Raidak and Murti forests�����������������������������������������������������  189 Table 6.4 Shannon – Wiener Index for plant diversity at two locations of Raidak and Murti using Excel’s data analysis tools�������������������  191 Table 6.5 Computation of diversity of species using Excel’s data analysis tools of planktons and floral communities identified in the Sunderbans���������������������������������������������������������������������������  196 Table 6.6 Computation of diversity of species using Excel’s data analysis tools of invertebrate species identified in the Sunderbans���������������������������������������������������������������������������  196 Table 6.7 Computation of diversity of species using Excel’s data analysis tools of vertebrate species identified in the Sunderbans���������������������������������������������������������������������������  197

xxiv

List of Tables

Table 6.8 Calculation of Shannon-Wiener index and diversity index using Excel’s data analysis tools of the macroinvertebrates in the tidal mudflat of Hana Char���������������������������������������������������  201 Table 6.9 Shannon – Wiener Index for elephant observations at two locations of Beliatore and Chapramari using Excel’s data analysis tools����������������������������������������������������������������������������������  207 Table 6.10 List of bird species at Kulik bird sanctuary������������������������������������  210 Table 6.11 Population of wild animals at Gorumara and Jaldapara national park�����������������������������������������������������������������������������������  212 Table 7.1 Number of elephant-attacks and name of the elephantattacked individuals������������������������������������������������������������������������  222 Table 7.2 Average month-wise data of tiger straying incidents and average number of persons killed by the tiger attacks�������������  226 Table 7.3 Number of tigers in two tiger zones based on the tiger census 2020 of the Sunderbans mangrove forest���������������������������  229 Table 7.4 Number of deer including the areas of the deer hubs in the Garchumuk, Parmadan, and Bethuadahari sanctuaries��������  231 Table 7.5 Observed and expected number of deer per hectare in different deer parks���������������������������������������������������������������������  233 Table 7.6 Record of human entry (permit holders) in the Sunderbans Tiger Reserve for collection of wood & honey, and for capture of fin fish, crabs, and prawns����������������������������������������������������������  234 Table 7.7 Year wise record of death cases of fishermen, honey collectors, and wood cutters in the Sunderbans�����������������������������������������������  235 Table 7.8 Data of computed pairwise correlation coefficients for the various variables using Excel’s correlation data analysis tool������������������������������������������������������������������������������������  236 Table 7.9 Name of the leaf puckers, number of plucked and shared leaves of Sal trees by each member of the group at Mayur Jharna Elephant Reserve in the Jhargram district�����������  238

Chapter 1

Forest Status of West Bengal

Abstract  District-wise forest cover change matrix reflects dwindling of South Bengal’s forest cover alarmingly from the 1980s of the last century; yet a shift to joint forest conservation by making stakeholders in forestry initiatives and social forestry plantation managed by the forest department has yielded the present results. Plantation under the social forestry scheme in almost all the districts of West Bengal has started showing results for the growing stocks. District-wise break-up revealed that South 24 Parganas, Uttar Dinajpur, Murshidabad and Howrah have recorded decrease in forest cover, while Bankura, Paschim Medinipur, Purulia and Birbhum have recorded a rise. District-wise statistics assessed from the biennial reports of the Forest Survey of India revealed the yearly changing scenario of the forest cover for the districts of West Bengal. Specifications of relevant forest related information like forest types, myths and history, current forestry practices, joint forest management, and eco-development committee are included for all districts. Keywords  Recorded forest area · Forest area assessment · Actual forest area · National park · Sanctuaries · Joint forest management · Eco-development committees

Physiography: At a Glance West Bengal is an outstandingly beautiful state of India for its geographical features like mountain, hills, rivers, sea, plateau, plains, and luxuriant green canopy of forests. The state of West Bengal occupies a crucial place in India by virtue of its unique location within three international frontiers bordering Bangladesh, Nepal, and Bhutan. West Bengal is the only Indian state to have a coastline in the south as well as the Himalayas in the north. The state is bounded on the east by Bangladesh and Assam, on the west by Bihar and Jharkhand, on the north by Sikkim, Nepal, and Bhutan and on the south by Orissa and the Bay of Bengal. It is located between

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_1

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1  Forest Status of West Bengal

21°30′'N and 27°12′N latitudes at the head of the Bay of Bengal and between 85°50′E and 89°52′E longitudes. West Bengal, located in the eastern part of India has a geographical area of 88,752 sq. km, which is 2.7% of the geographical area of the country. The state falls in the physiographic zones of Eastern Himalayas and Eastern Plains and has two distinct natural divisions viz. the Himalayan north and the fertile alluvial Gangetic plain stretching to its south. The topography of the northern territory varies from a maximum elevation of 3660  m at Sandakphu in Darjeeling district to an elevation of 89  m in the low-lying areas in Jalpaiguri, AlipurDuar and Cooch Behar districts, watered by the swift-flowing rivers like Teesta, Torsa and Jaldhaka. In the south-west, the land gets more and more sloppy and undulating, ultimately merging into the Chotanagpur plateau structure in Purulia district. The three main rivers in the north of the state viz. Teesta, Torsa and Jaldhaka are tributaries of the river Brahmaputra. Bhagirathi is the only channel left to West Bengal, which receives water from the Ganges. Bhagirathi River, namely Hooghly in the downstream, flowing through the central part of the state, drains into the Bay of Bengal and at its confluence, well-known mangrove forests in the Sunderbans exist. The state occupies the famous delta of Indian Sunderbans in the Hooghly-­ Matla estuarine complex. The state of West Bengal is drained by several rivers which include Hooghly, Damodar, Mayurakshi, Kangsabati, Rupnarayan, Teesta, Matla, Thakuran, and Saptamukhi. The state has a diverse climate, varying from moist tropical in the south-west and from subtropical to temperate in the mountains in the north. Moist wind from the Bay of Bengal makes the climate of the state highly humid, especially in the rainy and autumn season, but in the cold weather from November to February, the climate over the entire state is exceedingly pleasant. The annual temperature varies from sub-zero in the mountain area during the winter to about 46 °C in the parts of South Bengal during the summer. The average annual rainfall ranges from 900 mm in south-west to about 5000 mm in parts of the northern hilly region. The state West Bengal has 23 districts, among which 14 are tribal districts and 2 are hill districts. According to the census report, 2011, the tribal population is 5.80% of the state’s population. As per the 2011 census, West Bengal has a population of 91.28 million accounting to 7.54% of Indian population. The rural and urban population constitutes 68.13% and 31.87% respectively. The population density of the state is 1028 per sq. km which is higher than the national average of 382 persons per sq. km. The 19th Livestock Census 2012 has reported a total livestock population of 30.35 million (ISFR 1987–2019). Agriculture plays a pivotal role in the state’s income and nearly three out of four persons in the state are directly or indirectly involved in agriculture. The state of West Bengal is known for its rich variegated cultural pattern and dynamism that has always fascinated people both home and abroad.

Forest Covers of West Bengal

3

Forest Covers of West Bengal Forest of West Bengal is home to a rich and bewildering variety of wildlife and floral diversity including flowering plants. The forests cover about 19.04% of the total geographical area and lie chiefly in the districts of Darjeeling, Kalimpong, Jalpaiguri, Alipur Duar, South 24 Parganas, Jhargram, Paschim Medinipur, Bankura, Purulia, Paschim Bardhaman, and in some parts of Birbhum district (Fig. 1.1). Stray and scattered forests are present in Nadia, Purba Bardhaman, North 24 Parganas, Murshidabad, Malda, Uttar Dinajpur, Dakshin Dinajpur and Cooch Behar districts. Non-timber stray forests like Palas ban (Forest of Butea) are scattered almost all the blocks of Purulia district (Fig. 1.2). The principal forest products are timber, firewood, and charcoal. According to Champion and Seth Classification of forest Types (1968), the forests in West Bengal belong to 8 forest type groups, which are further divided into 30 forest types (Tables 1.1 and 1.2). Major forest types occurring in the state of West Bengal are Tropical Semi Evergreen Forests, Tropical Moist Deciduous Forests, Littoral and Swamp Forests, Tropical Dry Deciduous Forests, Subtropical Broad Leaved Hill Forests, Montane Wet Temperate Forests, Himalayan Moist Temperate Forests and Sub Alpine Forests (WB State Forest Report 2014). The recorded forest area of West Bengal is 11, 879 sq. km, which is 13.38% of the geographic area of the state (Table 1.3). Recorded Forest Area (RFA) is the forest area recorded as forests in government records. By legal status, Reserved Forests of the state constitute 59.38%, Protected Forests, 31.76%, and Unclassed Forests, 8.86%, i.e., out of 11,879 sq. km of Recorded Forest Area, 7054 sq. km is Reserved Forests, 3772 sq. km is Protected Forests, and 1053 sq. km is Unclassed Forests. There is a distinct difference between Reserved Forests and Protected Forests. According to India State of Forest Report 2019 (ISFR 2019) published by Forest Survey of India (FSI), Reserved Forest is an area so constituted under the provisions of the Indian Forest Acts, having full degree of protection. In Reserved Forests, all activities are prohibited unless permitted, whereas, Protected Forest is an area notified under the provisions of the Indian Forest Act or other State Forest Acts, having limited degree of protection. In Protected Forests, all activities are permitted unless prohibited. Unclassed Forest is an area recorded as forest but not included in reserved or protected forest category. Ownership status of such forests varies from state to state (ISFR 2019). The forest area of the state of West Bengal has gradually been changing, though the density of the Trees Outside Forest (TOF) is rather more than the green canopy and it is about 51.8% (ISFR 2019). Social forestry work in all districts over West Bengal has started showing results. South Bengal’s forest cover had dwindled alarmingly in the 1970s and 1980s; a shift to joint forest conservation by making stakeholders in forestry initiatives has yielded results. The present forest cover in West Bengal, based on interpretation of IRS Resourcesat-2 LISS III satellite data for the period from November 2017 to February 2018 is 16,901.51 sq. km, which is 19.04% of the geographical area of the state (Table  1.4). District-wise break-up revealed that South 24 Parganas, Uttar Dinajpur, Murshidabad and Howrah have

4

1  Forest Status of West Bengal

Fig. 1.1  Forest map of West Bengal showing locations of forest patches

Forest Covers of West Bengal

5

Fig. 1.2  Butea monosperma in the forest patches of Purulia district

recorded decrease in forest cover, while Bankura, Paschim Medinipur, Purulia and Birbhum have recorded a rise. While Bankura logged a growth of 15.6 sq. km in its forest cover, South 24 Parganas, which houses a vast stretch of the Sunderbans, has witnessed a dip in forest cover by 3.29 sq. km (Table  1.5). The mangrove forest cover of the Sunderbans is decreased by its area because of human encroachment for agricultural and aquacultural expansion (Das 2015).

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1  Forest Status of West Bengal

Table 1.1  Shannon-Wiener Index of tree, shrub, and herb species in different type groups of West Bengal and their location, area, and major species Shannon-Wiener Sl. no. Forest type group Index Tree Shrub Herb 1 Group 2: tropical 2.33 2.51 2.40 semi-evergreen forests 2 Group 3: tropical 2.76 1.21 1.59 moist deciduous forests a 3A Group 4: Littoral 1.28 1.10 and swamp forests 3B Group 4: Littoral and swamp mangroves

4

Group 5: tropical 2.32 2.49 dry deciduous forests

5

Group 8: subtropical broadleaved hill forests Group 11: montane wet temperate forests Group 12: Himalayan moist temperate forests

6

7

8

Group 14: sub alpine forests

1.76 2.36

1.51 2.66

1.96 2.72

1.19 2.03

Location North Bengal

North Bengal

1757 Sal, Champ, Sissoo, Chikrassi, Panisaj

20 Hijal Malda, Uttar and Dakshin Dinajpur 2112 Sundari, Baen, Sunderbans Dhundul, Gnewa, (North and Goran, Passur, South 24 Khalsi, Hental, Parganas), Purba Golpata, Tora, Medinipur Garjan, Kankra, Kaora, Kripal, Amur, Goria 4527 Sal, Peasal, Kend, 1.95 Bankura, Mahul, Kusum, Asan, Purulia, Paschim Bahera, Dhaw, Medinipur, Rahara Jhargram, Birbhum, Paschim Bardhaman 800 Chilouni, Panisaj, 2.33 North Bengal Gokul, Utis Hills 300– 1650 m altitude

1.76 North Bengal Hills 1650– 3000 m altitude 2.32 North Bengal Hills 1500– 1800 m altitude 1.24 North Bengal Hills 3000– 3700 m altitude

Modified after WB State Forest Report (2014) and ISFR (2019) Adequate numbers of sample plots were not available

a

Area (Sq. km) Major species 25 Champ, Panisaj, Gokul, Angare

150 Magnolia, Champ, Oaks, Kawla, Pipli 150 Chilouni, Katus Panisaj, Lampate, Angare, Utis, Toon, Malagiri 20 Rhododendrons, Salix, Berberis, Yew, Junipers, Birch

Forests in the Past

7

Table 1.2  Percentage area under different forest types of West Bengal Sl. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Forest type 2B/2S3 SubHimalayan secondary wet mixed Forest 3C/C1a(I) east Himalayan Sal 3C/C1b(I) east Himalayan upper Bhabar Sal 3C/C1b(ii) east Himalayan lower Bhabar Sal 3C/C1c eastern Tarai Sal Forest 3C/C2d(iii) eastern heavy Alluvium Plains Sal 3C/DS1 moist Sal Savannah 3C/C3a west Gangetic moist mixed deciduous forest 3C/C3b east Himalayan moist mixed deciduous forest 3C/C3/2S2 (secondary Euphorbiaceae scrub) 3C/1S1 low alluvial Savannah woodland (Salmalia albizia) 4B/TS1 mangrove scrub 4B/TS2 mangrove forest 4B/TS3 salt water mixed forest (Heritiera) 4B/TS4 brackish water mixed forest (Heritiera) 4B/E1 palm swamp 4C/FS2 Submontane hill-valley swamp forest 4D/SS2 Barringtonia swamp forest 4D/2S2 eastern wet alluvial grassland 5B/C1c dry peninsular Sal forest 5B/C2 northern dry mixed deciduous forest 5B/DS1 dry deciduous scrub 5/E5 Butea forest 5/1S2 Khair-Sissu forest 8B/C1 east Himalayan subtropical wet hill forest 11B/C1a lauraceous forest 11B/C1b Buk oak forest 11B/C1c high level oak forest 12/C3a east Himalayan mixed coniferous forest 14/C2 east Himalayan subalpine birch/fir forest Plantation/TOF Total

% of forest cover 2.09 2.35 0.90 0.69 1.91 0.02 0.07 0.08 2.45 0.06 0.18 0.71 5.89 2.80 1.80 0.87 0.03 0.02 0.01 16.31 1.49 0.21 0.12 1.18 2.81 0.72 0.33 0.12 1.90 0.08 51.8 100.00

Modified after ISFR (2019)

Forests in the Past The Bengal Basin at a time is covered with forest and coppice in major parts of its area in terms of its topographical diversity. Approximately 20,000 years ago, human beings started migrating towards the hilly region of south-eastern direction covered with forests and fixed villages of permanent nature for the first time in the Bengal Delta. But the areas were not safe after dark or in the daytime from the wild animals

8

1  Forest Status of West Bengal

Table 1.3  Recorded forest area of West Bengal Year 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Geographical area (sq. km) 87,850 87,850 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752

Forest area (sq. km) 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879 11,879

% of forest area to geographical area 13.5 13.5 13.4 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38 13.38

Per capita forest area in hectare 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Table 1.4  Actual forest cover of West Bengal Year 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Geographical area (sq. km) 87,850 87,850 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752 88,752

Forest area as finally assessed by FSI (sq. km) 8432 8015 8015 8186 8276 8349 8362 10,693 12,343 12,413 12,994 12,994 12,995 16,805 16,828 16,847 16,901.51

Actual forest cover as % of geographical area 9.5 9.03 9.03 9.22 9.36 9.40 9.42 12.05 13.91 13.99 14.64 14.64 14.64 18.93 18.96 18.98 19.04

Forests in the Past

9

Table 1.5  District-wise forest cover in West Bengal ISFR 2019 Assessment (in sq. km) Very Geographical dense forest District area Bankura 6882 222.33 Bardhaman 7024 57.53 Birbhum 4545 1.00 Dakshin 2219 0.00 Dinajpur Darjeeling 3149 720.76 Howrah 1467 0.00 Hugli 3149 0.00 Jalpaiguri 6227 724.22 Koch Bihar 3387 0.00 Kolkata 185 0.00 Malda 3733 0.00 Murshidabad 5324 0.00 Nadia 3927 1.00 4094 13.02 North 24 Parganas Paschim 9368 256.21 Medinipur Purba 4713 1.99 Medinipur Purulia 6259 37.36 South 24 9960 983.10 Parganas Uttar 3140 0.00 Dinajpur Grand Total 88,752 3018.52

Mod. dense Open % of w.r.t 2017 forest forest Total GA assessment Scrub 395.27 667.98 1285.58 18.68 15.58 28.59 91.78 190.00 339.31 4.83 4.31 7.35 34.14 149.66 184.80 4.07 7.80 8.90 5.83 81.29 87.12 3.93 0.12 0.00 654.52 992.52 50.00 253.77 14.00 146.00 434.92 1703.26 27.00 322.06 0.00 1.00 209.04 282.65 53.06 291.83 160.16 318.84 184.98 524.98

2367.80 303.77 160.00 2862.40 349.06 1.00 491.69 344.89 480.00 722.98

75.19 2.80 20.71 −0.23 5.08 0.00 45.97 5.40 10.31 0.06 0.54 0.00 13.17 0.69 6.48 −1.11 12.22 0.00 17.66 −0.02

9.21 0.00 0.00 39.65 0.00 0.00 0.00 0.00 0.00 0.00

591.64 1313.69

2161.54 23.07 10.54

20.24

197.96

620.10

0.05

2.50

915.88 14.63 11.88 2788.71 27.99 −3.29

28.68 1.00

7.48 −0.07

0.00

4160.26 9722.73 16,901.51 19.04 54.51

146.12

306.94 571.58 745.03 1060.58 3.99

230.94

820.05 17.40

234.93

or even by human beings of different types and nature. They took shelter in a safe place and in this way the human habitation zone had been formed in the clustered, compact, and linear pattern within the villages. They lifted soil to raise the place for the construction of their huts. The digging pit after being beautified with a definite shape introduced the pond in the rural domestic culture of Bengal. Pond was thus becoming an organ in the village households which was useful for drinking water for man and cattle, cooking, and fish culture. The earlier men, shepherds by nature, habituated with the rearing or tending of cattle, had changed their occupation and engaged the cattle in agricultural practices. For the preparation of food from the grains and greens, produced of their own cultivation, they used to collect fuel wood from their surrounding forests. Forest was still there in abundance and the word forest is still the opposite in meaning to the word – village. With time, more human habitation was established in the Bengal Basin, and more the forest area was cleared during the rules of the kings and the Muslim invaders in the Bengal Province, the

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1  Forest Status of West Bengal

reclaimed land for farming had been increased. After the rules of the kings, maximum areas of the forest cover in the undivided Bengal had been cleared by the British East India Company rulers in the soil of the Bengal to earn more and more revenue from the converted agricultural land of the reclaimed forest areas (Das 2011, 2017). It was only 11,879 sq. km area i.e. 13.38% as the recorded forest area of the geographical area of West Bengal when the British rulers left India dividing the province of Bengal into West and East Bengal. The government formed in 1977 in West Bengal, considered the right of land, is particularly important to the poor community. As a result of land reforms and decentralization of power, the poor land holders or landless villagers have received maximum benefit. From the data of National Sample Survey on ownership distribution of land holding in West Bengal, the ownership of 84% of the total agricultural land is in the hands of small and marginal farmers due to land reforms with respect to 43% of national ratio. The then West Bengal government has been continuing the act and taking initiatives to distribute land among the poor villagers in the 1970s of the last century. This kind of state sponsored act of empowerment of backward people particularly of the tribes is rarely experienced in other states or union territories of the country. Further, in West Bengal the share of permanent pasture and other grazing land, land under miscellaneous trees and groves, fallow land, waste land and current fallow land is extremely low except the land of non-agricultural use which is considered as recorded forest areas. Forests, fallows, and uncultivated land in the state are available in the districts of Birbhum Bankura Darjeeling Paschim Medinipur Purulia and Bardhaman. The then West Bengal government utilized its land reforms in a proper way as agriculture will continue to be the base of the state. This strategy of the Left Front government for distribution of forest land among the people particularly to the tribe triggers the encroachment activities of the forest land and whenever the encroachment has already been done, the people beyond such effort seek patta (Government land records) with the approval of the political leaders. Consequently, the forest land has gradually been decreasing slowly but permanently, and agricultural land in the shape of pocket lands encompassing the forest areas increased rapidly. This is how the forest cover of West Bengal declined to 8432 sq. km i.e., 9.5% with respect to its recorded forest area 11,879 sq. km i.e., 13.38% in 1987 (Fig. 1.3). Forest cover has been decreased rapidly for exercising land reforms and distribution of patta (deeds of land for ownership) to the landless and poor farmers particularly to the scheduled caste and tribal people in and around the forest areas in West Bengal. The aims of the then Left Front government regarding rural development and decentralization of power are worth mentioning for the people to bring about a change in the correlation of class forces in favour of the poor and working class by involving them in an organized manner in the process of development, though the forest area was remained almost the same for the period from the year of independence to the commencement of the left rules as per the records.

Joint Forest Management, Eco-Development Committee, and Self Help Groups

11

West Bengal Forest Cover (sq km)

18000 16000 14000 12000 10000 8000 6000 4000 2000 0

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2011 2013 2015 2017 2019

Forest Cover 8432 8015 8015 8186 8276 8349 8362 106931234312413129941299516805168281684716902

Year Fig. 1.3  Forest cover change matrix of West Bengal

J oint Forest Management, Eco-Development Committee, and Self Help Groups Joint Forest Management (JFM) has been working well in the two forest ranges of almost all the forests of West Bengal. In the forest sector, West Bengal is the pioneering state in implementing Joint Forest Management (JFM) with the initiatives taken by Ajit Kumar Banerjee, IFS, a Silviculturist, working for the forest department under Government of West Bengal as the Divisional Officer (Banerjee et al. 2010; Guha et al. 2017). Ajit Kumar Banerjee was able to involve 618 families living adjacent to the Arabari forest area under Paschim Medinipur district of Jungle Mahal subject to 25% of profits from the forests were shared with the villagers and thus managed 12.7 sq. km of forest area which was classified as degraded forest in 1971. The experiment led by Ajit Kumar Banerjee was successful and was expanded gradually to other parts of the state. The West Bengal Government approved this Joint Forest Management programme in terms of Government Order of West Bengal Forest Department Forest Branch No. 1118-For. /D/6M-76/65 dated 7 March 1987. Joint Forest Management has now been implemented not only in the state of West Bengal, but all over the country for managing the forests. Several Joint Forest Management Committees are registered in the vicinity of the forest for the purpose of the restoration and protection of forests, and up to 31 March 2018, about 4262 numbers of Joint Forest Management Committees (JFMC) have been constituted with total membership of 496,998 with an approximate ratio of male: female members at 9:1. Reportedly 4262 registered JFMCs are currently involved in protecting 563,344.134 hectare forest areas of West Bengal. Total coverage of scheduled caste (SC) and scheduled tribe (ST) members for the constitution of JFMC is about 55.54% as recorded in the Annual Administrative Report of the Department of Forest, Government of West Bengal 2017–18. The state has a total of

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1  Forest Status of West Bengal

114 numbers of Eco-Development Committees (EDCs) with total membership of 23,343 with male: female membership ratio 3: 1 (Annual Administrative Report, 2017–18). The EDC members are currently engaged in protecting approximately 80,076.1 hectare forest areas comprising about 58.80% SC & ST members in EDCs. About 3974 Self Help Groups (SHGs) have been constituted in different districts of the state in the vicinity of the forest areas. Out of 3974 SHGs, 2514 SHGs exclusively belong to the women groups i.e., 2514 are exclusively women SHGs in the state of West Bengal (Table 1.6).

National Parks and Wildlife Sanctuaries Forest areas of about 4706 sq. km are protected at present by the forest department which is about 39.50% of the total forest area of the state and about 5.47% of the geographical areas of West Bengal. The total protected areas are classified as National Park, Wildlife Sanctuary, Tiger Reserve, Elephant Reserve etc. for monitoring the long-term biodiversity, wildlife conservation and management. The state has 5.47% of its geographical area under Protected Areas comprising 6 National Parks, 16 Wildlife Sanctuaries and 5 Conservation Reserves (Table 1.7). There are two Tiger Reserves, namely, Sunderbans and Buxa in West Bengal. Sunderbans is a unique ecosystem covered with luxuriant mangroves. The vibrating mangrove ecosystem of Sunderbans has been identified as a special conservation value by the Government of India. Sunderbans, for such importance, has been declared as a Biosphere Reserve which includes a Tiger Reserve and a National Park. UNESCO recognized this amazing Biosphere Reserve and declared Sunderbans a World Heritage Site in 1987. There are two Elephant Reserves, namely, Eastern Dooars Elephant Reserve and Mayur Jharna Elephant Reserve, formed in the northern and southern parts of the State, respectively.

Current Forest Status The forest status of the state of West Bengal is almost healthy and in a developing state of forest regeneration with the participation of the people living adjacent to the forest. For monitoring overall forest management, the forest department has already implemented different steps like the formation of Mobile Patrolling Party (MPP) and Mobile Squad for minimizing man-animal conflict. The Mobile Patrolling Party and Mobile Squad wings are exclusively engaged for the protection of wildlife particularly when the straying of wild animals in the human inhabiting zone happens to occur in the vicinity of forests. Including such efforts, the forest department has been carrying on wildlife census operations on a regular basis by implementing the strategies for much involvement with the protection of critical habitats of endangered species (Table 1.8). Not only keeping vigil of forest vegetation and wildlife,

Current Forest Status

13

Table 1.6  Status of Joint Forest Management Committees (JFMC), Eco-Development Committees (EDC), and Self Help Groups (SHG) at the protected forest areas of West Bengal Name of forest divisions/ ranges/beats Darjeeling Mahananda WLS Senchal WLS Singalila NP Kurseong Darjeeling WLS Baikunthapur Jalpaiguri Buxa Tiger Reserve (E) Buxa Tiger Reserve (W) Coochbehar Jaldapara WLS Gorumara WLS Chapramari WLS Neora Valley NP Raiganj Kulik WLS Malda Bankura North Bankura South Panchet Kangsabati North Kangsabati South Purulia Bardhaman Durgapur Midnapore Jhargram Rupnarayan Kharagpur Purba Medinipur Birbhum Ballavpur WLS Nadia Murshidabad Bethuadahari WLS South 24 Parganas STR North 24 Parganas Bibhuti Bhusan WLS

No. of JFMC 77 0 0 0 46 0 66 62 17 27 24 27 0 0 0 21 0 5 543 641 231 246 305 225 70 23 364 474 216 254 19 198 0 11 0 40 26 0 0

No of EDC – 15 15 1 0 0 0 – 14 7 0 35 10 2 6 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 – 4 – 1 0 0 – 1

Number of self help group (SHG) Excluding Exclusively for women women 0 14 0 0 0 0 0 0 0 21 19 19 63 56 256 256 0 0 0 0 5 0 116 116 12 12 0 0 0 0 0 0 0 0 0 0 27 174 0 3 77 891 0 0 0 0 119 119 0 0 3 59 168 29 172 172 245 104 87 87 0 0 91 5 0 0 0 0 0 0 0 234 0 143 0 0 0 0

Total 14 0 0 0 21 38 119 512 0 0 5 232 24 0 0 0 0 0 201 3 968 0 0 238 0 62 197 344 349 174 0 96 0 0 0 234 143 0 0

(continued)

14

1  Forest Status of West Bengal

Table 1.6 (continued) Name of forest divisions/ ranges/beats Howrah Total

No. of JFMC 4 4262

No of EDC 0 114

Number of self help group (SHG) Excluding Exclusively for women women 0 0 1460 2514

Total 0 3974

Modified after Annual Administrative Report of the Department of Forest, Government of West Bengal (2017–18) Table 1.7  National parks, Wildlife Sanctuaries, and Conservation Reserves of West Bengal Sl. no. 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5

National parks (N.P.) Singalila N.P. Neora Valley N.P. Buxa N.P. Gorumara N.P. Sundarban N.P. Jaldapara N.P. Wildlife sanctuaries (W.L.S) Jorepokhri Salamander W.L.S. Senchal W.L.S. Chapramari W.L.S. Mahananda W.L.S. Raiganj W.L.S. Bethuadahari W.L.S. Ballavpur W.L.S. Ramnabagan W.L.S. Bibhutibhushan W.L.S. Chintamoni Kar Bird Sanctuary Sajnekhali W.L.S. Haliday Island W.L.S. Lothian Island W.L.S. Buxa W.L.S. West Sundarban W.L.S. Pakhi Bitan (Bird Sanctuary) Conservation reserves Deul Hijli Tekonia Mukutmanipur Garh Panchakot

Area in sq km

District

78.60 159.8917 117.10 79.45 1330.10 216.34

Darjeeling Darjeeling Alipurduar Jalpaiguri South 24 Parganas Alipurduar

0.04 38.88 9.60 158.04 1.30 0.6686 2.021 0.145 0.64 0.07 362.40 5.95 38.00 314.52 556.45 14.09 10.50 15.50 5.87 43.70 1340.34

Darjeeling Darjeeling Jalpaiguri Darjeeling & partly at Jalpaiguri Uttar Dinajpur Nadia Birbhum Purba Bardhaman North 24 Parganas South-24 Parganas South-24 Parganas South-24 Parganas South-24 Parganas Alipurduar South-24 Parganas Jalpaiguri Paschim Bardhaman Purba Medinipur Coochbehar Bankura Purulia

Summary

15

Table 1.8  Wildlife census operations in the forests of West Bengal Location name Sunderbans Tiger Reserve South 24 Parganas Forest Division Buxa Tiger Reserve

Area in sq. km 2584.89 556.45 760.8699

Mayur Jharna Elephant Reserve Eastern Dooars Elephant Reserve Jaldapara WLS

414.00 977.51 216.34

Gorumara NP

79.45

Chapramari WLS and Gorumara NP Mahananda WLS Sunderbans Tiger Reserve & South 24 Parganas Forest Division

9.60 158.04 2584.89 556.45

Name of wildlife Tiger Tiger Tiger Leopard Gaur Elephant Elephant Rhinoceros Leopard Rhinoceros Leopard Gaur Leopard Saltwater crocodile

Number of wildlife 73 23 3 105 782 194 488 204 28 50 43 901 26 140

Census year 2020 2020 2014 2012 2013–14 2017 2017 2015 2004 2015 2004 2009–10 2004 2012

Modified after Annual Administrative Report (2017–18), Department of Forest, Government of West Bengal

but the state forest department has also established several ecotourism centers to attract common people for enjoying the beauty of the forests staying within the natural environment, and thus, involved the common people for the conservation and forest restoration. For further assistance, the online booking system for accommodation in different ecotourism centers, cottages and huts has been initiated for the common people/tourists interested in forests under the supervision of the West Bengal State Forest Development Agency (Table 1.9). In this way, with the direct and indirect participation of the forest dwelling people and common people outside the forest, the forest cover of West Bengal has gradually been increasing with years (Annual Administrative Report, 2017–18).

Summary The forest scenario of the country, the goal of greening one-third part of the geographical areas of India is running beyond 12% as the total forest cover of India stands at 712,249 sq. km which is 21.67% of the total areas of the country, though the forest cover has gradually been increasing since 1987, the year mark of the first survey on the forests of the country by the Forest Survey of India. The forest cover recorded an increase of nearly 0.6% in 2019 in comparison to that of the 2017 survey as reported by the Forest Survey of India in their India State of Forest Report 2019. Country’s green cover is rather different including forest cover, tree cover and the trees outside the forest amounting to a total of 8,07,276 sq. km which is 24.56%

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1  Forest Status of West Bengal

Table 1.9  The inventory of accommodation of different ecotourism centers Sl. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Name Mouchuki Camp Hornbill Nest Murti Tents Kunjanagar South Khairbari Eco Park Mendabari Jungle Camp Garochira Village Eco Tourism Neora Camp Bandapani Camp Patlakhawa Cottages Gosanimari Twin Manebhanjan Trekkers Hut Susunia Eco Tourism Centre Biharinath Eco Tourism Centre Matha Tree House Gopegarh Eco Tourism Centre Parimal Kanan Khudiram Bose Park Bolpur NIC Bethuadahari Cottages

Location Samsing Bichabhanga Murti Kunjanagar Khairbari Mendabari Garochira Lataguri Bandapani Patlakhawa Gosanimari Manebhanjang Susunia Biharinath Matha Gopegarh Chandrakona Khudiram Bose Park Bolpur Betuaduari Park

Divisions Gorumara Wildlife Gorumara Wildlife Gorumara Wildlife Jaldapara Wildlife Jaldapara Wildlife Jaldapara Wildlife Jalpaiguri Jalpaiguri Jalpaiguri Coochbehar Coochbehar Darjeeling Bankura North Bankura North Purulia Medinipur URF URF Birbhum Nadia-Murshidabad

of the geographical areas of India. Tree cover, more specifically, Trees Outside Forest (TOF), accounting for 95,027 sq. km in 2019 comprises scattered trees on the roadside and canal side trees under social forestry scheme including personal or private plantation i.e., trees of all formations outside the forest. The increase of forest cover by 3976 sq. km and tree cover by 1212 sq. km accounts for the rise of total green cover of the country at 807,276 sq. km, which is 24.56% of the total geographical area of India. Recorded forest areas may or may not have forest cover; this area is recorded as forest in the Government records. Such recorded forest area in India is 767,419 and 11,879 sq. km in West Bengal inclusive of reserved forest, protected forest and unclassed forest as classified by the forest sector. The present forest cover of 21.67% in India is yet to reach the 23.34% of recorded forest areas of the country’s total geographical areas of 3,287,469 sq. km though the changing forest scenario of India as recorded by the India State of Forest Report 2019 (ISFR 2019) certainly raises a signature of hope for a green India in near future. Forest and forestry in West Bengal are likely to have a modest impact on wood production in changing situations of climate change. Favourable climatic conditions with changing temperature and precipitation patterns that produce a direct impact on natural and modified forest will eventually result in luxuriant growth and forest expansion thereon in the state of West Bengal (Das 2011, 2017; Raha et al. 2014). For the expansion of forests, different forest models, identified in the forest patches, are suggested to be introduced and that endeavour might recover the forest cover

References

17

from its present depleting status. Forest models not only will help to accelerate afforestation particularly in the forests of South Bengal, but these forest models will serve dual purposes like regeneration of forests both in urban and rural areas and simultaneously attract tourists and tree-lovers. Restoration of forest landscape not only protects nature while providing livelihoods for local people, but it also helps address issues like climate change and food and water security. Healthy soils limit the effects of climate change and provide food and water security, but the loss of soil biodiversity is undermining these benefits.

References Annual Administrative Report (2017–18) Department of Forest, Government of West Bengal, 270p Banerjee A, Ghosh S, Springate-Baginski O (2010) The creation of West Bengal’s forest underclass, an historical institutional analysis of forest rights deprivations. IPPG Programme Office, IDPM, School of Environment & Development University of Manchester, Manchester, 26p Champion HG, Seth SK (1968) A revised survey of forest types of India. Manager of Publication, Delhi, 404p Das GK (2011) Sunderbans  – environment and ecosystem. Sarat Book House, Kolkata, 254p. ISBN:81-87169-72-9 Das GK (2015) Estuarine morphodynamics of the Sunderbans. Springer, Cham, 211p. ISBN:978-3-319-11342-5 Das GK (2017) Tidal sedimentation in the Sunderban’s Thakuran Basin. Springer, Cham, 151p. ISBN:978-3-319-44190-0 Guha A, Pradhan A, Mondal K (2017) Joint forest management in West Bengal: a long way to go. J Hum Ecol 11(6):471–476 ISFR (1987–2019) Forest Survey of India (1987–2019) India State of Forest Report 1987–2019. Ministry of Environment, Forest & Climate Change. Government of India ISFR (2019) Forest Survey of India. India State of Forest Report. Ministry of Environment, Forest & Climate Change. Government of India. 187p Raha AK, Mishra AV, Das S, Zaman S, Ghatak S, Bhattacharjee S, Raha S, Mitra A (2014) Time series analysis of forest and tree cover of West Bengal from 1988 to 2010, using RS/GIS, for monitoring afforestation programmes. J Ecol 108:255–265 W B State Forest Report (2014) Directorate of Forest, Government of West Bengal, 2014

Chapter 2

District-Wise Forest Matrix, Forest Models and Strategies

Abstract  Forest cover mapping exercise for the state of West Bengal reflects the district-wise status of forests and its present trends and provides inputs for monitoring of forests with the implementation of different forest models and strategies for forest restoration. Several forest models are identified and recognized during the forest survey and the befitted models and strategies are prescribed for implementation in the suitable forest patches in the concerned districts in necessity. The current forest cover of 23 districts in West Bengal has recorded about half of a goal of nation-wide forest cover of 33% of the geographical area of the state which was envisaged in the National Forest Policy of India. The current forests cover about 19.04% of the total geographical area of West Bengal and lies chiefly in the districts of Darjeeling, Kalimpong, Jalpaiguri, Alipurduar, South 24 Parganas, Jhargram, Paschim Medinipur, Bankura, Purulia, Paschim Bardhaman, and in some parts of Birbhum district. District-wise statistics and classification of forest types, forest models, and forest strategies provide a scientific basis for forest research, management of wildlife and biodiversity, identification and classification of floral and faunal assemblages, assessment of biomass and carbon stock at forest floors of diverse applications. Keywords  Forest cover change matrix · Forest models · Restoration strategies · Very dense forest · Moderately dense forest · Open forest · Trees outside forest

District-Wise Forest Matrix Due to the implementation of the Joint Forest Management scheme, the forest scenario of West Bengal has gradually been changing. The forest cover in West Bengal, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 16901.51 km2, which is 19.04% of the geographical area of the state. In terms of forest canopy density classes, West Bengal has 3018.52  km2 under very dense forests, 4160.26  km2 under moderately dense © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_2

19

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2  District-Wise Forest Matrix, Forest Models and Strategies

forests and 9722.73 km2 under open forests. There is a net increase of 54.51 km2 in the forest cover from the reported area in India State of Forest Report 2019 in comparison to the data available in the assessment report published by Forest Survey of India (FSI) in 2017. Forest type mapping using IRS Resourcesat-2 LISS III satellite data has been undertaken by FSI with reference to Champion and Seth Classification (Champion and Seth 1968). As per this assessment, West Bengal has 30 forest types which belong to the 8 forest type groups – (1) Tropical Semi Evergreen Forests, (2) Tropical Moist Deciduous Forests, (3) Littoral and Swamp forests, (4) Tropical Dry Deciduous forests, (5) Subtropical Broad Leaved Hill forests, (6) Montane Wet Temperate forests, (7) Himalayan Moist Temperate forests and (8) Sub Alpine forests (ISFR 2019). The recorded forest area of West Bengal is 11,879 km2 which is 13.38% of the geographical area of the state. Reserved Forests constitute 59.38%, Protected Forests constitute 31.76% and Unclassed Forests constitute 8.86% of the Recorded Forest Area of West Bengal. Recorded Forest Area may or may not have forest cover. Recorded Forest Area means forest area recorded as forests in government records. In the present situation, the forest-lovers and the researchers would be hopeful in the facts that in different forest areas of West Bengal, local communities are coming together to replant forests on abandoned and blank forest areas on request and remuneration paid by the forest department of the state government keeping their patta-marked agricultural land aloof from the project. Bringing back the forests will help keep the communities together by reducing soil erosion and providing a source of income through selling of different forest produce, even the fishing in the mangrove swamps and marshes. The Forest Survey of India reported 13,418.77  km2 digitized boundary of recorded forest area of West Bengal, but the state has 11,879 km2 recorded forest area in the government records. Forest Survey of India observed that in West Bengal, during the period of 1 January 2015 to 5 February 2019, a total area of 305.77 ha of forest land was diverted for non-forestry uses under the Forest Conservation Act, 1980, though the state government assured an area of 568.42 ha of plantations were raised in 2018 and 2019. Tree cover of West Bengal has been estimated using a sampling-based method of Trees Outside Forests (TOF) inventory collected over a period of 2 years. Such estimated tree cover in the state is 2006 km2 which is 2.26% of the geographical area of West Bengal. The present tree cover of West Bengal has decreased by 130 km2 in comparison to the previous assessment report published in 2017.

Forest Cover of Bankura District Forest cover of Bankura district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Peasal, Kend, Mahul, Kusum, Bahera, Dhaw, Rahara etc. They are further classified into Northern Tropical Dry Deciduous Forest type

Forest Cover of Bankura District

21

according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Bankura district, Joint Forest Management have been adopted comprising jointly by the forest department and the local inhabitants and about 543 JFMC at Bankura (North), 641 JFMC at Bankura (South), and 231 Joint Forest Management Committees (JFMC) have been constituted in the Panchet forest divisions, respectively. There are 201 SHG at Bankura (North), 3 SHG at Bankura (South), and 968 Self Help Groups (SHGs) at Panchet, though there is no existence of an EcoDevelopment Committee in the forest divisions of Bankura. Forest stands of Bankura district are generally covered with the vegetation of Sal, Peasal, Kend, Mahul, Kusum, Asan, Bahera, Dhaw, Rahara of Tropical Dry Deciduous Forests type. The recorded forest area of Bankura District comprises three divisions, Bankura (North) Division, Bankura (South) Division and Panchet Division, is about 1482 km2, covering 21.53% of the total land area of the district. By legal status, there are 0.046 ha of forest per capita in the district, whereas the figure is 0.02  ha for the state of West Bengal. The forest comprises 80  km2 of reserved forests, 1311 km2 of protected forest, and 91 km2 of unclassified state forest and others (WB State Forest Report 2014). This recorded forest cover of Bankura district was once under the depleting status in the eighties of the last century, though it has now become regenerated gradually. Presently, the uniformly spread forest area of Bankura district varies considerably in terms of forest capital per unit area.

Forest Cover Change Matrix The forest area of Bankura is covered with approximately 21.53% of the land area and is well-distributed all over the district in record. But the inventory result, duly surveyed and computed by the Forest Survey of India in 1985, has shown about 16.2% of the total forest area is covered with scrubs leaving a major portion of the area as blank. Therefore, the actual forest cover of Bankura district was only 5.33% in 1985 as the scrub is not included in computing forest canopy. Such reduction in forest cover is due to drastic deforestation for demand of the local people, encroachment of forest areas, and conversion of forest land into agricultural land. Distribution of patta (government records for land holders) upon converted land and the changing land use patterns thereon has been a great loss for the forest resources for the Bankura district as well as for the country as the great ancient natural forest is lost forever. Still, lots of possibility for afforestation are to be implemented for the enhancement of the forest cover of the Bankura district in social forestry scheme, afforestation under the supervision of individual care and the last and the least is the introduction of huge plantation in the open space inside the recorded forest area with less greenery or blank which are proved to be successful as evidenced from the plantation in the Sarenga forest of the same district. With years, growth of the forest canopy of the district has gradually been accelerating with the proper measures

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2  District-Wise Forest Matrix, Forest Models and Strategies

taken by the forest department and people’s participation under Joint Forest Management policy. The forest cover of Bankura district is increased to 18.68% of the geographical areas of the district in 2019 (ISFR 2019). Needless to mention that the Forest Survey of India has conducted a survey every 2 years for assessing the forest and tree resources of India since 1987, and they published the result of that assessment in its biennial India State of Forest Report on a regular basis. Bankura district is very rich in forest canopy and dense forestry having plenty of timbers and trees. The forest cover in Bankura district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 1285.58 km2 which is 18.68% of the district’s geographical area. In terms of forest canopy density classes, the district has 222.33  km2 area under very dense forests, 395.27 km2 area under moderately dense forests and 667.98 km2 area under open forests. There is a net increase of 15.58 km2, the highest increase in the forest cover among all districts, in the state of West Bengal, as shown in the India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.1.

Forest Cover of Birbhum District Forest cover of Birbhum district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Sonajhuri, Peasal, Kend, Mahul, Kusum, Bahera, Dhaw, Rahara etc. They are further classified into Northern Tropical Dry Deciduous Forest and Dry Peninsular Sal Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Birbhum district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 198 Joint Forest Management Committees have been constituted in the Birbhum forest division. There are about 4 Eco-Development Committees (EDC) at Ballavpur Wildlife Sanctuary (WLS) and 96 Self Help Groups (SHGs) in the forest division of Birbhum. Birbhum Forest Division has 7 Forest Ranges and several Forest Beats in the district (Table 2.1). Forests of the Birbhum district are covered with the trees of Tropical dry deciduous types. Among the forests, Illambazar forest, locally known as 11 Miles Forest, extended parallel along the metal road in the Birbhum district, is a typical forest of mixed occurrences of both natural and social forestry origin where plenty of timber trees of different species like Sal Piyal Asan Arjun akashmani Jarul Kadam Babul Segun Mehagini Amalaki Hartaki Chhatim Bahera Neem Chatka Lambu Punyo Debdaru Tentul Jam Amaltas Champa Kanchan are very much common. Other notable forests in Birbhum district are Ballavpur forest, Gonpur forest, Amkhoi Fossil Park including Egaro Mile Forest under the Birbhum forest division and they are almost of social forestry origin.

Forest Cover of Birbhum District

23

Bankura 25

Forest Cover (%)

20

18.68 18.08 18.37 18.45

15 10.94

12.59 12.63 11.92 12.35

13.59

14.24

14.92 15.34 15.34

10 5.33 5

0 1985

1991

1993

1995

1997

1999

2001

2003

2005

2007

2011

2013

2015

2017

2019

Year Fig. 2.1  Forest cover change matrix of Bankura district, West Bengal Table 2.1  Name of the forest division and forest ranges of Birbhum district Name of the forest division Birbhum Forest Division

Name of the forest ranges 1. Bolpur 2. Dubrajpur 3. Suri 4. Sainthia 5. Mohammad Bazar 6. Rajnagar 7. Rampurhat

Past Presence of the Forest The forest area of the Birbhum District is 184.40 km2 which is only 4.07% of the district’s geographical area, but once the district was covered with the green canopy all around and for that reason, the district was named after the luxuriant occurrences of the forest (‘Bir’ means copses; bhum – place). Ruthless exploitation of the forests by the people, an ecological as well as social loss, cast a gloom in the entire area of the Birbhum District. Forests were free for use by local villagers for household purposes and cleaning for cultivation, which is available in the history of forest and its management during the sixteenth century in the entire West Bengal including the forests of Birbhum Division. Land revenue system was introduced by Todar Mal during the Mughal period, where local zamindars had to pay “Ruba” or share of ¼th part of revenue to Mughal Emperors for the protection being given by them. Permanent settlement was introduced by the East India Company in 1773 where the Zamindars were considered as the proprietor of forests including the landed properties. In the meantime, the Railways Division opened railway lines during 1890–1905

24

2  District-Wise Forest Matrix, Forest Models and Strategies

which helped the transport of forest products to far off places with ease and less cost and this followed by two world wars took a heavy toll of forest resources in the then Bengal. Zamindars or the local chiefs held the forests of this division who maintained their respective forests in a feudal tenure system. The forests of this area became accessible with the coming up of Railway lines. Due to easy transport to distant places by railway services with less cost and time, the value of the forest produce suddenly increased and the forests are considered as a source of earning with a higher return to the Zamindars. The forests were subjected to deforestation and in the advanced stages of degradation caused by ruthless exploitation on a rotation of 4–5  years by the savage acted Zamindars. The Zamindars continued this system of such mismanagement up to 1948. Later the Government of West Bengal took initiatives upon exercising control over the management of forests under the West Bengal Private Forests Act, 1948, but had not bettered the previous records of the occurrences of natural forests of the Birbhum District. Accordingly, possession of the forests was being taken by the Forest Department as per availability of records, evidence and court’s orders that came into consideration for scientific management and control. The Estates Acquisition Act came into force in 1953 and the forests so long captured by the Zamindars were vested to the government from 1954 to 1955. After taking over by the State Government, the forests were brought under scientific management, though, by this time, the productivity of the forests had declined to such a level that they could not meet the growing demands of forest produces from ever-increasing population of fringe areas outside the forests of the entire district of Birbhum. Natural forest and forestry of the Birbhum district, at present, is nearly void, though a few forests are grown up with the plantation programme of social forestry scheme under the supervision of the forest department. The increase in the forest cover in 2007 and 2013 respectively in the district is due to coppice growth and afforestation inside the forests and growth of commercial plantation under individual care. Total increase in the forest cover not only pertains to the period of 2011–2013, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Birbhum district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Birbhum district in the report of Forest Survey of India from 1987 to 1989. Forest area of Birbhum district is amalgamated with the other districts like Burdwan, Nadia, Kolkata, Hugli, Howrah, Malda, Medinipur, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of forest statistics for the year 2009. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987 in every two years consecutively.

25

Forest Cover of Cooch Behar District

Birbhum 5

Forest Cover (%)

4.5 4

3.87

3.85

3.89

2013

2015

2017

4.07

3.5 3 2.5 2 1.5

1.25

1.3

1.3

1999

2001

2003

2.31

2.31

2007

2011

1.5

1 0.5 0 2005

2019

Year Fig. 2.2  Forest cover change matrix of Birbhum district, West Bengal

Forest Cover Change Matrix Birbhum district is not enriched with the natural forest canopy. The forest cover of Birbhum district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 184.80 km2 which is 4.07% of the district’s geographical area. In terms of forest canopy density classes, the district has only 1 km2 area under very dense forests, 34.14 km2 area under moderately dense forests and 149.66 km2 area under open forests. There is a net increase of 7.80 km2 of the forest cover in Birbhum district as per the Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.2.

