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Frontiers in Forests and Global Change ·

Volume 9 - 2026 | https://doi.org/10.3389/ffgc.2026.1815587

Large tree and forest structural attributes drive biomass and carbon distribution in tropical dry deciduous forests of Rajasthan, India

  • Naveen Kumar1
  • Archana Meena1*corresponding author
  1. Department of Botany, University of Rajasthan, Jaipur, India
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Abstract

Dry tropical forests store significant amounts of biomass carbon in vegetation and play a critical role in global carbon sequestration. The present study estimated the biomass and carbon storage in different vegetation components across five forest stands in two wildlife sanctuaries representing tropical dry deciduous forests. Tree aboveground and belowground biomass were quantified using allometric equations, and shrub biomass, herb biomass, and litter biomass were evaluated following protocols by the Forest Survey of India and converted to carbon stocks using standard conversion factors. The findings revealed significant variation in vegetation biomass among two wildlife sanctuaries and forest stands. Jamwa Ramgarh sanctuary exhibited significantly higher biomass and carbon than Nahargarh (p < 0.0001), with Digota forest stand (S5) recording the highest values (3115.22 Mg/ha; 1480.08 Mg C/ha). Significant variations were observed among tree biomass carbon (359–1,477 Mg C/ha), understory biomass carbon (1.68–2.5 Mg C/ha), and litter biomass carbon (0.25–0.5 Mg C/ha) (p < 0.0001). Aboveground biomass contributed the largest share (66–70%) of total vegetation carbon, followed by belowground biomass (23–33%), with shrubs, herbs, and litter accounting for minor proportions. Tree density and the basal area emerged as the most significant forest structural parameters determining carbon storage. Dominating tree species such as Anogeissus pendula and Boswellia serrata contributed substantially to tree carbon storage. Furthermore, a 40–80% proportion of carbon accumulation by large diameter trees (DBH > 70 cm) in these forests highlights the importance of mature trees in overall carbon sequestration. The study emphasizes that sustainable management and conservation of mature trees are required to increase the vegetation biomass and carbon storage in tropical dry deciduous forests of semi-arid ecosystems.

Keywords

1 Introduction

Global warming and climate change, primarily driven by anthropogenic greenhouse gas emissions—particularly carbon dioxide (CO2) pose a severe threat to ecosystems. The continuous rise in atmospheric CO2 concentration has been recognized as the one of most urgent environmental challenges of recent decades. According to the National Oceanic and Atmospheric Administration (National Oceanic and Atmospheric Administration (NOAA), 2024), the average global atmospheric CO2 concentration reached 422.7 ppm, with 2024 recorded as the warmest year during the 1850–2024 period. Terrestrial ecosystems sequester approximately 28% of CO2 emitted to the atmosphere due to anthropogenic activities and are notable for climate change mitigation. Over 40% of the world’s terrestrial carbon reserves are made up of live biomass from forests (Pan et al., 2011). Forests play an important role in carbon sequestration by acting as major sinks of carbon in vegetation and soil. Numerous studies have aimed to quantify the carbon stocks and identify major factors controlling the storage and emissions in different carbon pools across diverse forest ecosystems (Huang et al., 2020; Harris et al., 2021; Hauck et al., 2023; Joshi and Garkoti, 2025).

In terrestrial ecosystems, carbon is stored in five major pools: aboveground biomass, belowground biomass, soil organic carbon, wood debris, and litter biomass (FSI, 2023). Forest structural attributes, including species composition, stand growth, forest age, diversity, tree density, and basal area (Cushman et al., 2022; Zhao et al., 2023), climatic and topographic parameters (temperature, precipitation) (Cheng et al., 2023), land use changes, and anthropogenic disturbances (Ao et al., 2024) influence forest productivity (Rai et al., 2021) and, hence, biomass and carbon storage (Chaudhury et al., 2022; Ma et al., 2025). Tree size in the forest stand determines its carbon storage potential, as large old trees contribute significantly to total aboveground biomass, thus enhancing the overall biomass of the forest. Previous studies highlighted approximately a 40–50% contribution by large diameter trees to the total aboveground carbon stock, which represents 1–3% of the total density (Lutz et al., 2012; Slik et al., 2013; Mildrexler et al., 2020). In comparison to large trees, forests with a higher density of young individuals are considered to be more productive but store lower biomass and carbon. Therefore, management and conservation of mature trees have implications for carbon sequestration in vegetation and soil organic matter (Hauck et al., 2023).

