Mapping the Emerald Forest: Exploring Structural Diversity and Regeneration Patterns in Panna Tiger reserve, Central India

Mapping the Emerald Forest: Exploring Structural Diversity and Regeneration Patterns in Panna Tiger reserve, Central India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping the Emerald Forest: Exploring Structural Diversity and Regeneration Patterns in Panna Tiger reserve, Central India Kamana Pokhariya, Ramesh Krishnamurthy, Chinnasamy Ramesh, Amit Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7939381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 13 You are reading this latest preprint version Abstract Forest extent, assemblage, and regeneration pattern influence the biodiversity and ecosystem functions, which are often sensitive to climatic and anthropogenic correlates, especially in tropical forest systems. We quantified the diversity, regeneration potential and mapped the forest types in Panna Tiger Reserve, Central India, using Sentinel−2A multi-temporal data with a Random Forest classifier. A total of 153 stratified random plots were sampled with a focus on trees, saplings, and seedlings. Of the six forest types reported by Champion and Seth in the region, five forest types, except Boswellia forest, could be mapped with an overall accuracy of 88.9% and a Kappa coefficient of 0.81. Area-wise, Northern dry mixed deciduous forest (NDDF) was the most widespread forest type (36.88%), followed by Dry deciduous scrub (DDS) (16.7%), Dry teak forest (DTF) (7.64%), Dry bamboo brakes (DBB) (3.78%), and Anogeissus pendula forest (APF) (0.62%), while non-forest and water consist of 33.26% of the reserve. Overall, 64 species from 48 genera and 25 families were identified. Trees (35 species), saplings and seedlings (39 species each) had the highest species richness across all life stages in NDDF. APF had the highest tree density of 625 individuals ha -1 and sapling density (634 ind. ha -1 ), while NDDF had the highest seedling density (1621 ind. ha -1 ). DDS had the highest regeneration potential (75%), followed by NDDF (74.5%) and BF (46%). Our results highlight that mapping forest types, together with assessing structural attributes, diversity, and regeneration potential, can contribute to better conservation planning and management actions. basal area conservation planning dry deciduous forest forest management Google Earth Engine Figures Figure 1 Figure 2 1. Introduction Forests around the globe are increasingly threatened by deforestation and degradation, driven by natural and anthropogenic factors. In the tropics, deforestation poses a greater risk due to the conversion of natural forests to other land uses. ( De la Barreda-Bautista et al. 2011 ; Keenan et al. 2015 ). These disturbances impact overall biodiversity and carbon storage capacity adversely and sometimes can lead to a complete shift in species composition ( Chazdon 2008 ; Poorter et al. 2016 ; Rozendaal et al. 2019 ; Stan and Sanchez-Azofeifa 2019 ). To support climate change mitigation, it is essential to understand forest structure and monitor species distribution and composition for informed decision-making ( Feldpausch et al. 2011 ; Pereira et al. 2013 ; Chheng et al. 2016 ; Vihervaara et al. 2017 ; Beirne et al. 2019 ). Accurate and updated information on forest type, diversity, and regeneration is essential for forest carbon management, biodiversity assessment, and conservation planning ( Pan et al. 2011 ; Bastin et al. 2019 ). Understanding the spatial distribution of forest types offers valuable insights into ecosystem structure and habitat suitability for different flora and fauna species ( Mo et al. 2023 ). Accounting for about 85% of total land vegetation biomass, forests are the largest land-based carbon reservoir, playing an irreplaceable role in the ecosystem ( Dixon et al. 1994 ; Schimel et al. 2001 ; Kuuluvainen & Gauthier 2018 ; Zhao et al. 2019 ). Tropical forests are among the most productive ecosystems on earth. While covering only ~ 6% of the world's surface area, these forests are the largest repository of above-ground biomass carbon ( Beer et al. 2010 ; Pan et al. 2013 ). Notably, tropical dry forests (TDFs) account for about 40% of all tropical forests and hold approximately half of the carbon storage capacity worldwide ( Miles et al. 2006 ; Keith et al. 2009 ; Joshi & Dhyani 2019 ; George-Chacón et al. 2022 ). However, recent studies show TDFs are increasingly shifting from being carbon sinks to carbon sources ( Le Quéré et al. 2015 ; Baccini et al. 2017 ). TDFs are multifunctional landscapes supporting livelihoods across the globe and are socio-economically significant, particularly for the marginalized communities ( Portillo-Quintero et al. 2015 ; Powers et al. 2018 ; Siyum 2020 ). Within a forest ecosystem, plant communities are the major component, especially trees that constitute dominant plant forms and shape biodiversity ( Haq et al. 2020 ; Rawat et al. 2020 ). Tree species maintain the overall physiognomy and determine the forest community structure ( Comita et al. 2007 ; Farooq et al. 2019 ). The current distribution of seedlings and saplings plays a critical role in shaping future species composition and forest structure ( Henle et al. 2004 ; Pokhriyal et al. 2010 ). Comprehensive information on the composition, structure, diversity, and regeneration potential supports ecological monitoring and can guide conservation and restoration strategies ( Eilu & Obua 2005 ; Mohanta et al. 2021 ; Haq et al. 2022 ). Studies on forest structure, diversity, and regeneration often do not include the mapping of forest extent or forest types and vice versa (Immitzer et al. 2016; Karasiak et al., 2017 ; Grabska et al. 2019 ; Hościło and Lewandowska 2019 ; Ray et al. 2021 ; Mohanta et al. 2021 ; Das et al. 2021 ). To enable data-driven management planning, integration of these two crucial components is essential for understanding a holistic view of a forest ecosystem. To map and monitor the extent of forest types, remote sensing technologies complement traditional methods ( Li et al. 2021 ). Further, remote sensing data offers consistent temporal frequency and spatially continuous coverage, enabling cost-effective and systematic monitoring of vegetation dynamics ( Lillesand et al. 2004 ; Reyes-Palomeque et al. 2021 ). Satellite datasets with high spectral and spatial resolution allow users to accurately map the forest types. Several studies have successfully utilized the spectral and temporal signatures to retrieve forest type information at regional and national levels ( Grabska et al. 2019 ; Aziz et al. 2024 ; Fassnacht et al. 2024 ; Hermosilla et al. 2025). Cloud-based platforms such as Google Earth Engine (GEE) with powerful geo-big data capabilities facilitate the handling of large datasets and support the integration of machine learning algorithms for enhanced forest type mapping and monitoring ( Gorelick et al. 2017 ; Liu et al. 2018 ; Pasquarella et al. 2018 ; Tamiminia et al. 2020 ; Li et al. 2021 ). Prior research on vegetation in the Panna Tiger Reserve (PTR) was limited to the core region with key research topics, i.e., tree diversity, regeneration, forest fires, carbon stock, and mapping of vegetation communities ( Porwal & Singh 2009 ; Jain et al. 2020 ; Ray et al. 2021 ; Parveen & Ilyas 2021 ; Parveen & Ilyas 2022 ; Ray et al. 2023 ; Parveen 2025 ). As we observe the UN Decade on Ecosystem Restoration, it is imperative to broaden our focus beyond core areas, which are typically under a high level of protection, to develop targeted conservation or restoration strategies. In this context, the present study bridges this gap by focusing on an extended area encompassing both the core and a large buffer zone of PTR, with the objective of mapping forest types and quantifying regeneration and diversity. 2. Methodology 2.1. Study area Our study area, Panna Tiger Reserve (PTR), is located in the state of Madhya Pradesh, central India. PTR constitutes an area of 576 km 2 in the core zone and a buffer region spanning over 1021 km 2 (Fig. 1 ). PTR has a step-like topography, constituting hilly terrain, gorges, escarpments, tablelands, and a valley along the river Ken, which flows 55 km inside the reserve. The elevation of PTR ranges from 123 meters to 566 meters with a varying slope of 0° to 48°. PTR receives approximately 1100 mm annual rainfall, and the monsoon season from July to October is the principal source of water for the large areas of the region. Summer months start from March to June, when the temperature often exceeds 45°C, and winter occurs from the month of November to February, when the minimum mean temperature remains around 7–8° Celsius. 2.2. Forest types of PTR According to Champion and Seth ( 1968 ), the forest type of PTR is broadly categorized as dry deciduous forest type, group 5, which includes sub-groups 5A, southern tropical dry deciduous forest, and 5B, northern tropical dry deciduous forest. In subgroup 5A, PTR harbors dry teak forest, making it the northernmost boundary for natural teak forest, while in 5B, five types of forest, i.e., Boswellia forest, dry bamboo brakes, dry deciduous scrub, northern dry mixed deciduous forest, and Annogeissus pendula forest are present. Additionally, according to Meher-Homji ( 2001 ) the forest communities viz., Acacia catechu - Annogeissus pendula vegetation type - dry deciduous low forest, Annogeissus latifolia - Terminalia type - dry deciduous forest, Terminalia - Annogeissus latifolia - Tectona grandis type - dry deciduous forest, and shrub savanna were also identified in PTR. We attempted to conduct mapping based on the Champion and Seth’s forest type classification owing to presence of distinct forest types. 2.3. Vegetation sampling This study was conducted during the post-monsoon months from August to November 2022 in PTR. A stratified random sampling method guided by the reconnaissance and literature was followed. A total number of 153 sampling plots measuring 20×20 m. were established across the reserve, including 68 plots in northern dry mixed deciduous forest (NDDF), 26 in dry teak forest (DTF), 8 in Annogeissus pendula forest (APF), 12 in dry bamboo brakes (DBB), 4 in Boswellia forest (BF), and 35 in dry deciduous scrub (DDS). To facilitate the sampling, these plots were further divided into sub-plots of four 10×10 m and 5×5 m (Kothandaraman et al. 2020 ). Tree individuals, with a girth at breast height (GBH) of ≥ 30 cm and saplings with GBH 10–30 cm were recorded within the 10×10 m subplots. Seedlings with GBH < 10 cm were assessed within the 5×5 m sub-plots. The spatial coordinates of all sampling locations were recorded using a handheld GPS unit. 2.4. Forest type mapping 2.4.1. Satellite data We utilized Sentinel-2 (S2) surface reflectance (SR) images from the GEE platform with less than 10% cloud cover per tile to get maximum information (Immitzer et al. 2019 ; Praticò et al. 2021 ). We used S2 bands available in 10-meter (B2, B3, B4, B8) and 20 meters (B5, B6, B7, B8a, B11, B12) spatial resolution. The 10-meter bands were resampled to 20-meter using a nearest neighbor resampling method (Hościło & Lewandowska 2019 ). Datasets for the months of October, December 2022 and February, May 2023 were analyzed, capturing pre- and post-monsoon seasons; monsoon season images (July to September) were excluded because forest types cannot be reliably distinguished when canopy cover is uniformly green. While the pre- and post-monsoon season captures various phases of phenology (Schieber et al. 2017 ; Jabłońska et al. 2015 ; Immitzer et al. 2019 ). For instance, Anogeissus pendula forest displays a purple to red tint in October before leaf fall, while Diospyros melanoxylon remains largely evergreen. Anogeissus latifolia shows coppery leaf coloration in November, followed by yellowing and shedding by January. Similarly, Tectona grandis begins leaf shedding in November, and new leaf emergence occurs in April and June (Krishen, 2013 ). Images from May were characterized by extensive leaf fall and predominantly brown vegetation; only some patches along the water bodies and ridges appear to be green. In addition to colour differentiation, forest types, mainly DBB and APF, exhibit distinctive textural patterns in satellite images. 