Forest Cover of Cooch Behar District Forest cover of Cooch Behar district is classified into North India Moist Deciduous and Northern Sub-Tropical Semi-Evergreen Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Champ, Sissoo, Chikrassi, Panisaj, Gamhar, Pithali, Simul etc. They are further classified into Moist Sal Savannah Forest and East Himalayan Lower Bhabar Sal Forest type according to the vegetation composition. The forest covers are grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Cooch Behar district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 24 Joint Forest Management Committees have been constituted in the Cooch Behar forest division. There are no

26

2  District-Wise Forest Matrix, Forest Models and Strategies

Eco-Development Committees (EDC) at Cooch Behar and 5 Self Help Groups (SHGs) in the forest division of Cooch Behar. In Cooch Behar district, the jungle flora includes Sal Malita Lampate Gamar Moyna Simul Champ bamboos creepers grass and fruit trees. The forests are inhabited by leopard elephant gaur (wild cattle) and rhinoceros, as well as other animals like reptiles and birds. Other than Kodalbasti forest, Rasomati forest is well known to almost all forest-lovers that is situated on the Torsa River basin under Pundibari range of Cooch Behar forest division of West Bengal. The Rasomati forest consists of mixed deciduous forest and the house of many important flora and fauna, which are highly vulnerable due to the anthropogenic activities as this forest is surrounded by many villages. In the forest area, in and around the water bodies, migratory birds, along with many local wildlife species, are found during the wintertime. Rasik Bill is that type of a small lake that attracts a lot of migratory birds which make nests in the trees around the lake during the winter. There are deer park, crocodile rehabilitation centre, leopard house, python house, aviary, and a tortoise rescue center in and on the Rasik Bill water bodies. Tree lines of the forests which cannot even move, in this way, grow up a symbolic social structure in the natural environment through green infrastructure. Trees cannot betray anybody else as the growing stocks in the forest stands never deceive but provide benefits to all including its wildlife in the forests and human beings of the society with their green infrastructure.

Green Infrastructure of Trees Green infrastructure is an illustrative example in the forests of Cooch Behar district under the Dooars region which is composed of natural framework in horizontal, vertical, crisscross, zigzag, or convoluted pattern by the tree lines, creepers, and bushes of herb and shrubs of the forests (Lockhart 2012). Influence and impact of that green infrastructure by the trees, not only on the wildlife, but on the forest vegetation, reveals the symbolic socialization of the tree community. Forest green serves as an infrastructure to the occurrence of forest vegetation as a whole and to the wildlife for their quiet living in the jungle atmosphere. Such green infrastructure is evidenced far and wide in the forest areas of the eastern Dooars elephant reserves. The tree lines of Sal (Shorea robusta) and other miscellaneous species under the category of tropical dry deciduous forest types stand in a regular interval, but their canopy on the top overlapped each other, not even maintaining social distancing or crown shyness (Das 2020a). This huge canopy without any gap all along the forest patches helps moving of the arboreal animals on the top and prevent predators of the wildlife dwelling in the forest floors. This type of forest looks like a huge structure framed for a social carnival where timber of Sal trees is assumed to be as poles and green canopy of foliage as tarpaulin covered on the top. In forest stands, creepers are numerous in number, grown up with huge length and diameter, sometimes as good as equal to the stems of the timber trees, undulating and strong that helps forest living animals to cross over from one tree to another and monkeys are seen

Forest Cover of Cooch Behar District

27

swinging from these creepers as these creepers are interconnected with the two or more trees. Sometimes these creepers act as barriers where swift moving herbivores like deer are trapped and their predators easily make them a prey. Such green infrastructure framed by the convoluted awesome creepers in a dense jungle, prevents the hunters from taking advantage of hunting wildlife with their arrow, spear, or shotguns. Herbs and shrubs, low and bushy, not only cover the forest floors or supplying fodder to the herbivores but trap the litterfall from the timber trees enhancing more nutrient supply contributing to the forest soils through microbial biomass decomposition and increases infiltration rate through precipitation from rainfall. Thus, herbs and shrubs looking like a green mosaic to the forest floor stop withering away of the dry leaves, barks, flowers, fruits and particularly seeds that promote regeneration of saplings and help in propagation of timber trees inside the forest stands. Forests in the high latitude, particularly in the upper Dooars of West Bengal, vegetation of soft stem like banana grows on the gentle slope of the elevated areas with a two-tier green infrastructure. These wild banana groves are surrounded by wild ginger, cardamom, chili and orange plants, the smell of which are disliked by elephant herds as their characteristic instinct. As a result of that green infrastructure, elephant herds halt abruptly towards banana groves and monkeys enjoy all green and ripe bananas of the banana groves. Taking such experience, the forest department at present, starts plantations of chili, ginger or plants bearing citrous fruits like orange along the borderline areas between the village and the forest patches to combat human-elephant conflict.

Forest Cover Change Matrix Cooch Behar district is very rich in forest canopy and dense forestry having plenty of deciduous trees. The forest cover of Cooch Behar district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 349.06 km2 which is 10.31% of the district’s geographical area. In terms of forest canopy density classes, the very dense forest area of the district is nil, 27 km2 area under moderately dense forests and 322.06 km2 area under open forests. There is a net increase of 0.06 km2 available from the reported area in India State of Forest Report 2019. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1991, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Cooch Behar district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Cooch Behar district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in Fig. 2.3.

28

2  District-Wise Forest Matrix, Forest Models and Strategies

Cooch Behar Forest Cover (%)

12

10.27

10.3

10.3

10.31

2013

2015

2017

2019

10 8 6 4 2

0.97

0.92

0.94

0.94

0.94

1.12

1991

1993

1995

1997

1999

2001

2.57

2.57

2.78

2.78

2003

2005

2007

2011

0 -2 -4

Year

Fig. 2.3  Forest cover change matrix of Cooch Behar district, West Bengal

Forest Cover of Darjeeling District Forest cover of Darjeeling district is classified into evergreen Northern Montane Wet Temperate and Sub-Alpine forests type composition according to Champion and Seth (1968) classification comprising the vegetation types like Oak Magnolia Champ Kawla Pipli Chilaune Katus Panisaj Lampate Angare Utis Toon Malangiri Rhododendrons Salix Berberis Yew Junipers Birch and Gokul etc. They are further classified into East-Himalayan Moist Temperate, Northern Tropical Wet Evergreen, and Northern Tropical Wet Evergreen Forest type according to the vegetation composition. The forest covers are grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Darjeeling district, Joint Forest Management have been adopted comprising jointly by the forest department and the local inhabitants and there are about 77 JFMC at Darjeeling, 64 JFMC at Kalimpong, and 46 Joint Forest Management Committees (JFMC) at Kurseong have been constituted in the Darjeeling forest division. There are 15 EDC at Mahananda WLS, 15 EDC at Senchal WLS, and 1 Eco-Development Committee (EDC) at Singalila Wildlife Sanctuary (WLS), and 14 SHG at Darjeeling, 21 at Kurseong, and 38 Self Help Groups (SHGs) in the forest division of Darjeeling WLS as per the Annual Administrative Report of the Department of Forest, Government of West Bengal 2017–2018. Darjeeling district consists of several forest divisions and forest ranges like Darjeeling, Kurseong, Singalila, Senchal, Mahananda etc. along with several beats. After splitting from Darjeeling district, Kalimpong district was formed on 14 February 2017 as the 21st district of West Bengal, though Forest Survey of India has not yet included separate district statistics for the Kalimpong district. Kalimpong district is covered with 36,435.79 ha forest areas and its Forest Division consists of 8 forest range and several beats (Table 2.2). Roadside area and mountain top are covered with the evergreen Northern Montane Wet Temperate and Sub-Alpine forests types where trees are particularly

Forest Cover of Darjeeling District

29

Table 2.2  Forest ranges and forest beats of Kalimpong forest division Sl. no 1 2 3 4 5 6 7 8 Total

Forest range Kalimpong Pankhasari Noam Neora Samsing Jaldhaka Lava Lolegaon

Forest beats Tashiding,Nazeok,Tarkhola & Kalimpong Damsang,Algarah & Dalapchand Noam & Ghish Gorubathan,Sakam,Dalim & Burikhola Samsing Khumani & Paren Lava & Kolbong Bokhim,Chumang,Pemling & Lolegaon

Areas in hectare 7111.73 2154.684 6594.00 4173.15 1174.660 2923.00 2203.008 3593.00 36,435.79

Oak Magnolia Champ Kawla Pipli Chilaune Katus Panisaj Lampate Angare Utis Toon Malangiri Rhododendrons Salix Berberis Yew Junipers Birch and Gokul having the characteristics of the Central Himalayan flora. Darjeeling is the only district of West Bengal, almost covered with the natural evergreen forests, and that evergreen forest of Darjeeling district is infested with the wild animals like leopard Asian elephant bison wild boars barking deer hog deer spotted deer tiger and clouded leopard. In Darjeeling, three-fourth part of its geographical areas is covered with evergreen forests. Hill Queen Darjeeling is known for her pine forests (Fig. 2.4) that are easily available and support forest bathing to the visitors during the peak period of the tourism season.

Forest Bathing Not only keeping away from diseases through social distancing for the humans, but trees also help healing human diseases too through other processes such as hugging trees which is good for health particularly in the recent crisis of global coronavirus pandemic during 2020–2021. People are emotional beings, and it is important for them, especially at times of fear, times of stress, to feel connected to someone, to feel comforted by someone. In this situation, hugging a tree is a solution. Five minutes for hugging a tree is good, and if someone can give five minutes of a day to hug, that is enough. It is nice to close eyes while hugging a tree that feels the warmth and current flowing from the tree into the hugger (Lohmann 2013). It starts warmth in toes, runs up legs and through the body into the brain. Someone gets such a good, relaxed feeling by hugging a tree that makes him ready for a new day and to take new challenges. The result of hugging trees is a simple biological treatment (Das 2020b). The hormone oxytocin and its measurement and significance of its presence is complex like a hugging partner that boosts the production of oxytocin. So, the act of hugging may be the stimulant for hormone production and such hugging trees is good for health as hugging a tree increases levels of hormone

30

2  District-Wise Forest Matrix, Forest Models and Strategies

Fig. 2.4  Pine tree lines (Pinus roxburghii) in the forest cover of Darjeeling

oxytocin. This hormone, oxytocin is responsible for feeling calm and emotional bonding. When hugging a tree, hormones serotonin and dopamine make the treehuggers feel happier. But aside from the novelty of the idea, there is plenty of science to back it up such as practicing forest bathing for years sending the message that spending time in nature has plenty of physical and mental benefits. Forest bathing is nothing but a nature therapy or ecotherapy specifically with an individual’s presence within nature or outdoor surroundings. Visitors, in reach, in the natural world of forest gather experience by receiving the essence of nature. Forests engage an individual with its soil, grass, and trees in the natural environment. In the forest atmosphere, chemicals emitted by the trees called Phytoncides and a nonpathogenic soil bacterium called Mycobacterium vaccae are proved to be health benefits derived from these chemicals emitted by the trees and soil-bacteria. Forest is the easy access to the visitors (in the Protected and Open Forests, but except the Reserved Forests) and generally the visitors can touch leaves, branches, stems, flowers, and fruits of the trees. The trees in the forest, when touched or injured somehow, emit essential oil Phytoncides. Even some trees bring out excessive Phytoncides to prevent insects that attack the plants. These trees emitted Phytoncides can lessen stress hormonal levels and help declining blood pressure of the forest visitors. Phytoncides also increase the immune system of the body and enhance the anti-cancer proteins production of the human body. Further, the soil-bacteria,

Forest Cover of Darjeeling District

31

Mycobacterium vaccae, strengthen serotonin in the prefrontal cortex that reduces anxiety particularly when the visitors come in touch with the exposure of forest-soil. Plants and soils of the forests look alike but are different in essence (Das 2020c). Not only for taking essence of Phytoncides from the plants and come with the contact of Mycobacterium vaccae, a soil-bacteria, but for absorbing every essence of the forests, the forest visitors must engage their five sense organs – eyes, nose, ears, tongue, and touch (skin). The visitors will see the scenic beauties of the forests, hear clear calls and loud calls of the birds and the crickets, get fragrance of the plants, touch leaves, branches, stem, flower, fruits and seeds, taste edible wild fruits or other foods from the forests. Researchers alert the forest visitors not to do any sorts of exercise during forest bathing, but get tired by walking, moving in and around in the natural environment of the forests. It is better to say that love to the forest bathing in its essence is spiritual fire. So, the hugging of trees and forest bathing are beneficial to the human beings concerned to their physical and mental health and this is due to the social distancing maintained by the trees in their canopy. Forest bathing model deals with the existing forests through ecotourism that acts as a stimulus for the restoration of forests by the implementation of the afforestation programme.

Forest Cover Change Matrix Darjeeling district is very much rich in forest canopy and dense forestry having plenty of evergreen trees. The forest cover of Darjeeling district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 2367.80 km2 which is 75.19% of the district’s geographical area. In terms of forest canopy density classes, the district has 720.76 km2 area under very dense forests, 654.52  km2 area under moderately dense forests and 992.52 km2 area under open forests. There is a net increase of 2.80 km2 forest area and has the highest percentage of the forest cover in Darjeeling among all the districts in the state of West Bengal, from the reported area in India State of Forest Report 2019. Total increase in the forest cover not only pertains to the year of 2001 with respect to 1987, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Darjeeling district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Darjeeling district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.5.

32

2  District-Wise Forest Matrix, Forest Models and Strategies

Darjeeling 90

Forest Cover (%)

80

69.74 70.53 70.53

70

72.69 72.69

75.52 75.52

75.1

75.19

2013

2017

2019

60 50

45.57 46.21 45.98

46.2

46.2

1991

1997

1999

40 30 20 10 0 1993

1995

2001

2003

2005

2007

2011

2015

Year Fig. 2.5  Forest cover change matrix of Darjeeling district, West Bengal

Forest Cover of Howrah Districts Forest cover of Howrah district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Khair, Kadam, Chhatim, Kotila, Sonajhuri, Kath badam, Bakul, Krishnachura, Radhachura etc. They are further classified into Biogeographical Zones of Lower Gangetic Plains according to the classification of Rodgers and Panwar (1998). The forest covers are then grouped into two broad classes  – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Howrah district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 4 Joint Forest Management Committees have been constituted in the Howrah forest division. There is no existence of an Eco-­Development Committee or Self Help Groups in the forest division of Howrah. Howrah district faces scarcity of forest cover all over its geographical areas. Among the forests, Garhchumuk is a notable one and is a man-made community forest in Howrah district created under the social forestry scheme. Besides the man-­ made forest of Garchumuk, another important greenery is The Acharya Jagadish Chandra Bose Indian Botanic Garden previously known as Indian Botanic Garden and the Calcutta Botanic Garden in Shibpur, Howrah. The garden, on the bank of the river Hooghly, exhibits a wide variety of rare plants and a total collection of 12,000 specimens spread over 109 has under the management and supervision of Botanical Survey of India. In the district, rescue forest models may be implemented for the recovery of greenery that may enhance the areas of the forests.

Forest Cover of Howrah Districts

33

Rescue Forest A man made forest was created within an area of approximately 13.40 ha earlier by Howrah forestry division under the social forestry scheme of forest directorate at Garchumuk located at the confluence of Hooghly and Damodar rivers in the district of Howrah. Considering its scenic beauty as well as suitability for wild animals, the spot, offering the spectacular view of the Ganges, a deer park within this man-made forest, was established on 31 January 1991 recognized by the Central Zoo Authority. It is a deer hub or deer park, rather than a deer forest as large areas are almost all devoid of trees. The forest land area, in the Damodar basin, is suitable for afforestation, though no step is taken by the forest department for plantation of trees of greater or less extent inside the deer hub. A few trees are seen standing outside the wire gauze surrounded area of the deer park, where plantation programmes may be taken as ‘rescue strategies’ for alternative afforestation or reforestation. Rescue strategies of forest regeneration means the rescue of the present forest environment by the settlement of an alternative one to be useful in necessity in near future (Gray and Hamann 2011). When the seedlings outside the deer park are grown up enough or mature by 5–7 years, deer are to be released in the newly established greenery, and the inside area of the existing deer park will undergo a plantation programme. In this way, both inside and outside areas of the deer park within the entire surroundings of the man made forest will be covered with the green canopy through the strategies of rescue forestry under social forestry schemes.

Forest Cover Change Matrix There are no separate statistics available for forest cover of Howrah district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Howrah district is amalgamated with the data of the other districts like Burdwan, Birbhum, Kolkata, Hugli, Midnapur, Nadia, Malda, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997. Separate data of these districts of the state of West Bengal have been inventoried in the forest report of 1999 for the first time. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-­ wise forest statistics for the year 2009. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Howrah district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987 in every two years consecutively (FSI 2019). The forest of Howrah district is covered with

34

2  District-Wise Forest Matrix, Forest Models and Strategies

Howrah

Forest cover (%)

30 25

20.72 20.72 20.72 20.71

20 15

9.95

10 5 0 -5

0

0.14

1999

2001

2.22 2003

9.95

5.45

2005

2007 2011 Year

2013

2015

2017

2019

Fig. 2.6  Forest cover change matrix of Howrah district, West Bengal

artificial plantations. Among fauna, jungle cats, jungle fowl, python, wild boars and varieties of birds and reptiles are increasingly being reported. Howrah is a district which is very much poor in forest canopy and forestry. The forest cover in Howrah district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 303.77 km2 which is 20.71% of the district’s geographical area. In terms of forest canopy density classes, the district has no area under very dense forests, 50 km2 area under moderately dense forests and 253.77 km2 area under open forests. There is a net decline of 0.23 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.6.

Forest Cover of Kolkata District The forest cover in the Kolkata metropolis is almost nil and it is not surprising to most of the people of the state (FSI 1997). In the past, Calcutta (presently Kolkata, The City of Joy), was covered with the jungle in the era of Job Charnock. At present there is no existence of any forest in and around the Kolkata metropolis, but the greenery covers a little space at Eden Gardens, Rabindra Sarobar and Subhas Sarovar areas in the form of pocket forest regenerated under the urban forestry scheme, though there are a lot of urban spaces that belong to various government agencies where urban forestry can be implanted. Likewise, HIDCO plans to set up an urban forest at New Town while a similar initiative is being taken by Kolkata Port Trust on a plot it owns near Hyde Road. This type of alternative social forestry initiatives helps enhance greenery inside the Kolkata metropolis as a strategy of rescue forest and pocket forest. Such urban social forestry schemes as an initiative of the forest department work in the city area has started showing results. Though there is no existence of JFMC, EDC, or SHGs in the metropolitan district of Kolkata, the

Forest Cover of Kolkata District

35

urban forestry department is involved in implementing tree equality strategies by the plantation of trees in the streets under urban forestry schemes.

Tree Equity and Urban Heat Island Effects Nothing could extinguish the hopes of an owner when his shop caught fire in the night in the urban crowded metropolitan area of Kolkata because these areas with less green space and vegetation create urban heat islands that causes an increase of as much as 22 °F during the night and the weather is too warm for fires. Urban areas are disproportionately affected by the rise in temperature that creates islands of heat from one area or locality to another which is referred to as urban heat island effects. An urban locality is cooler compared to the other locality is only due to the presence of trees. Trees reduce the rising temperature because of climate change and help refresh the area to cool down for easy and sound living (Enete et al. 2012; Voogt 2002; Wolf 1998). An area without trees has severe impact on the elderly and vulnerable inhabitants particularly for the low-income group communities as the urban heat islands increase temperature as much as 5–7 °F at daytime and becomes warmer up to 22  °F during night  – stated the researchers in the field of climate change. Generally, the locality with the effects of urban heat island is warmer than the cool normal areas by several degrees of Fahrenheit. Urban heat island is formed principally due to the metal roads and rooftops of the mega city areas like Kolkata. Metal roads constructed with the black pitch along with the cemented roof tops of the houses of the urban areas absorb heat from the radiation of the sun and the trapped heat slowly released by the rooftops and metal road create islands of heat in the different pocket areas of the city. Urban heat islands become hotter in the daytime, but they are clearly revealed in the night particularly in the area with scarcity of trees as the metal road and roof tops slowly release the trapped heat into the atmosphere during the night that leads to the area warmer. Areas covered with more green space and vegetation are less affected by the effects of such urban heat islands as this type of heat islands could not be formed in the area covered with the greenery (World Bank 2009, 2010). Tree equity is the solution to this urban heat island effect because only the trees can diminish the urban heat effectively. Equal distribution of the benefits of the nature and power of the trees is just not an environmental issue for climate change, but it is a remedial measure to the warmer state in the place for living purposes, and this equal benefits of the trees to the inhabitants of the locality are referred to as tree equity. In a place of tree equity, trees provide fresh oxygen ensuring for people’s breathing, clean water for drinking, comparatively cooler homes for living, solve heat-related illnesses, lower risk and hazards for flood, lower electricity bill and overall mental health. Further, rising cases of cardiovascular and lower respiratory tract illness are the results from such urban heat island effects that causes environmental degradation of a particular locality. Trees reduce such illnesses by trapping air pollutants, greenhouse gas and aerosols and keep the air of the locality

36

2  District-Wise Forest Matrix, Forest Models and Strategies

pollution-free, clear, and clean. Researchers interpreted that the trees could reduce air temperature by 9 °F in the surrounding environment and thus, can help reduce heat-related diseases and deaths. Urban metropolitan belt of the City of Joy suffers from the urban heat island effects though a few places like Eden Gardens, Dhakuria Lake, Subhas Sarobar and the surrounding areas of the Victoria Memorial Hall of the Kolkata Metropolis are covered with the green space and vegetation. About 10,000 numbers of such green spaces and roadside trees are destroyed by the super cyclone Amphan during May 2020, though a few numbers of them are restored by the local initiatives. Most of the trees are uprooted by the heavy winds blown during the storm, and some are dead due to prolonged flooding that cut off oxygen within the soil. Root systems of the trees are weakened because of oxygen lacking that leads to the death of the tree lines in the City of Joy. Tree species available in the City of Joy can tolerate a maximum duration of 24–36 h of water inundation for huge rainfall precipitation during the cyclonic storm depending upon the several factors like species variations, ages of the tree species etc. Jadavpur University campus containing 58 acres of areas had lost 181 numbers of trees by the ravaged cyclonic storm Amphan. Because of such destruction of trees for the cyclonic storms, the ratio of tree equity declines in the green space area of Kolkata metropolis. Tree equity not only helps in breathing fresh air to the citizens, but the trees are said to be the symbol of beautification for the City of Joy. Sitting under the trees are very much pretty and that becomes prettier when one can gaze at the tree lines of well-arranged plantations at both sides of the avenues and roads of the city. All these benefits of the trees could increase the infrastructure essential to human beings that improve the quality of life even for a man in the street.

Forest Cover Change Matrix Kolkata is a district which is very much poor in forest canopy and forestry. The forest cover in Kolkata metropolis, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is only 1 km2 which is 0.54% of the district’s geographical area. In terms of forest canopy density classes, the district has no area under very dense forests and moderately dense forests and only 1 km2 area under open forests. There is no increase or decline of the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.7.

Forest Cover of Hugli District

37

Kolkata 0.6

0.54

0.54

2017

2019

Forest cover (%)

0.5 0.4 0.3 0.2 0.1 0 -0.1

0

0

0

0

0

0

0

0

1999

2001

2003

2005

2007

2011

2013

2015

-0.2

Year

Fig. 2.7  Forest cover change matrix of Kolkata district, West Bengal

Forest Cover of Hugli District Forest cover of Hugli district is classified into Tropical Dry Deciduous Forest type composition comprising vegetation types like Sissoo, Krishnachura, Kadam, Chhatim, Khair, Kathbadam, wood apples etc. They are further classified into Biogeographical Zones of Lower Gangetic Plains and khair-Sissoo Forest type. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. There are no Joint Forest Management Committee, Eco-Development Committee, or Self Help Groups for monitoring such forest patches of Hugli Forest Division. The Hugli District gains credit for an island-forest, namely Sabuj Dwip. Sabuj Dwip, emerged at the confluence of Behula and Hooghly rivers with the accumulation of sand silt and clay, spanning over an area of 180 bigha (about 59.4 acre), is covered with the green canopy of Sonajhuri, Eucalyptus Mehagani Segun Kadam Chhatim Jarul Amaltas and other trees grown under the plantation programme of Social Forestry Scheme of the forest department. Likewise, Sabuj Dwip, another well-known forest area in Hugli district is the Garh Mandaran, a historical place of interest, which is to be regenerated similarly through the tree-islands strategy.

Tree Islands Sabuj Dwip, an island forest, is now a picnic spot and it hosts a watchtower, restaurant, children’s park, flower garden including the arrangement of mechanized boat rides in its surrounding river waters. But if the Sabuj Dwip is allowed for the picnic spot and open for visit to the tourists round the year, the Hugli district certainly will

38

2  District-Wise Forest Matrix, Forest Models and Strategies

lose a moderately dense island forest soon. Forest regeneration through afforestation is the solitary way for restoration of moderately dense forest at Sabuj Dwip. This effort certainly comes to be fruitful as the rural population in Sabuj Dwip is nil and the people in farming communities live in the mainland area, far away from the island. Well-organized local groups or the forest department ensure enforcement of the forest act by reforestation through plantation programmes in this isolated riverine island. For restoration of the forest, one of the most exciting assisted natural regeneration strategies is called applied nucleation, also known as ‘tree islands’, which involves plantation of only a small number of trees in the void area that attract birds and other seed dispersers, which can spread seeds around the tree islands (Holl et  al. 2010). Gradually, these tree islands turn into intact forests. Likewise, Sabuj Dwip, another well-known forest area in Hugli district is the Garh Mandaran which is also to be regenerated similarly through this tree-island strategy. Afforestation and regeneration of forest are very much essential to combat the environmental crisis due to global warming and climate change (Das 2020d). Regretfully, every second, more than a hectare of tropical forests is destroyed or drastically degrades.

Forest Cover Change Matrix There are no separate statistics available for forest cover of Hugli district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Hugli district is amalgamated with the data of the other districts like Burdwan, Birbhum, Kolkata, Malda, Howrah, Nadia, Medinipur, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-wise forest statistics for the year 2009. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987 in every two years consecutively. Hugli district is extremely poor in forest canopy having only deciduous trees generated through plantation. The forest cover of Hugli district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is only 160 km2 which is 5.08% of the district’s geographical area. In terms of forest canopy density classes, the very dense forests area of the district is nil, 14 km2 area under moderately dense forests and 146 km2 area under open forests. The report shows no rise or decline of forest area of the district in the India State of Forest Report 2019. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Hugli district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate

Forest Cover of Jalpaiguri District

39

Hugli

Forest Cover (%)

6 5.08

5.08

5.08

5.08

2013

2015

2017

2019

5 4 3

2.22

2.19

2003

2005

2 1 0

0 1999

1.94

1.94

2007

2011

0.41 2001

Year Fig. 2.8  Forest cover change matrix of Hugli district, West Bengal

statistics available for forest cover of Hugli district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.8.

Forest Cover of Jalpaiguri District Forest cover of Jalpaiguri district is classified into evergreen Northern Tropical Wet Evergreen and Northern Sub-Tropical Semi-Evergreen forests type composition according to Champion and Seth (1968) classification comprising the vegetation types like Simul Khair Champ Gamhar Pithali Sissoo Kawla Pipli Chilaune Katus Panisaj Lampate Malita Rain trees etc. They are further classified into East-­ Himalayan Moist Temperate and East Himalayan Lower Bhabar Sal Forest type according to the vegetation composition. The forest covers are grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Jalpaiguri district, Joint Forest Management have been adopted comprising jointly by the forest department and the local inhabitants and there are about 66 such JFMC at Baikunthapur, 62 JFMC at Jalpaiguri, 17 at Buxa Tiger Reserve East (Alipurduar), 27 at Buxa Tiger Reserve West (Alipurduar) and 27 Joint Forest Management Committees (JFMC) at Jaldapara WLS have been constituted in the Jalpaiguri forest division. There are 6 EDC at Neora Valley NP, 10 EDC at Gorumara NP (National Park), 2 at Chapramari WLS, 14 at Buxa Tiger Reserve (East), 7 at Buxa Tiger Reserve (West) and 35 Eco-Development Committee (EDC) at Jaldapara Wildlife Sanctuary (WLS), and 119 SHG at Baikunthapur, 512 SHG at Jalpaiguri, 232 SHG at Jaldapara WLS, and 24 Self Help Groups (SHG) at Gorumara WLS in the forest division of Jalpaiguri as per the Annual Administrative Report of the Department of

40

2  District-Wise Forest Matrix, Forest Models and Strategies

Forest, Government of West Bengal 2017–2018. Jalpaiguri Forest Division has several Forest Ranges like Mal, Gorumara, Chapramari, Barobisha, Nilpara, Falakata, Moynaguri, Jalpaiguri etc. and other Forest Ranges like Chilapata, Mendabari, Jaldapara, Rydak, Hatipota, Damanpur etc. are now included in the Alipurduar Division as because the Alipurduar district has been carved out from Jalpaiguri on 25th June 2014 as the 20th district in the state of West Bengal. In the territory of North Bengal, numerous rivers like Turturi, Gadadhar, Cheko, Kalkut, Nonai, Kaljani, Halong, Torsa, Ekti, Birbiti, Dimdima, Diana, Jaldhaka, Murti, Mal, Tuntunia, Chaiti, Jayanti are flowing, from north to south, from the hill to the plain, with icy cool water, round the year. These rivers traverse the forests covered with Gamar Champ Lampate Moyna Malita Sal Simul all along their meandering path. The herd of bison, a few elephants, rhinos, leopards are seen to wander within the forest off and on. Perhaps these wild animals win in the struggle for existence or are selected by nature in the ancient dense forest of Dooars in the Jalpaiguri district. Here too, a group of people from the urban area enjoy a lot inside the forest area which is befitted with the nature of ecotourism. Forest here seems to be nature’s poetry entangled with the green canopy, rivers, falls, wilds, and the birds. In the natural beauty and essence of the forest of the Dooars, even an imaginary hard cover felt by a solitary wayfarer, is going to be melted after encountering the billowy stream producing a sweet jingling sound. Forest area of Dooars in Jalpaiguri and Alipurduar districts infested with wild animals is rich in biodiversity. Leopards, Asian elephants, tigers, bison, wild boars, barking deer, spotted deer, clouded leopard, hispid hare, pigmy hog here are common among the wild animals. Varieties of snakes and birds are very much interesting to the naturalists and birdwatchers in different forests scattered in the Dooars. Dooars region, covered with the natural terrestrial forests, forms the gateway to Bhutan which is about 30 km wide and stretched over about 350 km from the Teesta River in West Bengal and Dhansiri River in Assam and such forest of Dooars is to be considered as forgotten forest for its origin of thousand years.

Forgotten Forest Forgotten forests, in general, are the most threatened forests where floral commensalism factors and functional approach among the species are still unknown to all (Das 2020e). Typically composed with the Tropical Dry Deciduous Forest types, the forgotten forests are scattered as completely isolated forest patches in different parts of Dooars with a huge carbon store in the subsoil (Särkinen et al. 2011). The seasonal phenomenon of the dry forests is associated with the grasslands which are partially dried during the winter but provide fodder to the herbivores living in the forests. Jaldapara, once a larger forest by area in the Dooars, is a striking example of such a forgotten forest. In Jaldapara, Jalda tribal races once lived but they meandered towards Bhutan, though the stead name of Jaldapara still exists in their name. Jalda, Tharu, Dhimal like tribal races were not only inhabitants in the present day

Forest Cover of Jalpaiguri District

41

Dooars, but they also saved the forest areas generations after generations living among the ferocious animals. Forest areas of the Dooars were once the abode of Koch, Mech and Tharu tribal races at a time which are evidenced by the name of places with ‘Guri’, ‘Bari’ and ‘Dabri’ syllables as pronounced and used by the ‘Tharu’ community. The tribal race, Dhimal was also influential in this area as reflected from the village names like Mallikhat, Mallikpara etc. which originated from the name of that tribal race. Dhimal, the tribal community, once carried such Mallik titles, presently having no existence like Tharu, Jalda etc. in this area. Jalda, Tharu, Dhimal like tribal races are completely absent from the area perhaps due to intermingling with the local denizen or migration towards hilly region in the north, although the rest of the tribal races, still existing in the present days, are engaged in the tea estates, and a few are visible in and around the forest areas. The present tribal races like Santal, Malpahari, Kheria, Munda, Oraon, Chik, Baraik, Magar, Ho, Khasi, Korhoy etc. of the Dooars area are accompanied by the son of the soil like Mech, Rava, Garo, Toto, Dukpa, Boro, Hazong, Kachhari etc. Other tribal races like Rai, Limbu, Mongor, Tamang, migrated from Nepal now share their livelihoods including social and cultural affairs with the Koch, Mech and Rajbangshi community. Tribal races, still living surrounding the Dooars, like forest, love forest, and worship the trees of the forgotten forest. Forgotten forest, the pristine forest in nature and once the denizen of the tribal community is the most precious one with its large soil carbon storage in the subsoil carbon pool through the carbon sink from the atmospheric carbon dioxide. Carbon is stored in the forest floors in the form of soil organic carbon which plays an important role in the mitigation of climate change. Soil carbon consists of soil organic matter and inorganic carbon as carbonate minerals and stored in the forest soils in the form of solid terrestrial matter. Soil carbon is often referred to as a carbon sink in the continuous process of the global carbon cycle and it is causally related to the biogeochemical cycles. The soil carbon accumulated to form storage or reservoir of carbon, referred to as a carbon pool, has the capability to lock or release carbon. If the soil carbon is transferred from one carbon pool to another, the process is to be called as carbon flux and for a specified time when the carbon is stored as soil carbon without such transfer or release, it is then termed as carbon stock. Absorption of carbon from the atmospheric carbon dioxide is referred to as carbon sequestration and inclusion of that carbon to the carbon pool is said to be the carbon uptake. Carbon is removed through the sequestration of the atmospheric carbon dioxide and such removal of carbon from the atmosphere is referred to as carbon sink. A carbon pool is converted to a carbon sink in a specific time interval when the process of carbon inflow exceeds the carbon outflow and on contrary, a carbon pool is referred to as a carbon source if the carbon outflow exceeds the carbon inflow. Thus, the carbon pool can be a carbon source if the carbon pool releases carbon dioxide to the atmosphere. And all these soil carbon related phenomena form the carbon budget which is measured with the estimation of carbon stocks and carbon fluxes. Further, atmospheric carbon dioxide is referred to as brown carbon, bye-products of the combustion of fossil fuels are black carbon, carbon stored in the subsoils of the terrestrial natural forests is said to be as green carbon, and the carbon

42

2  District-Wise Forest Matrix, Forest Models and Strategies

sink in the mangrove swamps and marshes, aquatic and marine environment is referred to as blue carbon. Dooars, a forgotten forest, is to be considered as a huge green carbon storage in its subsoils.

Forest Cover Change Matrix Natural forest and forestry are scattered all over the district of Jalpaiguri, though a few forests are grown up with the plantation programme of social forestry scheme under the supervision of the forest department. In 1991, 1537 km2 area of forest cover of the district was increased to 2344 km2 in 2001 as recorded in the India State of Forest Report 2001. There is a net increase of 807 km2 forest area in the district within a time span of only a decade. The increase in the forest cover in the district is due to coppice growth and afforestation inside the forests and growth of commercial plantations like tea gardens in the Dooars or canopy formation through plantation of saplings under individual care. Total increase of 1326 km2 with respect to 1991 in the forest cover not only pertains to the period of 2013, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Jalpaiguri district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Jalpaiguri district is very rich in forest canopy and dense forestry having plenty of timbers and trees. The forest cover in Jalpaiguri district in 2019 report, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 2862.40 km2 which is 45.97% of the district’s geographical area, the second highest forest cover among the districts of West Bengal. In terms of forest canopy density classes, the district has 724.22 km2 area under very dense forests, 434.92  km2 area under moderately dense forests and 1703.26  km2 area under open forests. There is a net increase of 5.40 km2 in the forest cover in the Jalpaiguri district of West Bengal reported in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.9.

Forest Cover of Malda District Forest cover of Malda district is classified into Littoral and Swamp Tropical Seasonal Swamp Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Hijal, Arjun, Mahua, Sonajhuri, Peasal, Kend, Kusum etc. They are further classified into Barringtonia Swamp Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes  – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such

43

Forest Cover of Malda District

Jalpaiguri Forest Cover (%)

60 45.98 45.86 45.88 45.97

50

40.24 40.21 37.64 38.43 38.75

40 30

24.68 25.23 25.34 25.37

25.4

1991

1999

20 10 0 1993

1995

1997

2001

2003

2005

2007

2011

2013

2015

2017

2019

Year Fig. 2.9  Forest cover change matrix of Jalpaiguri district, West Bengal

forest patches of Malda district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 5 such Joint Forest Management Committees have been constituted in the Malda forest division. There are no Eco-Development Committees (EDC) or Self Help Groups (SHGs) in the forest division of Malda. Malda Forest Division consists of several forest ranges like Chanchal, Kaliachak, Bhaluka Road, Malda, Gajol etc. Forest of Malda district is covered with plenty of deciduous trees. In this district, Tilason forest, the only natural habitat of Hizal trees (Barringtonia acutangula), a typical representative of the Tropical dry deciduous forest types, is covered in an area of about 150 mi2. Tilason forest, a Tropical Littoral Swamp natural forest of the Malda district, located in the western side of Habibpur Community Development Block at no man’s land of the India-Bangladesh border area is known for its habitation of Hizal trees grown naturally with abundant occurrences. In the Barrind region of North Bengal, the Hizal forest of Tilason, a part of littoral and swamp forest, is referred as northern tropical deciduous forest and has been taken up by the government forest department for conservation and protection. Tilason forest, covered with dense Hizal trees and diversified scrubs, is situated in between the Tangan and Punarbhaba rivers of Malda district. Among the forests of the district, Tilason forest is subject to an occurrence of a single large species of Hizal which is strongly influenced by climate and faces disturbances for global warming and climate change. Disturbances such as fire, drought, landslides, species invasions, and insect and disease outbreaks influence the structure, composition, and function of such Hizal forest. This type of large single species abundance can have the direct impact of climate change on such forest ecosystems because of the relationships between climate, disturbing agents, and forests. Any of these disturbances can increase forest susceptibility to other disturbances. Considering the forest of Tilason, for example, if Hizal forests become infested with the bark beetles, after suffering damage from devastated cyclonic storms, the beetle outbreaks will cause extensive tree mortality resulting in an increase of fuel loads which severely will increase the risk of wildfires. Further, making predictions on the future impacts of a changing climate on forest

44

2  District-Wise Forest Matrix, Forest Models and Strategies

disturbances is made more difficult by these interactions. Such effect of climate change is to be applicable on the other forests of Malda district enriched with species diversity. Other forests of Malda district, are Itabandha forest (Danga Akalpur, Rasikpur, Gajol); Salbona/Rajadighi forest (Chiriyadaha, Hatimari, Alampur); Adina forest (Gajol), Altar Forest; and Raniganj Dukla forest. Adjacent to these forests including the Tilasan forest, large numbers of tribes live in the Habibpur Block. The tribal women of that community are habituated to caring for the trees of the forest wholeheartedly.

Women’s Participation for Forest Restoration Most of the blocks of Malda district are dominated by the tribal population particularly in the vicinity of the forest areas. The tribal women living in the villages surrounding the forest areas are habituated to care for trees from the core of the heart like their own children. Affinity for the trees and caress for them by the women’s community are noticed when the tribal women are engaged in a plantation programme of the forest department in different forest patches. They care for every sapling of the trees like their siblings during the entire plantation period and watered them as if they offer the water to their kith and kin. Another group of tribal women, involved in collection of dry leaves, flowers, fruits, seeds, and flosses from the forest floor in a separate spot in the same forest areas, but they are noticed not to wound any tree of the tree lines of the forests in any form, though they affright of the sudden visit of the forest officials. Women have such several empowering tales to become tree people by making a balance between environment and occupation for the better nature (Coleman and Mwangi 2013). On the contrary, male-dominated working groups for managing forests sometimes do things against the harmony of the forests and forestry. Men categorically the tribal people are mostly habituated with smoking and usually they throw the end part of the smoking materials towards the jungle area that might cause a man-made forest fire. Sometimes they are engaged in preying the jungle wild hen and made a temporary arrangement for the preparation of their lunch inside the jungle area where they used the green fresh branches of the forest trees as fuel wood that caused a huge emission of nitric oxides, nitrous oxides, and carbon monoxides like harmful greenhouse gases. Even the male group leader of the women working group drinks huge quantities of haria and Mahua (indigenously made tribal drinks) and lies down on the forest floor unconsciously without performing his duties for which he is being appointed. If the male group leader is replaced by a female one, she might assist the other women labourer without any hesitation and consequently the progress of work will be carried on smoothly. Further, the rural tribal women, living in the vicinity of the forest stands, are generally detached from any political involvement, so at least they will never bias the officials of the forest department or the concerned authority by giving wrong information or fake news related to the health of the forests and forestry. Such women group leaders should be selected by the

Forest Cover of Malda District

45

local Gram Panchayat (the village body) as the Gram Panchayat is supposed to be a democratic platform for screening the women group leaders and their team members for caring and nourishing the tree lines of the forest patches (Das 2020f). Carbon is sequestered from the atmospheric carbon dioxide by the tree lines of the forest stands and thus, enrich the carbon pool beneath the forest floors. Sound forest health accelerates such absorption of carbon dioxide and keeps up the balance of the natural environment and helps to combat the effects of climate change because of excessive emission of carbon dioxide from the industrial belt. Rural tribal women are not benefited directly by the products of such industries, but they carry on their work for the removal of their bye-products. Further, these wonderful tribal women can campaign and discuss the plans and priorities for the action to be taken for mitigation of climate change through integrity monitoring of forest health if they are invited for a discussion organized by the forest department or climate managers. Overall, the women leaders would work in a fantastic network leading and helping to develop the young saplings into a forest of luxuriant green canopy with their motherly affectionate love and caress.

Forest Cover Change Matrix There are no separate statistics available for forest cover of Malda district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Malda district is amalgamated with the data of the other districts like Burdwan, Birbhum, Kolkata, Hugli, Howrah, Nadia, Medinipur, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-wise forest statistics for the year 2009. The forest cover of Malda district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 491.69 km2 which is 13.17% of the district’s geographical area. In terms of forest canopy density classes, the very dense forests area of the district is nil, 209.04 km2 area under moderately dense forests and 282.65 km2 area under open forests. There is a net increase of 0.69 km2 available from the reported area in India State of Forest Report 2019. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1991, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Malda district (mainly mango orchard) which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Malda district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.10.

46

2  District-Wise Forest Matrix, Forest Models and Strategies

Malda 16

Forest Cover (%)

14

13.5

13.5

13.15

13.17

2013

2015

2017

2019

12 10 8 6 4 2

2.89

2.89

3.13

2001

2003

2005

4.39

4.39

2007

2011

0.21

0 -2

1999

Year

Fig. 2.10  Forest cover change matrix of Malda district, West Bengal

Forest Cover of Murshidabad District Forest cover of Murshidabad district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Arjun, Siris, Simul, Kadam, Khair, Chhatim etc. though most of the forests are regenerated under social forestry scheme. They are further classified into Khair-Sissoo Forest and Northern Tropical Dry Deciduous Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes  – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants, but no such Joint Forest Management Committees have been constituted in the Murshidabad district. There are no Eco-­ Development Committees (EDC) or Self Help Groups (SHGs) in the Murshidabad district under Nadia-Murshidabad Forest Division. The green infrastructure sustaining life on the earth feat is the forest. Trees make the forest, and they are useful when they come to multi-solving, that is tackling multiple problems simultaneously. Not just carbon sinks and reserves for biodiversity are contributed by tree lines but also great allies are offered when tackling pollution, food supply, economic growth, to name just a few. Keeping this in mind the inhabitants of the Murshidabad district have drawn the framework of their own for years after years, through nature-based solutions, launched to show which trees are best for tackling economic crisis through mitigation of poverty of the people of the grass root level. Certainly, the people of Murshidabad chose the cultivation of mango and lichi, irrespective of much interest in the plantation programme under agroforestry schemes managed and planned by the government’s agricultural department, though the natural forest has been degraded over hundred years and land use pattern, thereon, has gradually been changing. At present, the area of green canopy of the district is moderately nice because of the agroforestry of both mango

Forest Cover of Murshidabad District

47

and lichi that enhances the economic growth of the people of the district of Murshidabad. Forest degradation leads to deforestation where forest degradation is a process in which the biological diversity of the forest is diminished permanently, and degradation makes the forest less valuable and results in deforestation. Ultimately forest degradation and deforestation accelerate the issue of land degradation of the Murshidabad district. A few forests, at present, is still there, namely Jitpur forest at Domkal, Deer forest at Farakka, Islampur forest, Aahiran forest, but with fewer trees, plants, or animals, though the luxuriant green cover is seen in the border area of the district like Lalgola etc. through the implementation of scheme of agroforestry strategy by the orchard plantation of mango, lichi, banana etc. in the district of Murshidabad. Other than the forest patches and orchard plantation, Murshidabad, a district of historical interest, is known for its large, old, and aged solitary trees that are scattered far and wide in the entire district.

Solitary Tree Solitary trees are the special characteristic features of the district of Murshidabad that are scattered here and there either inside or outside the forest areas or in the urban belt of Berhampur and Murshidabad. Look of a solitary tree bordering the forest stands on the roadside simply overwhelmed by any visitor followed by another one within a distance. The solitary trees are isolated and unaccompanied from the tree line of the forest patches of natural or social forestry origin. Tree lines in the forests are almost the same height and diameter of stems occurred in clusters maintaining a gap from the solitary tree. The shape of strong-built stems and branches of solitary trees indicates that they are well supplied with nutritious food like most essential nitrogen, phosphorus, and potassium for plants. Tree lines in the forest stands are slim in their appearance hardly with 12–18 cm in diameter at chest height and maximum up to 28 cm in contrast to the solitary trees’ DBH ranges of 142 & 156 cm respectively, though the trees in the tree lines have the same class, group, family, and even the same name i.e., Siris trees (Albizia lebbeck) or Simul trees (Bombax ceiba). Solitary Siris or Simul trees look attractive with luxuriant foliage, healthy flowers, fruits and seeds and they often enjoy an enchanting environment with the humming of bees and chirping of birds (Das 2020g). Even the birds prefer to make nests and bees frame their honeycomb in the twigs and branches of a solitary tree. Wildlife like monkeys are seen hooping and a large python taking a snap at noon on the branches of such trees. A solitary tree, a true representative of a forest-class in the forest stands, looks like a stout one when the trees of the tree lines maintain a social distance from the solitary one due to its healthy growth with respect to the growing stock surrounding their dominant representative. A solitary tree, a king tree in a forest patch with its huge strongly built branches covers a large area with its crown on its top (Lõhmus et al. 2006). Solitary tree, regarded as a king tree, often takes more but returns less

48

2  District-Wise Forest Matrix, Forest Models and Strategies

to its surrounding environment. On the contrary, tree lines in the forest patches receive huge precipitation for rainfall and enhance infiltration rate in the forest floor through leaching, and thus enrich and recharge the ground water table in the forest areas. A solitary tree enjoys major soil nutrients single-handed without sharing to its neighbour, though it hails from the same common descent of the tree community. Huge content of nutrients availability in the forest floor from the microbial biomass from decomposition process and absorption of those nutrients singly make the solitary tree gradually a healthy one. Trees in clusters in the forests generally share food, air, and water of the area jointly and almost equally that reflect their similarity in their health having almost same growth, same crown area and the same diameter class, and among them, a solitary tree is simply an exceptional existence with huge growth. Such huge growth sometimes becomes a cause of its destruction when gusty winds due to cyclonic storms start blowing over it. Then the branches and foliage of the solitary tree are severely damaged, even sometimes it is simply uprooted due to heavy winds of the storms, but the trees in clusters are less damaged in comparison to a king size tree due to lacking gap in between the tree cluster. Overall, tree lines stand or fall together in the natural calamities, tender a message that two heads are better than one.

Forest Cover Change Matrix For statistical interpretation, no data is available separately for Murshidabad district up to the forest survey report of 1997. There are no separate statistics available for forest cover of Murshidabad district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Murshidabad district is amalgamated with the data of the other districts like Birbhum, Kolkata, Hugli, Midnapur, Nadia, Malda, Bardhaman, 24 Parganas, Paschim Dinajpur and Howrah as shown in the report of FSI for the period from 1991 to 1997. Separate data of the district of the state of West Bengal have been inventoried in the forest report of 1999 for the first time. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-wise forest statistics for the year 2009. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of the Murshidabad districts which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987  in every two years consecutively. The forest of Murshidabad district is covered with artificial plantations. Among fauna, jungle cat, jungle fowl, python, monkeys, wild boar and varieties of birds and reptiles are increasingly being reported.

Forest Cover of Nadia District

49

Murshidabad 8

Forest Cover (%)

7

6.54

6.56

6.5

6.48

2013

2015

2017

2019

6 5 4 3 2 1

1.22

1.62

1.6

2003

2005

2.01

2.01

2007

2011

0.15

0 -1

1999

2001

Year

Fig. 2.11  Forest cover change matrix of Murshidabad district, West Bengal

Murshidabad is a district which is very much poor in forest canopy and forestry in terms of the percentage of its geographical area. The forest cover in Murshidabad district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 344.89 km2 which is 6.48% of the district’s geographical area. In terms of forest canopy density classes, the very dense forests area of the district is nil, 53.06 km2 area under moderately dense forests and 291.83 km2 area under open forests. There is a net decline of 1.11 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.11.