Tropical forests cover only 7% of the Earth’s land surface but contribute approximately 40% of terrestrial carbon storage, with a substantial portion held in biomass. However, deforestation and commercial exploitation have significantly reduced forest cover, making these ecosystems highly fragile and endangered (Chaudhury et al., 2022). In India, tropical forests constitute 86% of the total forest surface and play a crucial role in the global carbon cycle, carbon sequestration, maintaining biodiversity, climate change, and mitigation (Mansingh et al., 2025). Research on the forest biomass is crucial for quantifying the carbon stored in different vegetation components and estimating the carbon cycle at the regional and global levels. Naveenkumar et al. (2017) reported substantial biomass and carbon storage in the tropical dry forests of Eastern Ghats, India, with adult trees (diameter > 30 cm) accounting for 94% of total woody carbon stock. Tropical dry deciduous forests in northwestern India are characterized by hardy, drought-tolerant species that contribute significantly to regional ecological stability. The biomass and carbon studies from these forests show a diverse distribution of biomass and carbon stock (Joshi and Dhyani, 2019). Numerous studies have quantified the biomass and carbon stocks among different forest types across India. However, most of these studies are reported from tropical and sub-tropical forests in Himalayan ecosystems (Kaushal and Baishya, 2021; Joshi et al., 2021; Kalita and Yumnam, 2024) and from tropical dry deciduous forests of central and southern India (Srinivas and Sundarapandian, 2019; Joshi and Dhyani, 2019; Raha et al., 2020; Swamy et al., 2023). Contrastingly, limited studies have focused on the tropical dry deciduous forests of the northwest semi-arid regions of India, mainly Rajasthan. Given their ecological significance and vulnerability, there is a critical need for updated, site-specific data on biomass, carbon pools, and vegetation structure in semi-arid regions. Rapid urbanization, forest fragmentation, and biological invasion further threaten these ecosystems, underscoring the importance of establishing baseline information for conservation and management. Therefore, the present study aims to (1) assess biomass and carbon storage in different vegetation components, i.e., aboveground biomass, belowground biomass, shrub biomass, herb biomass, and litter biomass across two wildlife sanctuaries of the semi-arid forests, (2) evaluate the distribution of carbon storage across different diameter classes, and (3) identify the factors influencing carbon storage in vegetation pools. The findings will contribute to improved carbon accounting and support the development of effective strategies for sustainable forest management and climate mitigation in India’s dryland landscapes.

2 Materials and methods

2.1 Study area

Rajasthan, the largest state in India, covers an area of 342,239 km2 (10.41% of the country’s total land area) and is situated between 23°3′ and 30°12′N latitude and between 69°30′ and 78°17′E longitude. The region has a diverse range of plant species due to varying climate, physiography, geography, and ecological conditions. The Aravalli range, being the world’s oldest mountain range, stretches approximately 692 km in a south-westerly and north-eastern direction from Gujarat to Delhi through Rajasthan to Haryana. The vegetation of the study area falls under the 5B category of northern tropical dry deciduous (subgroup 5B; 5/E1 and 5/E2) and northern tropical thorn forests (subgroup 6B) as per Champion and Seth (1968). Forest types in the state vary from broadleaved hills, Anogeissus pendula (dhauk) forests, mixed teak, dry deciduous, thorn, tree/shrub savannahs, riverine forest, and forest plantations. The study was conducted in the forests of Jaipur district, Rajasthan, which lies near the northern-eastern fringe of the Aravalli range, roughly equidistant between 26°25′ and 27°51’ N latitudes and 74°55′ to 76°15′E longitudes. The district covers a geographical area of 11,143 km2 and a population of 6,626,178 (Census, 2011). Jaipur has a total forest area of 564.58 km2, accounting for 5.07% of the district’s total area (FSI, 2023). The region experiences very hot summers and bitterly frigid winters under a semi-arid environment, and the temperature ranges from 2 °C to 48 °C. The average annual rainfall per year is 673.9 mm (IMD, 2020–2024).