2.4.2. Ground truth data Overall, 958 reference data points were used in the analysis, belonging to six forest type classes and non-forest classes. Out of these, 153 data points were the sampling plot locations, while the remaining data points were collected randomly in the field and further supplemented via manual visual interpretation on Google Earth software (Hansen et al. 2008 ; De Sousa et al. 2020 ; Phan et al. 2020 ). The reference data were split, with 70% of sampling points used as training samples and 30% reserved for validation of the results (Gigović et al. 2019 ). To ensure better accuracy results, visual verification of reference samples was done using Google Earth images. We did not find any substantial differences; however, some selected reference points for dry bamboo brake were readjusted to pure bamboo patches near cliff edges for accurate categorization. 2.4.3. Classification We used the Random Forest (RF) Classifier in the GEE platform (smileRandomForest). The RF is a machine-learning algorithm approach that uses multiple self-learning decision trees to parameterize models and estimate variables (Breiman, 2001 ; Hastie et al. 2009 ; Belgiu & Dragut 2016 ). Over the past years, RF has been the most widely used classification algorithm for satellite imagery. (Millard & Richardson, 2015 ; Li et al. 2016 ; Cánovas-García et al. 2017 ; Jin et al. 2018 ; Adugna et al. 2022 ; Pande et al. 2024 ). Due to the ability to effectively handle outliers and noisy data, perform well with multi-source datasets, and superior accuracy over classifiers like SVM, kNN, and MLC, has made RF a preferred choice in many applications (Karasiak et al. 2017 ; Wessel et al. 2018 ). Following recommendations from previous studies (Abdel-Rahman et al. 2014 ; Xia et al. 2017; Mahdianpari et al. 2017 ) and preliminary tests on our dataset, the number of decision trees was fixed at 100 (ntree = 100), while the number of variables considered at each split (mtry) was set to the default, the Gini Index was used to define the impurity, and minimum number of samples in a node was set to one (Waske et al. 2012 ; Ghimire et al. 2013 ; Hościło & Lewandowska 2019 ; Phan et al. 2020 ). Variable importance index was measured based on the Gini criterion (Cheng & Wang 2019 ). Accuracy assessment was carried out using the reference validation datapoints, and overall accuracy, kappa coefficient, user’s accuracy, producer’s accuracy, and F1 score were calculated (Congalton et al. 2019; Hościło & Lewandowska 2019 ). 2.5. Phytosociological analysis Phytosociological analysis was carried out using the field datasets encompassing three life stages, i.e., tree, sapling, and seedling. Density, frequency, species richness, basal area, Shannon-Wiener diversity index, Simpson's index (concentration of dominance), evenness index, Important Value Index (IVI) were calculated to understand the forest characteristics (Shannon & Wiener 1963; Simpson 1949 , Curtis & McIntosh 1951 ; Mohanta et al. 2021 ). Additionally, density and basal area for bamboo were calculated. 2.6. Regeneration potential and conservation priority We analysed the regeneration status of the tree species in PTR by comparing their densities (individuals ha -1 ) at three life stages, i.e., tree, sapling, and seedling (Duchok et al. 2005 ; Bhadouria et al. 2017 ). The seedlings saplings > trees, Fair (seedlings > saplings ≤ trees), Poor (species present only at the sapling stage), and No regeneration when species present only at the tree stage following Ballabha et al. ( 2013 ) and Mohanta et al. ( 2021 ). Based on regeneration status, tree species were further categorized into conservation priority classes. Priority I is the highest conservation priority class, including species with no recorded seedlings or saplings. Priority II class comprised species lacking either seedlings or saplings, and Priority III category included species with no saplings and/or no mature trees (Woldearegay & Woldu 2020 ; Mohanta et al. 2021 ). 3. Results 3.1. Forest type mapping A total of seven classes were identified, of which five classes represent forest types, while the remaining two are water and non-forest areas, including the settlement and agricultural land, and bare land without green cover (Fig. 2 ). Table 1 presents the area statistics and accuracy assessment results, showing an overall accuracy of 88.9% and a Kappa coefficient of 0.81. Among the forest types, the northern dry mixed deciduous forest was the most widespread (37%), followed by DDS (17%), and DTF (8%). In PTR, Anogeissus pendula forest occurs on the gentler slopes, forming pure patches along the Ken River and its tributary, while dry deciduous scrub is distributed across the reserve, dominating the tablelands and much of the western part. DTF and DBB occur widely across the reserve, while DBB is largely confined to the slopes, DTF occurs along the slopes, the flatlands adjoining the Ken River, and in the upper plateau. Variable importance index ranks the predictor variables based on their contribution to distinguishing the classes in RF classifier (Online Resource 1). Band 5 from the month of February, October and December, band 11 from May, and band 6 from October were ranked top five contributing variable. Class-wise accuracy results show, Anogeissus pendula forest accounted for the highest accuracy with an F1-score of 92.74, followed by DBB (89.43). The lowest accuracy was observed for Boswellia forest 51.23, covering an area of less than 0.42km 2 . It is important to note that the Boswellia forest is endemic to central India; however, due to its scanty distribution, this class was represented by only six data points. To improve the overall classification accuracy, we merged this class with the northern mixed forest. Table 1 Details of area and accuracy of the forest type classification of PTR Forest type Area (km 2 ) Users accuracy % Producers accuracy % F1-score (%) Dry bamboo brakes 67.49 88.36 91.49 89.43 Dry deciduous scrub 298.41 86.25 89.25 87.72 Dry teak forest 136.54 85.26 90.62 87.86 Northern dry mixed deciduous forest 659.06 89.35 85.89 87.59 Anogeissus pendula Forest 11.09 94.42 91.12 92.74 Water 20.29 94.56 90.25 92.35 Non forest 594.39 95.12 91.52 93.29 3.2. Phytosociological analysis A total of 64 tree species belonging to 48 genera and 25 families were recorded from the study area. The Fabaceae family contributed the most species across all forest types, while the highest species rich genera were Acacia (3 species) and Anogeissus (2 species). Dendrocalamus strictus , the only bamboo species recorded in PTR, is distributed in four forest types, with the highest basal area of 8.09 and density of 4627 culms ha -1 in DBB (Table 3 ). 3.2.1. Tree Layer: With 35 tree species, the northern dry mixed deciduous forest had the highest species richness, where Acacia genera (3 species) and Fabaceae family (10 species) dominated the reserve. The Anogeissus pendula forest recorded the highest tree density (625 ind. ha -1 ), followed by the dry teak forest (518 ind. ha -1 ) and the NDDF (425 ind. ha -1 ), while the lowest density occurred in DDS (146 ind. ha -1 ). Basal area was maximum in Boswellia forest (15.84 m² ha -1 ), followed by APF (12.21 m² ha -1 ), and minimum in DDS (0.78 m² ha -1 ). The Shannon–Wiener diversity index, Simpson’s dominance index, and evenness were highest in the NDDF (H′=2.80, D = 0.92, E = 0.80) and lowest in the APF (H′=0.43, D = 0.17, E = 0.24) (Table 2 ). The species with the highest IVI within NDDF were Lannea coromandelica (IVI 34.55) and Diospyros melanoxylon (IVI 30.09). In dry bamboo brakes, Tectona grandis (IVI 86) and Diospyros melanoxylon (IVI 32) were dominant species, the genus Anogeissus (2 species) and Fabaceae family (5 species) contributed most to species richness. The Anogeissus pendula forest was strongly dominated by Anogeissus pendula (IVI 219) followed by Lannea coromandelica (IVI 34). Acacia was the most species-rich genus (3 species) in dry deciduous scrub, while the highest IVI values were observed for Lagerstroemia parviflora (54.25), Diospyros melanoxylon (36.51), Lannea coromandelica (33.12), and Ziziphus xylopyrus (30.72). The Boswellia forest was dominated by Boswellia serrata (IVI 184.65), followed by Lannea coromandelica (IVI 29.81), while Tectona grandi s (IVI 137.19) and Diospyros melanoxylon (IVI 46.26) were the dominant species in the dry teak forest (Table 3 ). 3.2.2. Sapling Layer In the sapling layer, similar to the tree layer, Accacia and Anogeissus were the most specious genera. While Fabaceae was the most specious family across forest types, with six species in NDDF and five in DDS. NDDF recorded highest species richness (28), followed by DDS (24), while the lowest species richness was recorded in the Boswellia forest (9). Anogeissus pendula had the highest IVI (218.4) in APF, followed by Tectona grandis (128.21) in the DTF, while the NDDF was dominated by Diospyros melanoxylon (42.39) and Lagerstroemia parviflora (32.23). In DBB, Diospyros melanoxylon has the highest IVI of 66.3, while in the Boswellia forest, Tectona grandis (48.9) and Wrightia tinctoria (43.9) were the most dominant species. Ziziphus xylopyrus (64.21) and Diospyros melanoxylon (56.56) were the most dominant species in the sapling stage in DDS (Table 3 ). Sapling density was highest in APF (634 ind. ha -1 ), followed by DTF (467 ind. ha -1 ), and lowest in DBB (127 ind. ha -1 ). Shannon–Wiener diversity index, Simpson’s dominance index shows similar patterns as tree layer, NDDF being highest (H′=2.70, D = 0.51) and APF being lowest (H′=0.51, D = 0.19), while evenness was highest in Boswellia forest (0.86) and lowest in APF (0.22) (Table 2 ). Table 2 Species richness, density, diversity across different forest types in PTR Forest type Species (n) Genera (n) Families (n) Density (n ha -1) Shannon’s index (0–ln(S)) Simpson’s index (0–1) Evenness (0–1) APF Tree 6 6 6 625 0.43 0.17 0.24 Sapling 10 9 7 634 0.52 0.19 0.22 Seedling 7 7 7 1013 1.32 0.62 0.68 BF Tree 10 10 8 388 1.17 0.48 0.51 Sapling 9 9 8 219 1.90 0.82 0.86 Seedling 5 5 5 625 1.30 0.66 0.81 DBB Tree 21 20 13 260 2.30 0.83 0.76 Sapling 15 14 11 127 2.23 0.85 0.82 Seedling 11 11 8 792 1.94 0.81 0.81 DDS Tree 25 21 15 146 2.55 0.89 0.79 Sapling 24 20 13 167 2.39 0.86 0.75 Seedling 23 20 13 917 2.31 0.84 0.74 DTF Tree 20 9 8 518 1.47 0.63 0.49 Sapling 13 8 7 467 1.38 0.66 0.54 Seedling 22 17 12 1428 2.35 0.85 0.76 NDDF Tree 35 32 18 425 2.83 0.92 0.80 Sapling 28 25 18 346 2.75 0.91 0.83 Seedling 39 32 17 1621 2.98 0.93 0.81 3.2.3. Seedling Layer The species richness is highest in the seedling layer among all three layers. NDDF comprises 39 species, followed by 23 species in DDS, while the lowest species richness was recorded in the Boswellia forest with only 5 species. Fabaceae, Rubiaceae and Rutaceae were the most specious families while the genera Acacia and Bauhinia were the most represented across forest types. Similar to the sapling layer, Anogeissus pendula had the highest IVI value (147.29), followed by Diospyros melanoxylon (46.47) in APF, while in DBB, Aegle marmelos recorded the highest IVI (108.9), followed by Wrightia tinctoria (40.30). Aegle marmelos (25.9) and D iospyros melanoxylon (25.6) were the most dominant species in NDDF, while in the Boswellia forest, Nyctanthes arbor-tristis , with IVI of 121.13, followed by Ziziphus xylopyrus (54.75), were the dominant species. In DDS, Ziziphus xylopyrus (89.22), and in DTF, Tectona grandis (78.33) and Diospyros melanoxylon (45.67) dominated respective forest types. (Table 3 ). Seedling density was highest in NDDF (1621 ind. ha -1 ), followed by DTF (1428 ind. ha -1 ), and lowest in Boswellia forest (625 ind. ha -1 ) (Table 2 ). Shannon’s diversity index and Simpson’s index were highest in NDDF (H′=2.98, D = 0.93) and lowest in Boswellia forest (H′=1.30) and APF (D = 0.62). Evenness was also highest in NDDF (0.82), followed closely by DBB (0.81), and lowest in APF (0.68) (Table 2 ). Table 3 Top five tree species ranked by Importance Value Index (IVI) with their frequency, density, and basal area, along with bamboo species showing frequency, density, and basal area across different forest types of PTR. Forest types Frequency (%) IVI Basal area Density (ind./ha) Trees Sapling Seedling Trees Sapling Seedling m 2 /ha Trees Sapling Seedling Anogeissus pendula forest Anogeissus pendula 100 100 50 219.49 218.45 147.3 12.21 569 572 