Forest Cover of Nadia District Forest cover of Nadia district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Teak, Arjun, Siris, Simul, Kadam, Khair, Chhatim etc. though most of the forests are regenerated under social forestry scheme. They are further classified into Khair-Sissoo Forest and Northern Tropical Dry Deciduous Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Nadia district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants, and 11 Joint Forest Management Committees have been constituted in the Nadia district under Nadia-Murshidabad Forest Division. There is 1 Eco-Development Committee (EDC) at Bethuadahari Wildlife Sanctuary, though there is no Self Help Group (SHGs) in the Nadia district under Nadia-Murshidabad Forest Division.

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2  District-Wise Forest Matrix, Forest Models and Strategies

A person living in a crowded metropolis breathes fresh air taking a solitary walk along the jungle passage covered with the green canopy as ‘solitude is needful to the imagination’. A place for such solitude is Bethuadahari sanctuary in the district of Nadia under Nadia-Murshidabad Forest Division. Bethuadahari sanctuary is a typical forest of social forestry origin where plenty of timber trees of different species like Arjun akashmani Jarul Kadam Babul Segun Mehagini Amalaki Haritaki Chhatim Neem Chatka Lambu Punyo Debdaru Tentul Jam Amaltas Champa Kanchan are very much common. Likewise, Bethuadahari sanctuary, Hijuli forest is established at Ranaghat range of Nadia district having dense vegetation of timber trees, plenty of flora and fauna and chirping sounds of birds. A visitor meandering inside the forest reported that there are many Bengal foxes in the jungle, and they often attack the visitors even in the daytime. Other notable forests in Nadia district are Anandanagar forest (Kalyani), Simanagar forest (Chapra), Banguria (Bagula), Mahatpur Galay Dari (Karimpur Road), Kulgachhi, Khisma (Birnagar) and Bahadurpur Reserved forest under Nadia – Murshidabad forest division and they are all social forestry origin.

Social Forestry Dire poverty made the people living around the forests take the desperate course of consumption of the forest produce that leads to the process of deforestation consequently. Further, poverty and climate change are positively correlated in the domain of the forests and forestry. Poor people collect forest produce not only for their household uses of cooking and heating, but simultaneously they used to sell the forest produce like fuel wood and poles for house constructions in the nearby markets to earn their bread, and thus, the forest stands have gradually been reduced by area and density. The loss of the green canopy of the forests causes a low rate of absorption of the atmospheric carbon dioxide and the overall situation has a major impact on the surrounding environment because of climate change and global warming. Only the plantation of tree lines can be the saviour of such environmental degradation under the social forestry schemes where every household would be the owner of the implanted trees from which they could collect fuel wood, even if they sell excess materials of these items if necessary. Government encouraged this social forestry scheme among the poor since 1976. Social forestry thus helps reduce poverty as well as the loss of forests, and with the implementation of social forestry even an entire forest has emerged in the form of sanctuary in the different parts of West Bengal. Bethuadahari sanctuary of the Nadia district of West Bengal is such an example of manmade forest created under the social forestry scheme (Fisher et al. 2018). Bethuadahari sanctuary is a typical forest of social forestry origin where plenty of timber trees of different species like Arjun akashmani Jarul Kadam Babul Segun Mehagini Amalaki Haritaki Chhatim Neem Chatka Lambu Punyo Debdaru Tentul Jam Amaltas Champa Kanchan are very much common (Das 2020h). Bethuadahari,

Forest Cover of Nadia District

51

a wildlife sanctuary, covering an area of 67 ha, and established in 1980 under social forestry scheme to preserve an eco-zone, has a large population of about 297 spotted deer, jackal, Bengal fox, porcupines, common langur, monitor lizards, gharial, and rock pythons. A nature interpretation center is also founded in the name of veteran dramatist of the district, Dwijendralal Roy where different ecological aspects about flora and fauna of the wildlife sanctuary are displayed. It is a good effort by the forest department to enrich in situ knowledge-based education for the students and young budding scientists of the state of West Bengal. Likewise, Bethuadahari sanctuary is established at Bethuadahari, Anandanagar forest at Kalyani, Bahadurpur forest at Bahadurpur, Seemanagar forest at Karimpur road and Hijuli forest at Ranaghat range of Nadia district covered with dense vegetation of timber trees along with plenty of flora and fauna and chirping sounds of birds. Natural forest and forestry of the Nadia district, at present, is without form and void, though a few forests are grown up with the government’s plantation programme of social forestry scheme under the supervision of the forest department. Bethuadahari, the man-made forest sanctuary under the social forestry scheme, stands in the Segun Bagicha Road i.e., the road is covered with teak plantation on the roadside. Roadside plantation is also a type of social forestry which is specifically termed as extension forestry. Teak plantations, maintained by the comparatively rich farmers in their agricultural land, are quite common and visibly scattered in Nadia district, and are specifically termed as farm forestry as the forest is grown up in the farming land. Gaining more profits from the farm forestry, a thriving class of farmers show interests in farm forestry, a kind of typical social forestry, rather than cultivating paddy or other vegetables and greens in the same land suitable for the crop production. A few farmers have implanted Eucalyptus and Acacia (Sonajhuri/Akashmoni) in their farming lands and gained more profits than that of the traditional farming of crops. Along the roadside, river and canal side, and the railway side plantations are performed by the direct initiatives by the government through the assistance of the Gram Panchayat (Village body), and this type of social forestry is classified as extension forests. The government encouraged the farmers for this type of plantation under social forestry scheme since 1976 through proper donation of seedlings/saplings of the trees free of cost on the days of celebrating forest week with the objective of reducing poverty and mitigation of global warming. Social forestry was introduced in the country by the National Commission of Agriculture in 1976 with the direct initiative by the then Government of India with an aim of democratic approach of afforestation from the households to wastelands for the improvement of rural, social, and environmental conditions. Plantation of trees in the unused and denuded or fallow lands not only reduces the pressure on the forests but reduces poverty among the rural people in Nadia district. Like forest patches, extension forestry or farm forestry/agroforestry with strip vegetation are grown up under the social forestry scheme of the Nadia district of West Bengal where trees are implanted in the degraded and denuded lands to improve standard of living and quality of life in urban and rural sectors. The poor people, particularly the farmers are now able to grow number of trees of their own under social forestry scheme that helps to raise their day by day financial support through the availability of the fuel wood for cooking and heating, selling of the

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2  District-Wise Forest Matrix, Forest Models and Strategies

excess of that fuel wood and marketable stems as poles for the house constructions. Farmers used to implant trees in the border areas surrounding their farming land to provide shade to the crops or to prevent the crops from the strong wind actions of storms or other natural calamities. This is too typical social forestry in terms of agroforestry. Tree lines grown up due to farm forestry, community forestry, extension forestry, agroforestry, urban forestry, or several other forms of the social forestry, locks carbon from the atmospheric carbon dioxide and that exchangeable carbon sink stores carbon in the carbon pool as green carbon down to the earth up to 1 m depth from the surface, and thus reduce the greenhouse gases leaving a suitable resilient environment for the human habitation in both rural and urban areas.

Forest Cover Change Matrix Natural forest and forestry of the Nadia district, at present, is without form and void, though a few forests are grown up with the plantation programme of social forestry scheme under the supervision of the forest department. In 1991, only 10 km2 area of forest cover of the district was increased to 480 km2 expansion in 2019 as recorded in the India State of Forest Report 2019. There is a net increase of 470 km2 forest area in the district within a time span of only two decades. A class-wise change in the district is given in the forest cover change matrix in Fig. 2.12. The increase in the forest cover in the district is due to coppice growth and afforestation inside the forests and growth of commercial plantation under individual care. Total increase in the forest cover not only pertains to the period of 2011–2013, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Nadia district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Nadia district in the report of Forest Survey of India from 1987 to 1989. Forest area of Nadia district is amalgamated with the other districts like Burdwan, Birbhum, Kolkata, Hugli, Howrah, Malda, Medinipur, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997 and no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of forest statistics for the year 2009. Nadia district, at present, is covered with 480 km2 area i.e., 12.22% forest cover with respect to its geographical areas of 3927 km2. The people of the district are very much keen for plantation of trees roadside, canal side and even in their own land for commercial exploitation for enhancement of the green cover of the district, and in this way the green canopy of the district is to be increased outside the forest in near future. In terms of forest canopy density classes, the Nadia district has only 1 km2 area under very dense forests, 160.16 km2 area under moderately dense forests and 318.84 km2 area under open forests. There is no change of the forest cover in Nadia district as per the Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.12.

53

Forest Cover of North 24 Parganas District

Nadia 16

Forest Cover (%)

14

12.2

12.2

12.22

12.22

2013

2015

2017

2019

12 10 8 6 4 2

0.24

2.67

2.42

2003

2005

1.12

3.28

3.28

2007

2011

0 -2

1999

2001

Year

Fig. 2.12  Forest cover change matrix of Nadia district, West Bengal

Forest Cover of North 24 Parganas District Forest cover of North 24 Parganas district is classified into Littoral and Swamp Mangroves Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Gewa, Bain, Garjan, Hental, Golpata, Khalsi, Pasur, Keora etc. though most of the forests are regenerated under social forestry scheme. They are further classified into Brackish Water Mixed Forest, Salt Water Mixed Forest, Mangrove Forest, and Mangrove Scrub type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants, but no such Joint Forest Management Committees have been constituted in the North 24 Parganas forest division. There is 1 Eco-Development Committee (EDC) at Bibhuti Bhusan Wildlife Sanctuary (Parmadan Forest), though there is no Self Help Group (SHG) in the North 24 Parganas Forest Division. North 24 Parganas district is covered with the world famous Sunderbans at the south-east part of the district.

Green Infrastructure Green infrastructure of the dense mangrove forests of the Sunderbans area of North 24 Parganas district occurred in the Hooghly-Matla estuarine environment in the confluence of the Bay of Bengal is quite different from that of the terrestrial forests. Penetrating such highly dense Sunderbans jungle covered with mangroves and its associated species is a most arduous undertaking task. The trees are intertwined,

54

2  District-Wise Forest Matrix, Forest Models and Strategies

supported, and upheld with each other forming a complex green infrastructure. Immense sized trees particularly Garjan (Rhizophora sp.) overwhelms the visitors with their spreading stems and stilt roots covering sometimes an acre area. This complex green infrastructure made up of stems and stilt roots of the Garjan trees are looking like a framework made outside of a built-up building under construction for shattering purposes. Further, abundantly occurred brushwood covering all over the forest areas with low height, bushy and interlocked with each other lead to almost impenetrable into the forest and thus they save mangroves through natural barriers. In such a green infrastructure, wood cutters and honey collectors are to be hacked away bit by bit in their attempts of penetration inside the mangrove forests of the Sunderbans. In this favourable situation the Royal Bengal Tiger hid themselves in the bushes of Hental (Phoenix paludosa) for prey of deer, monkey, or a wild boar.

Blue Carbon The world known mangrove forests of Sunderbans have a huge storage of blue carbon which is a pristine forest in nature (Macreadie et al. 2019). Once covered with about 25,500 km2 areas (in India and Bangladesh), the Indian part of the Sunderbans has now only 2108.11 km2 areas of mangrove covered forests as recorded in the India State of Forest Report 2019. Sunderbans not only enriched with blue carbon in its previous areas of about 25,500 km2, but carbon is locked in the subsoils in the form of peat of the mangrove trunks and roots still visible in dugout trenches like excavation of ponds, canals, or in the dugout trenches for the house constructions. Tribes who cleared the jungle are now sided to an edge; they are simply deprived from the promise given to them by the then landlords particularly the jamindar (landlords) class prior to the independence of India. Tribes are also forgotten in the forgotten forests who the original inhabitants of this pristine forest were. Further, in the forgotten forests, different facial characteristics of the different tree species are noticed; perhaps they are seeking not to be forgotten any more or uttering in silence with a sole appeal forget me not. These forest areas covered with the mangroves have gradually been cleared and reclaimed for the agricultural purposes. System of reclamation changes the dense mangrove forest canopy of the Sunderbans, particularly wilderness inclusive briefing of the natural habitat of the Royal Bengal tigers and crocodiles and as a result, human habitation and agricultural land is introduced within the reclaimed land during the last centuries. The Sunderbans of this district is also known for its huge storage of blue carbon in the mangrove swamps and marshy regions.

Forest Cover of North 24 Parganas District

55

Reclamation History of Forest Lands The Collector of Jessore, Tilman Henckell was the pioneer of the reclamation system in the Sunderbans in the present day North 24 Parganas district during his tenure of 1781–1790 (Hunter 1875). He played an incredibly positive role in taking initiative of reclamation clearing the dense jungle of mangroves and tried his utmost effort for the welfare of the molungees who were engaged in the salt preparation. His scheme failed as the members of the then Board of Revenue were not convinced with his proposal. But ultimately progress of reclamation has been going on steadily for about 40 years after the tenure of Collector Tilman Henckel. Further, he took initiatives for the welfare of the molungees of tribal origin namely Chandabandas, from which the name of the Sunderbans was supposed to be introduced. The British first noticed the vast low-lying forest area of the Sunderbans and they immediately resolved to reclaim this forestland to collect the revenue by the introduction of agriculture on that mangrove habitat zone. Mr. Claude Russel (1770), the Collector General of 24 Parganas district first took initiatives followed by Tilman Henckell (1781), the Judge and Magistrate of Jessore district. A scheme of arrangement was drawn up for cultivation in the reclaimed area in the Sunderbans of Jessore district (Das 2018). The Collector was directed to submit a separate report on the present state of forest areas by the Board of Revenue and to furnish information how far the original objects of the plan had been attained, together with an account of receipts and disbursements from the commencement of the undertaking to the present period. At the coastal areas, when salt manufacturing work was in progress adjacent to or inside the forest beauty of the Sunderbans in the present day North 24 Parganas district, the wild beast not only infested the area, but attacked molungees resulting in loss of so many lives. To avoid such a worst situation the Company rulers declared prize money for tiger-killing at the rate of Rs. 10 per tiger. Locals of the Sunderbans of the Jessore district only (presently in Bangladesh) killed 33 tigers in a calendar year of 1788 and the disbursement of Rs. 330 is recorded in the Board of Revenue Index (20 July 1789). Hard working labourers from Santhal, Orano, Munda categories of tribal community were engaged, and they were brought mainly from Hazaribagh, Singhbhum, Manbhum and Ranchi districts for the purpose of commencement of human habitation and settlement in the Sunderbans (O’Malley 1914). Though cultivators and latdars from the neighbouring district of Midnapore settled first at the reclaimed zone of the Sagar Island, Namkhana and Patharpratima, before reclamation of land was started at Hingalganj area in the present-day portion of the North 24 Parganas District. The duration of human habitation in the Sunderbans region is never more than 115 years. The first tube well for drinking water facilities was sunk in 1950 in the newly settled and reclaimed zone considered for human habitation. Except, mangrove swamps and marshes, Parmadan sanctuary was established in this district very recently under social forestry schemes at the bank of river Ichamati in the name of the great novelist Bibhuti Bhusan Bandyopadhyay.

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2  District-Wise Forest Matrix, Forest Models and Strategies

Forest Cover Change Matrix For statistical interpretation, no data is available separately for North 24 Parganas district up to the forest survey report of 1999, though North 24 Parganas district was formed on 1 March 1986 after partition of 24 Parganas district into North and South 24 Parganas. The forest of North 24 Parganas on an average of about 90% area is with mangroves and mangrove associated species and outside the forest, the greenery is covered with artificial plantations. The forest cover of North 24 Parganas district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 722.98 km2 which is 17.66% of the district’s geographical area. In terms of forest canopy density classes, the district has 13.02  km2 area under very dense forests, 184.98  km2 area under moderately dense forests and 524.98  km2 area under open forests. There is a net decline of 0.02 km2 of the forest cover in North 24 Parganas district as per the Forest Report 2019. With respect to 1999, total increase in the forest cover not only pertains to the year of 2013, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of North 24 Parganas district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of North 24 Parganas district in the report of Forest Survey of India from 1987 to 1989. Forest area of North 24 Parganas district is amalgamated with the data of the other districts like Birbhum, Kolkata, Hugli, Midnapur, Nadia, Malda, Murshidabad, 24 Parganas, Bardhaman, Paschim Dinajpur and Howrah as shown in the report of FSI for the period from 1991 to 1997. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.13.

North 24 Parganas

Forest Cover (%)

20

17.71

17.68

17.66

17.66

2013

2015

2017

2019

15 10 5

3.37

3.1

2.98

2001

2003

2005

0.73

2.17

2.17

2007

2011

0 1999 -5

Year

Fig. 2.13  Forest cover change matrix of North 24 Parganas, West Bengal

Forest Cover of Paschim Medinipur District

57

Forest Cover of Paschim Medinipur District Forest cover of Paschim Medinipur district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Simul, Peasal, Kend, Mahul, Kusum, Bahera, Dhaw, Rahara etc. They are further classified into Dry Peninsular Sal Forest and Northern Dry Mixed Deciduous Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Paschim Medinipur district, Joint Forest Management have been adopted comprising jointly by the forest department and the local inhabitants and about 364 such JFMC at Midnapore, 474 JFMC at Jhargram, 216 at Rupnarayan, and 254 Joint Forest Management Committees (JFMC) at Kharagpur Division have been constituted in the Paschim Medinipur forest division. Paschim Medinipur is the pioneer district for the implementation of Joint Forest Management in the country. There are 197 SHG at Midnapore, 344 SHG at Jhargram, 174 SHG at Kharagpur, 349 Self Help Groups (SHG) at Rupnarayan division, though there is no existence of Eco-Development Committee in the forest division of Paschim Medinipur (Annual Administrative Report 2017–2018). At Arabari, in the Paschim Medinipur district, local people or their residential places are not visible in and around this area other than the jungle. Dense jungle with a series of trees stands by the roadside. Except Sal trees, Kendu Mahua Kusum Bahera Muchukunda trees make the forest a dense greenery. Arabari, Jhitka, Lalgarh, Ramgarh, Karnagarh Garbeta and other natural forests, scattered in this area with a few kilometers distance from each other, are surrounded with Akashmani trees implanted outside the forest area. Arabari is known all over India as the pioneering forest range in implementing the Joint Forest Management (JFM) scheme (Guha et  al. 2017; Banerjee et  al. 2010). Thus, Arabari forest, a community forest by nature, is a famous one in the history of forest management.

Community Forests Arabari Forest of Paschim Medinipur district in the Jungle Mahal is likely a pictorial forest landscape. The day-long stay and enjoying the forest beauty at the Arabari forest stand must draw a picturesque poetry of the tree lines to any visitor. The Arabari forest is at the far end of Chandrakona Road, away from the urban crowd and is a solitary lonely place. Local tribal people or their huts are not visible in and around this area other than the jungle. Dense jungle with a series of tree lines stands by the roadside and is extended to miles after miles. Remarkably, the Arabari forest is famous in the history of forest management as this forest is known

58

2  District-Wise Forest Matrix, Forest Models and Strategies

all over the country as the pioneering forest range in implementing Joint Forest Management (JFM) scheme from which the concept of the community forest is introduced for managing and overall integrity monitoring of the forests (Hajjar and Oldekop 2018). Much more the resilient forest grown up, impact of climate change would be less, and that scenario is reflected not only in the Arabari forest patches, but almost all the forest stands in the Jungle Mahal is increased by area in the Paschim Medinipur, Jhargram, Bankura and Purulia districts which is recently recorded in the India State of Forest Report 2019. The changing jungle scenario certainly has a direct impact on the mitigation of climate change as noticed through the observations of more carbon sequestration from the atmosphere and stored in the forest floor that leads to the more nutrient supply to the forest by the process of microbial biomass decomposition. Chemical analysis of the soils of the forest floors in and around the Jungle Mahal area shows five times more concentration of nitrogen, phosphorus, and potassium, vital constituents for the forest vegetation, only in a period of three decades. And this enhancement with all aspects is the direct contribution of the community forest monitoring by the village people living adjacent to the forest patches. People-managed forest is the community forest, and as it is managed by the village people, the community forest is termed as the village forest. Community forests show how the common rural people make a drastic change in the nature as well as their own society by their inherited experiences for caress of the forests through the implementation of joint forest management programme. Taking these experiences from the direct participation of the community forestry jointly led by the people and the forest department, it is the right time from the part of the government to empower the people-led community forestry group without further delay and thus, strengthen the activities of the joint forest managers for built-­up resilient forest health smoothly that lead to the mitigation of the effects of climate change. The forest department of the government knows very well that the people living in the vicinity of the forests not only save the forests as their community forests for generations, but they also worship the trees of the forests, particularly by the tribal community, and forest to them is regarded as the sacred groves.

Forests and the Tribal Community Tribal people in majority living in and around the forest stands of Jungle Mahal earn their bread by plucking leaves from the Sal trees (Shorea robusta). They pluck fresh green Sal-leaf economically because of their awareness for the health of the trees as well as for the forest stands. During plucking generally by a group of girls they maintain teamwork and occasionally share leaves and move to the market for sale of those Sal leaves to the tea and snacks stalls. But those Sal leaves were not very abundant only a few decades back. The forest stands of Jungle Mahal, which stood in the south-west part of West Bengal, was in a depleting status, but now its health

Forest Cover of Paschim Medinipur District

59

is partially recovered. Forest area of Bankura, Purulia, Paschim Medinipur and Jhargram districts comprising Jungle Mahal is reportedly increased in comparison to the earlier record according to the India State of Forest Report of 2019. Trees in the forest stands look luxuriant probably due to more carbon dioxide in the atmosphere because of climate change. In this changing scenario of the forest stands in the Jungle Mahal, members of the tribal community are still economic in plucking of the Sal leaves that help to earn their bread and this scenario is reflected in the Mayur Jharna Elephant Reserve of Jhargram district where a group of girls of the tribal community used to pluck fresh green Sal leaves and supply them to the local marketplaces of Kakrajhor, Shilda or Belpahari. Generally, tea and snack stalls purchase fresh green Sal leaves for distribution of food items to the local customers. Food sellers never store green leaves because those leaves start rotting from the day after plucking. Naturally, girls of the tribal community living in and around the Sal forests of Mayur Jharna Elephant Reserve collect a limited number of Sal leaves daily and their collection varies seasonally based on demand and supply accordingly. The leaf-plucked girls usually collect around 50 leaves on an average, and if any member collects more leaves against her necessity, she shares excess leaves to the other members of their group. Tribal girls inherited such shared mentality like social behaviour from their ancestors who even did not have a cycle to reach the local markets to hand over the plucked Sal leaves to the owner of the tea or snacks stalls. The forest dwelling tribal girls are mostly illiterate or drop out of the school due to irregular attendance as they must carry on plucking of Sal leaves on the first half in the morning shift and to complete cooking and other domestic household works on the remaining part of the day. Their mothers are migratory agricultural labourers working for sowing and harvesting of crops during the rainy and winter season by rotation in almost all the districts in the state of West Bengal and they join their daughters for collection of fodders and fuel wood during the gap of their normal duties as migratory labourers. Therefore, the tribal women account for a substantial proportion of the agricultural labour force, the women farmers, who feed the districts of West Bengal. And occasionally, the tribal women are engaged by the forest department for plantation programmes during the autumn in between the sowing and harvesting season. And thus, the rural tribal women play an essential role in using and managing natural resources in forest and tree-based landscapes across the state of West Bengal. Though they are deprived of even staying at home round the clock, they or their daughters guard the forest areas for generations from which the forest department and other sectors like the tourism department get all the benefits of the forest stands. The tribal community living in the vicinity of the forests faces such societal inequality for hundreds of years and even in this modern time when the term of civilization is redefined by the United Nations (Bhargava 2002; Bhullar 2008). Forest-living tribal communities are exempted from the collection of fruits, seeds, flowers and leaves of the adjacent forest areas as per government order issued by the land revenue department of the Government of West Bengal in July 1980. For such collection of forest produce, free permits per two persons per tribal household

60

2  District-Wise Forest Matrix, Forest Models and Strategies

are issued by the forest department and these permits are duly renewed every year by the concerned department. Tribes are allowed one pole annually to be used as plough and three poles per 3 years for the purposes of house-constructions per tribal household, though all the tribal people are permitted to assign a tree as daharthan (place of their deity) for worship and offering prayers inside the forests. But allotment of patta (official land records by legal status) to the tribal community up to an acre area of land in or adjacent to the forest stands causes worse to the forest health as the members of the tribal community always try to increase land area up to the mark scheduled for them through encroachment by deforestation (Das 2020i). Government should take an alternate measure to assign and allot lands for the tribal people outside the periphery of the forest areas and both the tribal welfare department and the forest sector would be benefitted from such government’s decision only if it is implemented. Tribal practices of hunting ceremony as a part of their social culture are harmful to the forest faunal community that causes ruthless destruction of wildlife. The government should offer them an animal they admire most, reared in the government animal farm per five households on the day of their hunting ceremony as they fond of fresh animal meat and that step taken from the government’s side should gradually stop the tribal practices of killing wildlife in the forest on the day of celebration of their hunting festival. But even on the day of their celebration of the hunting ceremony like social culture, the tribal people in majority cannot manage food for their family members. To mitigate such a crisis of food, the basic needs for human beings, community kitchens should be introduced by the government to combat their starvation day after day. If the community kitchen for the distribution of midday meal is justified for the school-goers of urban municipal areas or even in the Kolkata metropolis, why the tribal people in the rural rustic forest lands are still deprived of that government’s policy particularly when the community kitchen and midday meal programme is financially supported by the direct taxpayers, though belated, it should be introduced with immediate effects. Sale of Sal leaves, mahua (indigenous wine prepared by the tribes from mahua flowers) and fuel wood are basic modes of day by day earnings of the tribal community. Other than those sources, the tribes particularly the tribal women perform tribal dance in a group before the tourists in the tourist spots of the forest areas duly organized and managed by the hotel or cottage owners. Diversity of such tribal dances is seen in the North Bengal forest areas as categorically different tribal communities live there and they used to perform dances of their own traditional culture. But in Jungle Mahal forest areas, only the Santali group perform tribal dance as the other tribal groups have no such practices like dance performances in their traditional culture, though earning from such dance performances is limited and restricted in the tourism season only and they usually cut the trees, sell as fuel wood, and earn their bread.

61

Forest Cover of Paschim Medinipur District

Forest Cover Change Matrix In this state of West Bengal, Paschim Medinipur is a district which is very rich in forest canopy and dense forestry. The forest cover in Paschim Medinipur district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 2161.54 km2 which is 23.07% of the district’s geographical area. In terms of forest canopy density classes, the district has 256.21 km2 area under very dense forests, 591.64 km2 area under moderately dense forests and 1313.69 km2 area under open forests. There is a net increase of 10.54 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.14. For statistical interpretation, no data is available separately for Paschim Medinipur district up to the forest survey report of 2015, though Paschim Medinipur district was formed on 1 January 2002 after partition of Medinipur district into Paschim Medinipur and Purba Medinipur. Further, on 4 April 2017, the Jhargram subdivision was converted into a district. Forest of Paschim Medinipur on an average 60% area is covered with Sal trees of coppice origin and the rest is covered with artificial plantations. Among fauna, elephant, jungle cat, jungle fowl, baboons, python, wild boar, and varieties of birds are increasingly being reported. Elephant-­ man conflict is common during paddy harvesting season due to the scattered nature of forests. Man could not understand the importance of forest when it is plentiful and the same one is dear to them when it is scarce. Due to scarcity of land for conversion of forest land into agricultural land, adequate area within the forest cover in Paschim Medinipur district is unavailable for further plantation, though plenty of barren land is covered with scrub. So, therefore, in the present scenario of the district, tree cover through social forestry is to be increased by taking thorough plantation programmes round the year on the roadside, canal side and riverside area. Villagers are to be

Paschim Medinipur Forest Cover (%)

30 25 20 15 10

9.85

10.43

1999

2001

18.27

18.15

18.43

2003

2005

2007

18.44

2011

21.19

21.26

2013

2015

22.96

23.07

2017

2019

5 0

Year Fig. 2.14  Forest cover change matrix of Paschim Medinipur district, West Bengal

62

2  District-Wise Forest Matrix, Forest Models and Strategies

encouraged for plantation through distribution of saplings from the forest sector by organizing gala festivals of afforestation annually. Only man could clearly perceive the good will for a better cause of recovery of greenery of their own surroundings for ecological balance of nature, if not, what man has made of man – lament over the consequences of deforestation.

 orest Cover of Purba Bardhaman and Paschim F Bardhaman Districts Forest cover of Purba and Paschim Bardhaman districts is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Sonajhuri, Peasal, Kend, Mahul, Kusum, Bahera, Dhaw, Rahara etc. They are further classified into Dry Peninsular Sal Forest and Khair-Sissoo Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Bardhaman district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 70 such Joint Forest Management Committees have been constituted in the Bardhaman forest division. There are no Eco-Development Committees (EDC) and 62 Self Help Groups (SHG) at Durgapur division in the forest division of Bardhaman. Forests are strewn across the districts of Purba and Paschim Bardhaman of West Bengal and the forests of the districts have wide variations. The natural vegetation of the forests in the district is Sal which is mostly in the protected and covering areas. Conservation of the forest is essential as the tropical forests alone are losing at least one higher plant species per day. It would require some effort to scale the forest area into its former size and shape, but the reality of the situation is quite different, sometimes the noble endeavour of the forest department has been desecrated by the inhabitants of the villages surrounding the forests. Garh Jangal and Aduria forests inclusive of all 38 forest patches are well-known to all. Other forest patches like Tilabani forest along the Jhajha Road, created, developed, and maintained by Eastern Coal Field Limited are created from artificial plantations under social forestry schemes. Ukhra lake, near the Tilabani forest houses varieties of migratory birds of about 45 species. The Bardhaman Forest Division is constituted with three forest ranges viz. Durgapur, Guskara and Panagarh. All these forest ranges are identified and studded with evidence of myth-history in the dense areas of the forest stands.

Forest Cover of Purba Bardhaman and Paschim Bardhaman Districts

63

Myths-History Myths-history of ancient happenings enriched fluvial geography, undulating topography, mixed geology, perhumid climate, Alfisol-red soil exposures and picturesque forest canopy narrate the dynamic environment of Garh Jangal and nearby Aduria forest under Bardhaman Forest Division of West Bengal that attracts researchers of different disciplines as well as common people for tourism. There are few places of historical interest inside the jungles; some notable myths about some structures and persons related to the forests are mentioned even in the Manasamangal. Dense forests of Bardhaman offer diverse set of habitats for plants, animals, and micro-­ organisms, but these increasingly threatened biologically rich systems along with Deul, Garh jungle etc., the places with dense forest, thick foliage, and greenery around, are the oldest places as believed by the historians (Peterson 1910). The deul of the famous Gopiraj Ishwar Ghosh, locally called Ichhai Ghosh, nestled in the bank of River Ajoy, is surrounded with the thick garh forest, and is frequented by herds of elephants from Bankura and Birbhum located nearby.

Divisional Forest Scenario The natural vegetation of the forests in the district is Sal forest which is mostly in the protected and covering areas. Conservation of the forest is essential as the Tropical Forests alone is losing at least one higher plant species per day (Das 2020j). It would require some effort to scale the forest area into its former size and shape, but the reality of the situation is quite different, sometimes the noble endeavour of the forest department has been desecrated by the inhabitants of the villages surrounding the forests. In the 38 forest patches in the Bardhaman Forest Division, four dominant timber tree species are enumerated during the survey. Occurrence of Sal (Shorea robusta) trees, enumerated to 610 in 1-hectare area, was recorded the highest among them, followed by 201 Kurchi (Holarrhena antidysenterica) trees, 56 Piyal (Buchanania lanzan) trees, and 52 Khoir (Acacia catechu) trees in a 1-hectare forest cover, though associated species exist along with these dominant tree species.

Forest Cover Change Matrix For statistical interpretation, no data is available separately for Bardhaman district up to the forest survey report of 1997. Needless to mention that the Purba and Paschim Bardhaman districts were formed on 7 April 2017 after partition of Bardhaman district into Purba and Paschim Bardhaman districts. There are no separate statistics available for forest cover of Bardhaman district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Bardhaman district is

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amalgamated with the data of the other districts like Birbhum, Kolkata, Hugli, Midnapur, Nadia, Malda, Murshidabad, 24 Parganas, Paschim Dinajpur and Howrah as shown in the report of FSI for the period from 1991 to 1997. Separate data of the district of the state of West Bengal have been inventoried in the forest report of 1999 for the first time. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-wise forest statistics for the year 2009. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of the Bardhaman districts which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987 in every two years consecutively. Forest of Bardhaman district is covered with both natural and artificial plantations. Among fauna, elephants, jungle cats, jungle fowl, python, monkeys, wild boars and varieties of birds and reptiles are increasingly being reported. Bardhaman is a district which is very poor in forest canopy and forestry in terms of the percentage of its geographical area. The forest cover in Bardhaman district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 339.31 km2 which is 4.83% of the district’s geographical area. In terms of forest canopy density classes, the district has 57.53 km2 area under very dense forests, 91.78 km2 area under moderately dense forests and 190 km2 area under open forests. There is a net increase of 4.31 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.15.

Bardhaman Forest Cover (%)

6 5

4.54

4 3

2.93

2.88

3.2

3.4

3.72

4.5

4.77

4.83

3.72

2 1 0 1999

2001

2003

2005

2007

2011

Year Fig. 2.15  Forest cover change matrix of Bardhaman district

2013

2015

2017

2019

Forest Cover of Purba Medinipur District

65

Forest Cover of Purba Medinipur District Forest cover of Purba Medinipur district is classified into Littoral and Swamp Mangroves Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Casuarina, Sissoo, Khair, Babul, Bain, Gewa etc. They are further classified into Salt Water Mixed Forest and Palm Swamp Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Purba Medinipur district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 19 Joint Forest Management Committees (JFMC) at Purba Medinipur forest division. There are no Self Help Groups (SHG) or Eco-Development Committee in the forest division of Purba Medinipur. Series of casuarina trees mixing with the mangrove swamps along the coastal Purba Medinipur district including Junput, Shankarpur, Mandarmani, Tajpur, Mohana, Old Digha, New Digha and Udaipur are the characteristics of Purba Medinipur district that connect the society with the forests with the same thread and similar thoughts. Likewise, the forest with the series of the casuarina trees all along the coastal stretch, the people too are involved with the social contract by their language, dress, food habits, and style of house building, marital relationship, and other rituals in the society of coastal forest areas of Purba Medinipur district. Further, the coastal belt of Purba Medinipur district is known for its rich history for centuries.

Historical Perspectives The coastline forest in and around the Hooghly estuary along the Bay of Bengal can turn into a labyrinth when a void labyrinthine forest path is visible like that of the then Kapalkundala, a mystic character brought to the light by the author Rishi Bankim Chandra Chattopadhyay. Rishi Bankim, the popular novelist, presents the nature of forests as necessary mysteries, in his many other works like Debi Chowdhurani, Durgesh Nandini, Anandamath, and Kapalkundala with a more similar tone. All his novels have the known historical characters once moved in this materialistic world, and certainly not like mysterious creatures dwelling deep into the unknown jungles. The great novelist uses forest as metaphors and the life of the characters in his novel can be quite mysterious like the deepest dense jungles. Kapalkundala is such a mysterious one, which we suppose that she is still living in the mystic coastal forest adjacent to the lighthouse of Dariapur near Rasulpur of Purba Medinipur, and such mystic feelings regarding a character of transitional state in between wilderness and societal approaches is certainly a universal spirit permeating all nature (O’Malley 1911). Bankim tries to connect the forest with the society as the relationship between human beings and forests has been important for the

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development of society. It is based on various productive, ecological, social, and cultural functions of forests. Industrialization and urbanization have contributed to an alienation from nature and weakened the connection of humans to forests, though almost all the trees, particularly the casuarina tree lines along the coastal stretch of Purba Medinipur district may keep distance socially to avoid touching each other.

Social Distancing of Trees In the forests, the casuarina canopy stretches for vast distances. Despite overlapping tree branches, canopy trees rarely interlock or even touch. They are separated from one another by a few feet. Researchers keep up working on why the branches of these trees do not touch which is still a mystery, but it might help the trees as protection from the attack of tree-eating caterpillars and other diseases including mangroves maintain mysterious gaps, also called as crown shyness that could serve trees share resources and help staying healthy (Keskinocak et al. 2020). It is noticed at the coastal areas of the district that the wind stirred the tops of the mangroves canopy driving the branches of trees towards each other but stopped touching the outermost leaves and branches with a continuity of a non-linear gap. Simply the tops of the trees so often refuse to touch, and this phenomenon is called crown shyness. The boundaries carved by bouts between branches may improve the plant’s access to resources, such as light, helping photosynthesis in the lower stories of the trees. Gaps in the treetops and reciprocal pruning of the outermost branches and leaves of the trees, thereon, serve protection of several diseases and spread of leaf-munching insects, parasitic vines, or infectious diseases. Such gaps in the treetops i.e., crown shyness is the arboreal version of social distancing. Plants keep away from physically touching each other, that increases productivity and that is the beauty of isolation and the tree is really safeguarding its own health by social distancing. Other than the social distancing nature of the coastal vegetation, the mangrove patches in the coastal stretch of the district play an important role for the accumulation of blue carbon in the mangrove sediments.

Blue Carbon Storage Carbon sink in the mangrove swamps and marshes, aquatic and marine environment is referred to as blue carbon, whereas carbon stored in the terrestrial natural forest patches is called green carbon (Macreadie et al. 2019). Green carbon and blue carbon are stored side by side along the coastal stretch of Digha  – Rasulpur of the Purba Medinipur district where casuarina tree stands on the land and mangroves in the tidal flood plain exist together, and this combination of casuarina and mangroves appearances is dominant particularly at Hijli in the coastal belt of Purba Medinipur district of West Bengal. All these criteria favour the forgotten forests to accumulate

Forest Cover of Purba Medinipur District

67

more carbon and thus form an enriched carbon storage in the subsoils of the forest floors. Thus, the soil carbon plays an important role in the global climate models that helps in the mitigation of climate change which is a world-wide phenomenon in recent times.

Forest Cover Change Matrix For statistical interpretation, no data is available separately for Purba Medinipur district up to the forest survey report of 2015, though Purba Medinipur district was formed on 1 January 2002 after partition of Medinipur district into Paschim Medinipur and Purba Medinipur. There are no separate statistics available for forest cover of Purba Medinipur district in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Purba Medinipur district is amalgamated with the data of the other districts like Burdwan, Birbhum, Kolkata, Hugli, Howrah, Nadia, Malda, Murshidabad, 24 Parganas and West Dinajpur as shown in the report of FSI for the period from 1991 to 1997. Separate data of these districts of the state of West Bengal have been inventoried in the forest report of 1999 for the first time. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-wise forest statistics for the year 2009. Total increase in the forest cover not only pertains to the year of 2003 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Purba Medinipur district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Needless to mention that the Forest Survey of India (FSI) took initiatives for forest survey of the entire country and commenced publishing forest reports since 1987 in every two years consecutively. Forest of Purba Medinipur on an average 60% area is with Casuarina trees and the rest is covered with artificial plantations. Among fauna, jungle cats, jungle fowl, python, wild boars and varieties of birds and reptiles are increasingly being reported. Purba Medinipur is a district which is rich in forest canopy and forestry. The forest cover in Purba Medinipur district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 820.05 km2 which is 17.40% of the district’s geographical area. In terms of forest canopy density classes, the district has 1.99 km2 area under very dense forests, 197.96 km2 area under moderately dense forests and 620.10 km2 area under open forests. There is a net increase of 0.05 km2 in the forest cover from the reported area in India State of Forest Report 2019. Based on ground truthing, the main reasons for the increase of forest cover in the district of Purba Medinipur are protection and plantation of Acacia, Eucalyptus, Akasmani and Casuarina in the coastal area. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.16.

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2  District-Wise Forest Matrix, Forest Models and Strategies

Purba Medinipur Forest Cover (%)

25

21.19

20

18.27

18.15

18.43

18.44

2003

2005

2007

2011

21.26 17.4

17.4

2017

2019

15 10

9.85

10.43

1999

2001

5 0 2013

2015

Year

Fig. 2.16  Forest cover change matrix of Purba Medinipur district, West Bengal

Forest Cover of Purulia District Forest cover of Purulia district is classified into Tropical Dry Deciduous Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sal, Simul, Peasal, Kend, Mahul, Kusum, Bahera, Dhaw, Rahara etc. They are further classified into Butea Forest and Dry Deciduous Scrub Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes  – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Purulia district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 225 Joint Forest Management Committees (JFMC) have been constituted in the Purulia forest division. There are 238 Self Help Groups (SHG), though there is no existence of an Eco-Development Committee in the forest division of Purulia. Forests in the Purulia District, the most important natural resources, exist in scattered patches and are unevenly distributed. Further, forests are the major land use in this district next to agriculture. The rural population depend on the forests for meeting their regular needs of fuel wood, fodder for their cattle and partially for earning their livelihood, though the villagers cannot meet all their requirements of fuel wood from the forest area alone and must depend on sources outside the forest. The rural population, particularly the tribes, economically extremely poor, depend upon the natural produce of the forests extended in and around Bandwan, Manbazar, Burra, Balarampur, Jhaldah, Joypur and Matha forest ranges of the district. Geographically the forest area of Purulia district covers 915.88 km2 of forest land. Physiographically the forest area under this district falls under a sub-region of North-Eastern part of Chhotanagpur plateau with undulating and rolling topography. The degradation processes are active in the area as the presence of isolated hills and dissected plateaus. Biogeographically, the district represents Deccan Peninsula Chhotanagpur zone having varieties of fauna like mammals, amphibians, reptiles,

Forest Cover of Purulia District

69

birds, and fishes including different species of invertebrates particularly befitted to the habitat of plateau region. Among the flora, most interesting is Madras Tree Shrew, found on the top hills of the forest ecosystem of Purulia district which is found nowhere else in the other districts of West Bengal. Forest canopy with greenery of this district is with very much outstanding heritage characteristics, though deforestation of 10.35% of the forest cover of the district for the period 1971–2011 remains one of the important problems in the plateau of the Purulia district that causes environmental degradation through agrarian invasion and huge loss of the forests. In the present situation, forests for the needs of the local people as well as for effort to uplift the environmental conditions in the district of Purulia, implementation of social forestry schemes has been suggested as the demand of the population cannot be met alone from the forest cover. Saplings must be planted by the inhabitants of the villages to meet their sufficient requirements for domestic and commercial uses through social forestry schemes. Social forestry schemes will endeavour to meet the rural requirements of fuel wood, poles, small timber, bamboo, fodder of forest produce primarily through plantation. Fast growing species, planted closely in land outside the forest, could be easily harvested within the time span of 5–7 years interval and it would provide sufficient fuel wood supply as required by the locals living in the villages. In this perspective, people of Purulia district, dependent on local forests for fuel wood and fodder, are to be best placed to look after the social forestry programme and that allowing communities to manage and use forest resources can have positive social, environmental, and economic impacts. It is now proved that social forestry can reduce deforestation, boost earnings, and settle conflicts over land use. Moreover, it could help the district in making progress towards the goal on climate change, forest protection and development. Social forestry is a broad term for approaches that empower communities to manage, protect and benefit from local forests  – states the Social Forest Organization. Social forestry is useful, and its necessity is applicable in different dimensions having different names in different places like community forestry, village forestry, participatory forestry, community-­ based forest management and people-centered forestry. Different approaches to social forestry vary in the extent to which they give communities rights to use and benefit from forest resources. Some allow communities to set up enterprises and sell forest products including timber commercially and such social forestry all over the world is rising. Studded with several forest patches, Purulia stands at the central part of Jungle Mahal.

Jungle Mahal Jungle Mahal is popular for its numerous forest patches and elephant corridor and is embarrassing to both the villagers and forest department due to man-elephant conflict in and around the forest areas of south-east part of West Bengal.

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2  District-Wise Forest Matrix, Forest Models and Strategies

Jungle Mahal (literally meaning Jungle Mahal is Jungle estates) was formed by the rulers of the British East India Company in 1805 as a district comprising 23 Parganas and Mahals in the Bengal Province of the British India in terms of Regulation XVIII of 1805. After 28 years of its existence officially, the British rulers abolished the district in 1833 by the implementation of Regulation XIII of 1833 due to several inconveniences caused for its vague jurisdiction for managing administration and collection of revenue (O’Malley 1908; Das 2020k). There has not been any district in the name of Jungle Mahal in West Bengal since then, but the name of Jungle Mahal is still popular to the people of West Bengal. At present, forest areas of Purulia, Bankura, Paschim Medinipur and Jhargram districts and part of Paschim Bardhaman and Birbhum districts form Jungle Mahal in the south-west part of West Bengal (Fig. 2.17), though the major portions of the forest areas of the then Jungle Mahal of eighteenth century are now reclaimed and converted into agricultural land. A few forest patches are still in existence under the jurisdiction of the forest department, and they are classified as Reserved Forests and Protected Forests according to their importance of wildlife

Fig. 2.17  Location map of Jungle Mahal in the south-west part of West Bengal

71

Forest Cover of Purulia District

conservation. Topographically the area is characterized by an alluvial plain in the east with maximum elevation of 150 m and covered with red soil in the west with maximum elevation of 200 m. The area is basically hot and humid with a small duration of cold weather and rainy days. Temperature ranges between 6 °C in the winter and about 44 °C during hot summer days. Annual average rainfall ranges from 900 to 1500  mm and the percentage of relative humidity varies between 49%, minimum in the month of April and 85%, maximum during the month of August.

Forest Cover Change Matrix Purulia district is very much enriched in forest canopy. The forest cover of Purulia district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 915.88 km2 which is 14.63% of the district’s geographical area. In terms of forest canopy density classes, the district has 37.36  km2 area under very dense forests, 306.94  km2 area under moderately dense forests and 571.58 km2 area under open forests. There is a net increase of 11.88 km2 of the forest cover in Purulia district as per the Forest Survey of India Report 2019. Total increase in the forest cover not only pertains to the year of 2013 with respect to 1991, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of Purulia district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of Purulia district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.18.

Purulia 16

Forest Cover (%)

14 11.1

12 10

9.2

9.31

9.55

9.69

9.61

1991

1993

1995

1997

1999

12.73 12.73 12.21 12.33

13.88 13.93

14.44 14.63

8 6 4 2 0 2001

2003

2005

2007

2011

Year

Fig. 2.18  Forest cover change matrix of Purulia district, West Bengal

2013

2015

2017

2019

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2  District-Wise Forest Matrix, Forest Models and Strategies

Forest Cover of South 24 Parganas District Forest cover of South 24 Parganas district is classified into Littoral and Swamp Mangroves Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Sundari, Gewa, Bain, Garjan, Hental, Golpata, Khalsi, Pasur, Keora etc. though most of the forests are regenerated under social forestry scheme. They are further classified into Brackish Water Mixed Forest, Salt Water Mixed Forest, Mangrove Forest, and Mangrove Scrub type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of South 24 Parganas district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants, and about 26 such JFMC at Sunderbans Tiger Reserve and 40 Joint Forest Management Committees have been constituted in the South 24 Parganas forest division. There is no Eco-Development Committee (EDC), though 143 SHG at Sunderbans Tiger Reserve (STR), and 234 Self Help Groups (SHG) are involved in protecting the forest areas under South 24 Parganas Forest Division. Here the amazingly quiet environment of the grand symphony of silence is uninterrupted. Wild bees in swarms come to Khalsi forest of Sunderbans during spring to produce honey and wax and cause pollination. Monkeys cover their bodies with muddy clay before tasting that honey from the honeycomb. Kaora tree opens its petals of flowers with tens of stamina to tempt the bats to feed on and ensure pollination. The snails crawl to climb up the trees, the animals quench their thirst by drinking salty water in the scarcity of sweet water, the roots of some mangroves grow upwards opposite in direction to that of centre of gravity instead of growing downwards and these roots are called pneumatophores which are breathing roots covered with mud and grow upward in search of oxygen. Some other mangroves grow on the stilt roots, which is an adaptive feature to stand erect on the shifting mud. During the high tidal phase of the day the semi-diurnal tide advances towards the land bathing it with water assuming a lover is clasping erotically his sweetheart; during the low tide, the water turns and goes back to the sea like one who slips off from the arms of his lover saturated after holding closely. This Sunderbans of Indian part covers most of the south-eastern region of the district of South 24 Parganas, the southernmost district of West Bengal. Sunderbans of the South 24 Parganas District – the largest prograding delta and the habitat of biggest contiguous mangrove patch of the world is with magnificent biodiversity including world famous Royal Bengal tiger and estuarine crocodiles. Sunderbans is a biogenous coast of numerous flora and fauna where the biological factors play significant roles in coastal evolution. There are 64 species of mangroves and its associated species and 1586 species of fauna are identified in Sunderbans. In such a dynamic ecosystem, blue carbon is stored in huge amounts in the subsoils of the mangrove swamps and marshes of the Sunderbans, a typical pristine forest, though a very few species of the mangroves are deciduous in nature. In the land

Forest Cover of South 24 Parganas District

73

systems, the amount of carbon sequestered and stored as the green carbon depends on the amount in the standing biomass, recalcitrant carbon remaining in the soil, and carbon removal in the wood products.