The study was conducted between 2020 and 2024 in two wildlife sanctuaries, namely Nahargarh and Jamwa Ramgarh, located in Jaipur district of Rajasthan, India (Figure 1). The forest sites were selected on the basis of forest types, crown density, and the extent of anthropogenic activities. Nahargarh wildlife sanctuary is located on the north-western border, around 12 km from the city. The sanctuary has an escalation of 52.40 km2 in total area and lies between 26°56′15.08”N to 26°57′5.81”N and 75°48′55.70″E to 75°46′54.65″E longitude. Jamwa Ramgarh forms the southernmost extension of the Sariska Tiger Reserve and is located 40 km to the northeast of Jaipur. It lies between 27ᴼ 02’ N and 76ᴼ 03′ E and covers an area of 300 km2, comprising 233.71 km2 of recognized forest area and 66.29 km2 of adjoining revenue land (Gazette of India, 2018). The soil of the study area is predominantly black and yellowish-brown in texture. To conduct the study, five forest stands were selected with two in the Nahargarh wildlife sanctuary, i.e., Nahargarh biological park (S1) and Mayla Bagh (S2), and three in the Jamwa Ramgarh wildlife sanctuary, i.e., Ramgarh (S3), Raisar (S4), and Digota (S5) (Figure 1).

Location of forest stands in Nahargarh and Jamwa Ramgarh wildlife sanctuaries in Jaipur district, Rajasthan, India. Adapted from Kumar et al. (2024b), licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
Figure 1. Location of forest stands in Nahargarh and Jamwa Ramgarh wildlife sanctuaries in Jaipur district, Rajasthan, India. Adapted from Kumar et al. (2024b), licensed under CC BY 4.0.

2.2 Vegetation sampling

Forest stand structural and compositional attributes—including tree species composition, diversity, basal area, and spatial distribution—were assessed across five selected stands (S1–S5) in two wildlife sanctuaries. The sampling design for vegetation analysis was hierarchical, using stratified random sampling by the nested quadrat sampling method. A total of 164 plots were laid out randomly to cover the forest area, with 68 (S1 = 44; S2 = 24) at Nahargarh and 96 (S3 = 58; S4 = 20; S5 = 18) at Jamwa Ramgarh forests, respectively. Due to the substantial difference in area between the two wildlife sanctuaries, the number of sampling plots varied between the forest stands to ensure adequate representation of the variation in vegetation and forest stands. For trees, 20 m × 20 m (0.04 ha) square-sized quadrats were laid randomly at each forest stand. The shrub, herb, and litter biomass were estimated from 15 tree quadrats of 20 m × 20 m within each forest stand. For shrub biomass, the samples were collected from a 5 m × 5 m sub-quadrat, while for herb and litter biomass a 1 m × 1 m quadrat was nested within a 20 m × 20 m. A nested sampling design could be a limitation within the forest stands and may result in spatial dependence among the sampling quadrats. Vegetation parameters, including tree density, tree basal area, diversity, biomass, and carbon stock, were estimated within each quadrat.

Tree density was measured as the total number of individuals per unit area. Trees were defined as any individual with a circumference at breast height (CBH) > 30 cm, while saplings were between 10 and 30 cm. For the estimation of DBH, all the individuals with circumference at breast height (CBH > 30) were measured at 1.37 m above the ground surface within a 20 m × 20 m quadrat and converted to DBH. Tree basal area was calculated as per Mishra (1968):

Basal area ( m 2 / ha ) = CBH 2 / 4 π

where CBH, Circumference at breast height.

All the tree individuals were identified using local and regional recognized floras (Flora of Rajasthan, Shetty and Singh, 1987). Seedlings and shrubs (CBH < 10 cm) were measured at 10 cm collar height above the surface.

For biodiversity assessment, the Shannon-Weiner (H′) diversity index was calculated as (Shannon and Weaver, 1963):

H ′ = − ∑ ( pi ∗ ln pi )

where ln is the log base n and pi is the species proportion.

To evaluate their structural distributions, tree biomass, density, and basal area were plotted across diameter classes for comparative visualization. Previous studies have suggested that the selection of the diameter threshold for defining large trees varies for different forest types and should consider the objectives under study. Widely used thresholds ranging from 60 cm to 100 cm diameter at breast (DBH) height have been suggested for large trees (Slik et al., 2013; Lutz et al., 2018; Ali and Wang, 2021). In the present study, trees with a diameter > 70 cm were considered as large trees as per previous studies on tropical forests (Kaushal and Baishya, 2021) and tropical dry deciduous forests of India (Gandhi and Sundarapandian, 2017; Mansingh et al., 2025). Trees with diameter > 70 cm contributed substantially to biomass and carbon storage; therefore, this threshold was appropriate for large trees in our study area.