463 Lannea coromandelica 38 0 0 34.42 0 0 1.63 22 0 0 Acacia catechu 25 25 0 17.1 12.88 0 0.39 16 6 0 Lagerstroemia parviflora 25 13 13 14.45 6.98 17.79 0.22 9 6 25 Diospyros melanoxylon 13 13 13 7.9 11.11 46.58 0.33 6 19 163 Dry bamboo brakes Tectona grandis 100 56 0 86.88 57.75 5.29 125 36 0 Diospyros melanoxylon 67 67 44 32.82 66.38 31.25 1.3 50 39 67 Acacia catechu 56 22 0 21.48 12.87 0 0.68 28 6 0 Lannea coromandelica 56 11 0 20.31 11.61 0 0.75 22 3 0 Madhuca longifolia 33 0 0 20.06 0 0 1.71 11 0 0 Dendrocalamus strictus 100 8.09 4627 culms ha − 1 Northern dry mixed deciduous forest Lannea coromandelica 48 9 3 34.55 3.73 1.11 2.01 46 3 4 Diospyros melanoxylon 59 52 35 30.09 42.39 25.61 1.39 47 46 146 Anogeissus latifolia 49 17 24 27.34 10.59 17.17 1.17 44 11 125 Lagerstroemia parviflora 51 51 30 21.73 32.24 16.53 0.67 32 31 49 Terminalia elliptica 25 9 14 20.56 9.95 15.09 1.12 36 11 85 Dendrocalamus strictus 17 2.16 956 culms ha − 1 Boswellia forest Boswellia serrata 100 0 25 184.65 0 25.82 15.84 275 0 25 Lannea coromandelica 75 0 0 29.81 0 0 0.9 19 0 0 Diospyros melanoxylon 25 25 0 18.74 79.22 0 0.7 38 69 0 Acacia catechu 25 0 0 11.08 0 0 0.22 13 0 0 Cassia fistula 25 25 0 10.45 13.78 0 0.11 13 6 0 Dendrocalamus strictus 25 0.07 206 culms ha − 1 Dry deciduous scrub Lagerstroemia parviflora 46 29 11 54.25 28.97 7.51 0.78 29 13 14 Diospyros melanoxylon 31 31 23 36.51 56.57 29.22 0.58 23 33 63 Lannea coromandelica 29 0 0 33.12 0 0 0.61 12 0 0 Ziziphus xylopyrus 37 37 63 30.72 64.22 89.22 0.54 16 44 263 Acacia catechu 20 17 26 24.37 20.98 21.62 0.35 15 9 57 Dry teak forest Tectona grandis 100 92 40 137.19 128.21 78.33 11.56 292 225 336 Diospyros melanoxylon 64 44 36 46.26 74.33 45.67 2.83 92 132 248 Lagerstroemia parviflora 64 56 12 39.76 46.7 17.4 2.34 63 69 76 Butea monosperma 32 16 4 16.56 6.94 3.16 0.97 17 4 12 Boswellia serrata 12 0 0 13.93 0 0 1.78 8 0 0 Dendrocalamus strictus 16 0.36 126 culms ha − 1 3.3. Regeneration and Conservation Priority Most of the dominant tree species at each forest type showed good regeneration. In NDDF, 55 species were recorded, with 41 regenerating (74.5%); DDS, with 36 species, showed 27 species in the regeneration stage (75%), while DBB recorded 29 species with 15 regenerating (51.7%). Similarly, APF documented 13 species, of which 9 were regenerating (69.2%); in Boswellia forest, 7 out of 15 species were regenerating (46.7%), and DTF had 26 species with 16 regenerating (61.5%). The conservation priority classes I, II, and III are represented as a, b, and c, respectively, along with the regeneration status of the species (Table 4 ). Tree species with highest IVI in APF were Anogeissus pendula, Acacia catechu and Lannea coromandelica , showed no regeneration. Lannea coromandelica represents no regeneration for Boswellia forest as well and falls under conservation priority class I for both APF and BF. In DBB, Tectona grandis showed no regeneration with conservation priority class II. IN DDS, Lagerstroemia parviflora and Diospyros melanoxylon showed fair to good regeneration. In DTF, Tectona grandis had fair regeneration, while Diospyros melanoxylon and Lagerstroemia parviflora showed good regeneration, though none of these fell under a conservation priority category. In PTR, Lannea coromandelica is among the top five species with the highest IVI in all forest types except DTF, yet it only shows regeneration in NDDF. Table 4 represents the tree species regeneration and their conservation priority status across forest types in PTR. In conservation priority class I (a), DBB has the highest species (10), followed by NDDF (8), DDS (7), DTF (5), BF (4), and APF (1). Priority class II (b) has the highest number of species in all forest types, including NDDF with 18 species, followed by DBB (12), DTF, DDS (11 each), BF (8), and APF (7). Table 4 Regeneration potential and priority class of tree species at different forest types in PTR Sr no. Species name APF BF DBB DDS DTF NDDF 1 Acacia catechu Noᵇ Noᵃ Noᵇ Fair Fair Fair 2 Acacia donaldii Poorᵇ Poorᵇ Poorᵇ Good Fairᵇ Good 3 Acacia leucophloea - - - Noᵇ - Fairᵇ 4 Acacia nilotica - - - - - Noᵃ 5 Aegle marmelos Noᵇ - Fair Fair Fair Good 6 Albizia procera - - Noᵃ Fairᵇ - Fair 7 Anogeissus latifolia - - Fairᵇ Fair Fairᵇ Fair 8 Anogeissus pendula No - Noᵃ Good Noᵇ Fair 9 Antidesma ghaesembilla - - - - - Fairᵇ 10 Azadirachta indica - - - - - Noᵃ 11 Bauhinia malabarica Poorᵇ Noᵃ Fairᵇ Poorᵇ - Fairᵇ 12 Bauhinia racemosa - - - Fairᵇ - Fairᵇ 13 Bauhinia variegata - - - - - Fairᵇ 14 Bombax ceiba - - - - - Noᵇ 15 Boswellia serrata - Fairᵇ - Noᵃ Noᵃ Noᵃ 16 Bridelia retusa - - - - - Fairᵇ 17 Buchanania cochinchinensis - - - Noᵃ Fairᵇ Noᵇ 18 Butea monosperma - - - Noᵃ Fair Fair 19 Cassia fistula Goodᶜ Noᵇ Good Goodᶜ Good Good 20 Cassine glauca - - - - Noᵃ Poorᵇ 21 Ceriscoides turgida - - Noᵃ - - - 22 Cochlospermum religiosum - - - Good - Fair 23 Desmodium oojeinense - - Poorᵇ - - Fairᵇ 24 Diospyros melanoxylon Good Noᵇ Fair Good Good Fair 25 Dolichandrone falcata - - - Noᵃ - - 26 Eriolaena hookerriana - - - - - Noᵃ 27 Erythrina stricta - - - - - Poorᵇ 28 Erythrina suberosa - - Noᵃ - - Noᵇ 29 Euphorbia nivulia - - Noᵃ - - Noᵃ 30 Ficus arnottiana - - - - - Noᵇ 31 Ficus mollis - - - Noᵃ Noᵃ Noᵃ 32 Flacourtia indica Goodᶜ - Fairᵇ Goodᶜ Fairᵇ Good 33 Gardenia latifolia - - Poorᵇ - - Fair 34 Grewia eriocarpa - - - - - Fairᵇ 35 Grewia orbiculata - - - - - Goodᶜ 36 Grewia tiliifolia - Noᵃ - - - Noᵇ 37 Haldina cordifolia - - Noᵃ - - Noᵃ 38 Holarrhena pubescens - - Fairᵇ Good Goodᶜ Goodᶜ 39 Holoptelea integrifolia - Noᵇ - Goodᶜ - Good 40 Kydia calycina - Noᵃ Noᵃ Fairᵇ - Fair 41 Lagerstroemia parviflora Fair Noᵇ Fair Fair Good Fair 42 Lannea coromandelica Noᵃ Noᵃ Noᵇ Noᵃ Noᵇ Fair 43 Limonia acidissima - - Noᵃ - No Noᵃ 44 Madhuca longifolia - - - - - Poorᵇ 45 Mallotus philippensis - - Noᵃ Noᵃ - Fair 46 Miliusa tomentosa - Poorᵇ Goodᶜ Poorᵇ - Goodᶜ 47 Mitragyna parvifolia - - - Good Fairᵇ Good 48 Murraya paniculata - - - Poorᵇ - Goodᶜ 49 Naringi crenulata Fairᵇ - Goodᶜ Good Fairᵇ Goodᶜ 50 Nyctanthes arbor-tristis - Fairᵇ Poorᵇ - Fairᵇ No 51 Phyllanthus emblica - - - Fairᵇ - Fair 52 Pterocarpus marsupium - - Noᵃ - - Fairᵇ 53 Schleichera oleosa - - Noᵃ - Noᵃ Noᵃ 54 Soymida febrifuga - - - - - Noᵃ 55 Sterculia urens - - - Poorᵇ - Noᵇ 56 Syzygium cumini - - - - Fairᵇ - 57 Tectona grandis - Goodᶜ Noᵇ No Fair Fair 58 Terminalia arjuna - - - - Noᵃ - 59 Terminalia bellirica - - - Poorᵇ - Noᵃ 60 Terminalia elliptica - - - Fair - Fair 61 Wrightia arborea - Fair - - - Fair 62 Wrightia tinctoria Fairᵇ Goodᶜ Good Good Good Good 63 Ziziphus mauritiana - - - Fairᵇ - Fairᵇ 64 Ziziphus xylopyrus Poorᵇ Fair Noᵇ Good Noᵇ Good Good : seedlings > saplings > trees; Fair : seedlings > saplings ≤ trees; Poor : species present only at the sapling stage; No: s pecies present only at the tree stage; a : conservation priority I; b : conservation priority II, c : conservation priority III. 4. Discussion Previous studies have classified forests in PTR primarily by density and community assemblages (Porwal & Singh 2009 ; Parveen & Ilyas 2022 ). However, we argue that to guide efficient management-level planning, an actionable scale of classification is required, which goes beyond community assemblage yet still distinguishes the major forest types. Apart from the five forest types in this study, during the field survey, we observed narrow strips of riverine patches, but due to their small size (< 10m) these were excluded from the classification. Similarly, the Bosewellia forest could not be mapped because of its scanty distribution. In addition, Anogeissus pendula scrub forest was recorded in the western part of PTR, which, according to Champion and Seth ( 1968 ), represents the final stage of degradation before complete elimination. We suggest that future studies optimise high-resolution data to map these ecologically important yet spatially limited forest types. Among the forest types, scrub forests emerge as the second largest in extent with the second highest species richness and the lowest in density. Studies have stated in India, savannas are often misclassified as scrub or degraded forest (Ratnam et al. 2016 ; Gopalakrishna et al. 2024 ). This nomenclature is an outcome of the colonial era’s timber-centric approach often leads to neglecting these crucial ecosystems in conservation planning (Ratnam et al. 2019 ). Based on our field observations, we support the argument of categorization of scrub forest in Ancient and Derived savannas (Ratnam et al. 2016 ). We observed in PTR, ancient savannas occur mainly on plateaus with continuous grass cover and scattered trees, while derived savannas are located primarily in areas of relocated villages and in buffer areas where anthropogenic pressures, along with edaphic factors, have formed this type. This can guide the conservation or restoration efforts for planning of these undervalued yet ecologically vital systems. In PTR, the proposed Ken–Betwa Link Project (KBLP) is estimated to submerge an area of 90.00 sq. km. Of which 40.26. sq. km has forest cover, this consists of 64% representing NDDF, 15.50% is DDS forest, 8.99% is DTF, 5.18% is APF, and 5.56% is DBB. This scenario reflects a broader global trend where forests are increasingly at risk due to developmental projects. Therefore, it is crucial to account for every forest patch regardless of its size and type to better understand the unique ecological roles and mitigate the impacts of such interventions. This study aims to meet this requirement and potentially serve as a reference for forest type mapping across the Greater Panna Landscape and provides scientific support for restoration choices or guidance in the context of various development projects. Previously, conservation induced village relocation took place in PTR and these relocated village sites were later on developed as grasslands. While this is helpful, we also recommend that the restoration of to be relocated village sites (as compensation for submergence) should be considered for woodland-grassland mosaic keeping in view the ecological characteristics of the surrounding areas thereby enhancing overall landscape integrity and functional connectivity. The diversity and density of different forests vary significantly depending on the biotic and abiotic factors, along with anthropogenic pressures (Timilsina et al. 2007 ; Mohanta et al. 2021 ). Studies by Ray et al. ( 2021 ) and Parveen et al. (2021) documented 45 trees and 46 tree species, respectively, in the core area of PTR, while our study, covering the entire PTR, recorded 64 tree species. In the central Indian region, with dry deciduous forest studies have recorded tree densities of 126–490 ind. ha - 1 (Chaturvedi and Raghubanshi 2013), 702–1671 ind. ha -1 (Joshi & Dhyani 2019 ), 225.28 ind. ha -1 (Parveen et al. 2021), 391.9 ind. ha -1 (Ray et al. 2021 ). In the present study, tree density ranged from 146–625 ind. ha -1 . Similarly, Parveen et al. (2021) recorded 70.289 seedlings ha -1 and 89.04 saplings ha -1 , whereas Ray et al. ( 2021 ) documented seedling densities of 3107.5 ind. ha -1 and 6777.5 saplings ha -1 in PTR. In our study highest sapling density was in the AP forest, 634 ind. ha -1 , and the lowest was in the DBB, 127 ind. ha -1 . Seedling density recorded the highest in NDDF 1621 ind. ha -1 , followed by DTF 1428 ind. ha -1 , and the lowest is Boswellia forest 625 ind. ha -1 . Such variations across studies are mainly due to sampling design and methodology, making direct comparisons misleading. Dry bamboo brake is one of the major forest types in PTR and is distributed across the reserve. Dendrocalamus srtictus forms clumps consisting of 12–58 culms in both core and buffer areas of PTR. However, despite its wide distribution, previous studies have largely overlooked bamboo species in PTR. DBB provides suitable habitat to the wildlife and serves as a source of livelihood for the dependent communities. During our field visit, we observed sporadic flowering throughout, indicating the high pressure on this species. We recommend that future studies focus on the ecology, distribution, and management of bamboo in PTR. Regeneration status results when compared to Parveen et al. (2025) show similar regeneration patterns across the species. In India, Rai and Saxena (1997) reported that nearly 72% of forests had already lost their regeneration potential, underscoring the importance of regular monitoring. Although regeneration in Anogeissus pendula forest was categorized as poor, it does not fall under the conservation priority group; notably, in PTR, this forest type remains highly intact, forming pure stands that indicate a climax stage. In our study, several species, including Terminalia bellerica , Pterocarpus marsupium , Terminalia arjuna , Limonia acidissima , Haldina cordifolia, Lannea coromandelica, Bosewellia serrata , and Schleichera oleosa , exhibited poor or no regeneration across all forest types and fell in high conservation priority classes. Hence, we suggest the highest priority should be given to these species along with other priority I-class species to ensure their long term survival. 