Mangroves and Marsh Vegetation A total number of 64 plant species are identified in the Sunderbans mangrove forest (Das 2011). Among these 34 species are true mangroves while the rest are mangrove associated plants. The species diversity of mangroves is relatively poor, and they all show similarity in their general occurrence and physiological adaptations. Mangrove zonation depends upon soil characteristics, soil and water salinity, tidal amplitude, gentle sloping, shallow mudflats, mud substrates etc. Floral changes and community structures are causally related to the mudflat elevation (Das 2015). It is observed that Porteresia coarctata (Dhani grass) and saplings of Avicennia marina (Baen) are the pioneer species in an emerging mudflat followed by Sonneratia sp. (Keora), Ceriops decandra (Garan) and Bruguiera sp. (Kankra), Aegialitis rotundifolia (Tara), Aegiceras sp. (Khalsi), Excoecaria sp. (Gewa) appear thereafter when the mud substrate of the newly emerged islands are stable, and the top of the island is no more inundated. Mangrove associated palm species are found to grow on the side of 31 large and narrow creeks and tidal inlets around the 48 mangroves dominated islands out of a total 102 islands of Sunderbans. Mangrove vegetation of intertidal mudflat accelerates the stability of the newly built-up islands and helps in configuring the new landmass.

Faunal Assemblages Among numerous and rich faunal diversity of many 1586 species, the tiger occupies the pinnacle of the mangrove ecosystem. The famous estuarine crocodiles of Sunderbans are declared endangered species of late. The occurrences of king cobras and Indian Rock Python are recorded around buffer areas of the Sunderbans. A few species among mammals like Indian Otter, Gangetic dolphin and Irrawaddy dolphin, Fishing cats and Leopard cats, Black porpoise are enlisted as endangered species. The fishes in estuaries of Sunderbans among fin fishes include a variety of about 120 species. There are abundant occurrences of tiger shark, dog shark and 22 species of prawn in the Sunderbans river waters. Common birds in the Sunderbans are predator birds like white bellied sea-eagle, osprey, fishing eagle, Brahmani kites, monsoon herons like open bill stork etc. Some migratory birds from Siberia region like curlew, Plover Goliath Heron also breed in the mangrove forest of Sunderbans. Traditionally, the wealth of natural resources of Sunderbans was assumed to be an unlimited gift of nature (Das 2017). However, with increased knowledge and development of awareness of the locals on conservation of nature,

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2  District-Wise Forest Matrix, Forest Models and Strategies

this myth of unlimited gift of nature has been demolished. Inhabitants of 54 islands for human habitation have realized that the natural resources, although renewable, are not infinite and need to be effectively managed and conserved, though the islanders of the lower Gangetic deltaic set up consider Sunderbans as a pristine forest covered with mangroves and marshes for its natural growth of vegetation in the estuarine environment.

Pristine Forests Mangrove covers of the Sunderbans keep the City of Joy away from the major devastation by the cyclonic storms. The scenic beauty of the evergreen mangrove forest of the Sunderbans is known to the rest of the world, which is a pristine and primary forest in nature (Das 2020l). The famous resilient and salt-tolerant mangrove forests of the Sunderbans is unique with abundant occurrences of 64 mangroves and its associated species including dominant species like Rhizophora, Avicennia, Heritiera and Sonneratia. Among those species, very recently, the age of Rhizophora mucronata is determined from the counting of annual growth rings. The oldest, recently fallen tree in the riverine forest displayed an average of 89  years with a height of 12 meter and a diameter of 28.6  cm. A tree of 34  cm diameter with an equal growth rate and a similar growth history is 106 years old. Age of mangrove species like Avicennia or others, lacking annual growth rings, thus barring from dendrochronological studies (Nature 2017). But the periodic growth layer allows accurate age determination only in trees with radical growth rates about 0.5  mm/year. Age determination of the largest trees collected in the primary forests revealed the relatively young age of the trees about 100  years, though age is one of the factors in the formation of a primary pristine forest (Ahlström et al. 2020). All forests grow to maturity at different rates based on their environment. But the term old growth is highly relative, and it is certainly not applicable for the mangrove forest of the Sunderbans. But when cyclonic storms ravage the Sunderbans, it destroys mangroves miles after miles. After days, nature starts to work through regeneration of the saplings from the dirt up biomass stored upon the substratum. Like such regeneration of mangroves, in Sunderbans, formation of new vegetation upon a newly built-up island, the first organisms to grow are called pioneer species, typically fast-growing Dhani grasses (Porteresia coarctata), then Kaora (Sonneratia sp) and Kali Bain (Avicennia marina) plants that can take quick advantage of loose silty substrates with plenty of nutrients, supplied by the tidal water inundated the islands twice daily. These pioneer species promote the abundant occurrence of vegetation succession allowing for a new guard of species to move in by trapping nutrients from the inundated sediment-laden tidal waters during flood tide. This generation of species assemblages repeats itself with the progress of years that helps to introduce ecosystems in the island-forest land. From the bushy grasses to quick-growing mangroves like Avicennia and Sonneratia trees and finally becomes a dense

Forest Cover of South 24 Parganas District

75

mangrove forest covering the newly built-up island in the Sunderbans. The ultimate grown up is often referred to as a climax community and it is these communities that are considered primary forests making the Sunderbans pristine in nature.

Forest Cover Change Matrix For statistical interpretation, no data is available separately for South 24 Parganas district up to the forest survey report of 1999, though South 24 Parganas district was formed on 1 March 1986 after partition of 24 Parganas district into North and South 24 Parganas. Forest of South 24 Parganas on an average of 100% area is with mangroves and mangroves associated plant species and outside the forest, the greenery is covered with artificial plantations. South 24 Parganas district is known for mangrove swamp. The forest cover of South 24 Parganas district in 2019, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 2788.71 km2 which is 27.99% of the district’s geographical area. In terms of forest canopy density classes, the district has 983.10 km2 area under very dense forests, 745.03 km2 area under moderately dense forests and 1060.58  km2 area under open forests. There is a net decline of 3.29 km2 of the forest cover in South 24 Parganas district as per the Forest Report 2019 (ISFR 2019). With respect to 1999, total increase in the forest cover not only pertains to the year of 2013, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of South 24 Parganas district which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. There are no separate statistics available for forest cover of South 24 Parganas district in the report of Forest Survey of India from 1987 to 1989. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.19.

South 24 Parganas Forest Cover (%)

35 30 25

20.84

22.49

22.94

22.92

2001

2003

2005

24.14

24.16

2007

2011

27.85

27.93

28.03

27.99

2013

2015

2017

2019

20 15 10 5 0 1999

Year

Fig. 2.19  Forest cover change matrix of South 24 Parganas, West Bengal

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2  District-Wise Forest Matrix, Forest Models and Strategies

Forest Cover of Uttar Dinajpur District Forest cover of Uttar Dinajpur district is classified into Littoral and Swamp Tropical Seasonal Swamp Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Hijal, Arjun, Mahua, Sonajhuri, Peasal, Kend, Kusum etc. They are further classified into Barringtonia Swamp Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes  – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches of Uttar Dinajpur district, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants and about 21 such Joint Forest Management Committees have been constituted at Raiganj in the Uttar Dinajpur forest division. There are 3 Eco-Development Committees (EDC) at Kulik Wildlife Sanctuary and there are no Self Help Groups (SHG) in the forest division of Uttar Dinajpur. Raiganj Forest Division of Uttar Dinajpur and Dakshin Dinajpur districts consists of Forest Ranges like Kulik, Chopra, Karandighi, Raiganj, Kushmundi, Balurghat etc. Effect of onslaught encroachment for habitation of the fearsome people enhances deforestation in the Uttar Dinajpur district. Loss of forests causes degradation of the soil of the land, though that soil is the biggest terrestrial carbon sink, but land degradation is reducing its ability to fight. Forests serve as natural storage for carbon, and deforestation is the second leading cause of carbon emissions that contribute to climate change. At present, there is no forest grown naturally in the district, but a few forests created through plantation programmes under social forestry schemes of the government. Among them, Kulik and Sapnikla forests of Uttar Dinajpur are popular and known to all. Kulik bird sanctuary is established at the outskirts of Raiganj town where the pocket forest model is likely to be introduced for its denser and luxuriant appearances.

Pocket Forest Pocket forests are considered for the urban areas only, either in and around the urban zone, or adjacent/outskirts of the town or metropolitan areas (Endreny 2018). Covering areas for this typical type of forest might be of several hectares or even less than a hectare because of the scarcity of land availability in the urban zone, and then this forest might be defined as Mini Forests. The mini forests can be squeezed into playgrounds or along the roadside areas where only the saplings of the native plants are considered for plantation programmes under social forestry schemes (Das 2020m). Native trees of the pocket forests grow vigorously, and it is ten times faster than those of the natural forests. Pocket forests recreate hundred times more biodiversity and sequester carbon forty times more to store carbon into the soils of the forest floors in comparison to the conventional terrestrial natural forests. Generation of pocket forests through the process of afforestation requires native or indigenous plant species where density of vegetation is the key and reach of sunlight upon the planted young

Forest Cover of Uttar Dinajpur District

77

saplings is essential for their growth. Growing stock of the pocket forests will form wildlife corridors for the common faunal community of the region such as butterflies, garden lizards, snails, amphibians and so on, and attract the birds as pollinators. As the pocket forest floor bursts into life there are plenty of colours to enjoy. Some wildflowers have a symbiotic relationship with the trees that shade them. Others attract and sustain bees and other beneficial pollinators that enrich the biodiversity. Present day biodiversity crisis might be solved by introducing such pocket forests in the urban areas that at least provide a snack for the songbirds. Pocket forests, studded with the tree lines and layers of local natural forest, is a green and cheap way to store carbon into soil, and thus, form a rich carbon pool in the urban zone (Das 2020m). Plantation of trees is the best way to combat the impact of climate change as forest health and climate change are intrinsically linked. Conserving and improving carbon through soil management and land use patterns can help mitigate climate change, improve the quality of degraded land and water and after all address the resilient nature of living for all. Kulik Bird Sanctuary, stood in the vicinity of Raiganj town of Uttar Dinajpur district, is an example of such pocket forest where 90,000–1,00,000 migratory birds come and stay for the period from June to February every year particularly in the breeding season. Among them, open billed stork (Anastomus oscitans), little cormorant (Phalacrocorax niger), night heron (Nycticorax nycticorax), grey heron (Ardea cinerea), cattle egret (Bubulcus ibis) and little egret (Egretta garzetta) are the dominant avian species. Open billed stork is found maximum out of the other bird species during the breeding season. There are no non-native plant species occurred in the Kulik Bird Sanctuary and the principal timber tree species are Jarul (Lagerstoemia speciosa), Hijal (Barringtonia acutangula), Pitali (Trewia nudiflora), Arjun (Terminalia arjuna), Chhatim (Alstonia scholaris) and Dewa (Phaleria macrocarpa). Covered with the green canopy along the entire forest areas, this avian sanctuary, situated adjacent to the Raiganj town area at the Kulik riverbank, not only attracts tourists round the year and become a source of income for the forest department, but decelerate the contents of greenhouse gases like carbon dioxide, nitrous oxide and such other gases emerged in the town areas due to greenhouse gas emission. Consequently, concentration of carbon dioxide is comparatively low in this district headquarter of the Uttar Dinajpur district as the green tree lines of the pocket forest, the Kulik Bird Sanctuary absorbs most of the volume of the carbon dioxide emitted from the urban belt, and thus, the pocket forest helps to mitigate the effects of the climate change and save the urban people from the effects of the air pollution. The forest officials and managers might format well-planned projects to set about plantations for the implementation of such pocket forests in the wastelands or bad lands of the municipal areas and metropolitan region under social forestry and urban agroforestry schemes all over the state of West Bengal. Even for the implementation of such pocket forest on the bank of the river Kulik, the fish resources in the river waters of Kulik has become enriched with the adequate supply of nutrients from the natural forest litter. Different native fishes like Bou, Piyali, Chapla and Ghaira fishes caught in the waters of the Kulik River help acceleration of the local economy in the region (Fig. 2.20).

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2  District-Wise Forest Matrix, Forest Models and Strategies

Fig. 2.20  Fisherman engaged in capturing fishes from the river waters of Kulik encompassing the Kulik Bird Sanctuary

Forest Cover Change Matrix For statistical interpretation, no data is available separately for Uttar Dinajpur district up to the forest survey report of 1997, though Uttar Dinajpur district was formed on 1 April 1992 after partition of Paschim Dinajpur district into Uttar Dinajpur and Dakshin Dinajpur districts. There are no separate statistics available for forest cover of Paschim Dinajpur district (known before 1 April 1992) in the report of Forest Survey of India from 1987 to 1989. Further, forest area of Paschim Dinajpur district is amalgamated with the data of the other districts like Burdwan, Birbhum, Kolkata, Hugli, Midnapur, Nadia, Malda, Murshidabad, 24 Parganas and Howrah as shown in the report of FSI for the period from 1991 to 1997. Separate data of the district of the state of West Bengal have been inventoried in the forest report of 1999 for the first time. Further, no report was published in 2007 by the Forest Survey of India and the report published in 2009 contains only the forest statistics for the year 2007. This is the reason behind non-availability of district-­ wise forest statistics for the year 2009. Total increase in the forest cover not only pertains to the year of 2001 with respect to 1999, but a major part of increase has been attributed to inclusion of Trees Outside Forest (TOF) areas of the Uttar Dinajpur districts which could not be captured earlier assessment by the Forest Survey of India (FSI) team due to limitation and dearth of modern device for computation. Needless to mention that the Forest Survey of India (FSI) took initiatives

79

Forest Cover of Dakshin Dinajpur District

Uttar Dinajpur 9

7.74

7.48

7.48

7.48

2013

2015

2017

2019

Forest Cover (%)

8 7 6 5

5.25

5.25

2003

2005

5.61

5.61

4.14

4 3 2 1

0.25

0 1999

2001

2007

2011

Year

Fig. 2.21  Forest cover change matrix of Uttar Dinajpur district, West Bengal

for forest survey of the entire country and commenced publishing forest reports since 1987  in every two years consecutively. Forest of Uttar Dinajpur district is almost covered with artificial plantations. Among fauna, jungle cats, jungle fowl, python, wild boars and varieties of birds and reptiles are increasingly being reported. Uttar Dinajpur is a district which is very much poor in forest canopy and forestry. The forest cover in Uttar Dinajpur district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 234.93 km2 which is 7.48% of the district’s geographical area. In terms of forest canopy density classes, the district has no area under very dense forests, 3.99 km2 area under moderately dense forests and 230.94 km2 area under open forests. There is a net decline of 0.07 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.21.

Forest Cover of Dakshin Dinajpur District Forest cover of Dakshin Dinajpur district is classified into Littoral and Swamp Tropical Seasonal Swamp Forest type composition according to Champion and Seth (1968) classification comprising the vegetation types like Hijal, Arjun, Mahua, Sonajhuri, Peasal, Kend, Kusum etc. They are further classified into Barringtonia Swamp Forest type according to the vegetation composition. The forest covers are then grouped into two broad classes – vegetated classes like very dense, moderately dense, open, and plantations, and less vegetated like scrub etc. For monitoring such forest patches, Joint Forest Management has been adopted jointly by the forest department and the local inhabitants, but no such Joint Forest Management Committees have been constituted in the Dakshin Dinajpur forest division. There are no Eco-Development Committees (EDC) or Self Help Groups (SHG) in the forest division of Dakshin Dinajpur.

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Myths-History Over the years, forests were revered by the people of undivided Bengal not only that, but many ceremonial occasions were also centered on trees and plants. The great emperor Ashoka started preservation and protection of forests and wild animals. But in the medieval era, many people had to flee from the attacks and take refuge in the forests during the Muslim invasions and the people cleared vast areas of forests to make way for settlements. This happened to the people of undivided Dinajpur district who destroyed forests for their settlement as evidenced by some myths still talked about in the districts. The myth has Pandavas (in the Mahabharata) hiding their arms and weapons in a hollow of a tree inside the forest at Harirampur of Dakshin Dinajpur and the forest gave them a good hiding. Another version of the story has the people hid themselves inside the forest feared after Muslim invasion and their arms and weapons were hidden reserves in the hollow of a tree inside the forest at Harirampur. The latter is most likely (Hunter 1876). At present, there is no forest grown naturally in the district, but a few forests created through plantation programmes under social forestry schemes of the government. Among them, Sarengbari and Dogachhi of Dakshin Dinajpur district are popular and known to all where a bioeconomy model is to be introduced for further regeneration and restoration of these forests.

Bioeconomy Model Humans destroyed almost all the forest patches in Dakshin Dinajpur district. As a result of such destruction, the tree lines are severely damaged leaving the area almost a desert-like look. The present day climate change crisis highlighted how vulnerable we are as people and a planet to health, environmental and other crises and how we desperately need to build resilience. The entire geographical area of Dakshin Dinajpur district, at present, is lacking green cover that needs the implementation of bioeconomy model (Das 2020n). Bioeconomy properly values forests, stimulating investments in sustainable growth which emphasizes the production of renewable biological resources from trees and plants and conversions into value-­ added products. To improve livelihoods and wellbeing, growth roles for forests, social forestry and agroforestry is referred to in the bioeconomy model. The Government of West Bengal, in this present miserable situation of both economy and environment must encourage the plantation of trees of their own land that stimulates green investment in landscapes and sustainable growth thereon. West Bengal needs such a new economic model that properly values trees, regeneration of forests like social forestry and agroforestry. Trees or products of trees are now welcomed by the present society. Many industries are seeking to replace non-renewable metals and fossil fuels with biological inputs. The construction industry is looking for wood in lieu of concrete in multistory buildings. And the agricultural waste can be

81

Forest Cover of Dakshin Dinajpur District

converted into biofuels. Again, it is untrue that the carbon in trees spews back into the atmosphere the moment the trees are harvested. Building materials and other long-lived wood products can preserve a significant share of that carbon for 50 years or more. Naturally, social forestry, forests, sustainable forest management and forest-­based solutions can be the impetus for transformation advancing the bioeconomy, while enhancing biodiversity and supporting the creation of wealth. If West Bengal Government gives the priority of bioeconomy model through encouraging people in social forestry and agroforestry, it stimulates not only economic growth to the state, but it stimulates to begin using wood more efficiently and strengthens the canopy of green cover upon the South Bengal areas as existed before the cyclonic storms. Bioeconomy model, undoubtedly, emphasizes the regeneration of forests of either social forestry or agroforestry, but the advent of social distancing of trees is a great and pleasant surprise to the lovers of nature (Ipate et al. 2020).

Forest Cover Change Matrix Dakshin Dinajpur is a district which is very much poor in forest canopy and forestry. The forest cover in Dakshin Dinajpur district, based on interpretation of IRS Resourcesat-2 LISS III satellite data of the period November 2017 to February 2018 is 87.12 km2 which is 3.93% of the district’s geographical area. In terms of forest canopy density classes, the district has no area under very dense forests, 5.83 km2 area under moderately dense forests and 81.29 km2 area under open forests. There is a net increase of 0.12 km2 in the forest cover from the reported area in India State of Forest Report 2019. A diagrammatic representation of the class-wise change is given in the forest cover change matrix in the Fig. 2.22.

Dakshin Dinajpur 4.5 4

3.92

3.92

3.92

3.93

2013

2015

2017

2019

Forest cover (%)

3.5 3 2.5 2 1.5 1 0.5

0.45

0.72

0.68

0.68

0.68

0.68

2001

2003

2005

2007

2011

0 -0.5

1999

Year

Fig. 2.22  Forest cover change matrix of Dakshin Dinajpur district, West Bengal

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2  District-Wise Forest Matrix, Forest Models and Strategies

Summary In comparison to the country’s forest cover, West Bengal state has recorded a rise by 0.3%, from 16,847 km2 in 2017 to 16,901.51 km2 in 2019, but trees outside forest i.e., green cover of non-forest areas has recorded a decrease by 6.1%, from 2136 km2 in 2017 to 2006 km2 in 2019. Loss of such greenery outside the forest areas in West Bengal is the consequences of construction of bridges, foot bridges, widening of roads and city urbanization projects. Forest cover of the districts like Howrah, Murshidabad, North Dinajpur, North 24 Parganas and South 24 Parganas has declined, however, forest cover of Bankura, Birbhum, Purulia and Paschim Midnapur has increased. While Bankura logged a growth of 15.6 km2 in its forest cover, South 24 Parganas, which houses a vast stretch of the Sunderbans, has witnessed a dip in forest cover by 3.29 km2. The India State of Forest Report 2019 has recorded an overall rise of forest cover in West Bengal and the loss of forest cover in a few districts of the state is only due to the cause of man-made interference. Strategies for forest regeneration and restoration through the implementation of forest models and introduction of small-scale forest types under social and community forestry schemes might increase the areas of the forest patches in West Bengal. For the maintenance of the forest health and to stop erosion, a wide-range campaigning for plantation of trees among the rural and urban people by the Village Panchayat (village body) and municipal authority of the urban belt will be fruitful, and in tribal areas, living standard of the tribal people of the forest areas should be considered first and their societal inequality still existing must be diminished through the proper steps to be taken by both state and central government. If the tribal people of the forest stand can gain the standard of living, then the green canopy of the tree lines of the forests breathes, and nobody could dare hamper their existence. People running the government have experienced such social inequality of the tribes living by the forests for years. Extinction of such experience and an emotional disconnect from the tribal community living in the vicinity of the forests are not inevitable on behalf of the government of the people, by the people, and for the people. And if the forest exists, trees of the forest help heal humans through forest bathing which is good for health. Further, trees in the forest teach humans how to avoid pathogens by maintaining social distancing.

References Ahlström A et al (2020) Primary productivity of managed and pristine forests in Sweden. Environ Res Lett 15:094067 Annual Administrative Report (2017–2018) Department of Forest, Government of West Bengal, 270p

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Banerjee A, Ghosh S, Springate-Baginski O (2010) The creation of West Bengal’s Forest underclass, an historical institutional analysis of Forest rights deprivations, IPPG Programme Office, IDPM, School of Environment & Development University of Manchester, 26p Bhargava M (2002) Forest, people and state. Econ Polit Wkly 37(43):4440–4446 Bhullar L (2008) The Indian Forest Rights Act 2006: a critical appraisal. Law Environ Dev J 4(1). http://www.lead-­journal.org/content/08020.pdf Champion HG, Seth SK (1968) A revised survey of Forest types of India. Manager of Publication, Delhi, 404p Coleman EA, Mwangi E (2013) Women’s participation in forest management: a cross-country analysis. Glob Environ Chang 23(1):193–205. https://doi.org/10.1016/j.gloenvcha.2012.10.005 Das GK (2020a) Green infrastructure of trees  – forest’s symbolic socialization. Frontier, 10 October 2020 Das GK (2020b) Wildlife, deforestation and spread of zoonotic diseases, frontier, 18 May 2020 Das GK (2020c) Required optimum sample size determination of Forest stands in West Bengal. eJournal Appl For Ecol (eJAFE) 8(2):1–6 Das GK (2020d) Impact of climate change in the forests of West Bengal. Frontier, 26 March 2020 Das GK (2020e) Carbon storage and tribal races of forgotten forests helps in climate solutions. Frontier, 14 November 2020 Das GK (2020f) Women’s participation might energize the forests and climate solutions. Frontier, 31 October 2020 Das GK (2020g) Solitary tree behaves like a nuclear family in the forest stands. Frontier, 26 September 2020 Das GK (2020h) Social forestry hoped to reduced poverty and climate change. Frontier, 7 November 2020 Das GK (2020i) Societal inequality of the tribal community – a cause for deforestation. Frontier, 17 October 2020 Das GK (2020j) Approachable facts for forests and forestry studies. Indian Sci Cruiser 34(4):8–9 Das GK (2020k) Effects of deforestation and climate change for zoonosis like coronavirus crisis. Frontier, 21 December 2020 Das GK (2020l) Mangroves of Sunderbans – a pristine forest in nature. Frontier, 11 June 2020 Das GK (2020m) Pocket Forest – a way for mitigation of climate change. Frontier, 24 October 2020 Das GK (2020n) Bioeconomy and forest bathing models for green recovery of Bengal. Indian Sci Cruiser 34(3):8–9 Das GK (2018) System of reclamation and salt preparation in Sunderbans. Frontier, 17 July 2018 Das GK (2017) Tidal sedimentation in the Sunderban’s Thakuran Basin. Springer, Cham, 151p. ISBN:978-3-319-44190-0 Das GK (2015) Estuarine morphodynamics of the Sunderbans. Springer, Cham, 211p. ISBN:978-3-319-11342-5 Das GK (2011) Sunderbans  – environment and ecosystem. Sarat Book House, Kolkata, 254p. ISBN:81-87169-72-9 Endreny TA (2018) Strategically growing the urban forest will improve our world. Nat Commun 9:1160. https://doi.org/10.1038/s41467-­018-­03622-­0 Enete IC, Alabi MO, Chukwudelunzu VU (2012) Tree canopy cover variation effects on urban Heat Island in Enugu City, Nigeria. Dev Ctry Stud 2(6):12–18 Fisher MR, Moeliono M, Mulyana A, Yuliani EL, Adriadiu A, Kamaluddin JJ, Sahide MAK (2018) Assessing the new social forestry project in Indonesia: recognition, livelihood and conservation? Int For Rev 20(3):346–361 FSI (1997) Forest Survey of India 1997, Report on inventory of trees in non-forest areas. A pilot survey in 25 villages of West Bengal, Forest Survey of India, Eastern Zone, Calcutta, 29, 1997 FSI (2019) Forest Survey of India 2019, India State of Forest Report (ISFR 2019). Ministry of Environment, Forest & Climate Change. Government of India, 187p Gray LK, Hamann A (2011) Strategies for reforestation under uncertain future climates: guidelines for Alberta, Canada. PLoS One 6(8):e22977. https://doi.org/10.1371/journal.pone.0022977

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Guha A, Pradhan A, Mondal K (2017) Joint Forest Management in West Bengal: a long way to go. J Hum Ecol:471–476 Hajjar R, Oldekop JA (2018) Research frontiers in community forest management. Curr Opin Environ Sustain 32:119–125. https://doi.org/10.1016/j.cosust.2018.06.003 Holl KD, Zahawi RA, Cole RJ, Ostertag R, Cordell S (2010) Planting seedlings in tree islands versus plantations as a large-scale tropical forest restoration strategy. Restor Ecol:1–10. https:// doi.org/10.1111/j.1526-­100X.2010.00674.x Hunter WW (1875) Statistical account of Bengal, Sunderbans. Trubner & Company, London, 176p Hunter WW (1876) A statistical account of Bengal  – Vol.7, districts of Maldah, Rangpur, and Dinajpur. Trubner and Co., London, 461p Ipate N, David KG, Ludith I, Bogdan A (2020) The bioeconomy model in future sustainable development. Studia Universitatis Vasile Goldis Arad  – Econ Ser 25(2). https://doi.org/10.1515/ sues-­2015-­0016 ISFR (2019) Forest Survey of India. India State of Forest Report. Ministry of Environment, Forest & Climate Change. Government of India, 187p Keskinocak P, Oruc BE, Baxter A, Asplund J, Serban N (2020) The impact of social distancing on COVID19 spread: State of Georgia case study. https://doi.org/10.1371/journal.pone.0239798 Lockhart J (2012) Green infrastructure: the strategic roles of trees, woodlands and forestry. Int J Urban For 32:33–49. https://doi.org/10.1080/03071375.2009.9747552 Lohmann A (2013) Is tree hugging the way to go? Classification trees and random forests in linguistic study. VIEWS c/o Department of English, University of Vienna, 1–15 Lõhmus P, Rosenvald R, Lõhmus A (2006) Effectiveness of solitary retention trees for conserving epiphytes: differential short-term responses of bryophytes and lichens. Can J For Res 36(5):1319–1330. https://doi.org/10.1139/X06-­032 Macreadie PI et al (2019) The future of blue carbon science. Nat Commun 10:3998. https://doi. org/10.1038/s41467-­019-­11693-­w. www.nature.com/naturecommunications Nature (2017) Pristine forests are shrinking fast, 541, 263. https://doi.org/10.1038/541263d O’Malley LSS (1908) Bengal District Gazetteers Manbhum. The Bengal Secretariat Book Depot, Kolkata, 300p O’Malley LSS (1911) Midnapore District Gazetteers. The Bengal Secretariat Book Depot, Kolkata, 228p O’Malley LSS (1914) Bengal District Gazetteers, 24 Parganas. The Bengal Secretariat Book Depot, Calcutta, 408p Peterson JCK (1910) Bengal District Gazetteers: Burdwan. Bengal Secretariat Book Depot, Calcutta, 222p Rodgers WA, Panwar HS (1998) Planning a wildlife protected area network in India. 2 volumes, Project FO: IND/82/003. FAO, Dehradun, India, 339p Särkinen T, Iganc J, Linares-Palomino R, Simon MF, Prado DE (2011) Forgotten forests – issues and prospects in biome mapping using seasonally dry tropical forests as a case study. BMC Ecol 11(1):27. https://doi.org/10.1186/1472-­6785-­11-­27 Voogt JA (2002) Urban heat island. In: Munn T (ed) Encyclopedia of global change. Wiley, New York, pp 660–666 W B State Forest Report (2014) Directorate of Forest, Government of West Bengal, 2014 Wolf K (1998) Urban nature benefits. Psycho-social dimensions of people and plants. Center for Urban Horticulture, College of forest Resources, University of Washington, Fact sheet I Seattle, WA World Bank (2009) World development report 2009. Reshaping economic geography. World Bank, Washington, DC World Bank (2010) World development report 2010. Development and climate change. World Bank, Washington, DC

Chapter 3

Forest Stands – Case Studies

Abstract  Forest patches are scattered in almost all the districts of West Bengal. Among them, four remarkably important forest patches of Bankura and Paschim Bardhaman districts are considered as model forest stands for mapping of the overall scenario of the forest patches of West Bengal. In the selected forest patches, the increased growth of growing stock in the forest cover needs to review the present forest health of the district through estimation of physico-chemical properties of forest soils and overall scenario of changing matrix of forest areas along with floral diversity. Physico-chemical properties of soil and soil organisms present in the forest floor as independent and/or dependent variables have a decisive influence on forest vegetation. Denser the forest, more soil-health potential is gained through improvement of soil characteristics by litter fall on the forest floor and microbial biomass decomposition thereon. Summing up data on soil physico-chemical properties, floral characteristics and gradual changing pattern of the forest cover all together will have immense help for further afforestation particularly in the degraded and wastelands in and around the forest areas of the districts. To examine the role of soil physico-chemical properties upon the forest vegetation, Joypur and Beliatore, two forest ranges of Bankura district, and Garh Jangal and Aduria forests of Paschim Bardhaman district are taken into consideration as experimental sites which have been found promising in rejuvenating forest vegetation. The soil type and its properties, and their impact on vegetation are investigated and evaluated after selecting different pedons in the study areas. Another reason for such enhanced status of forest cover of the districts might be the people’s participation adoring and rescuing the forest for their own interest through the implementation of Joint Forest Management (JFM) policy by the forest department. Keywords  Floral diversity · Soil characteristics · Groundwater · NPK · Micronutrients · Alfisols

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_3

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3  Forest Stands – Case Studies

Forest Management of Bankura District Bankura district consists of 4 major forest divisions comprising 32 forest ranges along with 1 working plan division which is non-territorial and functions for the districts of the entire South Bengal (Table 3.1). Out of 32 ranges in the forest areas of Bankura district, 5 ranges are non-territorial and function for the districts of South Bengal under Working Plan South Division – II. The quantum of forest capital per unit area varies in different parts of forest areas in the district of Bankura, though the forest areas of north-west and central-west part in the district are covered with poor growing stocks. Forest areas in the Ranges Saltora, Jhantipahari, Baddiha, Indpur and Khatra are less uniformly spread and consist of largely derelict poor forest areas primarily of regeneration vegetation.

Joypur and Beliatore Forests, Bankura District Forest cover of Bankura district, including the study areas of Joypur and Beliatore forests, offers a picturesque landscape with vast stretches of Sal trees, flowering trees of Palas and Mahua and lofty plants of Sonajhuri and Eucalyptus encircling the forest border. Forest Ranges Joypur and Beliatore in the Bankura district are in the south-west part of West Bengal having natural boundaries by the rivers Damodar and Dwarakeswar that indicates both the forest range areas are well-drained. Rivers Damodar and Dwarakeswar flow almost parallel towards south-east as the general slope of the district runs from north-west to south-east. Topographically the area is characterized by an alluvial plain in the east with a maximum elevation of 150 m. The area is basically hot and humid with a small duration of cold weather and rainy days. Temperature ranges between 6 °C in the winter and about 44 °C during hot summer days. Annual average rainfall is around 1500 mm and the percentage of relative humidity varies between 49%, minimum in the month of April and 85%, maximum during the month of August. Climate change is one of the probable causes that accelerates forest growth rate of Bankura district, though it is not clear yet as the study in this area of experiment remains still under the investigation (Das 2020a). Another factor may be changing nutrient status and chemical composition of forest soil. Soil of the present study area in Joypur and Beliatore Forest Ranges of Bankura district is characterized with mixed Alfisol and red soil. Alfisol exposure is visible over the surface configuration in the forest stands. Further, a ground water table with sufficient availability of groundwater, duly rechargeable annually, has tuned up the abundant occurrences of vegetation (Das 2021).

Joypur and Beliatore Forests, Bankura District

87

Table 3.1  Name of the forest divisions, forest ranges and beats of Bankura district Name of forest divisions Bankura north division

Name of forest ranges Sonamukhi

Radhanagar

Saltora

Gangajal Ghati

Bankura North

Patrasayer

Mejhia Beliatore

Barjora

Chhatna

Bankura south division

Bankura

Indpur

Kamalpur

Name of beats 1.Sonamukhi 2.Indkata 3.Manikbazar 4.Hamirhati 1.Radhanagar 2.Bhora 3.Panchal 4.Balarampur 1.Murlu 2.Saltora 3.Tiluri 1.Gangajal Ghati HQ 2.Gangajal Ghati 3.Ukhradihi 4.Ramharipur 1.Salboni 2.Belboni 3.Kanchanpur 1.Patrasayer 2.Kushadeep 3.Birsingha 1.Mejhia 2.Kusthal 1.Beliatore 2.Khag 3.Brindabanpur 4.Kundulia 1.Barjora 2.Sitla 3.Sangrampur 1.Jatipahari 2.Susunia 3.Chhatna 1.Bankura 2.Punisole 3.Ratanpur 1.Indpur 2.Hatagram 3.Chingra 4.Mahisdobra 1.Kamalpur 2.Bhuiyapara 3.Lakhanpur (continued)

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3  Forest Stands – Case Studies

Table 3.1 (continued) Name of forest divisions

Name of forest ranges Khatra

Hirbandh

Simlapal

Pirragari

Sarenga

Motgoda

Fulkusma

Ranibandh

Jhilimili Panchet division

Bishnupur

Joypur

Taldangra

Bankadaha

Name of beats 1.Khatra 2.Kesia 3.Mukutmonipur 1.Hirbandh – I 2.Hirbandh – II 3.Molian 1.Simlapal 2.Harmashra 3.Boricha 1.Pirragari – I 2.Pirragari – II 3.Persola 1.Sarenga 2.Dubrajpur 3.Ambakra 1.Motgoda – I 2.Motgoda – II 3.Pirrah 1.Fulkusma 2.Susunia 3.Bagdubi 1.Ranibandh 2.Punshya 3.Ambikanagar 1.Jhilimili 2.Muchikata 1.Bishnupur – I 2.Bishnupur – II 3.Basudevpur 4.Chougan 5.Hereparbat 1.Adhkata 2.Joypur 3.Machantola 4.Kuchiakole 1.Taldangra 2.Asna 3.Panchamora 1.Bankadaha 2.Amdangra 3.Peardoba 4.Uparsole 5.Amdhara (continued)

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Table 3.1 (continued) Name of forest divisions

Name of forest ranges Onda

Ranges of working plan south division – II

Camp – I

Name of beats 1.Chhagulia 2.Krishnagar 3.Onda 4.Chingani

Camp – II Camp – III Camp – IV Data Base

Ground Water Table Unconsolidated sediments occupy the entire study areas of the Ranges Joypur and Beliatore forests, though the western part of the Bankura district is partly occupied by the hard rocks having enough groundwater. The Ground water resources have been assessed block-wise in the district, but the Central Ground Water Board published the report district-wise (Dynamic Ground Water Resources of India Report 2017). As per assessment report, annual replenishable resources had been assessed as 178002.06 ham (hectare meter), net ground water availability as 161769.10 ham, and ground water draft for all uses as 74682.48 ham. The stage of development was worked out to be 46%, which is 1% more than the state average (Table 3.2). Ground water table is almost fair for Joypur and Beliatore areas, though the report shows semi-critical status for Bishnupur Block, 15 km away from Joypur and 35 km from Beliatore forest areas. Further, forests play an important role in soil water storage by increasing infiltration rate through intercepting maximum rainwater received in their canopy. Infiltration rate in the bare land, grass land and agricultural land is lower than forest land. Forests control rainwater precipitation in an enhanced manner by absorbing through litter fall converted biomass and humus, and thus not only check soil erosion from the forest floor, but recharge and enrich the groundwater table.

Forest Scenario Luxuriant forest area occurs in Bankura North Forest Division covering Ranges Joypur, Sonamukhi, Beliatore and part of Bishnupur having both a reasonable number of stems and volume per hectare. In Bankura South Forest Division, dense forest occurs in Ranibandh Range and part of Sarenga and Taldangra Ranges. North-west and central-west part of the district consists mainly of derelict areas and primarily of regenerated forest vegetation (FSI 1985). The forest area covering Bankura

178002.06 16232.96

Total annual ground water recharge 161769.10

69980.27 4702.22

Annual GW allocation for domestic use as on 2025 74682.48 7362.46

in Hectare Metre (ham) Current annual ground water Annual Total extractable extraction natural discharges ground Irrigation Domestic Total water & resources industrial

Source: Dynamic Ground Water Resources of India Report (2017)

Non-monsoon season Recharge Recharge Recharge Recharge from from from from other rainfall other rainfall sources sources 98905.53 21562.59 24886.50 32647.44

Monsoon season

Groundwater recharge

Table 3.2  Stage of groundwater resources in the district of Bankura

84426.38

Net ground water availability for future use

46

Stage of ground water extraction (%)

90 3  Forest Stands – Case Studies

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district is the habitat of Sal Segun Sishu Sonajhuri Piyal Kendu Lohajangi Palas Bamboo date palm Amlaki Hartuki etc. and infested with the wild animals like elephant wild boar monkey jackal Bengal fox wild hen fishing cat mongoose porcupine along with the varieties of birds and snakes. Saltora, Gangajalghati, Simlipal, Taldangra, Sarenga, Joypur, Beliatore, Sonamukhi, Susunia are covered with dense jungles in the district of Bankura.

Floral Diversity Joypur and Beliatore Forest Ranges of Bankura district, well-known for its floral diversity, is classified as Northern Tropical Dry Deciduous Forests as per the Champion and Seth’s classification (1968) where Sal tree is considered as dominant species. Other than Sal (Shorea robusta) trees, Madhuca latifolia, Diospyros melanoxylon, Buchanania latifolia, Terminalia tomentosa, Terminalia belerica, Terminalia chebula, Syzygium cumini, Alstonia scholaris, Aegle marmelos, Lagerstroemia parviflora, Butea frondosa are chief associated species. Several miscellaneous species exist with the Sal trees and its associated species. Very few among them, Azadirachta indica, Semecarpus anacardium, Holarrhena antidysenterica are scattered in the entire forest areas. Except these indigenous forest plant species, Eucalyptus, Acacia auriculiformis, Alstonia scholaris etc. are introduced by the forest department for marking the periphery of the forest border (Table 3.3). Dwarf, horny and bushy shrubs like Eupatorium, Combretum, Zizyphus etc. are found scattered in the blank space in between the timber tree species of the forest (FSI 1985). Both the timber and non-timber vegetation have medicinal values as well as commercially viable, if thoroughly investigated and extracted applying the traditional knowledge of the forest fringe dwelling tribal people. Tribal community, especially the Santali group, is dependent on non-timber shrubs that meet up their medicinal demands for generations, whereas, the leaves, fruits, flowers, seeds, fibers, flosses of timber trees are associated for their day-by-day earnings. And only the people of the tribal community are considered and permitted by the forest department to collect these forests produce for meeting up their livelihoods without felling trees as tribal people love trees, worship trees, and earn bread from the trees of the forests.

Elephant Corridor Joypur and Beliatore forests in the Bankura district, well-known for elephant corridor, are covered with densely occurred Sal, Sidha, Peasal, Gamar, Asna, Palash, Kusum, Mahua, Neem (Table 3.3) and other trees, which prevent even the scorching sun rays to reach the surface floor of the forest stands. Herds of deer are noticed meandering in the dense lush green forest canopy round the year in the forest of

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Table 3.3  Common timber trees and their associated species identified at Joypur and Beliatore forest ranges Local name Sal Mahua/Mahul Kendu Piyal Asna Bahera Haritaki Kalo Jam Chhattim Bel Sidha Palas Neem Bhela Kurchi Eucalyptus Sonajhuri

Scientific name Shorea robusta Madhuca indica Diospyros melanoxylon Buchanania lanzan Terminalia tomentosa Terminalia belerica Terminalia chebula Syzygium cumini Alstonia scholaris Aegle marmelos Lagerstroemia parviflora Butea frondosa Azadirachta indica Semecarpus anacardium Holarrhena antidysenterica Eucalyptus Acacia auriculiformis

Joypur which overwhelms the tourists and visitors. It is a sanctuary, almost isolated from the other forest patches of the district and enlisted in the map of tourism very recently for its famous elephant corridor (Fig. 3.1). Joypur forest is located 15 km from Bishnupur and 142 km from Kolkata lying between the Latitude and Longitude 23°3′33.1236″N and 87°26'36.132″E, respectively. It has an average elevation of 150  m. Beliatore forest is located between Latitude 23°16′5.592″N and Longitude 87°12′23.364″E in the district of Bankura. Topographically the area is characterized by an alluvial plain in the east with a maximum elevation of 79 m. River Shali, a tributary, flows over the area towards the south-east direction and joins the River Damodar. The heavily dense forest stippled with lush greenery is very well connected by road to others nearest places, and the Beliatore forest area is situated 23  km from Durgapur, 21  km from Bankura town and 23  km from Sonamukhi. Both the study area, Joypur and Beliatore Forests, containing forest range offices, are well connected by road with a 51 km distance in between the two Forest Ranges. Tourists can travel in both the forest stands from the month of August to December every year for a look for the herds of the elephants along the elephant corridor.

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Fig. 3.1  Elephant corridor at Joypur Forest

Soil Characteristics Forest ranges Joypur and Beliatore of Bankura district are characterized by Alfisol with slight admixture of red soil. Alfisol, a typical forest soil, is composed of aluminum and iron as main ingredients. The prefix ‘Alf’ of Alfisol is derived from aluminum (Al) and iron (Fe). Alfisol offers relatively native fertility to the forest vegetation through moderate leaching of clay minerals and soil nutrients from the surface layer to subsoil that enable food and fiber production of plants (Wang and Wang 2007; Wang and Yang 2007). Abundant occurrences of calcium, magnesium and

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potassium in the soil turns Alfisol saturated into at least 35% base composition that leads to high productivity and keeps it more fertile than other humid-climate soil. Alfisol is associated with semiarid to moist areas of relatively cooler, drier climates and younger landscapes of Joypur and Beliatore forest cover where rapid leaching, weathering and removal of bases are generally not occurring (Yang et  al. 2010, Zhao et al. 2012). These physico-chemical properties along with presence of inherently fertile parent materials of Alfisol assist luxuriant growth of the floral assemblages in the forest. Forest plants usually uptake available nutrients, ready for absorption and assimilation, derived from weathering and biomass decomposition. Weathering contributes calcium, magnesium, potassium, and sodium (base cations) along with iron, aluminum, and manganese (acid cations) to the forest soils. Trees are very much selective in choosing elements for absorption from among these elements. Forest plants absorb nitrogen, phosphorus, potassium, and manganese, on contrast, discards the elements like aluminum, chlorine and sodium (Noguez et  al. 2008; Nsabimana et al. 2004). For the decomposition process, microorganisms, agents for decomposition of forest biomass, classified as four major groups – bacteria, fungi, Actinomycetes and algae, cause microbial decomposition and contribute microbial biomass. This microbial biomass in the forest floor has contributed 1.9–4.6% to total nitrogen, 4.8–12.3% to total phosphorus and 1.8–3.5% to total organic carbon. Thus, available nutrients and microbial biomass are directly correlated in the dynamics of soil-­ health in the forest floor (Chandra et al. 2016). Forest lands have higher soil carbon content than the agricultural land due to production of detritus from leaves, bark, fruit, flower, seeds, flosses, and others from the plants. The soil carbon content becomes low with the conversion of forest land into agricultural land for decomposition of soil organic matter through the oxidation process (Horwath 2005). As a result of such degradation of soil carbon content, growth of forest plants encompassing the agricultural land becomes particularly in the forest areas of Joypur and Beliatore forest ranges. Organic carbon is recorded more in quantity in the natural forests (1.7%) than the relatively younger mixed plantations under social forestry scheme (1.5%) depending upon their nature of species composition (Sharma and Sharma 2004). Zhao et al. (2012) reported the availability of higher content of organic carbon particularly in the dry deciduous forest. Decomposition of litter fall such as leaves, bark, flosses, seeds, flowers, and fruits produce organic carbon and extract available nutrients to the forest vegetation. Amount of organic carbon in the forest floor of Sal forests is relatively lower (1.07%) as the Sal leaves are frequently collected by the local tribal community for its different uses and low rate of decomposability of Sal leaves. Another reason for lower organic carbon in the forest soils is due to the loss of litter fall in the runoff water during rainy days. Chandra et al. (2016) reported that organic carbon became enriched in the plantation forests and thus improved the forest floor with increasing amounts of organic carbon.

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Soil Analysis and Results Soil samples from a rooting depth of 15  cm were collected from several points selecting randomly distributed pedons from the study areas of Joypur and Beliatore forests of Bankura district (Table  3.4). Pedons were selected in the experimental stations to study the characteristics of soil as ‘pedon’ in the forest land surface is considered as the smallest unit. Soil samples were collected at the depth of 15 cm from the surface layer as the concentration of soil nutrients and other chemical properties are rather higher up to the depth of 15 cm and changes of soil chemical properties at soil depths are insignificant in the forest landscapes. The soil samples were oven-dried at 70 °C, then crushed using a pestle and mortar and passed through 2 mm sieve for chemical analysis. Electrical conductivity and pH of the soil samples are estimated following the methods of Jackson (1978). Organic carbon was estimated by wet digestion method as described by Walkley and Black (1934). Potassium Permanganate Oxidation method (Subbiah and Asiza, 1956) and Olsen’s Bicarbonate Extraction method (Olsen et al. 1954) were employed for estimation of available nitrogen and available phosphorus, respectively. Extracted potassium from soil in ammonium acetate solution was measured with a

Table 3.4  Soil physico-chemical properties for the samples collected at selected pedons of the forest patches of West Bengal Sample No J1 J2 J3 J11 J12 J13 J14 J15 B1 B11 B12 B13 B14 B15 G1 A1 A11 E1 E2

Soil pH 5.42 4.43 5.24 5.76 5.73 5.51 5.10 5.41 5.45 5.28 5.09 5.75 5.42 5.14 4.65 5.53 5.14 5.11 5.15

Electrical conductivity (dS m−1) 0.03 0.03 0.07 0.07 0.06 0.03 0.04 0.03 0.07 0.03 0.03 0.05 0.04 0.04 0.04 0.03 0.05 0.02 0.03

Organic carbon (%) 0.25 0.44 0.66 0.78 0.75 0.38 0.41 0.69 0.50 0.63 0.63 0.81 0.72 0.66 0.16 0.18 0.22 0.56 0.38

Nitrogen (kg/ha) 140 245 455 595 560 210 227.50 490 280 420 420 630 525 455 87 89.50 122.50 350 210

Phosphorus (kg/ha) 95.82 25.98 19.49 26.21 23.52 62.50 22.85 27.55 4.87 133.06 24.86 26.21 35.62 34.27 17.86 27.61 28.90 102.14 63.17

Potassium (kg/ha) 67.35 70.05 205.58 361.65 397.04 371.73 419.78 671.89 166.50 99.23 226.46 441.17 486.30 212.46 93.38 55.20 202.27 164.75 220.86

Sulphur (mg/kg) 3.09 2.85 6.41 0.48 0.24 3.80 2.38 5.23 3.80 7.84 12.11 4.75 14.01 4.28 8.55 4.75 8.08 0.24 9.50

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digital flame photometer. Micronutrients (Fe, Cu, Zn, B, and Mn) were analyzed using inductively coupled plasma atomic spectroscopy (ICP-AES). Soil samples (J1-J3 & J11-J15 for Joypur forest and B1 & B11–15 for Beliatore forest) from the study area are collected and soil chemical parameters are analyzed for interpreting their correlations with the growing stock vegetation of the forests (Tables 3.4 and 3.5). Among physico-chemical parameters obtained from the chemical analysis, organic carbon of forest soil in Joypur forest varies from 0.25% to 0.78% and from 0.50% to 0.81% in Beliatore forest. Soil carbon and forest floral assemblage density is directly correlated. The soil organic carbon shows higher value in Beliatore forest than that of Joypur forest and this happens for aggregation of more litter at the forest floor of Beliatore. Electrical Conductivity (EC) of the collected soil samples for Joypur and Beliatore ranges from 0.03 to 0.07 dS m−1. EC is controlled by the composition and nature of humus present in the forest soils including higher content of Calcium cation (Ca++) in Sal tree dominated forests. Soil pH varies from 4.43 to 5.76 for the forest soils of Joypur and 5.09 to 5.75 of the forest soils of Beliatore indicating acidic in nature and low base exchange capacity. Soil pH, acidic in nature for all the soil samples, has a major impact over nutrient availability and fertility of soil (Zhao et al. 2012). The available nitrogen content of the forest soils of Joypur varies from 140 to 595 kg/ha and at Beliatore forest available nitrogen ranges from 280 to 630  kg/ha. Soil samples with more content of red soil show low content of available nitrogen. Characteristically, Joypur and Beliatore forest area comprises Alfisol with slight admixture of red soil. Red soil is reported to be poor in available nutrients like nitrogen, phosphorus, and potassium (Roychaudhuri 1980). The content of available phosphorus of the forest Table 3.5  Micronutrient contents of forest soil samples collected at the forest patches of West Bengal Sample No J1 J2 J3 J11 J12 J13 J14 J15 B1 B11 B12 B13 B14 B15 G1 A1 A11 E1 E2

Boron (mg/kg) 0.82 0.84 0.81 0.83 0.79 0.79 0.78 0.87 0.96 0.86 0.84 1.18 0.79 1.06 0.91 0.83 0.79 0.77 0.76

Zinc (mg/kg) 2.64 1.82 2.90 3.16 2.89 4.04 0.95 1.68 4.37 0.96 1.43 3.30 7.39 3.02 1.13 1.61 2.11 1.18 0.97

Iron (mg/kg) 13.62 12.28 17.30 23.74 23.18 25.90 21.18 21.10 19.26 18.86 22.06 15.34 30.30 25.72 9.64 7.20 13.66 16.17 15.42

Copper (mg/kg) 1.78 1.52 2.36 1.75 1.45 1.37 1.31 1.32 3.12 0.26 0.76 7.61 5.26 3.54 0.76 0.70 0.54 0.72 0.46

Manganese (mg/kg) 11.64 17.83 26.76 58.32 25.54 35.20 51.26 58.60 17.96 14.22 23.68 71.08 73.48 43.40 22.52 11.50 39.34 16.86 66.56

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soils of Joypur varies from 19.49 to 95.82 kg/ha and at Beliatore forest available phosphorus ranges from 4.87 to 133.06 kg/ha. Available potassium content ranges from 67.35  kg/ha to 671.89  kg/ha in Joypur forest and from 99.23  kg/ha to 486.30 kg/ha for the soil samples of Beliatore forest (Table 3.4). Sulphur (S) is one of four major macroelements, after nitrogen, phosphorus, and potassium, which is considered as indispensable as far as appropriate plant growth and development. Sulphur (S) content in the sampled forest soils of Bankura district ranges from 0.24 to 6.41 mg/kg. The average concentrations of soil micronutrients like Fe, Mn, B, Cu, and Zn are not adequate in the selected pedons of the forest patches in the study area. Availability of soil micronutrients of both the forest is relatively low, though any concentration of copper above 60  mg/kg is considered to be toxic and the copper concentration 0.2  mg/kg is the critical limit for the forest soils (Sienkiewicz-Cholewa and Kieloch 2015). The present observation of copper content ranging from 0.26 to 7.61 mg/kg indicated that all the soil samples are adequate in available copper and is much lower than 60 mg/kg. Availability of micronutrients of both the forests is relatively low. Among them, Boron varies from 0.78 to 1.18 mg/kg, Zinc (Zn) ranges from 0.95 to 7.39 mg/kg, Iron (Fe) ranges from 12.28 to 30.30 mg/kg, and Manganese (Mn) ranges from 11.64 to 73.48 mg/kg in the soil samples collected from the forest floors of Joypur and Beliatore forests (Table 3.5).