2.3 Estimation of biomass

To evaluate the aboveground biomass of trees using a non-destructive approach, the volume tables and species-specific allometric equations developed by the Forest Survey of India (FSI, 1996) were followed. Multiple regression techniques were used to generate these equations, taking into account the diameter at breast height (DBH) in addition to the tree height or form factor. The growing stock volume (m3 ha−1) was first estimated and then multiplied by specific wood density (g cm−3) to determine the aboveground biomass of the tree (Mg/ha) (Rajput et al., 1996) (Supplementary Table 1). The general or local area-based volume equation, V = 0.00471 + 1.79326D2, was applied to a total of 33 species that lack volume equations. For the remaining species, the species or genus-specific volume equations were used. Species-specific wood density values were obtained from the global wood density database (Chave et al., 2009; Zanne et al., 2009). The following formula was used to determine biomass:

Aboveground biomass ( Mg / ha ) = volume of tree ( m 3 ) × species − specific gravity ( g cm − 3 )

Belowground biomass or root biomass was estimated using the generalized regression equations proposed by Cairns et al. (1997) as:

Belowground biomass ( Mg / ha ) = exp . [ − 1.059 + 0.884 × ln ( Aboveground biomass ) + 0.284 ]

This equation has been used widely for the estimation of belowground biomass across tropical forest ecosystems. However, considerable variation has been observed in root biomass predications worldwide, suggesting the uncertainty of using a generalized allometric equation for belowground biomass estimation across different forest ecosystems. In semi-arid ecosystems, factors including forest stand structure, soil parameters, and water availability potentially influence root-to-shoot allocation, highlighting the need for a region-specific allometric equation for accurate estimation.

Total tree biomass was computed as the sum of aboveground biomass and belowground biomass.

Shrubs were classified as woody species other than trees that had a stem diameter less than 5 cm and a height under 1 m. Within a 20 m × 20 m quadrat, a 25 m2 (5m × 5 m) subplot was created, and the basal diameter was estimated using a vernier caliper to measure the girth of the shrubs inside the plots at a height of 10 cm from the surface. Shrub biomass was estimated using the regression equation as:

Shrub biomass = exp [ − 3.42620 + 2.5031 ln ( drc ) ]

where, ln (drc), the basal diameter of the individual stem near the root collar.

Belowground biomass in shrubs was approximately 20% of the aboveground biomass (MacDicken, 1997).

For herb and litter biomass, the samples were taken from a 1 m × 1 m quadrat. All the herbaceous plants and dried leaves, bark, branch wood, etc. from the ground were taken, and their fresh weight was recorded immediately in the field before being packaged in a paper envelope. The representative herb and litter samples were brought to the lab and oven-dried for 48 h at 65 °C. Then an estimate of the sample’s dry weight was calculated as herb and litter biomass.

2.4 Estimation of carbon stock

Tree carbon stock was estimated as:

Carbon ( Mg C / ha ) = Biomass ( Mg / ha ) × C %

where C represents the carbon fraction of vegetation biomass. Biomass was converted to carbon stock using a standard conversion factor of 0.475, assuming that carbon constitutes approximately 47.5% of dry biomass (Intergovernmental Panel on Climate Change, 2006; Salunkhe et al., 2018). The total carbon content in each vegetation component was used to calculate the total ecosystem’s carbon stock.

As per Intergovernmental Panel on Climate Change (2006), soil organic carbon is an essential component of terrestrial carbon storage and should be included in forest carbon storage studies. In the present study, soil organic carbon was not assessed, as the primary objective was to estimate the vegetation biomass and carbon across the forest stands.

2.5 Statistical analysis

The mean values of forest stands were used to summarize the data and provide a graphical representation.

To assess variation in biomass across vegetation components, i.e., aboveground biomass, belowground biomass, shrub biomass, herb biomass, and litter biomass, among the selected forest sites and stands, one-way analysis of variance (ANOVA) was performed at the quadrat-level as replicates within the forest stands. Student’s t-test was used for comparison between Nahargarh and Jamwa Ramgarh wildlife sanctuaries. Two-tailed Pearson’s correlation was conducted to examine the association among variables related to stand structure and biomass, which are total tree density, shrub density, tree basal area, large tree density, large tree basal area, large tree biomass, aboveground biomass, belowground biomass, shrub biomass, herb biomass, Shannon-diversity index, tree species richness, and shrub richness. Principal component analysis (PCA) was conducted using packages “factoextra” and “factoextramine R,” and a biplot was presented by “ggplot2” and “ggbiplot” (R Core Team, 2025). PCA was applied for dimensionality reduction, and a correlation matrix was constructed for the selected variables. Relationships between variables and principal components were interpreted using the biplot and coefficient loadings. PCA analysis included only five forest stands; therefore, in this methodology, it was used as an exploratory tool to understand the relationship between the forest structural, diversity, biomass, and carbon storage. All the statistical analyses were performed using R Studio (Ver 4.5.0).