5. Conclusion Addressing the ongoing climatic and biodiversity crisis demands expanding our focus from protected areas to a landscape approach. This study supports this narrative by providing a spatially explicit understanding of forest type distribution and regeneration dynamics. Several key species including ecologically important species with high IVI values exhibited poor regeneration across all forest types, underscoring the need for better understanding of species-habitat relationship, population dynamics towards targeted restoration. The integrated assessment of forest type distribution and structural attributes offers a replicable framework that can guide management planning, enabling more informed decisions and effective conservation actions in PTR and other forested landscapes Declarations Acknowledgements We would like to express our sincere gratitude to the Director, Dean, Registrar and Research Coordinator of the Wildlife Institute of India, Dehradun, for their support and encouragement. We also thank the officials of the Madhya Pradesh Forest Department for granting research permissions and providing valuable assistance. Our heartfelt thanks to the field staff of Panna Tiger Reserve, for their constant support and dedication made it possible to conduct this work under challenging field conditions. We are especially grateful to our field team members Mr. Bablu Gond, Mr. Ramjan Khan, Mr. Pappu Pal, Mr. Rajkumar, and Mr. Darshan Singh for their invaluable help in facilitating data collection. Funding This research was funded by the National Water Development Agency, Government of India (No. WII/KR/PROJECT/PLMP/2017-18/F(1)). Contributions K.P. conducted the data collection, performed the analysis, and wrote the main manuscript text. R.K. contributed to the overall supervision and reviewed and edited the manuscript. C.R. and A.K. participated in reviewing and editing the manuscript. All authors reviewed and approved the final version of the manuscript. 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1","display":"","copyAsset":false,"role":"figure","size":62680,"visible":true,"origin":"","legend":"\u003cp\u003eMap representing core and buffer areas of Panna Tiger Reserve, Madhya Pradesh\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7939381/v1/e03d1c50d996af86adee8486.jpeg"},{"id":95368007,"identity":"41e075f0-7405-4c25-b895-4f23f3cd0267","added_by":"auto","created_at":"2025-11-07 09:05:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4406864,"visible":true,"origin":"","legend":"\u003cp\u003eForest type map of PTR, Madhya Pradesh\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7939381/v1/6dedf8bd0478917cfdfa5cd9.png"},{"id":104251541,"identity":"5dc4c932-4980-4227-af99-1014b5266c40","added_by":"auto","created_at":"2026-03-09 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Introduction","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eForests around the globe are increasingly threatened by deforestation and degradation, driven by natural and anthropogenic factors. In the tropics, deforestation poses a greater risk due to the conversion of natural forests to other land uses. (\u003c/span\u003eDe la Barreda-Bautista et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keenan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThese disturbances impact overall biodiversity and carbon storage capacity adversely and sometimes can lead to a complete shift in species composition (\u003c/span\u003eChazdon \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Poorter et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rozendaal et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stan and Sanchez-Azofeifa \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). To support climate change mitigation, it is essential to understand forest structure and monitor species distribution and composition for informed decision-making (\u003c/span\u003eFeldpausch et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chheng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vihervaara et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Beirne et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAccurate and updated information on forest type, diversity, and regeneration is essential for forest carbon management, biodiversity assessment, and conservation planning (\u003c/span\u003ePan et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bastin et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). Understanding the spatial distribution of forest types offers valuable insights into ecosystem structure and habitat suitability for different flora and fauna species (\u003c/span\u003eMo et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAccounting for about 85% of total land vegetation biomass, forests are the largest land-based carbon reservoir, playing an irreplaceable role in the ecosystem (\u003c/span\u003eDixon et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Schimel et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kuuluvainen \u0026amp; Gauthier \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTropical forests are among the most productive ecosystems on earth. While covering only\u0026thinsp;~\u0026thinsp;6% of the world's surface area, these forests are the largest repository of above-ground biomass carbon (\u003c/span\u003eBeer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNotably, tropical dry forests (TDFs) account for about 40% of all tropical forests and hold approximately half of the carbon storage capacity worldwide (\u003c/span\u003eMiles et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Keith et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Joshi \u0026amp; Dhyani \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; George-Chac\u0026oacute;n et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHowever, recent studies show TDFs are increasingly shifting from being carbon sinks to carbon sources (\u003c/span\u003eLe Qu\u0026eacute;r\u0026eacute; et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baccini et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTDFs are multifunctional landscapes supporting livelihoods across the globe and are socio-economically significant, particularly for the marginalized communities (\u003c/span\u003ePortillo-Quintero et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Powers et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Siyum \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWithin a forest ecosystem, plant communities are the major component, especially trees that constitute dominant plant forms and shape biodiversity (\u003c/span\u003eHaq et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rawat et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTree species maintain the overall physiognomy and determine the forest community structure (\u003c/span\u003eComita et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Farooq et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe current distribution of seedlings and saplings plays a critical role in shaping future species composition and forest structure (\u003c/span\u003eHenle et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pokhriyal et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eComprehensive information on the composition, structure, diversity, and regeneration potential supports ecological monitoring and can guide conservation and restoration strategies (\u003c/span\u003eEilu \u0026amp; Obua \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mohanta et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haq et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStudies on forest structure, diversity, and regeneration often do not include the mapping of forest extent or forest types and\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003evice versa\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e(Immitzer et al. 2016;\u003c/span\u003e Karasiak et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Grabska et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hościło and Lewandowska \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ray et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohanta et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTo enable data-driven management planning, integration of these two crucial components is essential for understanding a holistic view of a forest ecosystem.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTo map and monitor the extent of forest types, remote sensing technologies complement traditional methods (\u003c/span\u003eLi et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurther, remote sensing data offers consistent temporal frequency and spatially continuous coverage, enabling cost-effective and systematic monitoring of vegetation dynamics (\u003c/span\u003eLillesand et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Reyes-Palomeque et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSatellite datasets with high spectral and spatial resolution allow users to accurately map the forest types. Several studies have successfully utilized the spectral and temporal signatures to retrieve forest type information at regional and national levels (\u003c/span\u003eGrabska et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Aziz et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fassnacht et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHermosilla et al. 2025). Cloud-based platforms such as Google Earth Engine (GEE) with powerful geo-big data capabilities facilitate the handling of large datasets and support the integration of machine learning algorithms for enhanced forest type mapping and monitoring (\u003c/span\u003eGorelick et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pasquarella et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tamiminia et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePrior research on vegetation in the Panna Tiger Reserve (PTR) was limited to the core region with key research topics, i.e., tree diversity, regeneration, forest fires, carbon stock, and mapping of vegetation communities (\u003c/span\u003ePorwal \u0026amp; Singh \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jain et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ray et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Parveen \u0026amp; Ilyas \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Parveen \u0026amp; Ilyas \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ray et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Parveen \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). As we observe the UN Decade on Ecosystem Restoration, it is imperative to broaden our focus beyond core areas, which are typically under a high level of protection, to develop targeted conservation or restoration strategies. In this context, the present study bridges this gap by focusing on an extended area encompassing both the core and a large buffer zone of PTR, with the objective of mapping forest types and quantifying regeneration and diversity.