Discussion Calcium, magnesium, phosphorus, and nitrogen with maximum quantity returns to the soils of the forest floor of Sal forests through leaf litter. Mixing the maximum content of such calcium with soil depends upon the nature of tree species in the forests and the amount is determined with the content of calcium present in the tree leaves. Sal forests have higher content of exchangeable calcium and lower content of exchangeable magnesium in the plantation site with mixed vegetation (Chandra et al. 2016). Soil pH is recorded higher in the forests covered with Sal trees though litterfall and its decomposition contribute weak acids to the forest floor. Presence of calcium cation in the soil increases base saturation, exchangeable cations and EC in the soils covered with trees in the forests. Maximum EC in the forest soils was reported in the plantation sites with imported species like Eucalyptus and Acacia rather than the forest areas covered with indigenous species like Sal, Asna, Sidha, Mahua etc. Soils of Sal forests are reportedly with 10.6 me/100 g calcium cation that might control and maintain productivity and nutrient status of the forest through microbial activity and accumulation of organic matter thereon (Sharma and Sharma 2004).

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Remarks Micronutrients like Cu, Fe, Zn, B, and Mn are common in the forests where soils are older, more acidic, and higher in nitrogen and organic matter. Availability of the micronutrients in the soils of Joypur and Beliatore is low because both the forest stands are comparatively young. Low soil pH and high exchangeable aluminum might reduce the concentration of Fe, Cu, Zn, B, Mn like micronutrients as well as uptake in the forest lands (Zasoski et al. 1990). In forest soils, the metal content decreases with depth in most of the soil profiles. This is probably related to low soil organic matter content i.e., low binding capacity below 50 cm in combination with low soil pH. Concentration of micronutrients like Cu, Fe, Zn, B, and Mn decrease with increasing pH (Romkens and Salomons 1998).

Garh Jangal and Aduria Forest of Bardhaman Forest Division Forests are strewn across the districts of Purba and Paschim Bardhaman of West Bengal and the forests of the districts have wide variations. Inside the dense jungles, there are few places of historical interest; some notable myths about some structures and persons related to the forests are mentioned even in the Manasamangal. Such dense forests of Bardhaman Forest Division offer diverse set of habitats for plants, animals, and micro-organisms, but these increasingly threatened biologically rich systems along with Deul, Garh jungle etc. with dense forest, thick foliage, and greenery around, are the oldest places as believed by the historians. There are 38 patches of natural forests and plantation sites under three forest ranges in the Bardhaman Forest Division. Among them, Garh Jangal under Durgapur forest range and Aduria Forest under Panagarh forest range are accounted for in the studies of physico-chemical properties of soil and other environmental parameters. Garh Jangal is located between Latitude 23.604305 N and Longitude 87.431595E, and Aduria Forest lies between Latitude 23.572744 N and Longitude 87.523551E. The study area attains the elevation of 65 m and is bounded by Ajay River in the north-­ east and Damodar River in the south-west. The Ajay River changes the direction of its course along the entire stretch of Garh Jangal and looks like a pouch in shape that enhances rapid alluvial deposition to the study area. Such an accumulated thick soil layer enriched with available nutrients carried by Ajay river accelerates the establishment of the vegetation growth forming a dense mixed jungle at the riverside areas (Das 2020b). The jungle of Garh Jangal extends in the south-east direction towards the patches of Aduria Forest. Almost the same eco-floristic nature in forest vegetation is revealed in the forest floors of Garh Jangal and Aduria forest but they differ a little bit in soil physico-chemical properties and availability of nutrients. The Bardhaman Forest Division lies in the unpartitioned district Bardhaman which is almost hammer in shape, drained by Barakar, Damodar and Ajay Rivers, is known from the Mesolithic or Late Stone Age of about 5000 BC, and the anglicized

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99

name of Burdwan is derived from the Sanskrit word Vardhamana. At present, Burdwan is known to all as Bardhaman, and the district Bardhaman is divided to form separate two districts – Purba Bardhaman and Paschim Bardhaman with effect from 7 April 2017. The district is encountered in a mixed geologic condition with rock-blended soil on the west and thick alluvial low-level plains in the east. Topographically, the area is undulating, lying in the alluvial plain with high soil thickness brought down by the rivers Ajay and Damodar from the west to the east. The minimum temperature varied from 6 °C and maximum 44 °C round the year during the study period. The annual rainfall is recorded around 1500 mm that helps moderate infiltration rate in dry deciduous Sal forests in both the Durgapur and Panagarh forest ranges. Moderate infiltration rate and sufficient ground water table accelerates eco-floristic occurrences of the forest stands. The Bardhaman Forest Division, presently existing in both the districts of Purba and Paschim Bardhaman, is constituted with three ranges viz. Durgapur, Guskara and Panagarh (Table 3.6). Durgapur Range contains 5 beats namely Arrah, Basudha, Gopalpur, Molandighi, and Shibpur; Guskara Range includes 4 beats namely Ausgram, Bhalki, Orgram, and Pratappur; and Panagarh Range contains 4 beats namely Aduria, Kanksa, Khandari, and Sonai. There are several mouza (villages) included under the supervision of each forest beat.

Ground Water Table Unconsolidated sediments occupy the entire study areas of the Garh Jangal and Aduria forests, though the western part of the Bardhaman district is partly occupied by the hard rocks having enough groundwater. The Ground water resources have been assessed block-wise in the district, but the Central Ground Water Board Table 3.6  Name of forest ranges and beats under the Bardhaman forest division Division Bardhaman forest division

Range Durgapur

Gushkara

Panagarh

Beat Arrah Basudha Gopalpur Molandighi Shibpur Ausgram Bhalki Orgram Pratappur Aduria Kanksa Khandari Sonai

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published the report district-wise (Dynamic Ground Water Resources of India Report, 2017). As per assessment report, annual replenishable resources had been assessed as 261646.42 ham (hectare meter), net ground water availability as 236877.76 ham, and ground water draft for all uses as 103667.61 ham. The stage of development was worked out to be 44%, which is 1% less than the state average (Table 3.7). Groundwater table is almost fair for Garh Jangal under Durgapur forest range and Aduria forest under Panagarh forest range as the published report for ground water table shows that no blocks in the Bardhaman district adjacent to this forest stand is yet described under critical or semi-critical status. Further, forests play an important role in soil water storage by increasing infiltration rate through intercepting maximum rainwater received in their canopy. Infiltration rate in the bare land, grass land and agricultural land is lower than forest land. Forests control rainwater precipitation in an enhanced manner by absorbing through litter fall converted biomass and humus, and thus not only check soil erosion from the forest floor, but recharge and enrich the groundwater table.

Divisional Forest Scenario The natural vegetation of the forests in the district is Sal which is mostly in the protected and covering areas. Conservation of the forest is essential as the tropical forests alone are losing at least one higher plant species per day (Das 2020c). It would require some effort to scale the forest area into its former size and shape, but the reality of the situation is quite different, sometimes the noble endeavour of the forest department has been desecrated by the inhabitants of the villages surrounding the forests. Bouri and Mukherjee (2018) documented 187 Non-Timber Forest plant species and inventoried them in an account regarded as phytoresources in the forests of Durgapur Forest Range including Garh Jangal and Aduria Forests of Paschim Bardhaman district. All 187 Non-Timber Forest plant species are used for medicinal purposes based on their different usage as per the traditional knowledge from the tribal communities living in and around the forest areas about their utilities. Among 187 plant species, as resources of Non-Timber Forest Products (NTFP), Bouri and Mukherjee (2018) recorded 22 species as source of edible fruits; 10 species for minor wood works; 9 species each for miscellaneous purposes including making of basket, mat, hand-fan, broom and for leaves; 8 species each for flowers and tannin and gum, followed by 7 species for extraction of oil, 6 species as produce of fibers, 3 species for resins and 1 species for flosses. During the study, four dominant timber tree species covering 1-hectare forest area in Garh Jangal forest stands are enumerated. Occurrence of Sal (Shorea robusta) trees, enumerated to 610 in 1-hectare area, was recorded the highest among them, followed by 201 Kurchi (Holarrhena antidysenterica) trees, 56 Piyal (Buchanania lanzan) trees, and 52 Khoir (Acacia catechu) trees in a 1-hectare forest cover, though associated species exist along with these dominant tree species. Like the vegetation of Garh Jangal, dominant tree

261646.42 24768.66

Total annual ground water recharge 236877.76

94059.60 9608.01

Annual GW allocation for domestic use as on 2025 103667.61 15762.29

in Hectare Meter (ham) Current annual ground water Annual Total extractable extraction natural discharges ground Irrigation Domestic Total water & resources industrial

Source: Dynamic Ground Water Resources of India Report (2017)

Non-monsoon season Recharge Recharge Recharge Recharge from from from from other rainfall other rainfall sources sources 170643.19 13283.39 42504.98 35214.86

Monsoon season

Groundwater recharge

Table 3.7  Stage of groundwater resources in the district of Bardhaman

127055.87

Net ground water availability for future use

44

Stage of ground water extraction (%)

Garh Jangal and Aduria Forest of Bardhaman Forest Division 101

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species in Aduria forest remains almost the same as Garh Jangal and Aduria forest are in the same forest cover as the Garh Jangal forest under Durgapur forest range, situated on the western part of the forest, relates to the eastern part of the forest stand under Panagarh forest range of Bardhaman forest division.

Soil Characteristics The forest floor of Garh Jangal and Aduria forest is predominantly Alfisol and partly red soil derived from disintegration of rocks and stones. The common forest soils are yellowish in colour on the upper layer and reddish in depth of 30  cm, non-­ calcareous with low concentration of soil nutrients, relatively low pH value indicating slight to moderate acidic nature of soils. Texturally, soil exhibits a medium to coarse grain size distribution that contains a high amount of acid-soluble ferric oxide. Available iron, manganese and copper are high with respect to low percentage or almost deficient occurrence of available boron, molybdenum, and zinc in the soils of the forest floors where soils are generally cemented by iron and aluminum oxides. Overall, the soils are characterized with low pH, high exchangeable acidity, and low base exchange capacity (Aciego and Brooks 2009). Concentration of soil nutrients like NPK (nitrogen, phosphorus & potassium) and organic carbon content depends on the elevations, derived parent materials and pattern of vegetation in the forest stands of Garh Jangal and Aduria Forest. Nitrogen, phosphorus, and potassium availability as plant nutrients to the forest stands exhibit low availability in the Alfisol and red soils of Garh Jangal and Aduria forest under Bardhaman Forest Division. The forest soils of western part of the Bardhaman Forest Division are predominantly red soil derived from disintegration of rocks and stones of Chotanagpur plateau region. The common forest soils are yellowish in colour on the upper layer and reddish in depth of 30 cm with non-calcareous and low concentration of soil nutrients (Melle et al. 2020; Yu et al. 2020; Landesman and Dighton 2011). Soils are relatively low in pH value indicating slight to moderate acidic nature of red soils. Available iron, manganese and copper are high with respect to low percentage or almost deficient occurrence of available boron, molybdenum, and zinc in the soils of the forest floors where soils are generally cemented by iron and aluminum oxides. Overall, the soils are characterized with low pH, high exchangeable acidity, and low base exchange capacity. NPK availability as plant nutrients to the forest stands exhibits low availability in the soils, characteristics of lateritic soils. Physico-­ chemical properties of soil and soil organisms present in the forest floor as independent or dependent variables have decisive influence on forest vegetation (Tripathi and Singh 2013; Paudel and Sah 2003; Recous and Mary 1990; Six et al. 2004). Denser the forest, more soil-health potential is gained through improvement of soil characteristics by litter fall on the forest floor. Texturally, soil exhibits a medium to coarse grain size distribution that contains a high amount of acid-soluble ferric oxide. Average soil texture varies a little for the soil samples of different parts of

Garh Jangal and Aduria Forest of Bardhaman Forest Division

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Bardhaman Forest Division. On average, coarse sand ranged from 2–20%, fine sand 14–22%, very fine sand 11–24%, silt 21–39%, and clay 14–34% constitute the forest soils of the Bardhaman Forest Division.

Soil Analysis and Results Soil samples (G1 for Garh Jangal and A1 & A11 for Aduria forest) from the study area are collected and soil chemical parameters are analyzed for interpreting their correlations with the growing stock vegetation of the forests (Tables 3.4 and 3.5). Organic carbon of forest soil varies from 0.16% to 0.22% in Garh Jangal and Aduria forests. Soil carbon and forest floral assemblage density is directly correlated. The soil organic carbon shows higher value in Garh Jangal forest than that of Aduria forest and this happens for aggregation of more litter at forest floor. Electrical conductivity (EC) of the collected soil samples for Garh Jangal and Aduria forest ranges from 0.03 to 0.05 dS m−1. EC is controlled by the composition and nature of humus present in the forest soils including higher content of calcium cation (Ca++) in Sal forests. Soil pH varies from 4.65 to 5.53 for the forest soils of Garh Jangal and Aduria indicating acidic in nature and low base exchange capacity. Soil pH, acidic in nature for all the samples, has a major impact over nutrient availability and fertility of soil (Zhao et al. 2012). The available nitrogen content of the forest soils of Garh Jangal and Aduria forests varies from 87 to 122.50 kg/ha. Characteristically, Garh Jangal and Aduria forest area comprises Alfisol with slight admixture of red soil. Red soil is reported to be poor in available nutrients like nitrogen, phosphorus, and potassium (Roychaudhuri 1980). The content of available phosphorus of the forest soils of Garh Jangal and Aduria forests varies from 17.86 to 28.90 kg/ha and available potassium content ranges from 55.20 kg/ha to 202.27 kg/ha for the soil samples of Garh Jangal and Aduria forests (Table 3.4). Sulphur (S) is one of four major macroelements, after nitrogen, phosphorus, and potassium, which is considered as indispensable as far as appropriate plant growth and development. Sulphur (S) content in the forest soils of Paschim Bardhaman district ranges from 4.75 to 8.55 mg/kg. Availability of micronutrients of both the forest is relatively low, though any concentration of copper above 60 mg/kg is considered to be toxic and the copper concentration 0.2 mg/kg is the critical limit for the forest soils (Sienkiewicz-­ Cholewa and Kieloch 2015). The present observation of copper content ranging from 0.54 to 0.76 mg/kg indicated that all the soil samples are adequate in available copper and is much lower than 60 mg/kg. Among other important micronutrients, Boron ranges from 0.79 to 0.91 mg/kg, Zinc (Zn) ranges from 1.13 to 2.11 mg/kg, Iron (Fe) ranges from 7.20 to 13.66 mg/kg, and Manganese (Mn) varies from 11.50 to 39.34 mg/kg in the soil samples collected from Garh Jangal and Aduria forests (Table 3.5). The average concentrations of soil micronutrients like Fe, Mn, B, Cu, and Zn are not in sufficient ranges in the selected pedons of the forest patches in the study area.

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Discussion Available nitrogen is generally considered as the most important factor limiting the growth of the trees. Content of available nitrogen in the mineral horizons of forest soils are recorded higher than the amount accumulated in the organic horizons and the rate of accumulation of available nitrogen differs from one forest to another forest stand due to presence of different tree species composition and different physico-­ chemical properties of forest soils (Das 2021; Holden and Treseder 2013). Sharma and Sharma (2004) recorded available nitrogen, phosphorus, and potassium in open spaces with a range from 34–19 kg/ha, 9.3–10.9 and 132–143 kg/ha respectively, whereas the available nutrients under forest vegetation cover varies from 38–192, 9.9–13.9, and 159–184 kg/ha, respectively (Sharma and Sharma 2004). Phosphorus cannot alone improve forest-health, but it works well in combination with nitrogen for the forest stands. Available phosphorus accelerates growth of trees and thus is significantly correlated with the forest growth. In Perhumid climatic conditions, forest soils generally contain low amounts of available phosphorus (Fierer et  al. 2003; Holden and Treseder 2013). Physico-chemical parameters of the soils indicate positive trends towards growth of the growing stock of the forests in the two ranges of study area, though plantation sites inside the forest contribute relatively more nutrients to the soils of the forest floor in comparison to the natural forests. This indicates to take up more plantation programmes in the Bardhaman Forest Division. Even in different plots of the Garh Jangal and Aduria forest exhibits absence of growing stock and these plots are either filled with scrub growth or found to be blank. These areas are to be planted with quick growing species in shorter rotation to meet up demand of timber and fuel wood for the local people and thus, the forest health is likely to be managed in an eco-friendly way.

Impact of Potassium on Forest Vegetation Forest patches of Bankura, Paschim Bardhaman and Birbhum districts of the south-­ west part of West Bengal are dominated by four timber tree species like Shorea robusta (Sal), Holarrhena antidysenterica (Kurchi), Buchanania lanzan (Piyal) and Acacia catechu (Khoir) where Shorea robusta is recorded the highest among them. Substrate soils of these forest floors are covered with Alfisol comprising aluminum and iron as main ingredients. Collected soil samples from the selected pedons in the study area revealed higher content of potassium ranging from 55.20 to 671.89 kg/ha in comparison to nitrogen and phosphorus, the other two chief soil nutrients of the plants. Even a forest soil sample of the study area shows an exceedingly high value of 671.89 kg/ha obtained in the result of the soil chemical parameters analysis using the standard method for potassium extract. Further, the dominant four timber tree species of the vegetation type of the forest floors in the study area revealed different

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Garh Jangal and Aduria Forest of Bardhaman Forest Division

values for the potassium content and those values of potassium splendidly not only classify the habitat for each individual species but restrict the range of potassium content for the individual plant species. Forest soils covered with Holarrhena antidysenterica (Kurchi) trees show higher values of potassium content ranging from 93.38 to 671.89 kg/ha, in contrast, the substrate soils of the Sal forest show relatively lower values for potassium ranging from 55.2 to 397.04 kg/ha though the Sal trees (Shorea robusta) are abundantly occurred in these forest patches dominating over the other vegetation (Figs. 3.2 and 3.3). Districts of Bankura, Paschim Bardhaman and Birbhum are covered with several forest patches located particularly in the south-west part of West Bengal (Das 2021). Among them, remarkably important forests like Joypur and Beliatore forests of Bankura district, 11-miles forest of Birbhum district and Garh Jangal and Aduria forests of Paschim Bardhaman districts are considered as model forest stands for pilot survey to get overall forest vegetation scenario of the forest patches of three districts of West Bengal (Das 2020d). Forest cover of these three districts including the study areas of Aduria, Garh Jangal, 11-Miles, Joypur and Beliatore forests offer a picturesque landscape with vast stretches of Sal, Piyal, Kurchi, Khoir trees, flowering trees like Palas, Mahua and Kusum, and lofty plants like Sonajhuri or Akashmoni and Eucalyptus encompassing the forest stands surrounding the border. Generally, forest vegetation is often grown up on poorly fertile soils along with incredibly low available pools of nutrient cations for the plants. The loss of potassium in the forest floors will occur when the amount of rainfall exceeds the plant evaporation transpiration rates (Roberts 2008; Robinson et al. 2013). Over the long haul this will deplete the soils in potassium which is often found in older soils. Annual average rainfall around 1500 mm in these districts favours the high content of potassium. A closed climax forest minimizes the potassium losses under the natural biological web interactions, but the forest patches covered with mixed vegetation Soil chemical parameters OC

N

P

K

Soil chemical parameters (kg/ha)

800 700 600 500 400 300 200 100 0

J1

J2

J3

J11 J12 J13 J14 J15

B1

B11 B12 B13 B14 B15

G1

Sample No

Fig. 3.2  Soil chemical parameters of the forest soil samples of West Bengal

A1

A11

E1

E2

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Soil chemical parameters (kg/ha)

Soil substrate properties of classified vegetation 800 700 600 500 400 300 200 100 0

A1 B11 B12

E1

J11

J12 A11 B14

Sal

E2

G1

J14

J15 B13 B15

Kurchi

J3

Piyal

J13

B1

J1

J2

Khoir

Classified vegetation & Sample No OC

N

P

K

Fig. 3.3  Soil substrate characteristics of classified vegetation in the forest floors of West Bengal

enhances the potassium concentration. Other factors like the presence of a near neutral pH and ample calcium are particularly important (Schimel et  al. 2001; Schreeg et al. 2013). Potassium is a spectacular source of the soil nutrients for the plants and can stimulate a significant increase in the soil organic matter and soil nutrients. And the potassium has an amazing ability to accumulate the element and maximize the soil organic matter which is critical to optimize water and aeration in the soil condition (Quennehen et al. 2012). In a forest area potassium would foster an improved productivity and greater long term sustainability and utility where the limitation of nitrogen can be provided by legumes which might be considered as the need for phosphorus by rock phosphate and ensuring mycorrhizal activity in the forest floors (Rao et al. 2012). In the forest patches, the potassium leaching process has been accelerated with the decreasing soil pH of the forest soils. Soil samples revealed pH ranges from 4.65 to 5.76 might enhance potassium content of the sampled soils in the present study area in the three districts of south-west West Bengal. Variations of available potassium are observed more than nitrogen and phosphorus in the ecotypes of the terrestrial forest floors. Availability of potassium is strongly correlated with the water availability as observed in the recent stoichiometric studies that plays an important role in elemental composition of the individual timber tree species. Thus, the content of potassium is more important in the fundamental elemental composition of the individual tree species than the nitrogen and phosphorus revealed in the stoichiometric study. In plant cells, potassium is considered as the most abundant cation and in leaves, it is second most abundant after nitrogen (Sardans et al. 2008, 2011, 2012). About 2.6% potassium weighs the crust of the earth but still it is a neglected soil nutrient in the field of research for global change (Sardans and Peñuelas 2015).

Results and Discussion

107

Results and Discussion Forest soil samples are collected from the selected pedons from Joypur forest (J1-­ J3, J11-J15), Beliatore forest (B1, B11-B15), 11-Miles forest (E1-E2), Aduria forest (A1, A11) and Garh Jangal forest (G1) for the soil chemical analysis of different physico-chemical parameters following the standard methods. The obtained result shows the content of available potassium for a natural terrestrial forest soil sample an exceedingly high value of 671.89 kg/ha using the standard method for the soil chemical analysis, and therefore, a question might be arisen whether it is natural for the soils sampled at a terrestrial forest patch. The result obtained for potassium is above the threshold, but it is possible to get such high results given the other factors like weathering of minerals, fertilizers, and other pollutants as well as run off wastewater from communities which may trigger that values (Bel et al. 2020; Tripler et al. 2006; Yates et al. 2002). But there is no chance of mixing fertilizers with the soil samples in these forest patches from the agricultural run off as the vegetation is surrounded by badlands and no river or even a streamlet is flowing within the periphery of the forest stand. And the chance of mixing other pollutants like municipal wastes with the soils is nearly impossible as the nearest town Bishnupur is about 15 km far away from the forest stands of Joypur from where the collected soil sample revealed an exceedingly high value of potassium. It is certainly a remarkably interesting fact about NH4OAc-K, the higher value of potassium in the forest soils is quite dynamic. Ammonium-acetate (NH4OAc) is the most widely used soil test for potassium (K) based on air-dried or oven-dried samples applying soil potassium (K) extraction with neutral 1 M Ammonium-acetate. A huge variation in K (potassium) value is found for the soil samples collected during summer stress period versus rainy season samples (Talkner et al. 2019; van Sundert et al. 2020; Hansson et al. 2020; Legout et al. 2020). So, it is equally important to compare the soil test value taken at similar timings of sampling, which is comparable with the type of extractant, mineralogy, texture, pH etc. to give more facts about such values (Lucash et al. 2012; Rosenstock et al. 2019). For the collected soil samples in the forest patches of the south-west part of West Bengal, the texture of the soil samples in an average, coarse sand ranged from 2–20%, fine sand 14–22%, very fine sand 11–24%, silt 21–39%, and clay 14–34% that constitute the forest soils. Soil pH ranges from 4.65 to 5.76, electrical conductivity (EC) varies from 0.02 to 0.07 dSm−1, available nitrogen (N) ranges from 87 to 630 kg/ha, available phosphorus (P) ranges from 4.87 to 133.06  kg/ha, available potassium (K) from 55.20 to 671.89 and organic carbon (OC) from 0.16 to 0.81%. The samples were collected during the commencement of the spring in between summer and rainy season, yet the soil samples show such high value for potassium (Table  3.4 and Fig. 3.2). The percentage of relative humidity varies between 49% and 85%, minimum in the month of April and maximum during the month of August. For the soil moisture variability at those sampling sites, such values for the content of potassium are common, though the past-history of K-fertilization for the selected forest patches is not known.

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There is no surprise with this level of NH4OAc-K for so much variation in soil test values as the soils analyzed were under mixed vegetation (van der Heijden et al. 2017; van der Heijden et al. 2018). The samples were collected from the selected pedons covered with the mixed vegetation dominated by the four species like Sal (Shorea robusta), Kurchi (Holarrhena antidysenterica), Piyal (Buchanania lanzan) and Khoir (Acacia catechu) where Sal trees (Shorea robusta) are occurred as the highest timber tree species in the selected forest patches of the present pilot survey. The dominant four timber tree species of the vegetation type of the forest floors show different values for the potassium and those values of potassium splendidly not only classify each individual species but restrict the content of potassium for the individual species. Forest soils covered with Holarrhena antidysenterica (Kurchi) trees show higher values of potassium content ranging from 93.38 to 671.89 kg/ha, in contrast, the forest soils show relatively lower values for potassium ranging from 55.2 to 397.04 kg/ha though the Sal trees (Shorea robusta) are abundantly occurred in these forest patches dominating over other vegetation. Other dominant species Buchanania lanzan (Piyal) revealed values for the potassium content in between Holarrhena antidysenterica and Shorea robusta of the forest patch. Potassium content ranges from 205.58 to 441.17  kg/ha for Buchanania lanzan (Piyal) and the Acacia catechu (Khoir) tree species revealed the potassium values ranging from 67.35 to 166.5 kg/ha (Fig. 3.3).

Remarks The chemically analyzed value 671.89 kg/ha obtained for potassium of a terrestrial forest soil sample is quite natural for the forest soil of the Joypur forest in south-­ west Bengal including the ranges of potassium content for the individual timber tree species. Content of such potassium in the forest floors of south-west part of West Bengal can provide a clue that happens to soil test values when a soil type with uniform mineralogy and texture, is acted upon with different forest species (Sparks 2003; Kosmulski 2009; Schwertmann and Fechter 1982). An interesting feedback, we can get it with an additional data on different soil microbial communities plus microbial biomass carbon (Cmic), microbial biomass nitrogen (Nmic) and microbial biomass phosphorus (Pmic) including microbial biomass potassium (Kmic) with the variations in other nutrients as well in the subsequent studies (York et al. 2016). In such a case, rhizosphere effect will be particularly important while dealing with such a huge variation in soil test values, since soils are collected at almost the same point of time (Pradier et al. 2017). Textural variation is also one factor, and step down regression analysis would reveal the factors contributing towards such variation. The practical utility of such variations and these changes in soil test values offer possible changes on carbon footprint of these four dominated forest timber tree species, a big question to be answered which is to be considered in the future studies on these forest floors (Nezat et al. 2007). Further, if a distinct variation in soil test values as per stand of different forest species is found, the root density

Summary

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vis-­a- vis root education dictating such variation in soil test values will be observed including potassium for the individual timber tree species of the selected forest patches in the districts of Bankura, Birbhum, and Paschim Bardhaman of the south-­ west part of West Bengal.

Summary Soil chemical parameters indicated positive trends towards growth of the growing stock of the forests in the study areas, though plantation sites inside the forest contribute relatively more nutrients to the soils of the forest floor in comparison to the natural forests. This indicates taking up more plantation programmes in the district. Even in different plots of the ranges Joypur and Beliatore of Bankura district and Aduria and Garh Jangal of Paschim Bardhaman district, forest exhibits absence of growing stock and these plots are either filled with scrub growth or found to be blank. These areas are to be planted with quick growing species in shorter rotation to meet up demand of timber and fuel wood for the local people managing the forest health in an eco-friendly way. After maturity, harvesting of quick growing species, the natural indigenous growing stock like Sal, Asna, Sidha, Gamar, Mahua etc. will be reintroduced for plantation. For monitoring forest plantation, indigenous species must be the first choice, where quick growing alien species are to be considered only in necessity for a short term. As the physiography of the forest area, soil chemical properties and other physical conditions like fair and sufficient ground water table and rate of infiltration are favourable for tree growth of the present study areas, integrity monitoring through farsighted management policy might lead to a denser luxuriant forest canopy in the forest patches of the districts selected for the studies. Joypur and Beliatore forests are famous for corridors of elephants, where elephants migrate annually in the month of August from the Dalma Range of Jharkhand state and return generally in the month of December every year. The elephant herds like to choose the forest patches for their temporary dwelling purposes only if the plenty of foods are available in those forest areas and the environmental condition is befitted to them. Recently the forest department reported the settlement of about 194 elephants in the forest areas of Bankura, Jhargram, Purulia and Paschim Medinipur districts migrating from Dalma Range. Denser the jungle, more the numbers of wild animals and ultimately Joypur and Beliatore forest ranges would be enlisted as elephant reserves in near future with the direct participation of the tribal community living in and around the forests of Bankura district. Thus, at present, more plantations of fast-growing species, the need of the day and for the need of the people, on the short rotation basis ameliorating in the bare and degraded areas in the forest stands are suggested. Further, the tree lines of the forests provide a warming effect on a microclimate. Microclimates help to explain part of the patchiness in forest vegetation that occurs on smaller scales. The microclimate determines which plants can grow where and nature reveals a pattern that can be replicated in the forest patches scattered all over the state of West Bengal.

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Six J, Bossuyt H, Degryze S, Denef K (2004) A history of research on the link between (micro) aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res 79:7–31 Sparks DL (2003) Inorganic soil components. In: Sparks DL (ed) Environmental soil chemistry. Academic, Cambridge, pp 43–73 Subbiah BV, Asiza GL (1956) A rapid procedure for the estimation of available nitrogen in soils. Curr Sci 25(8):259–261 Talkner U et al (2019) Nutritional status of major forest tree species in Germany. In: Wellbrock N, Bolte A (eds) Status and dynamics of forests in Germany: results of the National Forest Monitoring. Springer, New York, pp 261–293 Tripathi N, Singh RS (2013) Cultivation impacts soil microbial dynamics in dry tropical forest ecosystems in India. Acta Ecol Sin 33:344–353 Tripler CE, Kaushal SS, Likens GE, Walter MT (2006) Patterns in potassium dynamics in forest ecosystems. Ecol Lett 9:451–466. https://doi.org/10.1111/j.1461-­0248.2006.00891.x van der Heijden G, Legout A, Mareschal L, Ranger J, Dambrine E (2017) Filling the gap in Ca input-output budgets in base-poor forest ecosystems: the contribution of non-crystalline phases evidenced by stable isotopic dilution. Geochim Cosmochim Acta 209:135–148 van der Heijden G et al (2018) Measuring plant-available Mg, Ca, and K pools in the soil—an isotopic dilution assay. ACS Earth Sp Chem 2:292–313 van Sundert K et  al (2020) Towards comparable assessment of the soil nutrient status across scales—review and development of nutrient metrics. Glob Chang Biol 26:392–409 Walkley A, Black IA (1934) An examination of the Degtjareff method for determining organic carbon in soils: effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci 63:251–263 Wang QK, Wang SL (2007) Soil organic matter under different forest types in Southern China. Geoderma 142:349–356. https://doi.org/10.1016/j.geoderma.2007.09.006 Wang CK, Yang JY (2007) Rhizospheric and heterotrophic components of soil respiration in six Chinese temperate forests. Glob Chang Biol 13:123–131. https://doi. org/10.1111/j.1365-­2486.2006 Yang K, Zhu J, Zhang M, Yan Q, Sun OJ (2010) Soil microbial biomass carbon and nitrogen in forest ecosystems of Northeast China: a comparison between natural secondary forest and larch plantation. J Plant Ecol 3:175–182. https://doi.org/10.1093/jpe/rtq022 Yates EJ, Ashwath N, Midmore DJ (2002) Responses to nitrogen, phosphorus, potassium, and sodium chloride by three mangrove species in pot culture. Trees 16:120–125 York LM, Carminati A, Mooney SJ, Ritz K, Bennett MM (2016) The holistic rhizosphere: integrating zones, processes, and semantics in the soil influenced by roots. J Exp Bot 67:3629–3643 Yu L, Ahrens B, Wutzler T, Zaehle S, Schrumpf M (2020) Modelling soil responses to nitrogen and phosphorus fertilization along a soil phosphorus stock gradient. Frontiers in Forest and Global Change. https://doi.org/10.3389/ffgc.2020.543112 Zasoski RJ, Paroda HJ, Ryan PJ, Jenkins JG, Gessel SP (1990) Observations of copper, zinc and iron and manganese status I western Wasington forest. Environ Climate Change 37(1–3):7–25 Zhao D, Li F, Wang R (2012) Soil inorganic nitrogen and microbial biomass carbon and nitrogen under pine plantations in Zhanggutai sandy soil, China. Acta Ecol Sin 32:144–149. https://doi. org/10.1016/S1002-­0160(08)60073-­9

Chapter 4

Statistical Analysis of Forest Soil Properties

Abstract  Statistical analysis of forest substrate soil parameters is an attempt for the interpretation of different aspects of the forest stands characteristics and monitoring of forest restoration. Such statistical estimation in the field of forest research plays multidisciplinary roles and the application of specific statistical techniques are useful directly or indirectly for the determination of vegetation pattern, species diversity, similarity indices, wildlife conservation, man-wildlife conflict, physico-­chemical parameters of forest soils or even applied for the occupational pattern of the tribal community living in the vicinity of the forest stands in West Bengal. Availability of such statistically estimated data pools has immense use for the researchers in the field of forests and forestry, for integrity monitoring of the forest stands and afforestation programme by the foresters, and for the overall management by the government sector for regeneration and restoration of the forests of the state. In this field of study, conventional standard statistical methods like random sampling with or without replacement, determination of variance and standard deviation, estimation of standard error and ratio estimator, analysis of multiple correlation coefficients, goodness of fit test, similarity indices are applied for the analysis of possible samples and for the data pool generation. Results obtained from statistical analysis come out to be unbiased in most cases showing truthfulness for different aspects of the forests and forestry research. Keywords  Random sampling · Fitted regression line · Variance · Standard error · Ratio data · Correlation coefficients · Goodness of fit test

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_4

113

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4  Statistical Analysis of Forest Soil Properties

Statistical Analysis The statistical measures for the different aspects of forests and forestry research are applied on the data collected either from the survey, or the data obtained from the soil physico-chemical analysis, and the data available from the forest department for the biodiversity and human-wildlife conflicts. Most of the data are obtained from the laboratory analysis of the forest samples as well as from the samples collected during the survey in different forest patches of West Bengal. The survey was conducted in both types of natural and stray forests patches during the period from October 2008 to December 2020. Only in a few cases, data used for statistical analysis are based on the record generated by the forest department of the Government of West Bengal. Considering such a view of research, analyzed data of soil physico-­chemical parameters are taken into consideration for the statistical analysis.

Forest Floor Substrate Soils Relationships of the Soil Nutrients Soil nutrients like nitrogen, phosphorus, and potassium (NPK) including other soil physico-chemical parameters like bulk density of soil, soil pH, salinity, organic carbon (C) is analyzed for the sediment samples collected from the different sampling stations of the Sunderbans mangrove ecosystems. The computation of C/N ratio of forest soils is observed in the standard literature, but the computation of C/P or C/K ratio is not found, probably for the release of C & N from the decomposed microbial biomass, and inclusion of P & K from the separate sources other than the biomass from the litter fall (Das 2011, 2015, 2017). The researchers do not lay much emphasis on C:P or C:K (though this ratio hardly works including the forest soils) because of the poor correlation compared to C:N ratio (Sharma and Sharma 2004). It is hardly seen any study concentrating on release of K from organic matter decomposition in the forest floors. All such observations lead to establishing a relationship of nitrogen, phosphorus, and potassium in the mangrove sediments. With an objective of the establishment of relationships, the present study is an attempt to find out correlation of nitrogen, phosphorus, and potassium through computation of correlation coefficients using the formula of multiple correlation coefficient of the standard statistical methods. Due to such heterogeneous combination and distribution of the soil nutrients shown in the Table 4.1, a relationship is to be established applying the formula of multiple correlation coefficient of statistical analysis.

Analysis and Results

115

Table 4.1  Soil nutrients parameters of mangrove sediment samples of the Sunderbans Sampling stations Sajnekhali Sudhanyakhali Banbibi Bharani Dobanki Pancha Mukhani Neti Dhopani

Sample number S1 S2 S3 S4 S5 S6

Available phosphorus (%) P 0.001 0.00098 0.0014 0.0014 0.0011 0.0006

Available nitrogen (%) N 0.061 0.085 0.085 0.073 0.048 0.061

Available potassium (%) K 0.071 0.0141 0.1157 0.164 0.134 0.149

Analysis and Results Correlation can be calculated between more than two variables applying the formula of multiple correlation coefficient. The multiple correlation coefficient is expressed simply as R though they are expanded to more than two independent variables with just one dependent variable (Zaiontz 2020). Computation of the multiple correlation coefficient is possible with the variables x, y and z using formula –. Rzxy 

rxz2  ryz2  2rxz ryz rxy 1  rxy2.



Where rxz, ryz, rxy are defined as basic concept of correlation. Here x and y are viewed as the independent variables and z is the dependent variable. In this statistical analysis, R is not an unbiased estimate of the population multiple correlation coefficient for the small samples. A relatively unbiased version of R is given by R adjusted. Where R is Rzxy as stated above (or similarly for more variables) then the adjusted multiple coefficient of determination is

1  R   n  1  1 2



2 adj

R

n  k 1



where k = the number of independent variables and n = the number of data elements in the sample for z (which should be the same as the samples for x and y). In addition to the various correlation functions described elsewhere, Excel provides the covariance and correlation data analysis tools (Zaiontz 2020). The covariance tool calculates the pairwise population covariances for all the variables in the data set. The pairwise correlation coefficients are computed for the various variables shown in the Table 4.1 using Excel’s correlation data analysis tool. The results are shown in Table 4.2. Three variables are computed, nitrogen, phosphorus and potassium and the multiple correlation coefficients is calculated (Table  4.2) assuming nitrogen is the dependent variable using the data in Table 4.1 to obtain the values rpnrknrpk.

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Table 4.2  Data of computed pairwise correlation coefficients for the various variables using Excel’s correlation data analysis tool Nitrogen (N) Phosphorus (P) Potassium (K)

Nitrogen (N)

Phosphorus (P)

0.397919 −0.01924

0.141694

Potassium (K)

rpn2  rkn2  2rpn rkn rpk

Rnpk 

1  rpk2





0.3972   0.019   2  0.397   0.019  0.141 2

Rnpk 



1  0.1412



Rnpk = 0.4028



2 = Rnpk 0= .40282 0.1622



As R value 0.4028 is not an unbiased estimate of the population multiple correlation coefficient for such small samples, a relatively unbiased version of R is given by R adjusted (Zaiontz 2020).

1  R   n  1  1 2

2 adj

R

Adjusted R2  1 



1  R2

  n  1

n  k 1

n  k 1

 1

1  0.1622   6  1 6  2 1

= −0.3963

A negative correlation coefficient is referred to as an inverse correlation. It means that there is an inverse relationship between nitrogen, phosphorus, and potassium.

Discussion Statistically nitrogen (N) shows inversely relationships with the phosphorus and potassium (P & K). When the data of NPK of the soil samples is computed with N as a dependent variable to P & K using statistical analysis of multiple correlation coefficient formulas, the value of r comes out to be negative, which means they are inversely correlated. From the standard research observations, concentration of NPK is low, in general, in the soil that shows high bulk density. For this reason, in the forest soil, NPK is high when the bulk density of soil is generally low. Bulk density of forest soils generally varies from 1.2 to 1.3 g/cubic cm in the observations of the present study areas (Das 2017; Sharma and Sharma 2004). Further, NPK is generally higher in the forest soils than that of the agricultural land, it does not solely depend on the bulk density of the soil or for the more nutrient supply from the

Discussion

117

decomposed microbial biomass (litterfall) which is generally scarce in the agricultural land. Soil pH, C and NPK of the forest soils are interrelated, they have contributions on controlling organo-chemical environment of the forest floors through biogeochemical cycles and thus, they certainly might have correlations with the soil fertility index, somehow this part is still ignored in the research field of soil chemistry for the forest stands. Soil C:N ratio is undoubtedly used so frequently because of the stoichiometry of the decomposition of organic matter releasing N corroborating with microbial C:N ratio, which relates so well with soil fertility index values. But soil C:P and soil C:K ratio unfortunately fail to relate with microbial C:P or C:K ratio. Because of these physico-chemical reactions happening continuously in the soil environment, the content of available NPK present in the forms of nutrients in the mangrove sediments shows a negative relationship. Inverse relation between N and K is common, especially in the mangrove soils. It reflects in the analysis of mangroves soil where available potassium is five times richer than that of the required amount for the natural growth of mangroves, which supply the nutrients to the ambient environment. Primarily mangroves forest ecosystems are totally different from terrestrial forest ecosystems (Das 2020). Organic matter content accumulates at the soil surface giving higher N content compared to PK, but in the lower horizon as organic matter goes down (and N), PK goes up due to its accumulation through leaching and the presence of primary and secondary minerals. It may be concluded that existence of inversely relationship between N and PK in the mangrove sediments as because the mangrove forests could have much lower rate of mineralization compared to terrestrial forest ecosystem and N (nitrogen) exists in both mineralizable N and non-mineralizable N forms in the mangroves sediments (Das 2021). The author’s understanding is that all the three nutrients cannot be brought under a single roof for availability. For P & K there are three forms -mobile, fixed, immobile, exchangeable, non-exchangeable, which is not the case for N. Further for K, the microbes (C dependent) play a little and minor role in exchangeable K in organic matter (again C deciding), which is also very minimum in soil. But for C & N (diffusion), both have a positive correlation already in soil. And thus, NPK is inversely related. This study for the establishment of relationships between different soil nutrients of the mangrove soils might be a reasonable observation in the mangrove swamps of the Sunderbans.

Statistical Prediction of Nitrogen Availability Organic carbon of sampled soils of the natural terrestrial forests varies from 0.00 to 145.2 kg/ha, whereas organic carbon content varies from 83.6 to 151.8 kg/ha in the mangrove sediments of the Sunderbans. The soil organic carbon shows higher value in Joypur forest and this happens for aggregation of more litter at forest floor as the soil carbon content and forest floral assemblage density is directly correlated. Presence of more organic carbon in the mangrove sediments reflects the huge

118

4  Statistical Analysis of Forest Soil Properties

quantity of carbon accumulation stored in the form of blue carbon in the mangrove swamps and marshes. Available nitrogen content of all samples varies from 0.00 to 455 kg/ha in the floors of natural terrestrial forests as red soil is reported to be poor in available nutrients like NPK (nitrogen, phosphorus, and potassium). Available nitrogen is relatively higher and varies from 880 to 1540 kg/ha in the Sunderbans mangroves soil. Available nitrogen is generally considered as the most important factor limiting the growth of the trees. Content of available nitrogen in the mineral horizons of forest soils are recorded higher than the amount accumulated in the organic horizons and the rate of accumulation of available nitrogen differs from one forest to another forest stand due to presence of different tree species composition and different physico-­ chemical properties of forest soils. Available nutrients are generally higher in the vegetation-covered forest areas than the available nitrogen, phosphorus, and potassium in open spaces. Available nitrogen is comparatively higher in the mangrove sediments for the enhanced microbial biomass decomposition of the litterfall due to regular inundation of the forest floors by the daily semidiurnal tidal river waters. Organic carbon and available nitrogen of the soils of the forest floors are positively correlated which is reflected in the fitted regression line drawn using the data of both the soil chemical parameters. Further, availability of nitrogen is to be predicted in a certain environment from the fitted regression line using the organic carbon as an independent variable. As the regression fit line reveals the relationships in between the organic carbon and available nitrogen in the two different types of forest environments, a regression fit line for each environment is calculated using the two variables of available nitrogen and organic carbon (Table  4.3) where y = a + bx is taken as the fitted regression line which can be used to predict the average value of dependent variable, y, associated with the value of the independent variable, x. The dependent variable is available nitrogen (y) and the independent variable is the organic carbon (x).

x = 73.7



y = 201.16

Table 4.3  Organic carbon and available nitrogen of the sediment samples collected from the terrestrial forest patches of West Bengal Sample location

Sample number

Joypur

JPR1 JPR2 JPR3 GRJ ADR BLT n = 6

Garh Jangal Aduria Beliatore

Organic carbon (kg/ha) x 55 96.8 145.2 35.2 0.00 110 Σ x = 442.2

Available nitrogen (kg/ha) y 140 245 455 87 0.00 280 Σy = 1207

Discussion

119

  x  x  y  y  b  x  x n

i

i 1

i

n



2

i 1

b

i

 442.2  73.7  1207  201.16  2  442.2  73.7 



b = 2.729



a  y bx



a  201.16  2.729  73.7 



a = 0.0327



y  a  b x



Result: y = 0.0327 + 2.729 x The result comes out to be y = 0.0327 + 2.729x which can be used to predict the value of nitrogen at any level of nutrient release or accumulation within the range of data, and thus, for an example, the expected nitrogen available at 1 kg/ha of organic carbon would be y = 0.0327 + 2.729(1) kg/ha. After getting the value of the regression fit line for the terrestrial forest patches, another regression fit line is drawn for the two variables of organic carbon and available nitrogen for comparison of the value of the mangroves soil of the Sunderbans (Table 4.4) using the same formula.

x = 121.36



y = 1246.66



  x  x  y  y  b  x  x n

i

i 1

i

n

i 1

2

i

Table 4.4  Organic carbon and available nitrogen of the sediment samples collected from the mangroves swamp of the Sunderbans Sampling station

Sample number

Sajnekhali Sudhanyakhali Banbibi Bharani Dobanki Pancha Mukhani Neti Dhopani

S1 S2 S3 S4 S5 S6 n = 6

Organic carbon (kg/ha) x 103.4 151.8 147.4 138.6 83.6 103.4 Σ x = 728.2

Available nitrogen (kg/ha) y 1100 1540 1540 1320 880 1100 Σy = 7480

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4  Statistical Analysis of Forest Soil Properties

b

 728.2  121.36   7480  1246.66  2  728.2  121.36 



b = 10.271



a  y  b x



a  1246.66  10.271121.36 



a = 0.18



y  a  b x



Result: y = 0.18 + 10.271 x The result comes out to be y = 0.18 + 10.271 x which can be used to predict the value of nitrogen at any level of nutrient release or accumulation within the range of data, and thus, for an example, the expected nitrogen available at 1 kg/ha of organic carbon would be y = 0.18 + 10.271(1) kg/ha. It may be concluded from the fitted regression line equation that for the 1 kg/ha of organic carbon, the expected nitrogen contents are available for the natural terrestrial forests 2.7617 kg/ha and for the mangroves soil 10.451 kg/ha, respectively. The higher value of expected available nitrogen concentration of mangroves soils in comparison to that of the terrestrial forests is quite natural that reflects the parity with the data of both the parameters shown in the Tables 4.3 and 4.4.

p H, Salinity, and Organic Carbon Relationships of the Marsh Sediments Among soil physico-chemical parameters, soil pH and soil organic carbon, and soil pH and soil salinity are highly correlated, though soil salinity and soil organic carbon have shown inverse relationships. So, for the establishment of relationships of these three parameters together, data of the marsh sediments collected from the outskirts of remote isolated island of Paschim Sripatinagar of the Sunderbans (Table 4.5) are taken into consideration applying the statistical formula of multiple correlation coefficients as the following,



R psc 

rps2  rpc2  2rps rpc rcs 1  rcs2



Where rps, rpc, rcs are defined as basic concept of correlation. Here, s (salinity) and c (organic carbon) are viewed as the independent variables and p (pH) is the dependent variable. In this statistical analysis, R is not an unbiased estimate of the population multiple correlation coefficient for the small samples. A relatively unbiased version of

Discussion

121

R is given by R adjusted. Where R is Rpsc as stated above (or similarly for more variables) then the adjusted multiple coefficient of determination is

1  R   n  1  1 2

2 adj

R



n  k 1



where k = the number of independent variables and n = the number of data elements in the sample for p (which should be the same as the samples for s and c). In addition to the various correlation functions described elsewhere, Excel provides the covariance and correlation data analysis tools (Zaiontz 2020). The covariance tool calculates the pairwise population covariances for all the variables in the data set. The pairwise correlation coefficients are computed for the various variables shown in the Table 4.5 using Excel’s correlation data analysis tool. The results are shown in Table 4.6. Three variables are computed, pH, salinity, and organic carbon and the multiple correlation coefficients is calculated (Table 4.6) assuming pH is the dependent variable using the data in Table 4.5 to obtain the values rpsrpcrcs.