3 Results

3.1 Biomass and carbon stock

The distribution of biomass and carbon among vegetation components across five forest stands is given in Table 1. Significant variations (p < 0.0001) were observed in biomass and carbon in aboveground (F = 11.21, η2 = 0.217, p < 0.0001), belowground (F = 13.34, η2 = 0.248, p < 0.0001), and tree biomass and carbon (F = 11.672, η2 = 0.224, p < 0.0001) among the forest stands. The mean aboveground biomass carbon, belowground biomass carbon, and tree biomass carbon were estimated as 260.21 Mg C/ha, 92.70 Mg C/ha, and 352.91 Mg C/ha for Nahargarh and 636.70 Mg C/ha, 206.19 Mg C/ha, and 842.89 Mg C/ha for Jamwa Ramgarh sanctuary, respectively. Within the forest stands, the aboveground biomass carbon and belowground biomass carbon was significantly higher (p < 0.0001) for Digota (S5) (1125.34 Mg C/ha; 351.68 Mg C/ha), followed by Ramgarh (S3) (570.19 Mg C/ha; 248.55 Mg C/ha), Raisar (S4) (403.29 Mg C/ha; 164.85 Mg C/ha), Nahargah Sanctuary (S1) (269.17 Mg C/ha; 133.95 Mg C/ha), and Nahargarh Biological Park (S2) (243.79 Mg C/ha; 115.50 Mg C/ha), with mean values of 522.36; 202.91 Mg C/ha, respectively. Among the forest stands, the highest mean tree biomass carbon (1477.01 Mg C/ha) was recorded for stand S5 followed by S3 (818.74 Mg C/ha), S4 (568.14 Mg C/ha), S1 (403.13 Mg C/ha) and S2 (359.29 Mg C/ha), with overall mean of 725.26 Mg C/ha, respectively, and varied significantly among forest stands and sanctuaries (p < 0.0001).

Table 1. Biomass and carbon stock of vegetation components in forest stands of Nahargarh and Jamwa Ramgarh Sanctuary forests.

Vegetation ComponentsNahargarh SanctuaryJamwa Ramgarh Sanctuary
S1S2S3S4S5
Aboveground biomass (Mg ha−1)566.68513.251200.39849.032369.13
Aboveground biomass carbon (Mg C ha−1)269.17243.79570.19403.291125.34
Belowground biomass (Mg ha−1)282.01243.15523.27347.05740.37
Belowground biomass carbon (Mg C ha−1)133.95115.50248.55164.85351.67
Shrub biomass (Mg ha−1)3.193.372.153.452.40
Shrub biomass carbon (Mg C ha−1)1.601.681.071.721.40
Herb biomass (Mg ha−1)2.422.891.882.362.52
Herb biomass carbon (Mg C ha−1)1.211.450.941.181.26
Litter biomass (Mg ha−1)1.050.570.500.410.80
Litter biomass carbon (Mg C ha−1)0.530.290.250.210.40

Understory biomass across all the forest stands ranged from 4.03 Mg/ha to 6.26 Mg/ha, with a mean of 5.33 Mg/ha. Among the forest stands, herb biomass showed a significant variation (F = 4.135, η2 = 0.093, p < 0.05), which was insignificant for shrub biomass (F = 4.93, η2 = 0.042, p > 0.05). The average values for shrub biomass was 3.28 Mg/ha and 2.58 Mg/ha in Nahargarh and Jamwa Ramgarh sanctuary, with the highest values recorded for forest stand S4 (3.45 Mg/ha), followed by S2 (3.37 Mg/ha), S1 (3.19 Mg/ha), S5 (2.40 Mg/ha) and S3 (2.15 Mg/ha), respectively. Shrub biomass carbon was also higher in Nahargarh (1.64 Mg C/ha) than in Jamwa Ramgarh (1.29 Mg C/ha), with a mean of 1.47 Mg C/ha. Herb biomass and carbon was highest (p < 0.05) for forest stand S2 (2.89 Mg/ha; 1.45 Mg C/ha), followed by S5 (2.52 Mg/ha; 1.26 Mg C/ha), S1 (2.42 Mg/ha; 1.23 Mg C/ha), S4 (2.36 Mg/ha; 1.18 Mg C/ha), and S3 (1.88 Mg/ha; 0.94 Mg C/ha). Herb biomass carbon was significantly higher for Nahargarh (1.29 Mg C/ha) than Jamwa Ramgarh sanctuary (1.05 Mg C/ha).