\u003c/span\u003e\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStudy area\u003c/span\u003e\u003c/h2\u003e\u003cp\u003eOur study area, Panna Tiger Reserve (PTR), is located in the state of Madhya Pradesh, central India. PTR constitutes an area of 576 km\u003csup\u003e2\u003c/sup\u003e in the core zone and a buffer region spanning over 1021 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PTR has a step-like topography, constituting hilly terrain, gorges, escarpments, tablelands, and a valley along the river Ken, which flows 55 km inside the reserve. The elevation of PTR ranges from 123 meters to 566 meters with a varying slope of 0\u0026deg; to 48\u0026deg;. PTR receives approximately 1100 mm annual rainfall, and the monsoon season from July to October is the principal source of water for the large areas of the region. Summer months start from March to June, when the temperature often exceeds 45\u0026deg;C, and winter occurs from the month of November to February, when the minimum mean temperature remains around 7\u0026ndash;8\u0026deg; Celsius.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Forest types of PTR\u003c/h2\u003e\u003cp\u003eAccording to Champion and Seth (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), the forest type of PTR is broadly categorized as dry deciduous forest type, group 5, which includes sub-groups 5A, southern tropical dry deciduous forest, and 5B, northern tropical dry deciduous forest. In subgroup 5A, PTR harbors dry teak forest, making it the northernmost boundary for natural teak forest, while in 5B, five types of forest, i.e., \u003cem\u003eBoswellia\u003c/em\u003e forest, dry bamboo brakes, dry deciduous scrub, northern dry mixed deciduous forest, and \u003cem\u003eAnnogeissus pendula\u003c/em\u003e forest are present. Additionally, according to Meher-Homji (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) the forest communities viz., \u003cem\u003eAcacia catechu\u003c/em\u003e - \u003cem\u003eAnnogeissus pendula\u003c/em\u003e vegetation type - dry deciduous low forest, \u003cem\u003eAnnogeissus latifolia\u003c/em\u003e - \u003cem\u003eTerminalia\u003c/em\u003e type - dry deciduous forest, \u003cem\u003eTerminalia\u003c/em\u003e - \u003cem\u003eAnnogeissus latifolia\u003c/em\u003e - \u003cem\u003eTectona grandis\u003c/em\u003e type - dry deciduous forest, and shrub savanna were also identified in PTR. We attempted to conduct mapping based on the Champion and Seth\u0026rsquo;s forest type classification owing to presence of distinct forest types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Vegetation sampling\u003c/h2\u003e\u003cp\u003eThis study was conducted during the post-monsoon months from August to November 2022 in PTR. A stratified random sampling method guided by the reconnaissance and literature was followed. A total number of 153 sampling plots measuring 20\u0026times;20 m. were established across the reserve, including 68 plots in northern dry mixed deciduous forest (NDDF), 26 in dry teak forest (DTF), 8 in \u003cem\u003eAnnogeissus pendula\u003c/em\u003e forest (APF), 12 in dry bamboo brakes (DBB), 4 in \u003cem\u003eBoswellia\u003c/em\u003e forest (BF), and 35 in dry deciduous scrub (DDS). To facilitate the sampling, these plots were further divided into sub-plots of four 10\u0026times;10 m and 5\u0026times;5 m (Kothandaraman et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tree individuals, with a girth at breast height (GBH) of \u0026ge;\u0026thinsp;30 cm and saplings with GBH 10\u0026ndash;30 cm were recorded within the 10\u0026times;10 m subplots. Seedlings with GBH\u0026thinsp;\u0026lt;\u0026thinsp;10 cm were assessed within the 5\u0026times;5 m sub-plots. The spatial coordinates of all sampling locations were recorded using a handheld GPS unit.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Forest type mapping\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Satellite data\u003c/h2\u003e\u003cp\u003eWe utilized Sentinel-2 (S2) surface reflectance (SR) images from the GEE platform with less than 10% cloud cover per tile to get maximum information (Immitzer et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pratic\u0026ograve; et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We used S2 bands available in 10-meter (B2, B3, B4, B8) and 20 meters (B5, B6, B7, B8a, B11, B12) spatial resolution. The 10-meter bands were resampled to 20-meter using a nearest neighbor resampling method (Hościło \u0026amp; Lewandowska \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Datasets for the months of October, December 2022 and February, May 2023 were analyzed, capturing pre- and post-monsoon seasons; monsoon season images (July to September) were excluded because forest types cannot be reliably distinguished when canopy cover is uniformly green. While the pre- and post-monsoon season captures various phases of phenology (Schieber et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jabłońska et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Immitzer et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest displays a purple to red tint in October before leaf fall, while \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e remains largely evergreen. \u003cem\u003eAnogeissus latifolia\u003c/em\u003e shows coppery leaf coloration in November, followed by yellowing and shedding by January. Similarly, \u003cem\u003eTectona grandis\u003c/em\u003e begins leaf shedding in November, and new leaf emergence occurs in April and June (Krishen, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Images from May were characterized by extensive leaf fall and predominantly brown vegetation; only some patches along the water bodies and ridges appear to be green. In addition to colour differentiation, forest types, mainly DBB and APF, exhibit distinctive textural patterns in satellite images.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Ground truth data\u003c/h2\u003e\u003cp\u003eOverall, 958 reference data points were used in the analysis, belonging to six forest type classes and non-forest classes. Out of these, 153 data points were the sampling plot locations, while the remaining data points were collected randomly in the field and further supplemented via manual visual interpretation on Google Earth software (Hansen et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; De Sousa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Phan et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reference data were split, with 70% of sampling points used as training samples and 30% reserved for validation of the results (Gigović et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To ensure better accuracy results, visual verification of reference samples was done using Google Earth images. We did not find any substantial differences; however, some selected reference points for dry bamboo brake were readjusted to pure bamboo patches near cliff edges for accurate categorization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3. Classification\u003c/h2\u003e\u003cp\u003eWe used the Random Forest (RF) Classifier in the GEE platform (smileRandomForest). The RF is a machine-learning algorithm approach that uses multiple self-learning decision trees to parameterize models and estimate variables (Breiman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hastie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Belgiu \u0026amp; Dragut \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Over the past years, RF has been the most widely used classification algorithm for satellite imagery. (Millard \u0026amp; Richardson, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; C\u0026aacute;novas-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jin et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Adugna et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pande et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to the ability to effectively handle outliers and noisy data, perform well with multi-source datasets, and superior accuracy over classifiers like SVM, kNN, and MLC, has made RF a preferred choice in many applications (Karasiak et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wessel et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following recommendations from previous studies (Abdel-Rahman et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xia et al. 2017; Mahdianpari et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and preliminary tests on our dataset, the number of decision trees was fixed at 100 (ntree\u0026thinsp;=\u0026thinsp;100), while the number of variables considered at each split (mtry) was set to the default, the Gini Index was used to define the impurity, and minimum number of samples in a node was set to one (Waske et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ghimire et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hościło \u0026amp; Lewandowska \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Phan et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Variable importance index was measured based on the Gini criterion (Cheng \u0026amp; Wang \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accuracy assessment was carried out using the reference validation datapoints, and overall accuracy, kappa coefficient, user\u0026rsquo;s accuracy, producer\u0026rsquo;s accuracy, and F1 score were calculated (Congalton et al. 2019; Hościło \u0026amp; Lewandowska \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Phytosociological analysis\u003c/h2\u003e\u003cp\u003ePhytosociological analysis was carried out using the field datasets encompassing three life stages, i.e., tree, sapling, and seedling. Density, frequency, species richness, basal area, Shannon-Wiener diversity index, Simpson's index (concentration of dominance), evenness index, Important Value Index (IVI) were calculated to understand the forest characteristics (Shannon \u0026amp; Wiener 1963; Simpson \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1949\u003c/span\u003e, Curtis \u0026amp; McIntosh \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1951\u003c/span\u003e; Mohanta et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, density and basal area for bamboo were calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Regeneration potential and conservation priority\u003c/h2\u003e\u003cp\u003eWe analysed the regeneration status of the tree species in PTR by comparing their densities (individuals ha\u003csup\u003e-1\u003c/sup\u003e) at three life stages, i.e., tree, sapling, and seedling (Duchok et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Bhadouria et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The seedlings\u0026thinsp;\u0026lt;\u0026thinsp;20 cm in height were excluded from the analysis, as they are considered ephemeral in nature. (Buragohain et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Regeneration status was classified into four categories: Good, when seedlings\u0026thinsp;\u0026gt;\u0026thinsp;saplings\u0026thinsp;\u0026gt;\u0026thinsp;trees, Fair (seedlings\u0026thinsp;\u0026gt;\u0026thinsp;saplings\u0026thinsp;\u0026le;\u0026thinsp;trees), Poor (species present only at the sapling stage), and No regeneration when species present only at the tree stage following Ballabha et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Mohanta et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on regeneration status, tree species were further categorized into conservation priority classes. Priority I is the highest conservation priority class, including species with no recorded seedlings or saplings. Priority II class comprised species lacking either seedlings or saplings, and Priority III category included species with no saplings and/or no mature trees (Woldearegay \u0026amp; Woldu \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mohanta et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Forest type mapping\u003c/h2\u003e\u003cp\u003eA total of seven classes were identified, of which five classes represent forest types, while the remaining two are water and non-forest areas, including the settlement and agricultural land, and bare land without green cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the area statistics and accuracy assessment results, showing an overall accuracy of 88.9% and a Kappa coefficient of 0.81.\u003c/p\u003e\u003cp\u003eAmong the forest types, the northern dry mixed deciduous forest was the most widespread (37%), followed by DDS (17%), and DTF (8%). In PTR, \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest occurs on the gentler slopes, forming pure patches along the Ken River and its tributary, while dry deciduous scrub is distributed across the reserve, dominating the tablelands and much of the western part. DTF and DBB occur widely across the reserve, while DBB is largely confined to the slopes, DTF occurs along the slopes, the flatlands adjoining the Ken River, and in the upper plateau.