Table 4.5  Physico-chemical parameters of the marsh sediments collected from the marginal bars of the Sunderbans Sample number K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 K14 K15

pH 7.30 7.68 7.29 6.75 7.35 7.05 7.69 7.19 6.73 6.75 7.18 7.31 6.96 7.56 7.37

Salinity (‰) 4.09 5.36 5.04 3.44 3.44 3.86 2.86 2.86 3.44 3.16 3.69 4.81 5.18 4.78 3.92

Organic carbon (%) 0.22 0.32 0.41 0.39 1.20 0.38 0.81 0.33 0.73 0.23 0.35 0.29 0.14 0.30 0.04

Table 4.6  Data of computed pairwise correlation coefficients for the various variables using Excel’s correlation data analysis tool pH Salinity Organic carbon

pH

Salinity

0.32953 0.114033

−0.3076

Organic carbon

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4  Statistical Analysis of Forest Soil Properties

R psc 

R psc 

rps2  rpc2  2rps rpc rcs 1  rcs2



0.3292  0.114 2  2  0.329  0.114   0.307  1   0.307 



2



R psc = 0.396



2 = R psc 0= .396 2 0.157



As R is not an unbiased estimate of the population multiple correlation coefficient for such small samples, a relatively unbiased version of R is given by R adjusted (Zaiontz 2020).

1  R   n  1  1 2

2 adj

R



n  k 1



1  R   n  1  1  1  0.157  15  1  0.9835 2

Adjusted R2  1 

n  k 1

15  2  1

The result comes out to be 0.9835 that indicates a high positive correlation between pH, salinity, and organic carbon exists in the marsh sediment samples of the Sunderbans. Soil pH, soil salinity and organic carbon of the marsh sediments are interrelated, they have contributions on controlling organo-chemical environment of the mangrove swamps and marsh floors through biogeochemical cycles and thus, they certainly might have correlations with the soil fertility index, somehow this part is still ignored in the research field of soil chemistry for the mangrove swamps and marshy lands.

Precision Test for Sediment Sampling Accumulated carbon forms the huge carbon stock in the mangroves swamp and marshes and this carbon sink in the mangroves forests is referred to as blue carbon (Macreadie et  al. 2019). Blue carbon has been introduced in the field of carbon study for climate change only a decade back and the mangroves has the capacity to take up four times more carbon dioxide than that of the green carbon of the terrestrial natural forest patches as studied in the recent research. The present study reveals that the carbon sequestered from the atmospheric carbon dioxide enriches the organic carbon content and can increase the content of organic carbon in the forest floors of the mangrove swamps. To examine the content of the organic carbon in the mangrove swamps, numbers of samples are collected and analyzed. Samples are collected from a remote isolated island of the Sunderbans along a linear profile of 375 m extended from the embankment to the lowest low water of the estuarine

Discussion

123

tidal river covered with the mangrove vegetation. The estimated contents of organic carbon of the collected sediment samples are almost alike and show no variations in comparison to that of the terrestrial forest soils. Confused with the obtained data of organic carbon of the mangrove sediment samples based on the present research view, all the obtained data are computed statistically for determination of accuracy of the sampling of the sediments from the mangroves forest to examine whether there is error in sampling of sediments, if any. Result of the statistical test comes out to be precise indicating smaller sampling error in sampling of the sediments collected along a profile of a mangrove vegetation. Sunderbans, stood on the deltaic estuarine morphodynamics situations, is covered, and characterized with 64 mangroves and its associated species (Das 2015). In Sunderbans, out of 102 islands, 52 islands are under human habitation, though these islands are surrounded with the dense mangrove vegetation outside the border area with an extension from the embankment to the waterline of the rivers and tidal inlets (Das 2017). From such an island with human habitation, namely Paschim Sripatinagar encircled by the Sibua River in the north, south and east, and Thakuran River in the east under Pathar Pratima police station of South 24 Parganas district in West Bengal, 15 sediment samples (K1 – K15) are collected with a regular gap of 25 m from each sampling spots along a straight-line linear profile length of 375 m from the Sibua River floodplain (Table  4.7). The profile is encircled with the embankment to restrict the flood tidal water into the agricultural land at the east, and the profile ends with the lowest low water level of the river at the western part. The entire area along the profile considered for sample collection in the river floodplain of Sibua is covered with the dense mangrove vegetation dominated by Avicennia marina, Excoecaria agallocha, Aegiceras corniculatum and Aegialitis rotundifolia along with the other mangrove species (Das 2011). Collected samples are oven Table 4.7  Soil chemical parameters of the sediment samples collected in the Sunderbans Sample number K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 K14 K15

pH 7.30 7.68 7.27 6.75 7.35 7.05 7.69 7.19 6.73 6.75 7.18 7.31 6.96 7.56 7.37

Salinity (‰) 4.09 5.36 5.04 3.44 3.86 3.44 2.86 2.86 3.44 3.16 3.69 4.81 5.18 4.78 3.92

Organic carbon (%) 0.22 0.32 0.41 0.39 1.20 0.38 0.81 0.33 0.73 0.23 0.35 0.29 0.14 0.30 0.04

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4  Statistical Analysis of Forest Soil Properties

dried and soil parameters of pH, salinity and organic carbon are analyzed followed by the standard methods of soil chemical analysis. The content of organic carbon varies from 0.04% to 1.20% which shows no differences compared to that of the soils of the terrestrial natural forests, though the mangroves absorb more carbon dioxide and lock more carbon in the sediments as blue carbon (Macreadie et  al. 2019). To avoid confusion, the obtained data of the organic carbon content are considered for statistical analysis for the precision test of sediment sampling applying random sampling methods with and without replacement.

Results and Discussion For total content of organic carbon in 15 samples (Table 4.7), the mean in the population is drawn using formula (Frerichs 2008) –

 x



n

Xi

i 1

N



Where xi is the total content of organic carbon in the population and N is the total number of collected sediment samples. Thus, the mean content of organic carbon in the population –

x 

0.22  0.32  0.41  0.39  1.20  0.38  0.81  0.33  0.73  0.23  0.35  0.29  0.14  0.30  0.04 15

 0.40

or 0.40 content of organic carbon per sediment samples. Population variance – Variance for the content of organic carbon in population of 15 sediment samples is calculated using the formula –

 x  n





2

i 1

i

N

 x

2



Where σ2 is the population variance, xi and N stated above, x is the mean content of organic carbon in the population. The above-mentioned formula is used to calculate the variance in the population. 

2



 0.22  0.40

 0.04



2

2 2 2 2   0.32  0.40    0.41  0.40      0.30  0.40    0.04  0.40  15



Results and Discussion

125

Sample mean – with an intention to make a statement about the total population of 15 sediment samples, a sample of 3 sediment samples containing organic carbon will be derived, and their measurements will be used to represent the group. The 3 will be selected by simple random sampling. The mean for a sample is drawn using formula –

 x



n

x

i 1 i

n



where xi is the content of organic carbon in each sampled sediment and n is the number of sediment samples. Assuming K7-K8-K9 is the sample where K7 had 0.81 organic carbon, K8 had 0.33 organic carbon and K9 had 0.73 organic carbon. Using the formula – x



0.81  0.33  0.73  0.62 3

The sample estimate of the mean content of organic carbon in the population (seen previously as 0.40) is 0.62. Sample variance – the variance of the sample is used to estimate the variance for the population and for statistical tests. Standard variance for a sample is calculated using formula –

 x  n

s



2

i 1

i

 x

n 1

2



Where s2 is the symbol for the sample variance, xi is the content of organic carbon for each of the i sampled in the sediment sample and x is the mean content of organic carbon in the sample. For sample K7-K8-K9 with a mean of 0.62, the variance is –

 0.81  0.62    0.33  0.62    0.73  0.62  2



s2 

2

3 1

2

 0.066



The variance 0.066 derived for the sampled sediments of 3 is beyond the truthfulness for the sample set. It shows the content of organic carbon and the collected sediment samples with the mean of 0.62 ± 0.066. Then the standard variance is to be derived using the method of possible sample with replacements. Possible samples with replacement  – there are many different combinations that could be selected in this method when drawing a sample from a population (Frerichs 2008). The number of possible samples could be drawn using this formula –

N n

where N is the number in the total population and n is the number of units being sampled when selected samples are three from the population of 15 collected

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4  Statistical Analysis of Forest Soil Properties

sediment samples, the sample could have been K1-K3-K9, or K6-K12-K15, or K3-K6-K11, or any of many other combinations. In sampling with replacement from the population, they are – n 3 N= 15 = 3375



or 3375 different combinations of three samples containing organic carbon that could have been selected. The frequency distribution of the mean number of sediment samples of the 3375 possible samples selected with replacement has 3 notable features of these 3375 possible samples. Notable feature 1 – while the range of the 3375 possible sample means is from a low of 0.04 to a high of 1.20, the average value of the sample means for the organic carbon is 0.40, the same as the population mean calculated noted above i.e., when sampled with replacement, on average the sample mean provides an unbiased estimate of the population mean. Notable feature 2 – the average variance of the 3375 possible samples of 3 selected with replacement is equal to the population variance of the 15 collected sediment samples is calculated using the formula – 



2

 

3375 i 1

Si 2

3375

Where si2 is the variance of sample i, where i goes from 1 to 3375, the total number of possible samples when selecting three from 15 with replacement. Notable feature 3 – for random samples of size n selected from a population with replacement, the variance of the mean of all possible samples is equal to the variance of the population divided by the sample size. For 3375 possible samples, the average variance of the mean for a sample of 3 from a population of 15 is calculated using the formula –

 x vx   3375 i 1



i

 x

3375

2



2 0.08   0.0266 n 3

Variance 0.0266 in the statistical measure reveals Mean 0.40 ± 0.0266 over the content of organic carbon and number of sediment samples in the sample set and the result is an unbiased estimate of the variance of the population provided with this form of sampling on average the variance of the sample mean divided by the sample size. Results obtained from the random sampling with replacement method in the sample set for the variance comes to be 0.0266 i.e., mean 0.40 ± 0.0266 considered in the sample set. A variance of 0.0266 shows that the content of organic carbon in each number of the sediment sample in consideration tends towards the mean. Since the variance is small it also signifies the absence of a major number of outliers.

Random Sampling Without Replacement

127

Random Sampling Without Replacement Further, a random sampling method without replacement is applied for determination of variance of the mean on the content of organic carbon estimated for 15 sediment samples to compare the result of the value in this statistical method. The mean, standard error, and confidence interval for all possible samples of three samples containing organic carbon without replacement from 15 sediment samples will be derived (Table 4.7). Since the sample is drawn without replacement, the possible samples are calculated using this formula – N! n!  N  n 



Where N is the number of samples in the population, n is the number of unit samples, and! is the factorial notation for the sequential multiplication of a number times a number minus 1, continuing until reaching 1 i.e. N! (termed N factorial) is N times N-1 times N-2 times and the like with the last number being 1. A sample of 3 unit samples (suppose K7-K8-K9) are selected without replacement and disregarding order using the formula – 15! 15  14  13  12 !   455 3! 15  3   3  2  1  12 !



or 455 possible samples. When using this formula, all factorial numbers do not have to be multiplied, the 15! in the numerator can be converted to 15 × 14 × 13 × 12!, and the 3! × (15–3)! in the denominator can be converted to 3 × 2 × 1 × 12!. By dividing 12! in the numerator by 12! in the denominator to get 1, the formula is reduced to 15 × 14 × 13 divided by 3 × 2 × 1 or 455 possible samples. Each sediment sample in the population of 15 sediment samples exists multiple times. The mean content of organic carbon in the population of 15 sediment samples is –

 x



n

Xi

i 1

N



Where xi is the total content of organic carbon in the population and N is the total number of collected sediment samples. Thus, the mean content of organic carbon in the population –

x 

0.22  0.32  0.41  0.39  1.20  0.38  0.81  0.33  0.73  0.23  0.35  0.29  0.14  0.30  0.04 15

or 0.40 content of organic carbon per sediment samples.

 0.40

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4  Statistical Analysis of Forest Soil Properties

With the small population of 15, there are 455 possible samples of 3 sediment samples that could be selected, assuming sampling without replacement and disregarding order. To be derived for each possible sample are the mean for total content of organic carbon (termed variable x), the standard error of the mean and the confidence interval for total sediment samples. The mean of each of the 455 possible samples is calculated with formula when K7-K8-K9 serves as an example.

 x



n

x

i 1 i

n



where xi is the content of organic carbon in each sampled sediment and n is the number of sediment samples. Assuming K7-K8-K9 is the sample where K7 had 0.81 organic carbon, K8 had 0.33 organic carbon and K9 had 0.73 organic carbon. Using the formula – x



0.81  0.33  0.73  0.62 3

The average value of the 455 possible sample means is 0.62, the same as the mean for the total population of 15 sediment samples. Thus, the sampling scheme for total samples is considered unbiased. The frequency distribution of the mean number of sediment samples of the 455 possible samples selected with replacement has 3 notable features of these 455 possible samples, while the range of the 455 possible sample means is from a low of 0.04 to a high of 1.20, the average value of the sample means for the organic carbon is 0.40, the same as the population mean calculated noted above i.e. when sampled without replacement, on average the sample mean provides an unbiased estimate of the population mean. To calculate each standard error, the variance of the sample mean is derived using formula – N  n  i 1  xi  x  vx   n  n  1 N n



2

(N  −  n)/N is the finite population correction, included only because this is a sample selected without replacement. For the sample, K7-K8-K9, the variance of the sample mean is



vx  

15  3  0.81  0.62    0.33  0.62    0.73  0.62   0.0 017 15 3  3  1 2

2

2

Results of the value of variance after the analysis of random sampling without replacement comes out to be 0.017 indicating a smaller estimation of sampling error.

Remarks

129

Remarks Result obtained for the variance of average sample mean is 0.017 for sampling without replacement compared to the value of 0.0266 for sampling with replacement resulting in smaller estimates of sampling error and greater efficiency in the sampling process in the collection of the large number of sediment samples along a single linear profile of 375 m length. Variance values for the random sampling with and without replacement ranging from 0.017 to 0.0266 might be considered as the existence of the other forms of carbon like mineralized carbon and recalcitrant with the soil organic carbon present in each soil sample collected from the sampling spots as the range of variances reveals the variances to the mean of the samples. Analysis of variance or ANOVA is the process of identifying causes of variations in the carbon flux and carbon stock through the accumulation of the soil organic carbon in the carbon budget. It helps to understand why fluctuation of the soil organic carbon happens and what can be done to manage the adverse variance. The ANOVA helps in better understanding of carbon budgeting consequently. Further, the variance analysis is the study of deviation of actual content of organic carbon versus carbon flux or carbon stock in carbon budgeting management (Park et al. 2019). The various values obtained from the variance analysis are concerned with the differences of carbon influx and outflow that indicates the actual behaviour of the blue carbon stock in the forest floors of the mangrove swamps to a high level of accuracy. Low content of organic carbon obtained from the collected samples from the Sunderbans is due to the construction of embankments for the protection of the human habitation that restrict the intrusion of saline tidal water into the agricultural lands (Das 2017). Other than that, the gentle sloping of the river floodplain washes away the litterfall of the mangroves and its associated species towards the river waters that reduce the organic carbon content in the forest floors and decelerate the rate of microbial biomass decomposition. Availability of low carbon content in the sampled sediments in the present study is thus, because of the relatively low rate of biogeochemical cycles in the mangrove swamps and marshes of the Sunderbans.

Bulk Density of Forest Soils Bulk Density of forest soils plays an important role for determining the soil characteristics which is directly correlated with the soil organic carbon content of the forest floors. Generally, bulk density (BD) increases with depth in soil profile and the bulk density is found increasing with decreased soil organic carbon in soil profile depth. For examining such characteristics of the soil profile, bulk density is estimated for the forest soils of the community forests of Bethuadahari sanctuary, Bahadurpur forest, and Seemanagar forests in the Nadia district of West Bengal. Bulk density of the forest soils ranges from 0.81 to 1.27 g/cm3 for the soil samples collected from the forest floors of Nadia district (Table  4.8). The average bulk

130

4  Statistical Analysis of Forest Soil Properties

Table 4.8  Bulk density (BD) of soil samples collected from the forest floors of Nadia district Sample locations Bethuadahari Bahadurpur Bahadurpur Seemanagar Seemanagar Seemanagar Average

Sample no. BTD1 BPR1 BPR2 SMN1 SMN2 SMN3

Bulk density (g/cm3) 1.06 0.81 1.09 1.09 1.20 1.27 1.08

Organic carbon (%) 0.29 0.33 0.31 0.28 0.26 0.24 0.285

density of the soil samples is 1.08 g/cm3. Bulk density value 0.81 g/cm3 of a forest soil sample is obtained in Bahadurpur community forest in the district of Nadia which is comparatively too low from the other sample of the same forest stands collected from 500 m distance. Visibly the sample is silty in nature, and the implanted trees of the said community forest are mostly of teak categories. The forest is situated in the alluvial plains of the lower Gangetic deltaic set up in the state of West Bengal. Bulk density (BD) measurement is done by core method for comparison to other soils’ BD. This bulk density value of 0.81 g/cm3 translates to a total soil porosity of 69.43% assuming a particle density value of 2.65 g/cm3. This value is normal for the young alluvial soils as the porosity is related to bulk density and this is the general trend for alluvial soils in different parts of the world (FAO 1988; FAO and ITPS 2015 – World Soil Resources Report 1994 and 2015; Soil Taxonomy 1999). The explanation for this is that alluvial sediments contain mixtures of primary and secondary minerals (three-layer silicates). Exceptions are the acid sulfate soils (Thionic Fluvisols) and organic soils (Histosols) which may have bulk density values as low as 0.6 g/cm3 for acid sulfate soils, and 0.1 g/cm3 for organic soils. Bulk density of 1.6 g/cm3restricts root growth; the same for sandy soils ranges between 1.3 and 1.7 while fine silts and clay is in the range of 1.1–1.6 g/cm3. The following is the range of bulk density for the different soil textures – clay, silt loam – 1.4–1.55; Silty clay, silty clay loam, silt  – 1.4–1.5; clay loam 1.45–1.55; loam 1.45–1.6; sandy clay 1.55–1.65; sandy clay loam 1.55–1.75; sandy loam 1.55–1.75; loamy sand, sand >1.75 (Harris 1990; Morris and Lowery 1988). If the soil consists mainly of organic parts and, additionally, when determining the bulk density, its moisture content is low (below 20%), the density will always be below 1 g/cm3. It is a characteristic feature of highly porous organic soils with a low degree of mineralization. Particularly, when the substrate soils are mostly organic, their bulk density depends mainly on moisture and compression. That is, if the organic soil has low moisture, the bulk density is often lower than 1 g/cm3. Increasing the humidity and compaction causes the approach to the value of 1 g/cm3, though the content of minerals, sand, and dolomite dust generally exceeds the value of 1 g/cm3. Understandably, bulk density increases with compaction. Bulk density (BD) may vary based on forest types (i.e., coniferous, or broadleaf) since they have an implication for organic

Remarks

131

matter accumulation under the forest floors. Regardless of the forest types, the bulk density value 0.81  g/cm3 of the soil sample of Bahadurpur community forest is within the range from 0.39 to 1.39 g/cm3. The obtained value of bulk density is low because of the high content of soil organic carbon present in the soil sample. Soil organic carbon varies from 0.24% to 0.33% with an average mean of 0.28% in the forest patches surveyed in the new alluvial belt of Nadia district. The soil organic carbon is found to decrease with increased bulk density. Low bulk density of the soil samples indicates the presence of high content of soil organic matter in the forest soils of the new alluvial belt of Nadia district (Kundu et al. 2017). For such a relationship between bulk density and soil organic carbon, the correlation of coefficients between these soil parameters is estimated using the values obtained from soil chemical analysis and applying conventional statistical formula for correlations measures (Table 4.9). For the estimation of correlation of coefficients, covariance of x and y, and variances of both x and y are to be computed first using the formula, 1 n  xi  i 1yi cov  x,y    xi yi  i 1 n  i 1 n  n



cov  x,y  

n

 6.52 1.71  1 1.8346   6 6   0.0039



   



Variance of x 2   n  1  n 2   i 1xi  v  x    xi  n  i 1 n 



    

Table 4.9  Bulk density and soil organic carbon obtained for the soil samples of the forest patches of Nadia district Sample no. BTD1 BPR1 BPR2 SMN1 SMN2 SMN3 N = 6

Bulk density (g/ cm3) (x) 1.06 0.81 1.09 1.09 1.20 1.27 Σ x = 6.52

Soil organic carbon (%) (y) 0.29 0.38 0.31 0.28 0.26 0.24 Σ y = 1.71

x2 1.1236 0.6561 1.1881 1.1881 1.44 1.6129 Σ x2 = 7.2088

y2 0.0841 0.1089 0.0961 0.0784 0.0676 0.0576 Σ y2 = 0.4927

xy 0.3074 0.2673 0.3379 0.3052 0.312 0.3048 Σ xy = 1.8346

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4  Statistical Analysis of Forest Soil Properties

6.52 1 v  x    7.2088  6 6   0.0206

2



  

Variance of y



2   n  1  n 2   i 1yi  v  y    yi  n  i 1 n 



1.71 1 v  y    0.4927  6 6   0.0009

2

Correlation coefficient (r) = r

       

cov  x,y  (v  x   v  y   0.0039

 0.0206  0.0009 )

r  0.91



The value of correlation of coefficients comes out to be negative i.e., bulk density and soil organic carbon are negatively correlated for the soils sampled at forest floors of Nadia district. This negative correlation is statistically significant (Curtis and Post 1964, Federer 1983, Huntington et al. 1989, Federer et al. 1993, Post and Kwon 2000, Tremblay et  al. 2002, Prevost 2004, Mestdagh et  al. 2006; Sakin et al. 2011).

Remarks Generally, the bulk density of a compact soil is greater than the loose or sandy or organic soil. Usually, the forest soil is an organic soil, so its bulk density will be lower. However, the obtained results might be normal for the soil samples of the community forest created by afforestation under the social forestry scheme. The present study site may of course be different and unique, and it is possible that the bulk density values are true values.

Paired T-Test for Organic Carbon and Nitrogen Stocks

133

Paired T-Test for Organic Carbon and Nitrogen Stocks  istribution Pattern of Organic Carbon and Nitrogen Stocks D within the Soil Profile The vertical distribution of soil organic carbon and nitrogen is generally found decreasing with increased value of bulk density with respect to depth and the average values show uniformity in each layer of 20 cm within the soil profile of 1 m depth, and such values are uniformly distributed for the soil samples found almost everywhere. Surface soil layer is so unique and maintains such identical uniformity, though the obtained values for the vertical distribution of organic carbon and nitrogen stocks of Joypur and Beliatore forest soils are not uniformly distributed with depth as the analyzed soil chemical parameters do not show unique and identical soil orders. The decrease in the soil organic carbon and nitrogen is not uniform in all soils, even in the same field some differences are noticed though they are insignificant but do exist (Smith 2004; Bonan 2008). The overall trend of the occurrences of soil organic carbon and nitrogen are found decreasing with depth (or with increasing bulk density) will always be the same among all soil orders. The weight of the surface soil layers, and as the traffic compresses the deep layers, then it is in fact compressing the pore space which is an important physical property and that is crucial for maintaining the biological and chemical properties of the soil balancing the dynamic equilibrium. Any disturbance in this equilibrium directly influences the microbial community first (as availability of moisture, nutrients, aeration, soil organic matter decreased) and hence the soil organic carbon and nitrogen mineralization process either reduced or ceased completely. Consequently, these are found in a relatively lesser amount in the soil layers with depth in the soil profile (Das 2021). Soil organic matter has three main fractions, fresh undecomposed residues, decomposing and partially decomposed materials, and a highly decomposed and stable product humus (it is not completely decomposed organic matter; complete decomposition of organic matter produces carbon dioxide, water, and other inorganics). Plant litters including dead leaves, stems, barks, flowers, fruits, and logs are the major sources of forest soil organic matter. Soil biota, microorganisms, and roots also contribute to the soil organic matter. The threshold value for organic matter in agricultural soil is 2% by weight, beyond which soil quality does not remain sustainable, but no threshold level for forest soils has so far been established. Forest mineral soils have generally 1–5% organic matter by weight. Forest soils usually have higher organic matter than agricultural soils (Castellano et al. 2015). There are two types of organic matter in soil, active or labile and passive or stable. Humus is the stable fraction of soil organic matter. Forest ecologists identify three types of humus: mull humus, mor humus, and moder humus depending on the degree of decomposition and integration with mineral matter, acidity, and base contents. This categorization is more pronounced in temperate and boreal forest soils. A deep O (organic) horizon also develops in these types of forests. So

134

4  Statistical Analysis of Forest Soil Properties

generally, no variation in soil organic carbon and nitrogen occurs in the forest soils and that may be due to the presence of a thick organic (O) horizon than other horizons within soil profile of 1 m depth. Deep into the forest soils, it is made of layers, or horizon, and that is an organic (O) horizon composed of mostly organic matter such as decomposing leaves and that layer is almost equivalent to the eluviated (E) layer (eluviation – the movement through the soil of materials brought into suspension or dissolved by the action of water) comprising leached of clay, minerals, and organic matter, leaving a concentration of sand and silt particles of quartz or other resistant materials that is too found in the forest soils. Leaching is a primary process for such horizons for passing soil nutrients and soil organic carbon with the increment of depth within the soil profile. The content of organic carbon and nitrogen concentration might depend upon the rate of leaching of soil organic matter towards bottom layers along the soil profile, but the organic carbon and nitrogen concentration do not depend on the leached clay as they are not available for absorption by the forest vegetation, and the content of soil organic carbon and nitrogen cannot be obtained from the soil sample analysis from the leached clay.

Paired T-Test As the observations and the results obtained for the stock of soil organic carbon and nitrogen in the Joypur and Beliatore forest floors of Bankura district show vertical variations without maintaining uniformity of decreasing trends of concentration with the increment of depth from top to bottom soil layers within the soil profile of 1 m depth, so the values obtained in soil chemical analysis for the two forest floors are compared with the application of paired t-test on the estimated content of organic carbon and nitrogen stocks. The statistical test used for comparing means of paired samples is generally called paired t-test. Two forests from where soil samples are collected from different layers of soil pits are considered as paired samples. Comparison of the means of organic carbon and nitrogen stocks in soil layers of soil pits at Joypur and Beliatore forests is observed applying statistical analysis, where the two groups of observations are not independent but paired, and two sets of observations come from a single set of experimental units like top or bottom layer of the soil pits. The observations obtained from such pairs can be correlated and the following formula is used for the test statistic. t= where

d Sd2 n

Paired T-Test for Organic Carbon and Nitrogen Stocks

Sd2 

135

2   di  1    di 2  n 1  n 

  

where, d is Mean differences and sd2 is the variance of the differences. The test statistics in formula follows a Student’s t distribution with n − 1 degrees of freedom (Jayaraman 1999). The computed value of t for n − 1 degrees of freedom, shows desired level of probability.

Paired T-Test of Organic Carbon Stocks For paired t-tests, values of organic carbon stocks are considered for the soil samples collected from Joypur and Beliatore forests of Bankura district. Stocks of organic carbon are estimated for sampled soils from five soil layers of each soil pit. The data pertain to soil organic carbon stock of different layers of several soil pits, so the observations are paired by soil layers of two forests. The paired t-test can be applied in this study to compare the organic carbon stock status of soil at each soil layer within the same depth of 1 m soil profile for two forests. Paired t-test is used to compare the vertical distributions of organic carbon stocks as the physiography and vegetation characters are almost the same for the two forests situated in the same district lying about 50  km away from each other, and the two forests once were supposed to be the same forest patches before unscientific deforestation. The statistical comparison applying paired t-test of organic carbon stocks (Table 4.10) is as following, Mean differences (d ) and variance of the differences (sd2) are calculated following the formula,

 d



n

d

i 1 i

n



Table 4.10  Mean organic carbon stock estimated from five soil layers of a set of soil pits from terrestrial natural forests of Joypur and Beliatore Soil layers 0–20 cm 21–40 cm 41–60 cm 61–80 cm 81–100 cm

Organic carbon stocks (t/ha) Joypur Beliatore 23.76 18.36 24.48 17.28 17.64 15.12 15.48 15.84 13.68 8.28

Differences 5.4 7.2 2.52 −0.36 5.4 Total = 20.16

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4  Statistical Analysis of Forest Soil Properties

20.16 = 4.032 5

=

2 Sd 

1 n 1



2  2  di   di  n 

  

 5.4 2   7.2 2   2.52 2   0.336 2     2 20.16    8.83875  2 5 1 5.4      5 1

The value calculated for t t= =

d Sd2 n

4.032

8.83875 5 = 3.03



The value calculated for t (3.03) is slightly more than the tabular value, 2.78 for 4 degrees of freedom at the 5% level of significance. From the value of t (3.03) obtained in the paired t-test, it may be concluded that there is a slight difference in the vertical distributions of the mean carbon stock for Joypur and Beliatore forest patches.

Paired T-Test for Nitrogen Stocks For paired t-tests, values of available nitrogen stocks are considered for the soil samples collected from Joypur and Beliatore forests of Bankura district. Available Nitrogen stocks are estimated for the soils sampled from five soil layers of each soil pit. The statistical comparison applying paired t-test of nitrogen (Table 4.11) is followed accordingly. Table 4.11  Mean available nitrogen stock estimated from five soil layers of a set of soil pits from terrestrial natural forests of Joypur and Beliatore Soil layers 0–20 cm 21–40 cm 41–60 cm 61–80 cm 81–100 cm

Nitrogen stock (t/ha) Joypur 34.20 11.88 4.32 3.60 1.44

Beliatore 27.72 11.52 3.96 3.24 0.72

Differences 6.48 0.36 0.36 0.36 0.72 Total = 8.28

Paired T-Test for Organic Carbon and Nitrogen Stocks

137

Mean differences (d ) and variance of the differences (sd2) are calculated following the formula,

 d

=



d

n



8.28 = 1.656 5

1 S  n 1 2 d



n

i 1 i

2    di    di 2  n 

2 8.28 1  2 2 2 2 2  6.48    0.36    0.36    0.36    0.72   5 1  5   7.2968



     

The value calculated for t t= =



d Sd2 n

1.656

7.2968 5 = 1.37

The value calculated for t (1.37) is less than the tabular value, 2.78 for 4 degrees of freedom at the 5% level of significance. From the value of t (1.37) obtained in the paired t-test, it may be concluded that there is no significant difference in vertical distributions of the mean nitrogen stock for Joypur and Beliatore forest patches.

Remarks On observing the vertical distributions of soil organic carbon and nitrogen in soil profile, it will decrease with increasing depth. The higher the bulk density in the lower layers of the soil profile is due to the less soil organic carbon content. The volume of soil is more important for bulk density of the soil which is directly and largely controlled by the soil organic carbon than soil structure and texture of the soil, and it is common in all the soils (generally the soil may have 50 percent of the pore space). The forest soil also follows the same trend. But the rate of decrease of

138

4  Statistical Analysis of Forest Soil Properties

soil organic carbon and nitrogen with depth may vary and depends on the impact of soil forming processes taking place in a particular soil which is observed in this study for the soil samples of the natural terrestrial forests of Joypur and Beliatore of Bankura district of West Bengal.

Summary Almost all the data sampled and analyzed for different forest soil characteristics and physico-chemical parameters are estimated to obtain values in different aspects applying statistical methods with high precision up to 95% accuracy of the probability level in the field of forests and forestry research. And to get true value in a population of random sampling, standard error is derived for the variable of interest which is useful to construct a confidence level. Several efforts for variance analysis are worked out with an objective of study of derivations of actual acts versus predicted or projected acts or plan in management, accounting, and budgeting of forest vegetation, soil nutrients, biomass content and carbon stock. From the value of t obtained in the paired t-test, it is easily inferred that there is no significant difference in vertical distributions of the mean soil carbon and nitrogen stock of the forest patches. The variance analysis helps in the deviation of actual content of organic carbon versus carbon flux or carbon stock in carbon budgeting management. Fitted regression line is used to determine and predict the value of biomass and carbon stock of the forest patches applying simple linear regression. Application of statistical methods in such different aspects of soil physico-chemical parameters in the forests and forestry related research of West Bengal is covered with some basic concepts and practice of analytical statistics based on the data either from sampled during the survey or obtained from the chemical analysis in the laboratory.

References Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320(5882):1444–1449 Castellano MJ, Mueller KE, Olk DC, Sawyer JE, Six J (2015) Integrating plant litter quality, soil organic matter stabilization and the carbon saturation concept. Glob Change Biol 21:3200–3209 Curtis RO, Post BW (1964) Estimating bulk density from organic matter content in some Vermont Forest soils. Soil Sci Soc Am Proc 28:285–286 Das GK (2011) Sunderbans  – environment and ecosystem. Sarat Book House, Kolkata, 254p. ISBN:81-87169-72-9 Das GK (2015) Estuarine morphodynamics of the Sunderbans. Springer, Cham, 211p. ISBN:978-3-319-11342-5 Das GK (2017) Tidal sedimentation in the Sunderban’s Thakuran Basin. Springer, Cham, 151p. ISBN:978-3-319-44190-0 Das GK (2020) Sunderbans rates faster than Jungle Mahal for climate change mitigation. Frontier, November 21

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Das GK (2021) Soil characteristics in the forest patches of Jungle Mahal in WB, India. Int Res J Environ Sci 10(1):81–85 FAO (1988) FAO/UNESCO Soil Map of the world, World resources Report 60, FAO, Rome, Reprinted as technical paper 20, ISRIC, Wageningen, 1994 FAO and ITPS (2015) Status of the world’s soil resources (SWSR) – technical summary. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome Federer CA (1983) Nitrogen mineralization and nitrification: depth variation in four new England forest soils. Soil Sci Soc Am J 47:1008–1014 Federer CA, Turcotte DE, Smith CT (1993) The organic fraction-bulk density relationship and the expression of nutrient content in forest soils. Can J For Res 23:1026–1033 Frerichs RR (2008) Simple random sampling, chapter three, in rapid surveys (unpublished), 44p Harris AS (1990) Picea sitchensis. In: Burns RM, Honkala BH (eds) Silvics of North America, vol. 1, Conifers, Agriculture handbook 654. U.S.D.A. Forest Service, Washington DC Huntington TG, Johnson CE, Johnson AH, Sicama TG, Ryan DF (1989) Carbon, organic matter and bulk density relationships in a forested. Spodosol Soil Sci 148:380–386 Jayaraman K (1999) A statistical manual for forestry research, food and agricultural organization of the united nations. Regional office for Asia and Pacific, Bangkok, 231p Kundu MC, Das T, Biswas PK, Mondal S, Ghosh GK (2017) Effect of different land uses on soil organic carbon in new alluvial belt of West Bengal. Int J Bio-Resour Environ Agric Sci 3(2):517–520 Macreadie PI et al (2019) The future of blue carbon science. Nat Commun 10:3998. https://doi. org/10.1038/s41467-­019-­11693-­w. www.nature.com/naturecommunications Mestdagh I, Lootens P, Van Cleemput O, Carlier L (2006) Variation in organic-carbon concentration and bulk density in Flemish grassland soils. J. Plant Nutr Soil Sci 169:616–622 Morris LA, Lowery RF (1988) Influence of site preparation on soil conditions affecting stand establishment and tree growth. South J Appl For 12(3):170–178. https://doi.org/10.1093/ sjaf/12.3.170 Park DSM, Lee HS, Rhee DS, Shin HS (2019) Improvement in the analytical procedure for total organic carbon measurements in particle-containing water samples. J Korean Soc Environ Anal 22:41–49 Post WM, Kwon KC (2000) Soil carbon sequestration and land-use change: processes and potential. Glob Chang Biol 6:317–327 Prevost M (2004) Predicting soil properties from organic matter content following mechanical site preparation of forest soils. Soil Sci Soc Am J 68(3). https://doi.org/10.2136/sssaj2004.0943 Sakin E, Deliboran A, Tutar E (2011) Bulk density of the Harran plain soils in relation to other soil properties. Afr J Agric Res 6(7):1750–1757 Sharma JC, Sharma Y (2004) Effect of forest ecosystems on soil properties – a review. Agric Rev 25(1):16–28 Smith P (2004) Carbon sequestration in croplands: the potential in Europe and the global context. Eur J Agron 20(3):229–236 Soil Taxonomy (1999) A basic system of soil classification for making and interpreting soil surveys, 2nd edn. Soil Survey Staff, United States Department of Agriculture, Agriculture Handbook, Natural Resources Conservation Service, Washington, DC Tremblay S, Ouimet R, Houle D (2002) Prediction of organic carbon content in upland forest soils of Quebec, Canada. Can J For Res 32:1–12 Zaiontz C (2020) Multiple correlation coefficient, real statistics using excel, real statistics 2020, www.real-­statistics.com, Correlation, 1–5

Chapter 5

Forest Vegetation Sampling and Analysis

Abstract  Analysis of vegetation characteristics and their occurrences including timber and non-timber species by random sampling, estimation of biomass stock and carbon content, and determination of wood volume are considered for several forest patches of West Bengal. For determination of required optimum sample size in the forests including the nature of the vegetation pattern, a survey has been taken up for statistical analysis on the sampled data collected from the 27 forests of West Bengal. Sampled data are analyzed in three phases separately applying probability measures of statistical methods. From the analysis, higher the survey spots, lower the required optimum sample size is revealed. Results obtained from the statistical analysis for the forest show likely indications and positive trends that help to understand the vegetation categories, types of dominant timber trees and stem-diameters of the other forest areas in the state. Similarity measurement of such timber trees is the determination of the properties of communities that helps to suggest whether the communities may be classified together or in necessity to be separated. Not only similarity measurements, but the biomass stock mapping of such uprooted timber trees helps for the estimation of carbon content of dead wood in the community forest created under the social forestry scheme. Keywords  Random sampling · Similarity indices · Relative abundance · Biomass stock · Carbon stock

Random Sampling and Analysis Similarity pattern check is a criterion for determination of reliability of the data sampled at random and in a relatively lesser number of sample spots in the survey area of the forests. Application of probability scale of statistical method may have some measures to interpret the required sample size for the entire study area. For determination of such required sample size in the forest areas, an attempt has been taken up for statistical analysis on the sampled data collected from 9 sample spots © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_5

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of 2 forest patches of Garh Jangal and Aduria Forest under Bardhaman Forest Division, 14 sample spots of the forest patches of 11 districts and overall, 27 sample spots from 19 districts situated all over the state of West Bengal during the period from 2008 to 2020 consecutively. Further, the sampled data are used in a repeated manner with the larger areas and a greater number of forests considered for the statistical analysis. The forest area of Bardhaman Forest Division is 339.31 sq. km comprising 38 forest patches under the administrative control of Guskara Forest Range of Purba Bardhaman district and Panagarh & Durgapur Forest Ranges of Paschim Bardhaman district. Among 38 forest patches, pilot surveys are conducted in two forest patches – Garh Jangal (forest area-3184.74 ha) and Aduria Forest (forest area-1777.14 ha) and the total coverage of the survey area is 4961.88 hectares. During the pilot survey, total number of trees (10 cm and above diameter) enumerated are 4630 in 9 sampling units with the large representations of 4 timber tree species such as Shorea robusta, Holarrhena antidysenterica, Buchanania lanzan and Acacia catechu (Das 2021). The total areas of forest in West Bengal (India) are 16901.51 sq. km comprising numerous forest patches scattered all over the districts of the state as recorded in the India State of Forest Report (FSI 2019). In such forest patches with rich and mixed floral diversity, an inventory survey is carried out with the random sample methodology for determination of optimum sample size for forests. Among them, a pilot survey is conducted in 27 forest patches in 19 districts out of 23 districts of West Bengal. The survey is conducted in both types of natural and stray forests patches (Das 2020a). During survey, the total number of trees (10 cm and above diameter) enumerated are 17,380 in 27 sampling units in 27 forest patches with the large representations of numerous timber tree species of eight major forest types of West Bengal (Champion and Seth 1968).

Data Sampling in Quadrats for Garh Jangal and Aduria Forests Number of data regarding categories of tree species including their diameter are collected in this inventory survey, though only timber trees of 10  cm and above diameter are inventoried, rest of the plant species including herbs and shrubs are considered as miscellaneous. For quantitative analysis, trees inside forests were sampled at random through 10 m × 10 m quadrats, equal to 0.1 hectare, and the collected data from 9 quadrats was transformed into the data for 1-hectare area after calculation with numerical conversion. Locations of all sampling units are indicated with the Latitude & Longitude taken on-spot from the Google Map (Table 5.1).

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143

Table 5.1  Collected data used for sample size determination from Garh Jangal and Aduria forests Latitude & Longitude of the Forest sampling spots Garh Jangal 23.603838N & 87.451219E 23.599647N & 87.451031E 23.596259N & 87.447513E 23.600971N & 87.432772E 23.595236N & 87.431461E Aduria 23.578570N & 87.534175E Forest 23.577743N & 87.529954E 23.578756N & 87.533068E 23.579643N & 87.531399E N = 9

Trees/ha (above 10 cm Diameter) 810 730 770 320 540 40 610 20 790 ∑ x = 4630

∑x2 656,100 532,900 592,900 102,400 291,600 1600 372,100 400 624,100 ∑ x 2 = 3,174,100

Data Analysis and Results Sampling units have been adopted considering quadrats of 0.1-hectare area in each spot of sampling in random sampling methodology. Each 0.1-hectare quadrat inside the forest is treated as a sampling unit during conducting the pilot survey. The data has been processed for calculation of sample size for the entire Garh Jangal and Aduria Forest and the following formula is used after completing the forest vegetation survey -.  1.96  c.v.     10  

n

2

1  1.96  c.v.     10 N  2.

1

Where, x is Mean of the variable tree/ha and N is the total number of forests in West Bengal. No of sample quadrats, n = 9  x  4630 x  514.4444  x 2  3174100

where, s = standard deviation



s2 

 x2 

 x

n

2

n

144



5  Forest Vegetation Sampling and Analysis

s = 296.68 c.v. = Coefficient of variation s x100 x = 57.66

c.v. =



x = Mean of the variable tree/ha = 514.4444 s = Standard deviation of tree/ha = 296.68 1  1.96  c.v.     is insignificant and the above forFor large N, the value of N 10  mula for sample size will become, 2



 1.96  c.v.   n  10  



 1.96  57.66   n  10  



n = 127

2

2

In statistical analysis using probability methods, the sample size obtained in the result for Garh Jangal and Aduria forest has come out to be 127 quadrats and the result of the sampled data shows positive patterns. The present pilot survey is based on the data from 9 quadrats of the study areas which may give some indication of vegetation pattern, number of stems having diameter classes of 10 cm and above, and types of dominant timber tree species.

 ata Sampling in Quadrats for the 14 Sample Spots D in 11 Districts Statistical measures of 14 forest patches scattered from east to west in the forest stands of Jungle Mahal are considered in this survey. The survey is conducted in both types of natural and stray forests patches. During the pilot survey, the total number of trees (10 cm and above diameter) enumerated are 7980 in 14 sampling units in 14 forest patches with the large representations of several timber tree species (Table 5.2).

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Table 5.2  Collected data used for sample size determination from 14 forest patches of West Bengal Name of the districts Name of the forests Paschim Bardhaman Garh Jangal Aduria Forest Bankura Joypur Beliatore Jhargram Mayur Jharna Lalgarh Paschim Medinipur Arabari Purulia Bundwan Nadia Bethuadahari Uttar Dinajpur Kulik Dakshin Dinajpur Dogachhi North 24 Parganas Parmadan Howrah Garh Chumuk Hugli Garh Mandaran Total n = 14

Trees (above 10 cm diameter)/ha 810 640 780 800 870 740 670 810 510 450 370 430 40 60 ∑ x = 7980

∑x2 656,100 409,600 608,400 640,000 756,900 547,600 448,900 656,100 260,100 202,500 136,900 184,900 1600 3600 ∑ x 2 = 5,513,200

Data Analysis and Results Number of data regarding categories of tree species including their diameter are collected in this inventory survey, though only timber trees of 10  cm and above diameter are inventoried, rest of the plant species including herbs and shrubs are considered as miscellaneous. For quantitative analysis, trees inside forests were sampled at random through 10 m × 10 m quadrats, equal to 0.1 hectare, and the collected data from 14 quadrats was transformed into the data for 1-hectare area after calculation with numerical conversion. List of all sampled forest patches and along with their occurrences in the districts of West Bengal are mentioned in Table 5.2. Sampling units have been adopted considering quadrats of 0.1-hectare area in each spot of sampling in random sampling methodology. Each 0.1-hectare quadrat inside the forest is treated as a sampling unit during conducting the pilot survey. The data has been processed for calculation of sample size for 14 Forest patches and the following formula is used after completing the forest vegetation survey -

n

 1.96  c.v.     10  

2

1  1.96  c.v.   1   10 N  2

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No of sample quadrats, n = 14  x  7980 x  570  x 2  5513200

where, s = standard deviation

s2 



 x2 

 x

2

n

n

s  262.48



c.v. = Coefficient of variation s c.v. = x100 x = 46.04



x = Mean of the variable tree/ha = 570 s = Standard deviation of tree/ha = 262.48 1  1.96  c.v.     is insignificant and the above forFor large N, the value of N 10  2

mula for sample size will become,  1.96  c.v.   n  10  

2

 1.96  46.04   n  10   n  81 2



In statistical analysis using probability methods, the sample size obtained in the result for 14 forest patches has come out to be 81 quadrats and the result of the sampled data shows positive patterns. The present pilot survey is based on the data from 14 quadrats of the study areas which may give some indication of vegetation pattern, number of stems having diameter classes of 10 cm and above, and types of dominant timber tree species.

Data Analysis and Results

147

 ata Sampling in Quadrats for the 27 Sample Spots from 19 D Districts of West Bengal Number of data regarding categories of tree species including their diameter are collected in this inventory survey, though only timber trees of 10  cm and above diameter are inventoried, rest of the plant species including herbs and shrubs are considered as miscellaneous. For quantitative analysis, trees inside forests were sampled at random through 10 m × 10 m quadrats, equal to 0.1 hectare, and the collected data from 27 quadrats was transformed into the data for 1-hectare area after calculation with numerical conversion. List of all sampled forest patches along with their occurrences in 19 districts of West Bengal are enlisted in Table 5.3. Sampling units have been adopted considering quadrats of 0.1-hectare area in each spot of sampling in random sampling methodology. Each 0.1-hectare quadrat inside the forest is treated as a sampling unit during conducting the survey. After processing data for estimation of sample size for 27 Forest patches, following formula (FSI 1997) is used after completing the survey of forest vegetation –

n

 1.96  c.v.     10  

2

1  1.96  c.v.   1   10 N  2

No of sample quadrats, n = 27  x  17380 x  643.70  x 2  12523800





where, s = standard deviation



s2 

 x2 

 x

2

n

n



s = 222.46 c.v. = Coefficient of variation s x100 x = 34.55

c.v. =



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x = Mean of the variable tree/ha = 643.70 s = Standard deviation of tree/ha = 222.46 N = Total number of forests in West Bengal 1  1.96  c.v.     is insignificant, thus for determination For large N, the value of N 10  of sample size the formula will become, 2

Table 5.3  Data collected for computation of optimum sample size from 27 forest patches of West Bengal Name of the districts Name of the forests Darjeeling Mirik Kalimpong Lava Lolegaon Alipur Duar Chilapata Hatipota Buxa Cooch Behar Kodal Basti Jalpaiguri Chapramari Gorumara Paschim Bardhaman Garh Jangal Aduria Forest Bankura Joypur Beliatore Birbhum 11 Mile Jhargram Mayur Jharna Lalgarh Paschim Medinipur Arabari Purba Medinipur Junput Purulia Bundwan Nadia Bethuadahari Uttar Dinajpur Kulik Dakshin Dinajpur Dogachhi 24 Parganas (North) Parmadan 24 Parganas (South) Dhanchi Luthian Howrah Garh Chumuk Hugli Garh Mandaran Total n = 27

Trees (above 10 cm diameter)/ha 830 920 930 870 650 740 590 670 710 810 640 780 800 610 870 740 670 770 810 510 450 370 430 530 580 40 60 ∑ x = 17,380

∑x2 688900 846400 864900 756900 422500 547600 348100 448900 504100 656100 409600 608400 640000 372100 756900 547600 448900 592900 656100 260100 202500 136900 184900 280900 336400 1600 3600 ∑ x 2 = 12,523,800

Remarks

149

 1.96  c.v.   n  10  

2

 1.96  34.55   n  10   n  45 2



In statistical analysis using probability methods, the derived sample size from 27 forest patches has appeared to raise 45 quadrats and the result of the sampled data shows positive patterns. The collected data of the pilot survey is from 27 quadrats of the 27 forest areas which reflect similarity patterns of growing stocks, number of stems having diameter classes of 10 cm and above, and types of timber trees dominated in the forests.

Remarks Determination of required number of optimum sample size through the inventory study with the estimation of growing stock of standing trees in terms of tree density of major timber species for 9 sampling units taken up in Garh Jangal and Aduria forest are likely and possess similarity in pattern to 127 such quadrats (obtained in the result) for all 38 forest patches under Bardhaman Forest Division. The sample size obtained in the result interprets homogeneity of the vegetation pattern in all 38 forest patches of Bardhaman Forest Division. Required number of optimum sample size determination through the inventory study with the estimation of growing stock of standing trees in terms of tree density of major timber species for 14 sampling units taken up in 14 forests are likely and possess similarity in pattern to 81 such quadrats (obtained in the result) for rest of the forest patches of West Bengal. In statistical analysis using probability methods, the derived sample size from 27 forest patches has appeared to raise 45 quadrats and the result of the sampled data shows positive patterns. The collected data of the pilot survey is from 27 quadrats of the 27 forest areas which reflect similarity patterns of growing stocks, number of stems having diameter classes of 10 cm and above, and types of timber trees dominated in the forests. The survey result and statistical analysis for optimum sample size ascertainment should be ±10% accuracy at 95% probability level (FSI 1997) in terms of statistical random sampling methodology. Determination of required number of optimum sample size through the inventory study with the estimation of timber trees for 27 sampling units taken up in 27 forests are likely and possess similarity in pattern to 45 such quadrats (obtained in the result) for the rest of the forest patches of West Bengal. Application of statistical measures for determination of optimum required sample size reveals that larger the area surveyed higher the data pool obtained

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minimizes the required optimum sample size for the forest stands. From this pilot survey it is noticed that the number of more surveys in the study areas enhances precision of data and the probability level. Therefore, surveys to be conducted in the broader perspectives for generating more data pools and for the precise accuracy for further interpretation on vegetation patterns and overall integrity monitoring of the forest stands is suggested. From measurements of statistics in the pilot survey of the forest stands of West Bengal, the difference between sampling unit and elementary unit is observed. Selection of simple random sampling with or without replacement, calculation of variance, standard errors, confidence interval is accomplished and the findings from the statistical analysis are especially significant as well as important to the people interested about the forest sectors irrespective of their lacking much knowledge or understanding about statistical measures. Ratio estimator is analyzed as findings when the elementary unit is the unit of interest. Variability is expressed in the size of confidence interval in this pilot survey as the variability of an attribute exists within aggregated units assuming this pilot survey conducted for statistical estimation on the different parameters of the forests of West Bengal as a preliminary status of the study. The statistical analysis is to be reflected more on the different parameters after subsequent sampling and collection of additional data pools regarding composition of the forest stands of West Bengal (Das 2020a).

Timber and Non-timber Plant Species of Garh Jangal Different plant species aggregated to form a forest cluster are apparently of two types – timber and non-timber tree species (Fig. 5.1). Among 187 total identified species of the forest stand of Garh Jangal under Bardhaman Forest Division, 6 species are considered as the timber tree species. Those 6 timber tree species are sampled from a population frame of 187 identified plant species (Bouri and Mukherjee 2018). Thus, the sample units and elementary units are both plant species and the outcome of the analysis is a binomial variable. The sampling is done with replacement, discarding order. The number of possible samples of 6 from a population of 187 is, 187! 6! 187  6 ! 



187 186 185 184 183 182  x181!

6 x 5 x 4 x3 x 2 x11x181!  54768908194



An exceptionally large number of possible samples, though the mean, standard error and 95% confidence interval are to be derived from each of these possible samples. This is simply a formidable task and certainly the results could not be

Remarks

151

Fig. 5.1  A data sampling quadrat covered with timber tree species at Garh Jangal

viewed graphically. Thus, the confidence interval is to be drawn and to derive the 95% confidence interval, the finite population correction term is included and a t-value of 1.986 is to be used. The 95% confidence interval for each of the surveys is derived using formula,



 187  6  p  1  p  CI  p   p  1.986    187  6  1

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5  Forest Vegetation Sampling and Analysis

Where p is the proportion in the sample, a is the count of sample (timber trees), n is the number of sampled plant species, the proportion of sample is, P=



a 6 = = 0.032 n 187

Thus, confidence interval is  187  6  p  1  p  CI  p   p  1.986    187  6  1  187  6  0.032  1  0.032   p  1.986   6 1  187  CI  p   0.032  1.986  0.076 





The confidence interval for the sample with a proportion 0.032 is, CI  p   0.032  1.986  0.076 





Or a lower limit of 0 and an upper limit of 1.

Remarks With such a small sample, most of the confidence intervals are very wide, extending from 0 to 1, assuming there was no bias in the sample selection at Garh Jangal forest under Bardhaman Forest Division.

Pocket Forest Tree Inventory Tree Inventory of a Pocket Forest in Urban Zone Pocket forests provide huge quantities of oxygen to the urban people and they are considered for the urban areas only, either in and around the urban zone, or adjacent/outskirts of the town or metropolitan areas (Endreny 2018). Covering areas for this typical type of forest might be of several hectares or even less than a hectare because of the scarcity of land availability in the urban zone, and then this forest might be defined as Mini Forests. The mini forests can be squeezed into playgrounds or along the roadside areas where only the saplings of the native plants are considered for plantation programmes under social forestry schemes (Das 2020b). Native trees of the pocket forests grow vigorously, and it is ten times faster than those of

Pocket Forest Tree Inventory

153

the natural forests. Pocket forests recreate hundred times more biodiversity and sequester carbon forty times more than that of the conventional forests. Generation of pocket forests through the process of afforestation requires native or indigenous plant species where density of vegetation is the key and reach of sunlight upon the planted young saplings is essential for their growth. Growing stock of the pocket forests will form wildlife corridors for the common faunal community of the region such as butterflies, snails, amphibians and so on, and attract the birds as pollinators. Present day biodiversity crisis might be enriched by the introduction of the pocket forests in the urban areas that at least provide the snack for the songbirds. Pocket forests, studded with the tree lines and layers of local natural forest, is a green and cheap way to store carbon into soil, and thus, form a rich carbon pool in the urban zone. Generally, in the urban areas, a mature tree can consume 48 pounds of carbon dioxide annually including greenhouse gases like ozone. The amount of oxygen released by a mature tree is about 260 pounds each year which is almost equal to the quantity of oxygen inhaled by 10 people in a year. A mature tree of height 30.5 m and 45 cm basal diameter produces 260 pounds of oxygen, and the tree lines comprising forests release about 28% of oxygen globally. The quantity of oxygen produced by the trees in the urban areas varies with several factors like species diversity, age and health of the trees, and the surrounding environments. The trees identified and measured in a pocket forest in the urban zone of Kolkata megacity are healthy in nature, comparatively young aged, and 18  in numbers including 11 species (Table 5.4). Trees of the pocket forest produce a huge amount of oxygen that reduces the heat in the urban zone. For estimation of such oxygen of a pocket forest, the means of basal diameter, diameter at breast height (DBH), and height of the trees are to be calculated using statistical methods to justify the maturity of the trees. In the present study area, the pocket forest consists of 18 trees of 11 species located at an institutional campus in Jadavpur area of Kolkata megacity (Tables 5.5, 5.6 and 5.7). The value of mean is calculated from the formula, f x f 760   42.22 cm 18 Mean for basal diameter  42.22 cm Mean 



The values to be substituted in the following formula, f x f 680   37.77 cm 18 Mean for DBH  37.77 cm Mean 







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5  Forest Vegetation Sampling and Analysis

The values to be substituted in the following formula, f x f 570   31.66 m 18 Mean for height  31.66 cm Mean 





Table 5.4  Inventory of trees of a pocket forest in the urban areas of Kolkata metropolis Sl. no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Name of the trees Tamarind (Tamarindus indica) Shimul (Bombax ceiba) Chhatim (Alstonia scholaris) Pakur (Ficus rumphii) Bakul (Mimusops elengi) Shimul (Bombax ceiba) Shimul (Bombax ceiba) Jackfruit (Artocarpus heterophyllus) Anshfal (Dimocarpus longan) Anshfal (Dimocarpus longan) Dewa (Phaleria macrocarpa) Jackfruit (Artocarpus heterophyllus) Tamarind (Tamarindus indica) Kamini (Murraya paniculata) Kamini (Murraya paniculata) Arjun (Terminalia arjuna) Krishnachura (Delonix regia) Dewa (Phaleria macrocarpa)

Basal diameter (cm) 19.92 56.21 44.14 56.27 71.68 68.37 50.54 52.33

Diameter at breast height (cm) 17.79 50.96 38.82 51.14 66.0 60.66 46.91 45.29

Height (m) 31.1 37.3 35.1 33.4 28.2 43.4 40.9 30.7

12.71 12.33 26.12 29.31

10.88 10.51 23.45 25.07

21.8 20.7 25.8 29.1

31.11 59.42 21.18 46.25 84.21 14.88

25.88 56.62 17.79 38.82 76.03 10.77

31.8 20.7 20.9 34.4 31.5 24.1

Table 5.5  Computation of mean for basal diameters of the tree inventory from grouped data Basal diameter class (cm) 11–20 21–30 31–40 41–50 51–60 61–70 71–80 81–90

Midpoint x 15 25 35 45 55 65 75 85

ƒ 4 3 1 2 5 1 1 1 ∑ƒ = 18

ƒx 60 75 35 90 275 65 75 85 ∑ƒx = 760

Measurements of Similarity Index

155

Table 5.6  Computation of mean for DBH of the tree inventory from grouped data DBH class (cm) 11–20 21–30 31–40 41–50 51–60 61–70 71–80

Midpoint x 15 25 35 45 55 65 75

ƒ 5 3 2 2 3 2 1 ∑ƒ = 18

ƒx 75 75 70 90 165 130 75 ∑ƒx = 680

Table 5.7  Computation of mean for heights of the tree inventory from grouped data Height class (m) 21–30 31–40 41–50

Midpoint x 25 35 45

ƒ 8 8 2 ∑ƒ = 18

ƒx 200 280 90 ∑ƒx = 570

Remarks The obtained values of means for basal diameter 42.22 cm, diameter at breast height 37.77  cm, and height 31.66  m are close to the parameters of basal diameter and height for a mature tree in an urban surrounding. As a mature tree releases 260 pounds of oxygen, then 18 trees in the pocket forest of the urban set up produce about 4680 pounds of oxygen each year and consume about 864 pounds of carbon dioxide annually at the rate of 48 pounds carbon dioxide consumption by a mature tree per year.

Measurements of Similarity Index Similarity measurement is the determination of the properties of communities that helps to suggest whether the communities may be classified together or in necessity to be separated. Similarity index sometimes is estimated by reformulating the indices of abundance coefficients. Estimation of abundances is an important way to characterize and measure the properties of plant and animal communities as the species abundances comprise the community. Like abundances, similarity, a parameter, is utilized for the interpretation of the communities in necessity. Measurement of such similarity as a community parameter has been attempted by the several workers in the field of modern ecology. Measurements of abundances and similarity are almost the same properties of the plant and animal communities. Similarity can

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5  Forest Vegetation Sampling and Analysis

be measured using multivariate statistical methods in the applied forest ecology. Among several ways of measures, very closely correlated Jaccard index and Sorensen index are taken into consideration for the present studies of tree communities of Dooars, though Chao et al. (2006) modified both Jaccard and Sorensen indices taking together the indices of presence-absence data and indices of relative abundances for measurement of similarity.

 imilarity index Determination of Tree Species of Chilapata S and Mendabari Forests of Dooars The jungle-beauty Dooars is known for her abundant occurrences of several tree species. Dooars is a vast area comprising numerous forest patches. Among them, two natural forest patches namely Chilapata and Mendabari consist of dense forest vegetation covered with luxuriant green canopy. Almost similar types of tree species have occurred in both forests of Dooars origin as the Chilapata forest is situated only about 8 kilometers away from the Mendabari forest with the similar type of climatic condition, physiography, and topographical features. For the occurrences of similar types of tree species at Chilapata and Mendabari, the similarity index of tree species is measured for both forests after proper identification of the tree species and number counts during the survey. Measures of similarity for the sampled tree species of the two forests during the survey with an objective of the classification or separation between two such plant community samples together. Determination of similarity index is calculated by the estimation of binary coefficients when data of only presence or absence of the numbers of individuals of species is available and by the quantitative similarity coefficients depending upon the measure of relative abundance that quantifies the importance of the individuals of species in a specific community. Estimation of relative abundance depends on the data of number of individuals, productivity, cover, biomass etc. and are usually measured by the following Jaccard and Sorensen indices.



Jaccard Similarity Index  Sorensen Similarity Index 

a abc 2a 2a  b  c

For measuring relative abundance and similarity between the communities, Binary coefficients are introduced by Jaccard and Sorensen which is later modified by Chao et al. (2006). The reformulated Jaccard and Sorensen indices by Chao et al. (2006) for the estimation of similarity index are as following,

Measurements of Similarity Index

157

Xi  m  1 f1 a Xi   / Yi  1 i 1 n m 2 f2 i 1 n a

U

Yi  n  1 f1 a Yi   /  Xi  1 i 1 m n 2 f2  i 1 m



a

V



Where, Xi = number of individuals of species i in sample 1 a = number of shared species between samples 1 and 2 n = total number of individuals in sample 1 m = total number of individuals in sample 2 f+1 = observed number of shared species that occur once in sample 1 f+2 = observed number of shared species that occur twice in sample 1 / = indicator function (/ = 1 if the expression is true, / = 0 if false) Yi = number of individuals of species i in sample 2 f1+ = observed number of shared species that occur once in sample 2 f2+ = observed number of shared species that occur twice in sample 2 Relative abundance is the division of the count of individuals of species in sample and total count of the individuals of species in sample, Relative abundance =

count of individuals of species in sample total count of the individuals of species in sample



Chao et al. (2006) generalized the abundance data instead of presence-absence data and modified the Jaccard Index and Sorensen Index, where U is the total relative abundances of the shared species in sample 1, and V is the total relative abundances of the shared species in sample 2. After modification of both indices, Adjusted Jaccard abundance index and Adjusted Sorensen abundance index are as the following,



Adjusted Jaccard abundance index 

UV U  V  UV

Adjusted Sorensen abundance index 

2UV U V



In total 495 and 479 numbers of tree species are sampled in both Chilapata and Mendabari forest, respectively. Among them, 11 number of shared tree species occur between Chilapata and Mendabari forest (Table 5.8).

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Table 5.8  List of identified tree species and tree density per hectare area at Chilapata and Mendabari forests Common name Simul Udal Makri sal Bon chalta Sal Teak Kanak champa Khair Gamhar Pithali Rain tree Lampate Malita Haritaki Bondarphulla Amlaki Rudraksha a = (17–6) = 11

Scientific name Bombax ceiba Sterculia villosa Schima wallichii Dillenia indica Shorea robusta Tectona grandis Pterospermum acerifolium Acacia catechu Gmelina arborea Mallotus nudiflorus Albizia saman Duabanga grandiflora Terminalia elliptica Terminalia chebula Duabanga sonneratioides Phyllanthus emblica Elaeocarpus ganitrus

Tree density/ha area Chilapata (sample 1) 19 22 19 11 7 22 0 126 77 83 53 22 0 11 0 22 1 n = 495

Mendabari (sample 2) 24 29 11 27 11 0 24 89 66 54 66 27 24 0 24 0 3 m = 479

From Table 5.8, out of the total number of 17 identified plant species, the shared species present at the forests of Chilapata and Mendabari is 17–6 = 11 species.

Chilapata (sample 1) Number of species present Number of species absent

Mendabari (sample 2) Number of species present 11 3

Number of species absent 3 Unknown

Here in the tabular format, the number of tree species of joint occurrences at Chilapata and Mendabari forests is 11 i.e., shared species; number of species absent in Mendabari is 3 but not in Chilapata forest; number of species absent in Chilapata is 3 but not at Mendabari; number of species i.e., zero-zero matches for both forests are in the unknown category. Jaccard and Sorensen indices are estimated based on presence-absence data sampled from the two forests.



Jaccard Index  Sorensen Index 



a 11   0.65 a  b  c 11  3  3

2 11 2a   0.78 2a  b  c 2 11  3  3



To calculate the estimates of the adjusted Jaccard and Sorensen indices, the following parameters are required.

Measurements of Similarity Index

159

Xi = 440 = number of individuals of species i in sample 1 a = 11 = number of shared species between samples 1 and 2 n = 495 = total number of individuals in sample 1 m = 479 = total number of individuals in sample 2 f+1 = 2 = observed number of shared species that occur once in sample 1 f+2 = 1 = observed number of shared species that occur twice in sample 1 / = indicator function (/ = 1 if the expression is true, / = 0 if false) Yi = 407 = number of individuals of species i in sample 2 f1+ = 3 = observed number of shared species that occur once in sample 2 f2+ = 1 = observed number of shared species that occur twice in sample 2 for estimation of adjusted Jaccard abundance index and adjusted Sorensen abundance index, the following formula is used. Xi  m  1 f1 a Xi   / Yi  1 i 1 n m 2 f2 i 1 n a

U

 0.8888  0.9979  1  0.00757  0.9643





Yi  n  1 f1 a Yi   /  Xi  1 i 1 m n 2 f2  i 1 m a

V

 0.8496  0.9979  1.5  0.0723  0.9579



Adjusted Jaccard abundance index , SJ  

UV U  V  UV

0.9643  0.9579 0.9643  0.9579   0.9643  0.9579   0.9250



Adjusted Sorensen abundance index , SS  

2  0.9643  0.9579 0.9643  0.9579  0.9610

2UV U V



Jaccard abundance index and Sorensen abundance indices can be converted into a dissimilarity coefficient by taking the inverse values of computed data.

Jaccard s dissimilarity coefficient  1  SJ  1  0.9250  0.075



Sorensen s dissimilarity coefficient  1  SS  1  0.9610  0.039

Values of dissimilarity coefficients show a very closeness in matching of the species composition in two forests where tree species are sampled in several quadrats of 1 hectare areas.

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5  Forest Vegetation Sampling and Analysis

Remarks The estimated value 0.9250 obtained applying the formula of adjusted Jaccard abundance index reveals about 92 percentage similarity in tree species identified between Chilapata and Mendabari forest, whereas the estimated value 0.9610 obtained using the formula of adjusted Sorensen abundance index shows about 96 percent similarity of the tree species of both forests of the Dooars region. Values of abundance indices match significantly between the tree species of Chilapata and Mendabari forests than mismatches revealing a close similarity tree species composition pattern. Relative abundance of tree species at Chilapata and Mendabari forest of Dooars thus varies from 92–96 percent that represents the similarity index of the tree species of two forests identified during the survey.

 imilarity Determination Using Methods S of Correlation Coefficient Correlation coefficient is a frequently used measure for estimation of similarity between the two variables assuming a linear relationship between species abundances in two communities and that correlation coefficient ranges from −1.0 to +1.0. Correlation coefficient is an approach for measuring similarity which is completely intensive to additive or proportional difference between the samples of tree communities, but it shows large bias because of the zero abundances of many species in a community sample (Wolda 1981). Measurements of correlation coefficients are disturbed by such sample types within a higher species diversity of the forest floors. Correlation coefficient is computed with the calculation of covariance and variances of two variables applying the statistical methods (Jayaraman 1999). The correlation coefficient is measured using the data for tree species collected from Chilapata and Mendabari forests (Table 5.9). Covariance of x and y, and variances of both x and y are to be computed first using the formula, n

n

 x  y  1n cov  x,y     xi yi  i 1 i i 1 i  i  1 n n 



cov  x,y   Variance of x

 495 479   1  26550   17  17   741.33

Remarks

161

Table 5.9  List of tree species identified and sampled at Chilapata and Mendabari forests Name of the trees Simul Udal Makri Sal Bon chalta Sal Teak Kanakchampa Khair Gamhar Pithali Rain tree Lampate Malita Haritaki Bondarphulla Amalaki Rudraksha n = 17

Chilapata (x) 19 22 19 11 7 22 0 126 77 83 53 22 0 11 0 22 1 Σ x = 495

Mendabari (y) 24 29 11 27 11 0 24 89 66 54 66 27 24 0 24 0 3 Σ y = 479

x2 361 484 361 121 49 484 0 15,876 5929 6889 2809 484 0 121 0 484 1 Σ x2 = 34,453

2 n  1  n 2  i 1 xi  v x   xi  n  i 1 n 



 495 1  v  x    34453  17  17   1178.81

2



y2 576 841 121 729 121 0 576 7921 4356 2916 4356 729 576 0 576 0 9 Σ y2 = 24,403

      

Variance of y



2 n  1  n 2  i 1 yi  v  y   yi  n  i 1 n 

 479 1  v  y    24403  17  17   641.55

2



      

xy 456 638 209 297 77 0 0 11,214 5082 4482 3498 594 0 0 0 0 3 Σ xy = 26,550

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5  Forest Vegetation Sampling and Analysis

Correlation coefficient  

cov  x,y 

 v  x  (v  y  

741.33

1178.81  641.55) r  0.85





Remarks The obtained correlation coefficient (r) value of 0.85 is close to the adjusted Jaccard abundance coefficient and adjusted Sorensen abundance coefficient for estimation of similarity index of the tree species sampled at Chilapata and Mendabari forests of Dooars. Though the researchers show a large bias for estimation of similarity index applying correlation coefficients because of the zero abundances of many species in a community sample, the present study shows an unbiased estimate for measuring similarity using statistical formula of correlation coefficients.

Percentage Similarity Each community sample is standardized first into percentages for the estimation of percentage similarity that leads to the relative abundances all sum to hundred percent in each sample. The percentage similarity is measured using the formula, P   minimum  p1i ,p2 i 



i



Where, PS = percentage similarity between sample 1 and 2, p1i = percentage of species i in community sample 1, p2i = percentage of species i in community sample 2 Determination of the percentage similarity (PS) of the tree species of Chilapata and Mendabari (Table 5.10) is the simplest one using the formula, PS   minimum  p1i ,p2 i  i



 3.84  4.44  2.30  2.22  1.42  0  0  18.57  13.78  11.27  10.71  4.44  0  0  0  0  0.21  73.20 PS  73.20

Result shows the value 73.20 as percentage similarity for the tree communities of Chilapata and Mendabari forests of Dooars.

Remarks

163

Morisita Index of Similarity Measuring of similarity between two communities is propounded by Morisita with the following formula, C 

2  Xij X ik

 1  2  N j N k

Where, Cλ is Morisita’s index of similarity between sample j and k, Xij,  Xik is number of individuals of species i in sample j and sample k, Nj = ∑Xij = total number of individuals in sample j and Nk = ∑Xik = total number of individuals in sample k

1 

2 

  Xij  Xij  1  N j  N j  1



  Xik  Xik  1  N k  N k  1



Similarity index for the tree species of two forests of Chilapata and Mendabari (Table 5.10) is calculated using the formula of Morisita’s index of similarity. Table 5.10  Percentage composition of tree species sampled at Chilapata and Mendabari forests Name of tree species Simul Udal Makri Sal Bon chalta Sal Teak Kanakchampa Khair Gamhar Pithali Rain tree Lampate Malita Haritaki Bondarphulla Amalaki Rudraksha Total = 17

Number of each tree species Chilapata Mendabari 19 24 22 29 19 11 11 27 7 11 22 0 0 24 126 89 77 66 83 54 53 66 22 27 0 24 11 0 0 24 22 0 1 3 495 479

Percentage composition Chilapata Mendabari 3.84 5.01 4.44 6.05 3.84 2.30 2.22 5.64 1.42 2.30 4.44 0 0 5.01 25.45 18.57 15.56 13.78 16.77 11.27 10.71 13.78 4.44 5.64 0 5.01 2.22 0 0 5.01 4.44 0 0.21 0.63 100.0 100.0

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5  Forest Vegetation Sampling and Analysis

C 

 1  2  N j N k



1 

19 18    22  21  19 18  22  21  1 0   0.1384 495  494 

2 

 24  23   29  28   1110  24  23   3 2   0.1044 479  478 







2  Xij X ik

2 19  24    22  29   19 11  11 27   53100 C     0.92 57569.09  0.1384  0.1044  495 479 



Morisita-Horn Index of Similarity Morisita-Horn Index of similarity is another measure for similarity of species community which is reformulated by Horn generalizing the Morisita’s similarity index. Morisita-Horn Index of similarity can be calculated with the following formula, C MH  







2  Xij Xik

 X /N  2 ij

2 j

X

2 ik



/ N k2 N j N k 

53100

 

 19  22  19   / 495  24 2  292  / 4792   0.9070 2

2

2

2

  495 479  

The obtained value of 0.9070 for the Morisita’s index of similarity is within the range of the Morisita index as the Morisita’s index of similarity varies from 0 (no similarity) to 1.0 (complete similarity). Morisita’s index of similarity is related to probability theory and can be defined as the probability that an individual drawn from sample j and one drawn from sample k will belong to the same species divided by probability that two individuals drawn from either j or k will belong to the same species.

Remarks

165

Horn’s Index of Similarity Horn proposed a measure for similarity popularly known as Horn’s index of similarity based on information theory is as the following,  ( Xij  Xik ) log  Xij X ik      Xij log Xij     X ik log Xik 

R0 

 N j  N k  log  N j  N k ]   N j log N j   ( N k log N k 







Where R0 is Horn’s index of similarity for samples j and k, Xij, Xik is number of individuals of species i in sample j and sample k, Nj = ∑Xij = total number of individuals in sample j and Nk = ∑Xik = total number of individuals in sample k For estimation of Horn’s index of similarity for the sampled tree species of Chilapata and Mendabari forests, the following formula is used after breaking down the summation term of the numerator.  ( Xij  Xik ) log  Xij Xik  

 19  22  log 41   22  29  log 51  19  11 log 30  11  27  log 38 

 1884.61



  Xij log Xij 

 19  log 19   22  log 22   19  log 19   11  log 11   856..06



  Xik log Xik   24  log 24   29  log 29   11  log 11  27  log 27    783..68



Horn’s index of similarity R0 

1884.61  856.06  783.68  0.8353  495  479  log 974 ]  495  log 495   479 (log 479 



Result obtained for the Horn’s index of similarity of tree species of Chilapata and Mendabari is 0.8353. Horn’s index of similarity is acceptable to the forest ecologists as it can be calculated from numbers, proportions, or percentages directly.

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5  Forest Vegetation Sampling and Analysis

Table 5.11  Comparison of data obtained from the different measures of similarity indices of the tree species samples of Chilapata and Mendabari Similarity measuring indices Jaccard index Sorensen index Bray-Curtis index Canberra metric index Morisita index Morisita-Horn index Horn’s index Percentage similarity index Correlation coefficient Adjusted Jaccard index Adjusted Sorensen index

Presence-absence based 0.65 0.78

Abundance based

0.49 0.62 0.92 0.90 0.83 0.73 0.85 0.92 0.96

Preference of Choices Similarity indices Measures Similarity measuring indices are broadly classified based on presence-absence and abundance of the sampled species in the communities. Presence-absence indices are a biased measure for high species numbers and many rare species of the communities with limited options. Among presence-absence based measures, Jaccard and Sorensen indices are the best preferred measurements for the estimation of similarity index as they are low biased measures. On the contrary, there are more options for preferring similarity measures from the abundance based indices. Out of all such measures based on abundances, again the Adjusted Jaccard and Adjusted Sorensen indices are the best choices as the other measures like Bray-Curtis and Morisita-­ Horn indices perform extremely poor in determining relative abundances for the samples collected from the communities (Table 5.11).

Biomass stock and Carbon Content Estimation Estimation of Biomass stock and Carbon Content of Dead Wood Biomass stock mapping of dead wood of the uprooted trees at Bethuadahari Sanctuary helps for the estimation of carbon content of dead wood in the community forest created under the social forestry scheme (Salunkhe et  al. 2018). The estimated amount of biomass 42.5 tons and carbon content 21.25 tons for the dead wood in a particular catastrophic event of devastating storm namely Super Cyclone Amphan are obtained applying regression fit line formula of statistical methods using the calculated value of stem biomass, branches & foliage biomass, and root biomass of four uprooted tree samples outside the forest in the same day of super cyclonic storm on 20 May 2020. Out of four uprooted tree samples, one tree sample is lacking root biomass value as the entire root systems are not exposed above the ground even though the tree is uprooted and ravaged by the cyclonic storm. So,

Biomass stock and Carbon Content Estimation

167

three uprooted tree samples are considered on rotation basis keeping parity and maintaining proportional distribution for the estimation of stem biomass, branches & foliage biomass, and root biomass of the dead wood inside the sanctuary. As the estimation of biomass stock is computed indirectly using the biomass value of the outside trees of the forest, the degree of uncertainty is tested by calculating biomass density methodology using the same data collected from the dead wood inside the forest and the obtained amount of biomass shows almost close to the value obtained from the regression fit line analysis. The obtained amount of biomass stock for the forest stand of Bethuadahari sanctuary might be considered as a forest database for the dead wood biomass estimation of the other forest patches of West Bengal and the data might be implemented for climate change mitigation measures that is of late a major issue globally (Das 2020c). Forest vegetation is affected mostly in the several forest stands of the south districts including Nadia district of West Bengal as the Cyclone Amphan, the maiden super cyclonic storm of the century formed over the Bay of Bengal ravaged the southern districts of India and Bangladesh on 20 May 2020. Numerous trees are uprooted in the scattered community forests of Nadia in this cyclonic event. Among the uprooted trees, teak (Tectona grandis) is the dominant species for their average height of 33–42 feet particularly in the Bethuadahari Wildlife Sanctuary of Nadia district. Apart from the teak, other tree species like Nona ata (Annona reticulata), Debdaru (Polyalthia longifolia) and Arjun (Terminalia arjuna) are uprooted due to the strong winds of the cyclonic storm in the forest floors of Bethuadahari wildlife sanctuary. Those uprooted dead tree species are in the intermediate density class category as classified by Pearson et  al. 2007. Intermediate density class of dead wood category is to be identified in the forest floors by the strike of a sharp blade and it enters slight in the dead wood, and if it is rotten, woods fall apart, and again the dead wood is to be identified under sound density class in the forest floors if the sharp blade bounces with its strike on the dead wood. For estimation of density of such dead wood of intermediate density class, weight is to be taken by making the entire dead wood into pieces and weighed, but this process is not permissible in the areas of a forest like Bethuadahari wildlife sanctuary under the control of the forest department managed by the Government of West Bengal. In such a situation, density of dead wood is measured from three dead wood trees outside and away from the forest which are uprooted in the same cyclonic event and biomass stock of 45 dead wood is estimated applying the regression fit line equation of statistical method using the estimated volume and density of three sampled dead trees away from the forest (Table 5.12). The measurements of basal diameter, diameter at breast height (DBH), stem top diameter, and height of 45 dead trees are taken during the field Table 5.12  Volume and biomass of dead wood of three tree samples from outside the forest Tree samples Sample 1 Sample 2 Sample 3 n = 3

Stem volume (cubic meter) x 0.049 0.0846 1.977 Σ x = 2.1106

Stem biomass (kg) y 72.307 111.2875 128.01 Σ y = 1463.60

x2 0.0024 0.00715 3.908 Σ x2 = 3.918

xy 3.54 9.41 2530.57 Σ xy = 2543.52

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5  Forest Vegetation Sampling and Analysis

survey (Table 5.12). As the collected data of basal diameter and DBH assumes to be almost the same for these 45 dead trees, the average of DBH and top diameter of stem is considered for the estimation of biomass stock and carbon stock applying a regression fit line equation. Volume and biomass of dead wood of three outside tree samples are estimated for biomass stock calculation of dead wood of the forest floors of Bethuadahari wildlife sanctuary using regression fit line formula where biomass is considered as dependent variable (y) and stem volume as independent variable (x) for statistical analysis.

x = 0.70



y = 487.86

Fitted regression line is to be estimated with the following formula of statistical measures,



 xy 



n i 1

xi

 

i i

n i 1

n i 1

2543.52 

xi  2

yi



n

 i 1 xi  n









n i 1

2

n



 2.1106 1463.60  3

 2.1106  3.918 

2

3



  620.93



  y x  487.86  620.93  0.70   53.21



y    x y  53.21  620.93x Result : y  53.21  620.93x



The result comes out to be y = 53.21 + 620.93x which can be used to calculate the biomass stock of dead wood by applying 1 cubic meter of stem volume, the stem biomass stock of the dead wood would be y = 53.21 + 620.93 (1). Applying the linear regression equation, biomass stock is calculated after estimation of the volume of dead wood from the data of average DBH and stem top diameter, and height of the dead trees (Table 5.13). Total stem biomass stock estimated = 36673.35 kg = 36.67 tons

Biomass stock and Carbon Content Estimation

169

Table 5.13  Amount of stem biomass stock calculated after estimation of dead wood volume using the data of average DBH and stem top diameter, and height of the dead trees collected from Bethuadahari wildlife sanctuary Sl. no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Name of trees Teak Arjun Arjun Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Nona ata Teak Teak Teak Teak Teak Teak Nona ata Nona ata Nona ata Nona ata Nona ata Nona ata Teak Teak Teak Teak Teak Nona ata Teak Teak Nona ata

DBH (m) 0.77 0.70 0.46 0.65 0.75 0.28 0.19 0.73 0.69 0.77 0.64 0.58 0.38 0.36 0.40 0.58 0.29 0.27 0.48 0.39 0.46 0.44 0.75 0.77 0.22 0.23 0.19 0.24 0.20 0.10 0.59 0.68 0.77 0.85 0.49 0.23 0.73 0.79 0.10

Stem top diameter (m) 0.34 0.30 0.26 0.28 0.30 0.11 0.08 0.29 0.27 0.33 0.28 0.28 0.17 0.14 0.16 0.25 0.19 0.11 0.27 0.25 0.25 0.26 0.31 0.35 0.11 0.10 0.10 0.10 0.088 0.064 0.27 0.28 0.36 0.38 0.26 0.10 0.30 0.36 0.054

Height (m) 12.10 11.31 10.64 11.34 11.25 10.09 9.42 11.43 11.16 12.23 11.65 11.25 10.21 9.88 10.40 10.82 9.88 8.78 10.30 9.24 9.91 10.15 11.13 12.33 6.80 7.53 6.37 7.77 7.10 6.58 10.43 11.10 12.16 12.38 10.43 8.69 11.55 12.41 6.19

Volume (cubic meter) 2.77 2.22 1.08 1.92 2.43 0.30 0.13 2.33 2.02 2.90 1.93 1.63 0.61 0.48 0.64 1.46 0.45 0.25 1.14 0.74 0.98 0.97 2.45 3.01 0.15 0.16 0.11 0.18 0.12 0.034 1.51 2.00 3.05 3.67 1.51 0.19 2.40 3.22 0.029

Stem Biomass (kg) 1773.18 1431.67 723.81 1245.39 1562.07 239.48 133.93 1499.97 1307.48 1853.90 1251.60 1065.32 431.97 351.25 450.60 959.76 332.63 208.44 761.07 512.69 661.72 655.51 1574.48 1922.21 146.35 152.55 121.53 164.97 127.72 74.32 990.81 1295.07 1947.04 2332.02 990.81 171.19 1543.44 2052.60 71.21 (continued)

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5  Forest Vegetation Sampling and Analysis

Table 5.13 (continued) Sl. no 40 41 42 43 44 45

Name of trees Nona ata Debdaru Debdaru Teak Debdaru Debdaru

DBH (m) 0.11 0.40 0.25 0.33 0.20 0.37

Stem top diameter Height (m) (m) 0.067 7.10 0.17 10.09 0.14 9.51 0.15 9.52 0.097 8.44 0.16 9.08

Volume (cubic meter) 0.043 0.64 0.28 0.43 0.14 0.50

Stem Biomass (kg) 79.90 450.60 227.07 320.21 140.14 363.67

Multiplying by 0.5 gives the amount of carbon = 36.67*0.5 = 18.33 tons Total stem biomass stock for the dead wood uprooted in the cyclonic storm is 36.67 tons using a fitted regression line of statistical analysis. For feasibility tests of the obtained data for stem biomass stock from the fitted regression line analysis, the biomass stock of dead wood is further determined by applying another method of estimation of biomass stock of the dead wood by calculating biomass density of dead wood of the forest floors. For estimation of biomass stock by calculating biomass density, three diameter classes are considered based on their average of diameter at breast height (DBH) and stem top diameter (Table 5.14). Wood density is available from the data generated from the three tree samples of dead wood and that data of wood density is considered for determination of stem biomass stock. Volume, biomass stock, and carbon content of the dead wood of the forest floor of Bethuadahari wildlife sanctuary are estimated using the formula of Pearson et al. 2007. Diameter classes are determined based on the collected data of dead wood from the forest floors.

D1 : 1 − 30 cm − 19.01 cm



D2 : 31 − 50 cm − 41.80 cm



D3: 51 − 65 cm − 55.27 cm



Density  mass / volume  data from the three sampled dead wood trees   1463.60 / 2.1106  691.68 kg / m3  0.69 t / m3

Dividing by the total area of Bethuadahari wildlife sanctuary gives the volume per hectare area, total area of the sanctuary is 67 hectares i.e., 67,000 sq. meter. Volume   2  D12  D2 2  D32 .  D n 2 / area   9.8596 19.012  41.80 2  55.272 / 67000 

 0.75 m 3 / ha



Biomass = stock 0= .75 x 0.69 0.52 t / ha



Biomass stock and Carbon Content Estimation

171

Table 5.14  Data collected for dead wood biomass estimation at Bethuadahari wildlife sanctuary Sl. no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Name of trees Teak Arjun Arjun Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Nona ata Teak Teak Teak Teak Teak Teak Nona ata Nona ata Nona ata Nona ata Nona ata Nona ata Teak Teak Teak Teak Teak Nona ata Teak Teak Nona ata Nona ata Debdaru Debdaru

DBH (cm) 77 70 46 65 75 28 19 73 69 77 64 58 38 36 40 58 29 27 48 39 46 44 75 77 22 23 19 24 20 10 59 68 77 85 49 23 73 79 10 11 40 25

Stem top diameter (cm) 34 30 26 28 30 11 08 29 27 33 28 28 17 14 16 25 19 11 27 25 25 26 31 35 11 10 10 10 8.8 6.4 27 28 36 38 26 10 30 36 54 6.7 17 14

Height (cm) 1210 1131 1064 1134 1125 1009 942 1143 1116 1223 1165 1125 1021 988 1040 1082 988 878 1030 924 991 1015 1113 1233 680 753 637 777 710 658 1043 1110 1216 1238 1043 869 1155 1241 619 710 1009 951 (continued)

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5  Forest Vegetation Sampling and Analysis

Table 5.14 (continued) Sl. no 43 44 45

Name of trees Teak Debdaru Debdaru

DBH (cm) 33 20 37

Stem top diameter (cm) 15 9.7 16

Height (cm) 952 844 908

Table 5.15  Stem biomass and root biomass of dead wood of three tree samples from outside the forest Tree samples Sample 1 Sample 2 Sample 4 n = 3

Stem biomass (kg) x 72.307 111.28 105.93 Σ x = 289.517

Root biomass (kg) y 12.755 14.1325 15.98 Σ y = 42.8675

x2 5228.30 12383.23 11221.16 Σ x2 = 28832.69

xy 922.27 1572.66 1677.90 Σ xy = 4172.83

For 67 hectares areas, stem biomass stock will be 0.52 × 67 = 34.84 tons Multiplying by 0.5 the carbon stock will be 34.84*0.5 = 17.42 tons The result comes out to be 34.84 tons for stem biomass stock and 17.42 tons carbon content is obtained multiplying the biomass stock by 0.5 (Pearson et  al. 2007). The amount of stem biomass stock (34.84 tons) and carbon content (17.42 tons) is close to the amount estimated by the fitted regression line of statistical analysis.

Estimation of Root Biomass of Dead Wood Root biomass of the dead wood is estimated by applying a fitted regression line of the statistical method using stem biomass as independent variable (x) and root biomass as dependent variable (y). Stem biomass and root biomass of dead wood of three sampled trees (sampled tree 4 instead of sampled tree 3) outside the forest are considered for the estimation of root biomass stock using regression fit analysis (Table 5.15).

x = 96.505



y = 14.289

Fitted regression line is to be estimated with the following formula of statistical measures,





n i 1

 xy 

n i 1

xi

i i



n i 1

xi

2

 

 n

n i 1

n

xi

n i 1



yi



2



Biomass stock and Carbon Content Estimation



4172.83 

173

 289.517  42.8675

28832.69 

3 2  289.517  3



  0.04



  y x  13.289  0.04  96.505   10.4288



y    x y  10.4288  0.04 x Result : y  10.4288  0.04 x



The result comes out to be y = 10.4288 + 0.04x which can be used to calculate the root biomass stock of dead wood by applying 1 cubic meter of volume, the root biomass stock of the dead wood would be y = 10.4288 + 0.04 (1). The root biomass stock is estimated using this fitted regression line considering root biomass as a dependent variable and stem biomass as independent variable.

 stimation of Biomass of Branches and Foliage E of the Dead Wood Biomass of branches and foliage of the dead wood is estimated by applying a fitted regression line of the statistical method using root biomass as independent variable (x) and branches & foliage biomass as dependent variable (y). Branches & foliage biomass and root biomass of dead wood of three sample trees outside the forest are considered for the estimation of branches and foliage biomass stock using regression fit analysis (Table 5.16).

x = 14.28



y = 30.55

Fitted regression line is to be estimated with the following formula of statistical measures,

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5  Forest Vegetation Sampling and Analysis

Table 5.16  Root biomass and branches & foliage biomass of dead wood of three tree samples from outside the forest Tree samples Sample 1 Sample 2 Sample 4 n = 3

Branches & foliage biomass (kg) y 20.537 42.515 28.62 Σ y = 91.67

Root biomass (kg) x 12.755 14.1325 15.98 Σ x = 42.86





 xy 

n i 1

xi

i i

 

n i 1





n i 1

xi 

1320.12 

2

yi

xy 261.94 600.84 457.34 Σ xy = 1320.12



n

 i 1 xi  n





n i 1

x2 162.69 199.72 255.36 Σ x2 = 617.77

2

n



 42.86  91.67  3

 42.86  617.77 

2

3



  1.92



  y x  30.55  1.92 14.28   3.14



y    x y  3.14  1.92 x Result : y  3.14  1.92 x



The result comes out to be y = 3.14 + 1.92x which can be used to calculate the branches and foliage biomass stock of dead wood by applying 1 cubic meter of volume, the biomass stock of the dead wood would be y  =  3.14  +  1.92 (1). The branches and foliage biomass stock are estimated using this fitted regression line considering branches and foliage biomass as dependent variables (Table 5.17). Biomass stock of stem, branches & foliage, and root of the dead wood is estimated after rigorous calculation applying the regression fit line formula of the statistical methods. The obtained biomass stock in Bethuadahari sanctuary is about 42.5 tons for the stem, branches & foliage, and root of the dead wood of the trees uprooted due to the strong gusty winds by the Super Cyclone Amphan on 20 May 2020 (Table 5.17).

Biomass stock and Carbon Content Estimation

175

Table 5.17  Estimation of biomass stock of stem, branches & foliage, and roots of dead wood of Bethuadahari sanctuary using regression fit line of statistical methods Sl. no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Name of trees Teak Arjun Arjun Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Teak Nona ata Teak Teak Teak Teak Teak Teak Nona ata Nona ata Nona ata Nona ata Nona ata Nona ata Teak Teak Teak Teak Teak Nona ata Teak Teak Nona ata Nona ata

Stem biomass (kg) 1773.18 1431.67 723.81 1245.39 1562.07 239.48 133.93 1499.97 1307.48 1853.90 1251.60 1065.32 431.97 351.25 450.60 959.76 332.63 208.44 761.07 512.69 661.72 655.51 1574.48 1922.21 146.35 152.55 121.53 164.97 127.72 74.32 990.81 1295.07 1947.04 2332.02 990.81 171.19 1543.44 2052.60 71.21 79.90

Branches & foliage biomass (kg) 159.33 133.10 78.74 118.80 143.08 41.54 33.43 138.34 123.56 165.53 119.28 104.97 56.32 50.12 57.76 96.85 48.70 39.15 81.61 62.52 73.96 73.48 144.06 170.77 34.39 34.87 32.49 35.81 52.98 28.86 99.25 122.62 172.69 202.24 99.25 36.29 141.68 180.79 28.61 29.29

Root biomass (kg) 81.35 67.69 39.38 60.24 72.91 20.00 15.78 70.42 62.72 84.58 60.49 53.04 27.70 24.47 28.45 48.81 23.73 18.76 40.87 30.93 36.89 36.64 73.40 87.31 16.28 16.53 15.29 17.02 25.96 13.40 50.06 62.23 88.31 103.70 50.06 17.27 72.16 92.53 13.27 13.62

Total biomass (kg) 2013.86 1632.46 841.93 1424.43 1778.06 301.02 183.14 1708.73 1493.76 2104.01 1431.37 1223.33 515.99 425.84 536.81 1105.42 405.06 266.35 883.55 606.14 772.57 765.63 1791.94 2180.29 197.02 203.95 169.31 217.80 206.66 116.58 1140.12 1479.92 2208.04 2637.96 1140.12 224.75 1757.28 2325.92 113.09 122.81 (continued)

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5  Forest Vegetation Sampling and Analysis

Table 5.17 (continued) Sl. no 41 42 43 44 45

Name of trees Debdaru Debdaru Teak Debdaru Debdaru Total

Stem biomass (kg) 450.60 227.07 320.21 140.14 363.67 36673.35

Branches & foliage biomass (kg) 57.76 40.59 47.74 33.91 51.08 3878.19

Root biomass (kg) 28.45 19.51 23.23 16.03 24.97 1946.44

Total biomass (kg) 536.81 287.17 391.18 190.08 439.72 42497.98

Total biomass stock of the dead wood of the uprooted trees in the Bethuadahari Sanctuary is estimated: 42497.98 kg i.e., 42.50 tons. From the value of total biomass stock, the amount of carbon (multiplying by 0.5 with the content of biomass stock) is obtained and the content of carbon is 42.50*0.5 = 21.25 tons.

Remarks Estimation of biomass stock of the dead wood in the Bethuadahari sanctuary is helpful for identifying causes of variation in the carbon flux and carbon stock or inflow-outflow of the soil organic carbon from the carbon budget (Yohannes and Soromessa 2015; Pan et al. 2011). It helps to understand why fluctuations happen and what should be done to reduce the adverse variance. The value of biomass stock eventually helps in better understanding of the carbon budgeting process in the rest of the forest stands in the state of West Bengal.