\u003c/p\u003e\u003cp\u003eVariable importance index ranks the predictor variables based on their contribution to distinguishing the classes in RF classifier (Online Resource 1). Band 5 from the month of February, October and December, band 11 from May, and band 6 from October were ranked top five contributing variable. Class-wise accuracy results show, \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest accounted for the highest accuracy with an F1-score of 92.74, followed by DBB (89.43). The lowest accuracy was observed for \u003cem\u003eBoswellia\u003c/em\u003e forest 51.23, covering an area of less than 0.42km\u003csup\u003e2\u003c/sup\u003e. It is important to note that the \u003cem\u003eBoswellia\u003c/em\u003e forest is endemic to central India; however, due to its scanty distribution, this class was represented by only six data points. To improve the overall classification accuracy, we merged this class with the northern mixed forest.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of area and accuracy of the forest type classification of PTR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUsers accuracy %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProducers accuracy %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF1-score (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry bamboo brakes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry deciduous scrub\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e298.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry teak forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern dry mixed deciduous forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e659.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus pendula\u003c/em\u003e Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e594.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Phytosociological analysis\u003c/h2\u003e\u003cp\u003eA total of 64 tree species belonging to 48 genera and 25 families were recorded from the study area. The Fabaceae family contributed the most species across all forest types, while the highest species rich genera were \u003cem\u003eAcacia\u003c/em\u003e (3 species) and \u003cem\u003eAnogeissus\u003c/em\u003e (2 species). \u003cem\u003eDendrocalamus strictus\u003c/em\u003e, the only bamboo species recorded in PTR, is distributed in four forest types, with the highest basal area of 8.09 and density of 4627 culms ha\u003csup\u003e-1\u003c/sup\u003e in DBB (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Tree Layer:\u003c/h2\u003e\u003cp\u003eWith 35 tree species, the northern dry mixed deciduous forest had the highest species richness, where \u003cem\u003eAcacia\u003c/em\u003e genera (3 species) and Fabaceae family (10 species) dominated the reserve. The \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest recorded the highest tree density (625 ind. ha\u003csup\u003e-1\u003c/sup\u003e), followed by the dry teak forest (518 ind. ha\u003csup\u003e-1\u003c/sup\u003e) and the NDDF (425 ind. ha\u003csup\u003e-1\u003c/sup\u003e), while the lowest density occurred in DDS (146 ind. ha\u003csup\u003e-1\u003c/sup\u003e). Basal area was maximum in \u003cem\u003eBoswellia\u003c/em\u003e forest (15.84 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e), followed by APF (12.21 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e), and minimum in DDS (0.78 m\u0026sup2; ha\u003csup\u003e-1\u003c/sup\u003e). The Shannon\u0026ndash;Wiener diversity index, Simpson\u0026rsquo;s dominance index, and evenness were highest in the NDDF (H\u0026prime;=2.80, D\u0026thinsp;=\u0026thinsp;0.92, E\u0026thinsp;=\u0026thinsp;0.80) and lowest in the APF (H\u0026prime;=0.43, D\u0026thinsp;=\u0026thinsp;0.17, E\u0026thinsp;=\u0026thinsp;0.24) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe species with the highest IVI within NDDF were \u003cem\u003eLannea coromandelica\u003c/em\u003e (IVI 34.55) and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (IVI 30.09). In dry bamboo brakes, \u003cem\u003eTectona grandis\u003c/em\u003e (IVI 86) and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (IVI 32) were dominant species, the genus \u003cem\u003eAnogeissus\u003c/em\u003e (2 species) and Fabaceae family (5 species) contributed most to species richness. The \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest was strongly dominated by \u003cem\u003eAnogeissus pendula\u003c/em\u003e (IVI 219) followed by \u003cem\u003eLannea coromandelica\u003c/em\u003e (IVI 34). Acacia was the most species-rich genus (3 species) in dry deciduous scrub, while the highest IVI values were observed for \u003cem\u003eLagerstroemia parviflora\u003c/em\u003e (54.25), \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (36.51), \u003cem\u003eLannea coromandelica\u003c/em\u003e (33.12), and \u003cem\u003eZiziphus xylopyrus\u003c/em\u003e (30.72). The \u003cem\u003eBoswellia\u003c/em\u003e forest was dominated by \u003cem\u003eBoswellia serrata\u003c/em\u003e (IVI 184.65), followed by \u003cem\u003eLannea coromandelica\u003c/em\u003e (IVI 29.81), while \u003cem\u003eTectona grandi\u003c/em\u003es (IVI 137.19) and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (IVI 46.26) were the dominant species in the dry teak forest (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Sapling Layer\u003c/h2\u003e\u003cp\u003eIn the sapling layer, similar to the tree layer, \u003cem\u003eAccacia\u003c/em\u003e and \u003cem\u003eAnogeissus\u003c/em\u003e were the most specious genera. While Fabaceae was the most specious family across forest types, with six species in NDDF and five in DDS. NDDF recorded highest species richness (28), followed by DDS (24), while the lowest species richness was recorded in the \u003cem\u003eBoswellia\u003c/em\u003e forest (9). \u003cem\u003eAnogeissus pendula\u003c/em\u003e had the highest IVI (218.4) in APF, followed by \u003cem\u003eTectona grandis\u003c/em\u003e (128.21) in the DTF, while the NDDF was dominated by \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (42.39) and \u003cem\u003eLagerstroemia parviflora\u003c/em\u003e (32.23). In DBB, \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e has the highest IVI of 66.3, while in the \u003cem\u003eBoswellia\u003c/em\u003e forest, \u003cem\u003eTectona grandis\u003c/em\u003e (48.9) and \u003cem\u003eWrightia tinctoria\u003c/em\u003e (43.9) were the most dominant species. \u003cem\u003eZiziphus xylopyrus\u003c/em\u003e (64.21) and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (56.56) were the most dominant species in the sapling stage in DDS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sapling density was highest in APF (634 ind. ha\u003csup\u003e-1\u003c/sup\u003e), followed by DTF (467 ind. ha\u003csup\u003e-1\u003c/sup\u003e), and lowest in DBB (127 ind. ha\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eShannon\u0026ndash;Wiener diversity index, Simpson\u0026rsquo;s dominance index shows similar patterns as tree layer, NDDF being highest (H\u0026prime;=2.70, D\u0026thinsp;=\u0026thinsp;0.51) and APF being lowest (H\u0026prime;=0.51, D\u0026thinsp;=\u0026thinsp;0.19), while evenness was highest in \u003cem\u003eBoswellia\u003c/em\u003e forest (0.86) and lowest in APF (0.22) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecies richness, density, diversity across different forest types in PTR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecies (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGenera (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFamilies (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDensity (n ha\u003csup\u003e-1)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eShannon\u0026rsquo;s index (0\u0026ndash;ln(S))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSimpson\u0026rsquo;s index (0\u0026ndash;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eEvenness (0\u0026ndash;1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAPF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDTF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNDDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Seedling Layer\u003c/h2\u003e\u003cp\u003eThe species richness is highest in the seedling layer among all three layers. NDDF comprises 39 species, followed by 23 species in DDS, while the lowest species richness was recorded in the \u003cem\u003eBoswellia\u003c/em\u003e forest with only 5 species. Fabaceae, Rubiaceae and Rutaceae were the most specious families while the genera \u003cem\u003eAcacia\u003c/em\u003e and \u003cem\u003eBauhinia\u003c/em\u003e were the most represented across forest types. Similar to the sapling layer, \u003cem\u003eAnogeissus pendula\u003c/em\u003e had the highest IVI value (147.29), followed by \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (46.47) in APF, while in DBB, \u003cem\u003eAegle marmelos\u003c/em\u003e recorded the highest IVI (108.9), followed by \u003cem\u003eWrightia tinctoria\u003c/em\u003e (40.30). \u003cem\u003eAegle marmelos\u003c/em\u003e (25.9) and D\u003cem\u003eiospyros melanoxylon\u003c/em\u003e (25.6) were the most dominant species in NDDF, while in the \u003cem\u003eBoswellia\u003c/em\u003e forest, \u003cem\u003eNyctanthes arbor-tristis\u003c/em\u003e, with IVI of 121.13, followed by \u003cem\u003eZiziphus xylopyrus\u003c/em\u003e (54.75), were the dominant species. In DDS, \u003cem\u003eZiziphus xylopyrus\u003c/em\u003e (89.22), and in DTF, \u003cem\u003eTectona grandis\u003c/em\u003e (78.33) and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e (45.67) dominated respective forest types. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Seedling density was highest in NDDF (1621 ind. ha\u003csup\u003e-1\u003c/sup\u003e), followed by DTF (1428 ind. ha\u003csup\u003e-1\u003c/sup\u003e), and lowest in Boswellia forest (625 ind. ha\u003csup\u003e-1\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eShannon\u0026rsquo;s diversity index and Simpson\u0026rsquo;s index were highest in NDDF (H\u0026prime;=2.98, D\u0026thinsp;=\u0026thinsp;0.93) and lowest in Boswellia forest (H\u0026prime;=1.30) and APF (D\u0026thinsp;=\u0026thinsp;0.62). Evenness was also highest in NDDF (0.82), followed closely by DBB (0.81), and lowest in APF (0.68) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop five tree species ranked by Importance Value Index (IVI) with their frequency, density, and basal area, along with bamboo species showing frequency, density, and basal area across different forest types of PTR.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eForest types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eIVI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBasal area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eDensity (ind./ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTrees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003em\u003csup\u003e2\u003c/sup\u003e/ha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTrees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSapling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eSeedling\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus pendula\u003c/em\u003e forest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus pendula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e219.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e218.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e147.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcacia catechu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLagerstroemia parviflora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDry bamboo brakes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTectona grandis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcacia catechu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMadhuca longifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDendrocalamus strictus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e4627 culms ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNorthern dry mixed deciduous forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus latifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLagerstroemia parviflora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTerminalia elliptica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDendrocalamus strictus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e956 culms ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBoswellia\u003c/b\u003e \u003cb\u003eforest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoswellia serrata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcacia catechu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCassia fistula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDendrocalamus strictus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e206 culms ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDry deciduous scrub\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLagerstroemia parviflora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eZiziphus xylopyrus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcacia catechu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDry teak forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTectona grandis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLagerstroemia parviflora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eButea monosperma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoswellia serrata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDendrocalamus strictus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e126 culms ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Regeneration and Conservation Priority\u003c/h2\u003e\u003cp\u003eMost of the dominant tree species at each forest type showed good regeneration. In NDDF, 55 species were recorded, with 41 regenerating (74.5%); DDS, with 36 species, showed 27 species in the regeneration stage (75%), while DBB recorded 29 species with 15 regenerating (51.7%). Similarly, APF documented 13 species, of which 9 were regenerating (69.2%); in \u003cem\u003eBoswellia\u003c/em\u003e forest, 7 out of 15 species were regenerating (46.7%), and DTF had 26 species with 16 regenerating (61.5%). The conservation priority classes I, II, and III are represented as a, b, and c, respectively, along with the regeneration status of the species (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTree species with highest IVI in APF were \u003cem\u003eAnogeissus pendula, Acacia catechu\u003c/em\u003e and \u003cem\u003eLannea coromandelica\u003c/em\u003e, showed no regeneration. \u003cem\u003eLannea coromandelica\u003c/em\u003e represents no regeneration for Boswellia forest as well and falls under conservation priority class I for both APF and BF. In DBB, \u003cem\u003eTectona grandis\u003c/em\u003e showed no regeneration with conservation priority class II. IN DDS, \u003cem\u003eLagerstroemia parviflora\u003c/em\u003e and \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e showed fair to good regeneration. In DTF, \u003cem\u003eTectona grandis\u003c/em\u003e had fair regeneration, while \u003cem\u003eDiospyros melanoxylon\u003c/em\u003e and \u003cem\u003eLagerstroemia parviflora\u003c/em\u003e showed good regeneration, though none of these fell under a conservation priority category. In PTR, \u003cem\u003eLannea coromandelica\u003c/em\u003e is among the top five species with the highest IVI in all forest types except DTF, yet it only shows regeneration in NDDF.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the tree species regeneration and their conservation priority status across forest types in PTR. In conservation priority class I (a), DBB has the highest species (10), followed by NDDF (8), DDS (7), DTF (5), BF (4), and APF (1). Priority class II (b) has the highest number of species in all forest types, including NDDF with 18 species, followed by DBB (12), DTF, DDS (11 each), BF (8), and APF (7).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegeneration potential and priority class of tree species at different forest types in PTR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr no.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDBB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDDS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDTF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNDDF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcacia catechu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcacia donaldii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcacia leucophloea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcacia nilotica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAegle marmelos\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAlbizia procera\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus latifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnogeissus pendula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAntidesma ghaesembilla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAzadirachta indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBauhinia malabarica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBauhinia racemosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBauhinia variegata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBombax ceiba\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBoswellia serrata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBridelia retusa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBuchanania cochinchinensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eButea monosperma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCassia fistula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCassine glauca\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCeriscoides turgida\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCochlospermum religiosum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDesmodium oojeinense\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDiospyros melanoxylon\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDolichandrone falcata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEriolaena hookerriana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eErythrina stricta\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eErythrina suberosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEuphorbia nivulia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFicus arnottiana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFicus mollis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFlacourtia indica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGardenia latifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGrewia eriocarpa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGrewia orbiculata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGrewia tiliifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHaldina cordifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHolarrhena pubescens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHoloptelea integrifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKydia calycina\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLagerstroemia parviflora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLannea coromandelica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLimonia acidissima\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMadhuca longifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMallotus philippensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMiliusa tomentosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMitragyna parvifolia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMurraya paniculata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNaringi crenulata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNyctanthes arbor-tristis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePhyllanthus emblica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePterocarpus marsupium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSchleichera oleosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSoymida febrifuga\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSterculia urens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSyzygium cumini\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTectona grandis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTerminalia arjuna\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTerminalia bellirica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNoᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTerminalia elliptica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eWrightia arborea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eWrightia tinctoria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoodᶜ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eZiziphus mauritiana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFairᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eZiziphus xylopyrus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNoᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGood\u003c/b\u003e: seedlings\u0026thinsp;\u0026gt;\u0026thinsp;saplings\u0026thinsp;\u0026gt;\u0026thinsp;trees; \u003cb\u003eFair\u003c/b\u003e: seedlings\u0026thinsp;\u0026gt;\u0026thinsp;saplings\u0026thinsp;\u0026le;\u0026thinsp;trees; \u003cb\u003ePoor\u003c/b\u003e: species present only at the sapling stage; \u003cb\u003eNo: s\u003c/b\u003epecies present only at the tree stage; \u003cb\u003ea\u003c/b\u003e: conservation priority I; \u003cb\u003eb\u003c/b\u003e: conservation priority II, \u003cb\u003ec\u003c/b\u003e: conservation priority III.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious studies have classified forests in PTR primarily by density and community assemblages (Porwal \u0026amp; Singh \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Parveen \u0026amp; Ilyas \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, we argue that to guide efficient management-level planning, an actionable scale of classification is required, which goes beyond community assemblage yet still distinguishes the major forest types. Apart from the five forest types in this study, during the field survey, we observed narrow strips of riverine patches, but due to their small size (\u0026lt;\u0026thinsp;10m) these were excluded from the classification. Similarly, the \u003cem\u003eBosewellia\u003c/em\u003e forest could not be mapped because of its scanty distribution. In addition, \u003cem\u003eAnogeissus pendula\u003c/em\u003e scrub forest was recorded in the western part of PTR, which, according to Champion and Seth (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), represents the final stage of degradation before complete elimination. We suggest that future studies optimise high-resolution data to map these ecologically important yet spatially limited forest types.\u003c/p\u003e\u003cp\u003eAmong the forest types, scrub forests emerge as the second largest in extent with the second highest species richness and the lowest in density. Studies have stated in India, savannas are often misclassified as scrub or degraded forest (Ratnam et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gopalakrishna et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This nomenclature is an outcome of the colonial era\u0026rsquo;s timber-centric approach often leads to neglecting these crucial ecosystems in conservation planning (Ratnam et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Based on our field observations, we support the argument of categorization of scrub forest in Ancient and Derived savannas (Ratnam et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We observed in PTR, ancient savannas occur mainly on plateaus with continuous grass cover and scattered trees, while derived savannas are located primarily in areas of relocated villages and in buffer areas where anthropogenic pressures, along with edaphic factors, have formed this type. This can guide the conservation or restoration efforts for planning of these undervalued yet ecologically vital systems.