Wood Volume Determination Bankura district is enriched with the dense forest vegetation containing predominantly Sal forest along with the miscellaneous vegetation and plantation species that leads to the three distinct strata of occurrences – i) Sal stratum, ii) miscellaneous stratum, and iii) plantation stratum. Sal forest, classified as Northern Tropical Dry Deciduous Forest under Champion and Seth classification, consists of 86% of the forest vegetation under Sal stratum, miscellaneous species under miscellaneous stratum contain scattered Sal trees along with other species, and the forest of implanted plants leads to the formation of plantation stratum. In the three distinct strata, total volume of wood differs, and as these have been considered as distinct strata for the vegetation characteristics and growth rate of the growing stock, the estimate of the wood volume is very much essential for further attention of protection of the forest vegetation well before its loss due to a few adverse biotic factors. With the objective of the estimate of wood volume, wood volume for Sal,

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177

miscellaneous, and plantations are separately derived in all the forest stands of Bankura district, though the data of wood volume for only six forest patches are available (FSI 1985). Wood volume of forest patches of the district is determined by applying selection based simple random sampling in the forest stands of Bankura district, West Bengal. For determination of wood volume, an area of 10 m × 10 m is chosen in each quadrat for taking measurements of the tree samples from 5 cm DBH and above in a plot of such 0.1 hectare quadrat in almost all the forest patches of the district of Bankura. Bankura district consists of 4 major forest divisions comprising 32 forest ranges along with 1 working plan division which is non-territorial and functions for the districts of the entire South Bengal. Out of 32 ranges in the forest areas of Bankura district, 5 ranges are non-territorial and function for the districts of South Bengal under Working Plan South Division – II. Therefore, 27 ranges out of 32 total ranges are covered with uniformly spread forest areas. Among 27 forest covered ranges, a simple random sample of maximum volume of tree inventory has been selected for 6 ranges and for each of these sample forest ranges, the wood volumes in m3/ha were recorded by the Forest Survey of India (FSI 1985). The quantum of forest capital per unit area varies in different parts of forest areas in the district of Bankura, though the forest areas of north-west and central-west part in the district are covered with poor growing stocks. Forest areas in the ranges Saltora, Jhantipahari, Baddiha, Indpur and Khatra are less uniformly spread and consist of largely derelict poor forest areas primarily of regenerated vegetation. Forest health depends on the availability of both a reasonable number of stems and the volume per hectare area, which is found at Sonamukhi, Joypur, Bishnupur and Beliatore ranges of the Bankura North Forest Division. Maximum volume of 132.4 m3/ha is found at Sonamukhi range and minimum volume of 30.0 m3/ha is found at Bishnupur range as per inventory results in the Survey operation by the Forest Survey of India, Eastern Zone. Among them, 6 forest ranges are considered for the determination of wood volume in the district of Bankura (Table 5.18). The wood volume is designated as Yi on the ith sampling unit. The population mean Y is calculated using the equation, n

Y 



i 1 Yi n



Table 5.18  Wood volumes in the forest ranges of Bankura district Name of the forest ranges Sonamukhi Ranibandh Joypur Bishnupur Taldangra Beliatore

Wood volume (m3/ha) 132.4 102.5 60 30 35.44 39

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5  Forest Vegetation Sampling and Analysis

Where Y is an unbiased estimator of the population mean, Y 



132.4  102.5  60  30  35.44  39  66.55 6

An unbiased estimate of the sampling variance of Y is calculated by using the following equation, N n 2 Sy Nn

v Y  



i 1  yi  y  n

2 where S y 

2

 n  1

S y2 

132.4  66.55

2

 102.5  66.55    60  66.55    30  66.55    35.44  66.55    39  66.55   6  1 2

2

2

2

 1746.85



The unbiased estimate of sampling variance of Y is, v Y  

27  6

 27  6 

 

 209.62 m 3

1746.85

2



The standard error (SE) of the unbiased estimate of sampling variance of Y is calculated with the formula,

SE Y   209.62  14.47 The relative standard error is



SE Y  Y

Thus, RSE Y  



(100)

209.62 100   6.90% 209.62

The upper and lower confidence limits on the population mean Y are estimated using formula,



YL  Y  Z

SY n

2

N n N

Wood Volume Determination



179

YU  Y  Z

SY n

N n N

Where z is the table value which depends on how many observations there are in the sample, but in this estimation, t value is considered using n - 1 degree of freedom instead of z value as there are less than 30 observations as sample population.



Lower limit  66.55   2.57  209.62  29.37 cords





Upper limit  66.55   2.57  209.62  103.73 cords



The 95% confidence interval for the population mean is (29.37, 103.73) m3. Now the confidence interval (29.37, 103.73) m3 will be included in the 95% confidence interval of population mean. The total wood volume of the forest areas of Bankura district can be calculated multiplying the estimate mean (66.55) by the total number of forest ranges (27) in the population,



Y  66.55  27   1796.85 m 3

with a confidence interval of (792.99, 2800.71) estimated by multiplying the confidence limits on the mean (29.37, 103.73) by N = 27 and the estimation of wood volume for the forest inventory will not change the relative standard error (RSE) of Y in the population.

Remarks In the forest inventory report, the total wood volume for the forests of the Bankura district was estimated to 1494.605 m3 (1135.489 m3 in Sal stratum +239.917 m3 in miscellaneous stratum +119.199 m3 in plantation stratum), though total forest areas of the district were not then divided into 27 ranges under 3 forest divisions. The obtained wood volume of 1796.85 m3 looks slightly more in quantum as it is estimated for 27 forest ranges classified by the present forest administration and out of such 27 forest ranges, wood volumes of 6 forest ranges (previously existed) are taken into consideration from the tree inventory data recorded earlier by the Forest Survey of India in 1985 (FSI 1985).

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Summary Application of statistical measures for determination of optimum required sample size reveals that larger the area surveyed higher the data pool obtained minimizes the required optimum sample size for the forest stands. Biomass of the dead wood is estimated by applying fitted regression line of the statistical method using stem biomass as independent variable and root biomass as dependent variable, and thus, from the value of total biomass stock, the amount of carbon stock in the forest floor is obtained multiplying by 0.5 with the content of biomass stock. For forest vegetation, similarity measuring indices for the forest vegetation are broadly classified based on presence-absence and abundance of the sampled species in the communities. Presence-absence indices are biased measure for high species number and many rare species of the communities with limited options, though the estimated values obtained applying the reformulated equation of Adjusted Jaccard abundance index and Adjusted Sorensen abundance index revealed the percentage similarity in identified tree species even between the forest patches of the same geographical region. And among all indices for similarity measures based on abundances, the Adjusted Jaccard and Adjusted Sorensen indices are the best choices in determining relative abundances for the samples collected from the floral and faunal communities of the forest stands. Estimation of dissimilarity shows different results for the different formulas used for measuring distant coefficients. The values of distance coefficient serve for measuring dissimilarity as well as the similarity of the species composition of the collected samples.

References Bouri T, Mukherjee A (2018) Phytoresources from Durgapur forest range, West Bengal and their sustainable use. J Environ Sociobiol 15(1):89–92 Champion HG, Seth SK (1968) A revised survey of Forest types of India. Manager of Publication, Delhi, 404p Chao A, Chazdon RL, Colwell RK, Shen TJ (2006) Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62(2):361–371 Das GK (2020a) Required optimum sample size determination of Forest stands in West Bengal. E-J Appl Forest Ecol 8(2):1–6 Das GK (2020b) Pocket Forest – a way for mitigation of Climate Change, frontier. 24 October 2020 Das GK (2020c) Impact of climate change in the forests of West Bengal, frontier. 26 March 2020 Das GK (2021) Soil characteristics in the forest patches of Jungle Mahal in WB, India. Int Res J Environ Sci 10(1):81–85 Endreny TA (2018) Strategically growing the urban forest will improve our world. Nat Commun 9:1160. https://doi.org/10.1038/s41467-­018-­03622-­0 FSI (1985) Forest survey of India 1985, report on Forest resources of Bankura District of West Bengal, Forest Survey of India, Eastern Zone, Ministry of Environment and Forest, Department of Forests and Wildlife, Government of India, 82p FSI (1997) Forest Survey of India 1997, Report on inventory of trees in non-forest areas. A pilot survey in 25 villages of West Bengal, Forest Survey of India, Eastern Zone, Calcutta, 29p

References

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FSI (2019) Forest Survey of India 2019, India State of Forest Report (ISFR 2019). Ministry of Environment, Forest & Climate Change. Government of India, 187p Jayaraman K (1999) A statistical manual for forestry research, food and agricultural organization of the united nations. Regional office for Asia and Pacific, Bangkok, 231p Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE et al (2011) A large and persistent carbon sink in the world's forests. Science 333:988–993 Pearson TRH, Brown SL, Birdsey RA (2007) Measurement guidelines for the sequestration of forest carbon, General Technical Report, NRS-18, United States Department of Agriculture, Forest Service, Northern Research Station, 42p Salunkhe O, Khare PK, Kumari R, Khan ML (2018) A systematic review on the aboveground biomass and carbon stocks of Indian forest ecosystems. Ecol Process 7(1). https://doi.org/10.1186/ s13717-­018-­0130-­z Wolda H (1981) Similarity indices, sample size and diversity. Oecologia 50:296–302 Yohannes H, Soromessa T (2015) Carbon stock analysis along Forest disturbance gradient in Gedo Forest: implications of managing Forest for climate change mitigation. J Ecosyst Ecogr 05(03). https://doi.org/10.4172/2157-­7625.1000170

Chapter 6

Estimation of Biodiversity Indices and Species Richness

Abstract  The forest areas of West Bengal are well-known for its evergreen vegetation situated in the north and south with numerous plant species that reflects relative abundances of species, evenness, and species richness. Species richness is simply a count of species living in a certain location indicating the number of different species as the representatives in an ecological community, whereas the number of species and their abundances of each species in a particular ecological community is the species diversity. Species richness never reveals the accountability of species abundances or relative abundance distribution of the species in that ecological community. Occurrences of common or rare species relative to other species in an ecological community is referred to as the relative species abundances. A quantitative measure of different types of individuals in a dataset and their phylogenetic relationships among each other including distributions of all types of individuals such as divergence, evenness, or richness is referred to as a diversity index. The biodiversity of floral assemblages and faunal community of the forest stands is estimated using Shannon-Wiener Index which is a commonly used measure among other diversity indices as Shannon-Wiener Index is a comparatively better way of representing biodiversity, species diversity, species richness, evenness. Biodiversity among plant and animal communities at different forest patches has been compared and the diversity indices reflect the way in which abundance is distributed among the different species constituting the community. The result obtained after the computation of Shannon-Wiener Index shows the maximum possibility of diversity in terms of natural log of species richness for the forest vegetation. Such diversity indices of the floral assemblages and faunal community in the forest patches of West Bengal are measured using standard statistical methods and the results obtained from the statistical analysis are assumed to be unbiased in nature. Keywords  Biodiversity index · Species richness · Species diversity index · Evenness · Relative abundance

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. K. Das, Forests and Forestry of West Bengal, https://doi.org/10.1007/978-3-030-80706-1_6

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6  Estimation of Biodiversity Indices and Species Richness

Estimation of Biodiversity and Species Diversity Indices The forest patches of West Bengal nurture the ground-dwelling wildlife and floral assemblages that in turn support arboreal life and biodiversity. In the present scenario, forest stands of the state again support such wildlife and forest vegetation with the diversity of numerous species and turns into its previous state of luxuriant greenery with natural forest covers of known native terrestrial plant species after drastic protective measures taken by the government. Such distinct, different, and unlike living systems of the forests of West Bengal are the properties of biodiversity as the property of groups and classes are to be varied with space and time. Relative abundance is a measure of abundance of a species compared to other species in a site. Evenness is the measure of how relative abundance is distributed amongst the species of that site, and species richness is the number of the species that exist in that site, and these components help to interpret the species diversity. Like species diversity, species richness plays as one of the vital components in the forest ecosystems of terrestrial natural forest stands.

Diversity Index and Species Richness Estimation Floristic composition and wildlife habitat have created a unique biotic region in the forest ecosystems of Dooars that reflect a major impact in the forest ecology. Floral assemblages of Dooars provide habitat for many forms of terrestrial biota that form an enriched biodiversity comprising natural vegetation, planktons, invertebrates, and vertebrates. This biotic region is enriched with numerous flora and fauna of different communities which are yet to be identified. At present, biodiversity of both flora and fauna of Dooars and the conservation of nature and natural resources are endangered by anthropogenic interference (Morris et al. 2014; Leinster and Cobbold 2012). It is now time to realize that the decline of plants and animals will cause natural catastrophe, and this is only due to reckless and nefarious activities of human beings. For such gradual declination of floral species of Dooars, an estimation of species diversity indices might help to frame the plans and strategies for monitoring restoration and management of the forest floors by mapping of the plant communities of the region. A quantitative measure of such different types of individuals of plant species in a dataset and their phylogenetic relationships among each other including distributions of all types of individuals such as divergence, evenness, or species richness is defined as the diversity index. Species diversity (H′) is determined for both forest areas using the Shannon-Wiener Index and the species richness index is calculated using the formula of Magurran which have been used extensively in the field of ecology. H value of Shannon-Wiener index includes not only richness, but also evenness of abundance distribution of species. H is a wonderful indicator in forest ecology, and its measuring is understood with a holistic approach. Such diversity indices of the floral pattern at Raidak and Murti forests of

Estimation of Biodiversity and Species Diversity Indices

185

Dooars are estimated in the present study. Maximum possibility of diversity for both forests shows a close resemblance to each other. After estimation, the obtained result shows 79% of possibility of diversity for Raidak forest and 81% for Murti forest in the Dooars. The species richness index shows the same value for two forests of identified 12 tree species and the species richness index value 0.59 is obtained for the Raidak and Murti forests of Dooars in West Bengal. Deforestation might be an ultimate consequence of necessity driven largely by human beings on demands of the market that leads to an influence of forest degradation as an environmentally significant behaviour either caused by policy or by governance failure like factors. Broadly the factors are attitudinal and contextual factors like personal capabilities and habits, or routine works day by day. Due to that type of reasons and degradation of forest areas consequently, the famous Dooars had lost its dense canopy forest scenario only a century back during the British India period and became a forgotten forest. The forests of Dooars with its natural formatting nurture the ground-dwelling wildlife and floral assemblages that in turn support arboreal life and biodiversity (Juwarkar et al. 2016). At present, Dooars again support such wildlife and forest vegetation with the diversity of numerous species and turns into its previous state of luxuriant greenery with natural forest covers of known native terrestrial plant species after drastic protective measures taken by the government side (Shameem et al. 2010; Das 2021). Dooars is well-known for its evergreen vegetation situated in the extreme north of West Bengal with numerous plant species that reflects relative abundances of species, evenness, and species richness (Norton 1994; Simpson 1949; Peet 1974; Pant et al. 2013; Pielou 1966; Jost 2006). Species richness is simply a count of species living in a certain location indicating the number of different species as the representatives in an ecological community, whereas the number of species and their abundances of each species in a particular ecological community is the species diversity. Species richness never reveals the accountability of species abundances or relative abundance distribution of the species in that ecological community (Lande 1996; Hill 1973; Heip 1974; Gaston 1994; Beisel et al. 2003). Occurrences of common or rare species relative to other species in an ecological community is referred to as the relative species abundances. A quantitative measure of different types of individuals in a dataset and their phylogenetic relationships among each other including distributions of all types of individuals such as divergence, evenness, or richness is referred to as a diversity index. Such diversity indices are estimated for the plant species of Raidak and Murti forests of Dooars in West Bengal. Relative abundance is a measure of abundance of a species compared to other species in a site. Evenness is the measure of how relative abundance is distributed amongst the species of that site, and species richness is the number of the species that exist in that site, and these components help to interpret the species diversity (Buckland et al. 2005; Berger and Parker 1970; Wilsey and Stirling 2007; Smith and Wilson 1996). A complete illustration of the relative abundance of different species in a community can be obtained through a species abundance model from the randomly sampled individuals which is conventionally employed in the studies of

186

6  Estimation of Biodiversity Indices and Species Richness

biodiversity (Parangpe and Gore 1997; Moreno and Rodríguez 2010; Walker et al. 1999; Warwick and Clarke 1995). Species richness is simply a count of species living in a certain location indicating number of different species as the representatives in an ecological community, whereas the number of species and their abundances of each species in a particular ecological community is the species diversity (Shannon and Weaver 1949; Margalef 1958; Magurran 2004). Species richness never reveals the accountability of species abundances or relative abundance distribution of the species in that ecological community. Occurrences of common or rare species relative to other species in an ecological community is referred to as the relative species abundances (Rogers et al. 1999; Purvis and Hector 2000; Clarke and Warwick, 1998, 1999, 2001; Gaston and Spicer 1998; Boyle et al. 1990; Chao et al. 2010). A quantitative measure of different types of individuals in a dataset and their phylogenetic relationships among each other including distributions of all types of individuals such as divergence, evenness, or richness is referred to as diversity index (Chiarucci et al. 2011; Mérigot and Gaertner 2011; Mouchet et al. 2010; Tolimieri and Anderson 2010; Tuomisto 2010; Wilsey et al. 2005). Estimation of such diversity indices of the floral assemblages at Raidak and Murti forests of the Dooars areas is the objective of the present study. Ten quadrats of 10 m × 10 m size are laid in the Raidak and Murti forests as at both forest sites a similar pattern of temperature and rainfall prevails throughout the year (Das 2021). Winter dominates with a short duration of hot and humid climate. Rainfall continues from June to October almost every year. In such a climatic situation, in each quadrat, all the plant communities comprising 12 species are identified and their diameter at breast height are recorded. Numbers of trees are sampled and identified in each quadrat at Raidak and Murti forests of Dooars for determination of species diversity in terms of relative abundances applying Shannon-Wiener Index and the species diversity between the plant communities of the two forests is compared in percentage applying the statistical methods (Gorelick 2011; Gotelli and Colwell 2010). Estimated diversity index reflects the pattern of species abundance which is uniformly distributed among the individual species constituting the community in each site considered during the survey. Species sampled at Raidak and Murti forests of Dooars have been properly identified in a large-scale survey for maintaining the degree of sampling accuracy and for measuring the differences between taxonomic relationships, phylogeny (evolutionary history, and function of the individual sampled floral species (Lyons et  al. 2005; Lande et  al. 2000; Hurlbert 1971).

Determination of Species Diversity The biodiversity of identified timber tree floral communities of Dooars is estimated using Shannon-Wiener Index which is a commonly used measure among other diversity indices as Shannon-Wiener Index is a comparatively better way of representing biodiversity, species diversity, species richness, evenness etc. (Ellingsen

Results

187

et al. 2007; Gaertner et al. 2010) The measures of variability cannot be used in calculation of Shannon’s index of diversity because there is no mean or median, or measures of variations for categorical data for the species (Zaiontz 2020) and Shannon’s Diversity Index can be calculated for a random of observation which is defined as H   logn 



1 s  ni log ni n i 1

Where n is the number of observations from the sample in the i (species) of S n categories and n =  ni is the sample size. An equivalent formula is, i 1

s



H   

i 1

ni ni ln n n

ni Where n is the proportion of observations in the i th of S categories; ni is the number of individuals, and N is the total number of species. The diversity (D) is estimated using the formula, D  1

iS1 ni  ni  1 N  N  1



The maximum value of H′ occurs when all categories have the same number of observations. Relative diversity i.e., evenness or homogeneity is measured using the formula, E

H H  log S Hmax



Shannon-Wiener Index and the species diversity at Raidak and Murti of Dooars are estimated by applying the stated equations of the statistical methods using Excel’s data analysis tool (Tables 6.1 and 6.2).

Results The Table  6.1 shows the obtained value of H  =  1.96755 i.e., 1.97 which is the Shannon’s diversity index of the floral communities of the Raidak and the value of D = 0.825238136 is the diversity of species, where N (total number of species) = 407, and S (total number of species category) = 12, where S represents species richness (Table 6.1). Evenness (E) of the species diversity is obtained 1.82 i.e., E = 1.82 by calculating H/Hmax (1.97/1.079) after estimation of Hmax using ln(S) i.e., ln (12) = 1.079 i.e., Hmax = 1.079, where Hmax indicates maximum diversity possible in the community.

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6  Estimation of Biodiversity Indices and Species Richness

Table 6.1  Computation of diversity of species using Excel’s data analysis tools of the floral communities identified at Raidak forest areas Name of the trees Acacia catechu Gmelina arborea Mallotus nudiflorus Albizia saman Duabanga grandiflora Bombax ceiba Sterculia villosa Schima wallichii Dillenia indica Shorea robusta Tectona grandis Pterospermum acerifolium Total S = 12

Numbers of sampled species 117 88 66 39 32 21 18 11 6 4 3 2 N = 407

H calculation −0.35837 −0.33113 −0.295 −0.22473 −0.19995 −0.15295 −0.13792 −0.09759 −0.06217 −0.04543 −0.03619 −0.02612

D calculation 0.082134082 0.046332046 0.025961923 0.008968664 0.006003316 0.002541727 0.001851829 0.00066569 0.000181552 7.26208E-05 3.63104E-05 1.21035E-05

−1.96755 H = − (−1.96755) = 1.96755

0.174761864 D = 1–0.174761864 = 0.825238136

Table 6.2  Computation of diversity of species using Excel’s data analysis tools of the floral communities identified at Murti forest areas Name of the trees Acacia catechu Gmelina arborea Mallotus nudiflorus Albizia saman Duabanga grandiflora Bombax ceiba Sterculia villosa Schima wallichii Dillenia indica Shorea robusta Tectona grandis Pterospermum acerifolium Total S = 12

Numbers of sampled species 121 91 55 41 34 23 19 15 9 6 5 2 N = 421

H calculation −0.35836 −0.3311 −0.26589 −0.22682 −0.20321 −0.15882 −0.13982 −0.11881 −0.08221 −0.06058 −0.05265 −0.02541

D calculation 0.082117408 0.04631829 0.016796742 0.009274969 0.006345436 0.002861667 0.00193417 0.001187648 0.000407194 0.000169664 0.000113109 1.13109E-05

−2.02369 H = − (−2.02369) = 2.02369

0.167537609 D = 1–0.167537609 = 0.832462391

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189

Results The Table  6.2 shows the obtained value of H  =  2.02369 i.e., 2.02 which is the Shannon’s diversity index of the floral communities of Murti forest areas and the value of D = 0.832462391 is the diversity of species, where N (total number of species) = 421, and S (total number of species category) = 12, where S represents species richness (Table 6.2). Evenness (E) of the species diversity is obtained 1.87 i.e., E = 1.87 by calculating H/Hmax (2.02/1.079) after estimation of Hmax using ln(S) i.e., ln (12) = 1.079 i.e., Hmax = 1.079, where Hmax indicates maximum diversity possible in the community.

Relative Abundance of Species Diversity Relative abundance of species diversity is estimated for identified plant species of Raidak and Murti forest areas of Dooars. Total 12 species are identified in the two forests of Dooars with an average tree density of 407 and 421 per hectare for Raidak and Murti forests respectively (Table 6.3).

Results The relative abundance of species diversity is usually measured in terms of diversity indices, a well-known example of which is Shannon- Wiener Index (H). Values of pi, pi ln pi, and pi (ln pi)2 are calculated for the Shannon-Wiener index using Excel’s Table 6.3  List of identified tree species and tree density per hectare area at Raidak and Murti forests Common name Khair Gamhar Pithali Rain tree Lampate Simul Udal Makri Sal Bon chalta Sal Teak Kanak Champa Total = 12

Scientific name Acacia catechu Gmelina arborea Mallotus nudiflorus Albizia saman Duabanga grandiflora Bombax ceiba Sterculia villosa Schima wallichii Dillenia indica Shorea robusta Tectona grandis Pterospermum acerifolium

Tree density/ha area Raidak Murti 117 121 88 91 66 55 39 41 32 34 21 23 18 19 11 15 6 9 4 6 3 5 2 2 407 421

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6  Estimation of Biodiversity Indices and Species Richness

data analysis tools and a table with those values is drawn up (Table 6.4). The species diversity at Raidak of Dooars is H1 = 1.967545 i.e., the index value of 1.967545, where the species diversity at Murti of Dooars is H2 = 2.023689 i.e., the index value of 2.023689. These values represent the sum of the pi ln pi column. The formula of the Shannon-Wiener Index commences with a negative sign to cancel out the negative signs created by taking logarithms of proportions. The corresponding values of Shannon-Wiener index using Excel’s data analysis tools are as the following, For Raidak forest areas H1′(-SUM) = 1.967545 i.e., the Shannon-Wiener index value is 1.967545 ln(S)  =  2.484907 i.e., the value reveals the maximum possibility of diversity as natural log of species richness. H1′/ln(S) = 0.791798 i.e., the value shows approximately 79% of maximum possibility of diversity. For Murti forest areas H2′(-SUM) = 2.023689 i.e., the Shannon-Wiener index value is 2.023689. ln(S)  =  2.484907 i.e., the value reflects the maximum possibility of diversity as natural log of species richness. H2′/ln(S) = 0.814392 i.e., the value shows approximately 81% of maximum possibility of diversity.

Estimation of Variance of Diversity The variance in diversity is to be calculated using the formula,



Var  H  

 pi  ln pi     pi ln pi  2

2

N



S 1 2N 2



S 1 2N 2

For Raidak forest areas Var  H1   

 pi  ln pi     pi ln pi  2

2

N

4.55059  1.96755  407

2



12  1 2  407 

2

 0.0017





For Murti forest areas Var  H 2  

 0.0016

4.76884   2.0236  421

2



12  1 2  421

2



Name of plant species Khair Gamhar Pithali Rain tree Lampate Simul Udal Makri Sal Bon chalta Sal Teak Kanakchampa Total = 12

Raidak Numbers of plant species 117 88 66 39 32 21 18 11 6 4 3 2 407

pi 0.287469 0.216216 0.162162 0.095823 0.078624 0.051597 0.044226 0.027027 0.014742 0.009828 0.007371 0.004914 1

ln pi −1.24664 −1.53148 −1.81916 −2.34525 −2.54308 −2.96429 −3.11844 −3.61092 −4.21705 −4.62252 −4.9102 −5.31567 −38.2447

pi* ln pi −0.35837 −0.33113 −0.295 −0.22473 −0.19995 −0.15295 −0.13792 −0.09759 −0.06217 −0.04543 −0.03619 −0.02612 −1.96755

pi (ln pi)2 0.44675 0.50711 0.53664 0.52704 0.50848 0.45338 0.43008 0.35239 0.26216 0.21 0.17771 0.13885 4.55059

Murti Numbers of plant species 121 91 55 41 34 23 19 15 9 6 5 2 421 pi 0.287411 0.216152 0.130641 0.097387 0.08076 0.054632 0.045131 0.035629 0.021378 0.014252 0.011876 0.004751 1

Table 6.4  Shannon – Wiener Index for plant diversity at two locations of Raidak and Murti using Excel’s data analysis tools

ln pi −1.24684 −1.53177 −2.0353 −2.32906 −2.51627 −2.90714 −3.09819 −3.33458 −3.84541 −4.25087 −4.43319 −5.34949 −36.8781

pi* ln pi −0.35836 −0.3311 −0.26589 −0.22682 −0.20321 −0.15882 −0.13982 −0.11881 −0.08221 −0.06058 −0.05265 −0.02541 −2.02369

pi (ln pi)2 0.44681 0.50716 0.54117 0.52827 0.51134 0.46172 0.4332 0.39617 0.31612 0.25753 0.2334 0.13595 4.76884

Results 191

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6  Estimation of Biodiversity Indices and Species Richness

Species diversity observed at two different locations is compared with the t test applying the following formula, H1  H 2

t

Var  H1   Var  H 2 





Var  H   Var  H   Var  H   / N1  Var  H   2

v

1

2

2

1



2

2

/ N2



In this estimation t

1.967545  2.023689



0.0017  0.0016  0.9773



 0.0017  0.0016  v 2 2  0.0017  / 407   0.0016  / 421 2



v  837.69



The table value of t corresponding to 837 degrees of freedom shows that the difference between diversity indices of two locations is nonsignificant. Estimation of diversity indices depends on the sample size, particularly the large sample size of minimum one thousand randomly selected individuals is required for the significant result, though the variance of species diversity shows a close resemblance of 0.0017 and 0.0016 for Raidak and Murti forests, respectively.

Species Richness Index Like species diversity, species richness plays as one of the vital components in the forest ecosystems of terrestrial natural forest stands. Distinct, different, and unlike living systems are the properties of biodiversity as the property of groups and classes are to be varied with space and time. Further, biodiversity is the manifestation of two dimensions  – variety and relative abundance of species. The variety is often measured as species richness index (Magurran 1988). Species richness index is to be estimated using the following formula, Species richness index =

S N

Where S is the number of species identified and N is the number of individual species counted during the survey.

Remarks

193

As at Raidak and Murti forests, 12 species are identified among 407 and 421 number of timber trees counted for two forests respectively, the species richness index would be, Species richness index of Raidak forest =

S = N

Again , for Murti forest,species richness index =

S = N

12 407

= 0.59

12 421

= 0.59

The species richness index remains the same for two forests of 12 species identified and the species richness index for Raidak and Murti forests is 0.59.

Remarks For assessing biodiversity, the number of species is considered as the main criteria where the number of species in a unit area (species density) and the number of species per number of individual species (numerical species richness) have been used extensively in the field of applied ecology (Plazzi et al. 2010; Ricotta et al. 2001; Hillebrand et al. 2008). Categorically these indices are useful for measuring species richness representing the same phenomena of biodiversity (Ma 2005; Somerfield et al. 2008; Thomas and Mallorie 1985; Modica et al. 2011; Peet 1974). The present work is driven to draw the multicomponent aspect of diversity of species richness, evenness, and relative abundance by analyzing the degree of reproducibility and applying the formulas of diversity indices (Hoffmann and Hoffmann 2008). Maximum possibility of diversity for both forests shows a close resemblance to each other. After estimation, the obtained result shows 79% of possibility of diversity for Raidak forest and 81% for Murti forest in the Dooars. Species diversity (H′) is determined for both forest areas using the Shannon-Wiener Index. The obtained value of H′ in Shannon-Wiener index of 1.967545 is converted into entropy that is exp.(1.967545) = 7.15309 because of a community with Shannon-Wiener index of H′ has an equivalent diversity as a community containing equally-common species of exp.(H′), indicating that a community with Shannon-Wiener index of 1.967545 has an equivalent diversity as a community with about 7 equally-common species. Index value of H′ includes not only richness, but also evenness of abundance distribution. H′ did not change from 2008 to 2020 but the forest area has increased with a greater number of individuals of the species (a few frequent but many infrequent species) because of the decreased evenness. Two or more Shannon-Wiener Index might be compared to infer which is more diverse according to the variations of values from 0 to 1. The value 0 shows the least diversity, whereas 1 indicates the greater diversity. The obtained value of H in Shannon-Wiener index of 2.023689 is converted into entropy that is exp.(2.023689) = 7.56618 indicating that a community with Shannon index of 2.023689 has an equivalent diversity as a community

194

6  Estimation of Biodiversity Indices and Species Richness

with about 8 equally-common species. Biodiversity among communities at Raidak and Murti has been compared and the diversity indices reflect the way in which abundance is distributed among the different species constituting the community. Most of the native species abundantly occurred far and wide of the forest patches of the Dooars including Raidak and Murti forests. Species diversity indices of those native species including computation of species richness of two forests in the present survey shows approximately 79% of maximum possibility of diversity at Raidak forest, whereas approximately 81% of maximum possibility of diversity at Murti forest floors in the Dooars. The sample mean of H′ tends to underestimate the corresponding population index of diversity H i.e., it is a biased estimate. The value of relative diversity or evenness tends to overestimate the corresponding population since some categories from the population may not be present in the sample particularly for a small sample collected from the Raidak and Murti forest areas of Dooars in West Bengal. The result obtained after the computation of Shannon-Wiener Index shows approximately 79% of maximum possibility of diversity with maximum possibility of diversity 2.48 as natural log of species richness for Raidak forest vegetation, whereas, approximately 81% of maximum possibility of diversity with maximum possibility of diversity 2.48 as natural log of species richness for forest vegetation of Murti forest. The maximum possibility of diversity of species richness for forest vegetation of Raidak and Murti forest shows the same value of 2.48. The species richness index reveals the same value for two forests of identified 12 tree species and the species richness index obtained for the Raidak and Murti forests is 0.59. As the value of Shannon-Wiener Diversity Index 0 shows the least diversity, whereas 1 indicates the greater diversity, the values of 0.82 and 0.83 for Raidak and Murti forests respectively indicate a greater diversity on the occurrences of timber tree species in Dooars of West Bengal.

Estimation of Diversity Index in Sunderbans Floristic composition and wildlife habitat have created a unique biotic region in the mangrove ecosystems of the Sunderbans. Mangroves provide habitat for many forms of both aquatic and terrestrial biota that form an enriched biodiversity comprising halophytic vegetation, planktons, invertebrates, and vertebrates (Barton and Bowers 2006). This biotic region is enriched with numerous flora and fauna of different communities which are yet to be identified. At present biodiversity of both flora and fauna of the Sunderbans and the conservation of nature and natural resources are endangered by anthropogenic interferences. It is now time to realize that the decline of plants and animals will cause natural catastrophe, and this is only due to reckless and nefarious activities of human beings. For such gradual declination of floral and faunal species of the Sunderbans, an estimation of biodiversity might help to frame the plans and strategies for its further restoration. The biodiversity of identified planktons, flora, and faunal community of the Sunderbans till date

Remarks

195

is estimated using Shannon-Wiener Index which is a commonly used measures among other diversity indices as Shannon-Wiener Index is comparatively better way of representing biodiversity, species diversity, species richness, evenness etc. The measures of variability cannot be used in calculation of Shannon’s index of diversity because there is no mean or median, or measures of variations for categorical data for the species (Zaiontz 2020) and Shannon’s Diversity Index can be calculated for a random of observation which is defined as H   logn 



1 s  ni log ni n i 1

Where n is the number of observations from the sample in the i (species) of S n

categories and n =  ni is the sample size. An equivalent formula is, i 1

s

H   

i 1



ni ni ln n n

ni is the proportion of observations in the i th of S categories; ni is the n number of individuals, and N is the total number of species. The diversity (D) is estimated by using the formula, Where

D  1

iS1 ni  ni  1 N  N  1



The maximum value of H′ occurs when all categories have the same number of observations. Relative diversity i.e., evenness or homogeneity is measured using the formula, E

H H  log S Hmax



Shannon-Wiener Index and the diversity of species of the Sunderbans are estimated by applying the mentioned equations of the statistical methods using Excel’s data analysis tool. The obtained value of H = 1.46 is the Shannon’s diversity index of the floral and faunal communities of the Sunderbans and the value of D = 0.70239 is the diversity of species, where N (total number of species) = 381, and S (total number of species category) = 6, where S represents species richness (Table 6.5). Evenness (E) of the species diversity is obtained 0.81 i.e., E = 0.81 followed by H/Hmax (1.46/1.79) after estimation of Hmax, ln(S) i.e., ln(6)  =  1.79 i.e., Hmax = 1.79, where Hmax indicates maximum diversity possible in the community. The obtained value of H = 1.89 is the Shannon’s diversity index for the identified species of invertebrates of the Sunderbans and the value of D = 0.81 is the diversity

196

6  Estimation of Biodiversity Indices and Species Richness

Table 6.5  Computation of diversity of species using Excel’s data analysis tools of planktons and floral communities identified in the Sunderbans Plankton & Flora Phytoplankton Zooplankton Bacteria Algae Fungi Mangroves Total S = 6

Numbers of identified species 36 59 22 16 184 64 N = 381

H calculation −0.22292 −0.28885 −0.16467 −0.13313 −0.35151 −0.29966 1.460746

D calculation 0.008703 0.023636 0.003191 0.001658 0.232574 0.027849 0.70239

Table 6.6  Computation of diversity of species using Excel’s data analysis tools of invertebrate species identified in the Sunderbans Name of invertebrates Protozoa Cnidaria Ctenophora Platyhelminthes Nemathelminths Nemertines Annelida Sipunculid Echiuroid Arthropods Mollusca Echinodermata Ectoprocta Entoprocta Hemichordate Total S = 15

Numbers of identified species 106 21 2 13 68 2 48 1 4 151 61 8 1 1 1 N = 488

H calculation −0.33166 −0.13537 −0.02253 −0.09658 −0.27462 −0.02253 −0.22811 −0.01269 −0.03938 −0.36297 −0.25993 −0.06739 −0.01269 −0.01269 −0.01269 1.891803

D calculation 0.046832396 0.00176726 8.41552E-06 0.000656411 0.019170566 8.41552E-06 0.009492712 0 5.04931E-05 0.09530582 0.015400411 0.000235635 0 0 0 0.811071465

of species, where N (total number of species) = 488, and S (total number of species category) = 15, where S represents species richness (Table 6.6). Evenness (E) of the species diversity is obtained 0.70 i.e., E = 0.70 followed by H/Hmax (1.89/2.71) after estimation of Hmax, ln(S) i.e., ln(15)  =  2.71 i.e., Hmax = 2.71, where Hmax indicates maximum diversity possible in the community. The obtained value of H = 1.15 is the Shannon’s diversity index for the identified species of vertebrates of the Sunderbans and the value of D = 0.62 is the diversity of species, where N (total number of species) = 388, and S (total number of species category) = 5, where S represents species richness (Table 6.7). Evenness (E) of the species diversity is obtained 0.71 i.e., E = 0.71 followed by H/Hmax (1.15/1.61) after estimation of Hmax, ln(S) i.e., ln(5)  =  1.61 i.e., Hmax = 1.61, where Hmax indicates maximum diversity possible in the community.

Remarks

197

Table 6.7  Computation of diversity of species using Excel’s data analysis tools of vertebrate species identified in the Sunderbans Name of vertebrates Fishes Amphibia Reptilia Birds Mammals Total S = 5

Numbers of identified species 116 6 21 170 25 N = 338

H calculation −0.36703 −0.07156 −0.17263 −0.34566 −0.19262 1.149497

D calculation 0.117114 0.000263 0.003687 0.252226 0.005268 0.621442

Remarks The results obtained for species diversity indices and biodiversity show almost likely values for all categories of flora and fauna of the Sunderbans. The sample value of H′ (Shannon-Wiener Index) tends to estimate the corresponding population index of diversity indicating an unbiased estimate. The value of relative diversity or evenness tends to estimate the corresponding population since some categories from the population may not be present in the sample, particularly for a small sample of the floral and planktonic communities and vertebrate species. Thus, it may be concluded from the obtained values of Shannon-Wiener Index and diversity of species of the Sunderbans that the present floral and faunal communities of the Sunderbans are stable and the existing faunal diversity is to be restored properly.

Assessing Diversity Indices for the Macroinvertebrates Shannon-Wiener diversity index is undoubtedly the best choice for the estimation of diversity indices of a particular ecological community among several such conventional indices like Margalef index or Magurran index used by the researchers in the field of applied forest ecology, marine ecology, or brackish water ecology in the estuarine environment though the Margalef index and Magurran index categorically state about species richness indices sensitive to abundance based sampling of species. And after application of all these indices for the estimation of diversity index for the macro-invertebrates identified and sampled at a tidal mudflat of Hana Char in the estuarine environment of the world famous mangrove ecosystem of the Sunderbans, the obtained values show completely different results like the values of Shannon-Wiener diversity index 0.74, Margalef diversity index 1.066, and Magurran diversity index 0.30 because of their sensitiveness to the presence-absence based data and abundance based data of the sample populations. For obtaining such different diversity indices values, a thorough literature search and survey has been conducted for finding a modified combined form or reformulated version of the

198

6  Estimation of Biodiversity Indices and Species Richness

Margalef diversity index and the Magurran diversity index that will be befitted for the better interpretation of the diversity indices in broader aspects and shows similarity in values with that of the Shannon-Wiener Index. Finding out no such scientific literatures related to the combined formulas of these indices, an attempt has been taken to reformulate the diversity indices measurement combining and modifying both Margalef diversity index and Magurran diversity index which are well-­ known as species richness indices in the field of applied ecology. The estimated value 0.72 for the macroinvertebrates of Hana Char applying the modified and reformulated version for measuring diversity indices is remarkably close to the value 0.74 obtained from the Shannon-Wiener diversity index which might be used for the estimation of diversity indices in a particular ecological community and might be compared with the index’s values of the Shannon-Wiener index. Hana Char, a tiny island of only 0.7 sq. km area, lies between Latitude 22.2279710N and Longitude 88.745971°E under Basanti Police Station of South 24 Parganas district of West Bengal. The island is almost resembling the Greek letter delta (Δ) and it forms on the riverbed of Pathankhali Nadi (river), a branch of the Hogol river near Hogolduri village which is situated about 5  km away from the Basanti Police Station of South 24 Parganas district in West Bengal. Pathankhali Nadi is locally known as Hana river and the newly emerged island on the riverbed of Hana river is called Hana Char by the local inhabitants. The tiny island is almost young in origin and emerged from a tidal shoal only 50 years back as reflected in the toposheet (no. 79B/16) published by the Survey of India in 1969. The toposheet map of 1969 shows Hogol River, a branch of Matla River, related to a tidal inlet namely Pathankhali Nadi at right angles. The tidal inlet containing a tidal shoal in the riverbed was in the stage of formation of an island. Initially terrigenous mud eroded from the left bank of Pathankhali Nadi settled upon the tidal shoal and accumulated sands at the riverbed. Thereafter, accretion was in progress by the sediments carried by the tidal current with the process of sedimentation through suspension during slack water condition during the transitional period of flood and ebb tide. At present, Hana Char is almost covered with the mangroves vegetation which is a rich habitat for the macroinvertebrates of different species. Species richness is simply a count of species living in a certain location indicating the number of different species as the representatives in an ecological community, whereas the number of species and their abundances of each species in a particular ecological community is the species diversity (Lyons et al. 2005). Species richness never reveals the accountability of species abundances or relative abundance distribution of the species in the ecological community. Occurrences of common or rare species relative to other species in an ecological community is referred to as the relative species abundances. A quantitative measure of different types of individuals in a dataset and their phylogenetic relationships among each other including distributions of all types of individuals such as divergence, evenness, or richness is referred to as diversity index (Gorelick 2011). Estimation of such diversity indices of the macroinvertebrates at the mudflat of Hana Char in the estuarine environment of the Sunderbans is the objective of the present study.

Materials and Methods

199

Depositional Environment Hana Char is characterized by the clayey-silt dominated mudflat at its periphery restricted to the mesotidal estuarine environment of the Sunderbans. Sedimentation pattern at Hana Char is controlled by the influence of flooding and the ebbing phase of the tidal cycle. Each tidal cycle produces a cyclic sequence of sand and mud and that sequence leads to the arrangement of four distinct strata citing periodic states of tidal rise and fall (Das 2017). Higher velocities of ebb and flood currents accelerate accumulation of sands whereas deposition of mud consisting of silt and clay takes place from suspension in the slack water condition. Depositional process in this tide dominated environment depends on the phenomenon of time velocity asymmetry where maximum flood current velocity occurs before achieving mid tide and maximum ebb current is observed much later the ebb when water starts to recede (Das 2015). In shallower water depths, particularly at less than 5  m depth of east and north directions of Hana Char, the speed differential increases rapidly with a corresponding increase in the tidal distortion. In these circumstances the distortion becomes so pronounced that the front of the tide is vertical, much like the front of a breaking wave in the mudflat. The discharge volume through this tidal channel on the flooding tide closely matches the discharge volume on the ebbing tide; the inequality between the flood and ebb durations must produce a velocity-magnitude asymmetry between the tidal currents (Das 2011). Tidal mudflat of this tiny island Hana Char contains many infaunal organisms like macro-invertebrates that produce bioturbation. Bioturbation features are visible in the upper portion of the tidal flat at the western part of the island that lacks presence of vegetation and in the relatively low physical energy condition. Formation of flaser bedding results in accumulation of pellets left by the infaunal organisms like macroinvertebrates in the mudflat of Hana Char.

Materials and Methods Hana Char, a tiny island, emerged on the riverbed of Hana river, is mapped, and the areas of the island have been measured using measuring tapes. Mudflat of the Hana Char located at its southern part is only exposed after the recession of water during ebb tide when sampling and collection of the macroinvertebrate species are possible only during that period. Categorically, one of each individual species of the macroinvertebrates are collected and preserved in 4% formalin solutions. The arthropods are preserved in the 60% alcohol as their chitinous exoskeleton comprising calcium carbonate reacts with the formalin solutions. The collected and preserved macroinvertebrates are sent to the Zoological Survey of India for proper identification. The macroinvertebrates of Hana Char are characterized with the brackish water origin and these species prefer mangrove swamp and marshy areas as their natural habitat. Individual species of these macroinvertebrates and their total numbers of presence

200

6  Estimation of Biodiversity Indices and Species Richness

Fig. 6.1  Formation of dome-shaped bioturbation structures by the Thalassina anomala scattered around the mudflat of Hana Char of the Sunderbans

in the mudflat are carefully sampled following the presence-absence based method of sampling. The burrow-dwelling macroinvertebrates like Thalassina anomala are sampled by counting their dome-shaped bioturbation structures without destroying their habitat that might hamper the ecological balance of the community structure of Hana Char of the Sunderbans (Fig. 6.1). About 8 individual species of macroinvertebrates are properly identified and their total number of occurrences are 710 at the mudflat that are enlisted in the inventory for such a tiny island like Hana Char.

Determination of Species Diversity Indices Bioturbation structures forming macro-invertebrates at the mudflat of Hana Char are identified and sampled during the survey and the diversity indices of sampled biota are estimated using tools and formulas of different diversity indices measuring methods like Shannon-Wiener diversity index, Margalef diversity index, and Magurran diversity index. The biodiversity of identified macro-invertebrates of Hana Char is estimated using the formula of Shannon-Wiener Index (Table  6.8). The Shannon-Wiener Index is a commonly used measure among other diversity indices as the

Determination of Species Diversity Indices

201

Table 6.8  Calculation of Shannon-Wiener index and diversity index using Excel’s data analysis tools of the macroinvertebrates in the tidal mudflat of Hana Char Sl. no 1 2 3 4 5 6 7 8

Name of the macro-invertebrates Thalassina anomala Pelocoetes exul Uca acuta Virgularia sp. Ocypode macrocera Telescopium telescopium Cerithidea cingulata Coenobita cavipes S = 8

Number of species 292 6 54 2 17 95 141 103 N = 710

H′ calculation −0.36542 −0.04034 −0.19594 −0.01654 −0.08936 −0.26913 −0.32102 −0.28006 H′ = 1.577815

D calculation 0.168799539 5.95959E-05 0.005685453 3.97306E-06 0.000540337 0.017739725 0.039214128 0.020870498 D = 0.747086752

Shannon-­Wiener Index is a comparatively better way of representing biodiversity, species diversity, species richness, evenness etc. (Gaertner et al. 2010). The measures of variability cannot be used in calculation of Shannon’s index of diversity because there is no mean or median, or measures of variations for categorical data for the species (Zaiontz 2020). Shannon’s diversity index can be calculated for a random of observation which is defined as H   logn 



1 s  ni log ni n i 1

Where n is the number of observations from the sample in the i (species) of S n

categories and n =  ni is the sample size. An equivalent formula is, i 1

ni ni ln i 1 n n s



H   

ni Where n is the proportion of observations in the i th of S categories; ni is the number of individuals, and N is the total number of species. The diversity (D) is estimated using the formula, D  1

iS1 ni  ni  1 N  N  1



The maximum value of H′ occurs when all categories have the same number of observations. Relative diversity i.e., evenness or homogeneity is measured using the formula, E

H H  log S Hmax



202

6  Estimation of Biodiversity Indices and Species Richness

Shannon-Wiener Index and the species diversity index of Hana Char are estimated by applying the stated equations of the statistical methods using Excel’s data analysis tool. Another two important diversity indices are Margalef diversity index and Magurran diversity index (Ma 2005). The Margalef diversity index can easily be calculated with the following formula using the sampled data of macroinvertebrates collected from the mudflat of Hana char. D

S 1 ln N



Where S is the number of species, and N is the total number of individuals in the sample. Magurran diversity index is calculated through the estimation of species richness index using the following formula stated by Magurran (1988, 2004). Magurran diversity index =

S N

Results and Discussion Measuring diversity indices using methods of Shannon-Wiener index is widely acceptable to the researchers in the field of applied ecology where the range of richness index is observed species to species distribution. Shannon’s entropy and evenness also reflect richness and distribution (Modica et  al. 2011). High level of Shannon’s entropy means even distribution (Somerfield et al. 2008). Species like the Thalassina anomala are dominant at Hana Char mudflat, and that species has dominance-richness and reduces the distribution of species. The richness focuses on the amount, evenness focuses on distribution, but the range remains the same. For assessing biodiversity, the number of species is considered as the main criteria where the number of species in a unit area i.e., species diversity and the number of species per number of individual species i.e., numerical species richness have been used extensively in the field of brackish water or marine ecology (Chao et al. 2010). Both indices have been used for measuring species richness representing the same phenomenon of biodiversity. Species sampled at Hana char have been identified properly in a large-scale survey for maintaining the degree of sampling accuracy and for measuring the differences between taxonomic relationships, phylogeny (evolutionary history), and function of the sampled species. The number of species sampled at the mudflat of Hana Char has been analyzed choosing Shannon-Wiener index, Margalef species richness index, and Magurran species richness index for their ease of calculation and extensive uses (Plazzi et al. 2010).

Margalef Diversity Index

203

Shannon-Wiener Index The estimated Shannon-Wiener’s index is 1.577815 and the value in exp. (1.577815) is 4.84435931368 that indicates a community with Shannon-Wiener index of 1.577815 has an equivalent diversity as a community with about 5 equally common macroinvertebrate species (Table 6.8). And these 5 equally common macroinvertebrate species are Thalassina anomala, Cerithidea cingulata, Coenobita cavipes, Telescopium Telescopium and Uca acuta identified in the mudflat of Hana Char of the Sunderbans. Considering the value of Shannon-Wiener’s index 1.577815, evenness (E) or relative diversity is calculated using the following formula, E E

H H  log S Hmax



1.577815 1.577815   1.7471 log  8  0.903089  1.75





The value 1.75 reveals the relative diversity of species identified in the mudflat of Hana Char of the Sunderbans.

Margalef Diversity Index The Margalef diversity index can easily be calculated with the following formula using the sampled data from Table 6.8 (Margalef 1958). D

S 1 ln N



Where S is the number of species, and N is the total number of individuals in the sample. Results will become different if densities are used for the estimation of Margalef diversity index instead of total numbers. Margalef diversity index of the macro-­ invertebrates identified at Hana Char will become, D

S 1 8 1 7   1.066  ln N ln 710 6.56526



A log normal species distribution results in geometric distributions of ecologically relevant communities. Thus, by extension, the Margalef index applies to log

204

6  Estimation of Biodiversity Indices and Species Richness

normal species distribution. As Margalef index, a good index of diversity, is followed by replication principle and independent of the sample size, so, the effective number of species is to be used. Sometimes, Margalef index formula D = (S -1)/ log(N) is used by the researchers but they are calculating log value rather than ln value. There is a difference between log and ln as log is defined for base 10 and ln is denoted for base e; ln is a natural logarithm that can be referred to as the power to which the base ‘e’ that has to be raised to obtain a number called its log number.

Magurran Diversity Index Magurran diversity index is calculated using the data of macroinvertebrates sampled at Hana Char (Table 6.8) through the estimation of species richness index following the formula as propounded by Magurran (2004). Magurran diversity index = =

S N 8 = 0.30 710

Magurran (2004) states the range of values calculated for the diversity index – (i) H′ ≥ 3 means low species diversity, (ii) 1