\u003c/p\u003e\u003cp\u003eIn PTR, the proposed Ken\u0026ndash;Betwa Link Project (KBLP) is estimated to submerge an area of 90.00 sq. km. Of which 40.26. sq. km has forest cover, this consists of 64% representing NDDF, 15.50% is DDS forest, 8.99% is DTF, 5.18% is APF, and 5.56% is DBB. This scenario reflects a broader global trend where forests are increasingly at risk due to developmental projects. Therefore, it is crucial to account for every forest patch regardless of its size and type to better understand the unique ecological roles and mitigate the impacts of such interventions. This study aims to meet this requirement and potentially serve as a reference for forest type mapping across the Greater Panna Landscape and provides scientific support for restoration choices or guidance in the context of various development projects. Previously, conservation induced village relocation took place in PTR and these relocated village sites were later on developed as grasslands. While this is helpful, we also recommend that the restoration of to be relocated village sites (as compensation for submergence) should be considered for woodland-grassland mosaic keeping in view the ecological characteristics of the surrounding areas thereby enhancing overall landscape integrity and functional connectivity.\u003c/p\u003e\u003cp\u003eThe diversity and density of different forests vary significantly depending on the biotic and abiotic factors, along with anthropogenic pressures (Timilsina et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mohanta et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies by Ray et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Parveen et al. (2021) documented 45 trees and 46 tree species, respectively, in the core area of PTR, while our study, covering the entire PTR, recorded 64 tree species. In the central Indian region, with dry deciduous forest studies have recorded tree densities of 126\u0026ndash;490 ind. ha\u003csup\u003e\u003cem\u003e-\u003c/em\u003e1\u003c/sup\u003e (Chaturvedi and Raghubanshi 2013), 702\u0026ndash;1671 ind. ha\u003csup\u003e-1\u003c/sup\u003e (Joshi \u0026amp; Dhyani \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 225.28 ind. ha\u003csup\u003e-1\u003c/sup\u003e (Parveen et al. 2021), 391.9 ind. ha\u003csup\u003e-1\u003c/sup\u003e (Ray et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the present study, tree density ranged from 146\u0026ndash;625 ind. ha\u003csup\u003e-1\u003c/sup\u003e. Similarly, Parveen et al. (2021) recorded 70.289 seedlings ha\u003csup\u003e-1\u003c/sup\u003e and 89.04 saplings ha\u003csup\u003e-1\u003c/sup\u003e, whereas Ray et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) documented seedling densities of 3107.5 ind. ha\u003csup\u003e-1\u003c/sup\u003e and 6777.5 saplings ha\u003csup\u003e-1\u003c/sup\u003e in PTR. In our study highest sapling density was in the AP forest, 634 ind. ha\u003csup\u003e-1\u003c/sup\u003e, and the lowest was in the DBB, 127 ind. ha\u003csup\u003e-1\u003c/sup\u003e. Seedling density recorded the highest in NDDF 1621 ind. ha\u003csup\u003e-1\u003c/sup\u003e, followed by DTF 1428 ind. ha\u003csup\u003e-1\u003c/sup\u003e, and the lowest is \u003cem\u003eBoswellia\u003c/em\u003e forest 625 ind. ha\u003csup\u003e-1\u003c/sup\u003e. Such variations across studies are mainly due to sampling design and methodology, making direct comparisons misleading.\u003c/p\u003e\u003cp\u003eDry bamboo brake is one of the major forest types in PTR and is distributed across the reserve. \u003cem\u003eDendrocalamus srtictus\u003c/em\u003e forms clumps consisting of 12\u0026ndash;58 culms in both core and buffer areas of PTR. However, despite its wide distribution, previous studies have largely overlooked bamboo species in PTR. DBB provides suitable habitat to the wildlife and serves as a source of livelihood for the dependent communities. During our field visit, we observed sporadic flowering throughout, indicating the high pressure on this species. We recommend that future studies focus on the ecology, distribution, and management of bamboo in PTR.\u003c/p\u003e\u003cp\u003eRegeneration status results when compared to Parveen et al. (2025) show similar regeneration patterns across the species. In India, Rai and Saxena (1997) reported that nearly 72% of forests had already lost their regeneration potential, underscoring the importance of regular monitoring. Although regeneration in \u003cem\u003eAnogeissus pendula\u003c/em\u003e forest was categorized as poor, it does not fall under the conservation priority group; notably, in PTR, this forest type remains highly intact, forming pure stands that indicate a climax stage. In our study, several species, including \u003cem\u003eTerminalia bellerica\u003c/em\u003e, \u003cem\u003ePterocarpus marsupium\u003c/em\u003e, \u003cem\u003eTerminalia arjuna\u003c/em\u003e, \u003cem\u003eLimonia acidissima\u003c/em\u003e, \u003cem\u003eHaldina cordifolia, Lannea coromandelica, Bosewellia serrata\u003c/em\u003e, and \u003cem\u003eSchleichera oleosa\u003c/em\u003e, exhibited poor or no regeneration across all forest types and fell in high conservation priority classes. Hence, we suggest the highest priority should be given to these species along with other priority I-class species to ensure their long term survival.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAddressing the ongoing climatic and biodiversity crisis demands expanding our focus from protected areas to a landscape approach. This study supports this narrative by providing a spatially explicit understanding of forest type distribution and regeneration dynamics. Several key species including ecologically important species with high IVI values exhibited poor regeneration across all forest types, underscoring the need for better understanding of species-habitat relationship, population dynamics towards targeted restoration. The integrated assessment of forest type distribution and structural attributes offers a replicable framework that can guide management planning, enabling more informed decisions and effective conservation actions in PTR and other forested landscapes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Director, Dean, Registrar and Research Coordinator of the Wildlife Institute of India, Dehradun, for their support and encouragement. We also thank the officials of the Madhya Pradesh Forest Department for granting research permissions and providing valuable assistance. Our heartfelt thanks to the field staff of Panna Tiger Reserve, for their constant support and dedication made it possible to conduct this work under challenging field conditions. We are especially grateful to our field team members Mr. Bablu Gond, Mr. Ramjan Khan, Mr. Pappu Pal, Mr. Rajkumar, and Mr. Darshan Singh for their invaluable help in facilitating data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by\u0026nbsp;the National Water Development Agency, Government of India (No. WII/KR/PROJECT/PLMP/2017-18/F(1)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.P. conducted the data collection, performed the analysis, and wrote the main manuscript text. R.K. contributed to the overall supervision and reviewed and edited the manuscript. C.R. and A.K. participated in reviewing and editing the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors have read, understood, and complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel-Rahman, E. M., Mutanga, O., Adam, E., \u0026amp; Ismail, R. (2014). 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Vegetation structure and regeneration status of the moist, evergreen, afromontane Bore\u0026ndash;Anferara\u0026ndash;Wadera forest in southern Ethiopia. \u003cem\u003eJournal of Forestry Research, 31\u003c/em\u003e(3), 953\u0026ndash;967. https://doi.org/10.1007/s11676-018-00872-y \u003c/li\u003e\n\u003cli\u003eZhao, Q., Yu, S., Zhao, F., Tian, L., \u0026amp; Zhao, Z. (2019). Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. \u003cem\u003eForest Ecology and Management, 434\u003c/em\u003e, 224\u0026ndash;234. https://doi.org/10.1016/j.foreco.2018.12.019\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"basal area, conservation planning, dry deciduous forest, forest management, Google Earth Engine","lastPublishedDoi":"10.21203/rs.3.rs-7939381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7939381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eForest extent, assemblage, and regeneration pattern influence the biodiversity and ecosystem functions, which are often sensitive to climatic and anthropogenic correlates, especially in tropical forest systems. We quantified the diversity, regeneration potential and mapped the forest types in Panna Tiger Reserve, Central India, using Sentinel\u0026minus;2A multi-temporal data with a Random Forest classifier. A total of 153 stratified random plots were sampled with a focus on trees, saplings, and seedlings. Of the six forest types reported by Champion and Seth in the region, five forest types, except\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eBoswellia\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eforest, could be mapped with an overall accuracy of 88.9% and a Kappa coefficient of 0.81. Area-wise, Northern dry mixed deciduous forest (NDDF) was the most widespread forest type (36.88%), followed by Dry deciduous scrub (DDS) (16.7%), Dry teak forest (DTF) (7.64%), Dry bamboo brakes (DBB) (3.78%), and\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eAnogeissus pendula\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eforest (APF) (0.62%), while non-forest and water consist of 33.26% of the reserve. Overall, 64 species from 48 genera and 25 families were identified. Trees (35 species), saplings and seedlings (39 species each) had the highest species richness across all life stages in NDDF. APF had the highest tree density of 625 individuals ha\u003c/span\u003e\u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-1\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand sapling density (634 ind. ha\u003c/span\u003e\u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-1\u003c/span\u003e\u003c/sup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e), while NDDF had the highest seedling density (1621 ind. ha\u003c/span\u003e\u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e-1\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). DDS had the highest regeneration potential (75%), followed by NDDF (74.5%) and BF (46%). Our results highlight that mapping forest types, together with assessing structural attributes, diversity, and regeneration potential, can contribute to better conservation planning and management actions.\u003c/span\u003e\u003c/p\u003e","manuscriptTitle":"Mapping the Emerald Forest: Exploring Structural Diversity and Regeneration Patterns in Panna Tiger reserve, Central India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 09:05:38","doi":"10.21203/rs.3.rs-7939381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T00:47:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T17:27:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T13:29:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T02:53:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242908767405547074785614031310986398966","date":"2025-12-19T18:32:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129161485751144996117695712647825323673","date":"2025-12-08T12:27:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51402692367964386434686524293100097121","date":"2025-12-03T22:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98883684535214035595187077782924137215","date":"2025-11-13T10:27:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163853999670447053460587981152123311736","date":"2025-11-09T04:42:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T00:32:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-27T01:47:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T01:47:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-10-24T09:52:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"105882ff-aeda-4938-af0c-d860165f1d2c","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:08:26+00:00","versionOfRecord":{"articleIdentity":"rs-7939381","link":"https://doi.org/10.1007/s10661-026-15125-3","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2026-03-02 15:58:44","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-11-07 09:05:38","video":"","vorDoi":"10.1007/s10661-026-15125-3","vorDoiUrl":"https://doi.org/10.1007/s10661-026-15125-3","workflowStages":[]},"version":"v1","identity":"rs-7939381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7939381","identity":"rs-7939381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}