Mean litter biomass, i.e., 0.81 Mg/ha and 0.57 Mg/ha, and litter biomass carbon, i.e., 0.41 Mg C/ha and 0.29 Mg C/ha, were reported for Nahargarh and Jamwa Ramgarh sanctuary and were significantly different among the forest stands (F = 9.528, η2 = 0.464, p < 0.0001). Both litter biomass and litter biomass carbon differed significantly among the sanctuaries and were highest for stand S1 (1.053 Mg/ha; 0.52 Mg C/ha) (F = 4.135, η2 = 0.093, p < 0.0001).

Total vegetation biomass and vegetation carbon were recorded as 1264.70 Mg/ha and 600.89 Mg C/ha, respectively, with highest values for forest stand S5 (3115.22 Mg/ha; 1480.08 Mg C/ha), followed by S3 (1728.19 Mg/ha; 821 Mg C/ha), S4 (1202.30 Mg/ha; 571.25 Mg C/ha), S1 (855.35 Mg/ha; 406.46 Mg C/ha) and S2 (763.23 Mg/ha; 362.71 Mg C/ha) (Table 2).

Table 2. Comparison of vegetation biomass and carbon stock estimates reported from forests in India.

RegionForest TypeAboveground Biomass(Mg/ha)Belowground biomass(Mg/ha)Vegetation Biomass (Mg/ha)Vegetation Biomass Carbon (Mg C/ha)References
Northwestern HimalayaSubtropical forests50.17–301.7811.54–75.4561.71–377.23Banday et al. (2017)
Central HimalayanTropical forests457.06–1001.76109.11–279.03566.17–1280.79258.22–577.77Kaushal and Baishya (2021)
Central HimalayaSubtropical and Temperate forests227.23–577.16107.93–274.15Joshi et al. (2021)
Garhwal HimalayaSubtropical forests185.59–1572.182.64–700.06Tiwari et al. (2025)
Garhwal HimalayaSubtropical and Temperate forests271–703.2083.21–163.92354.18–729.78177.06–364.89Tiwari et al. (2023)
Western HimalayaSubtropical forests226.6–641.759.9–140.9286.6–782.6128.9–360Kumar et al. (2024a)
Northwest HimalayaMoist Temperate Forests209.7–564.252.35–151.15262.05–715.35131–357.7Kumar et al. (2025)
Western HimalayaGori Valley198.1–544.258.6–147.9256.6–692.1115.5–311.4Bisht et al. (2023)
Binsar Wildlife Sanctuary, Uttarakhand173.01–279.1240.85–65.58213.85–344.70101.58–163.73Joshi et al. (2024)
Western GhatsTropical forest160.8–868.225.7–121.6188.7–997.589.8–455.7Kothandaraman et al. (2020)
AssamTropical Moist Deciduous Forest782.19203.37985.56492.78Kalita and Yumnam (2024)
OdishaTropical Moist Deciduous Forest379.08–440.6255.68–64.17434.76–504.79206.51–239.78Mohanta et al. (2020)
Madhya PradeshTropical dry deciduous forest128.5–179.832.2–43.2160.7–223.375.3–104.7Raha et al. (2020)
Central IndiaTropical dry deciduous forest83.23–370.12103.32–453.5448.97–214.97Joshi and Dhyani (2019)
Central IndiaTropical forests58.65–334.0824.21–129.63Swamy et al. (2023)
Andhra PradeshTropical dry forest58.04–368.3915.09–95.7873.12–464.1744.51–218.84Srinivas and Sundarapandian (2019)
DelhiSemi-arid forest44.95–75.2424.25–32.1669.2–107.8533.53–52.05Meena et al. (2019)
HaryanaTropical dry deciduous forest33.1 to 75.89.0 to 18.524.3 to 53.9Yadav et al. (2022)
RajasthanTropical dry deciduous forest547.82–1340.43195.15–434.08742.97–1774.51352.91–842.89Present study

3.2 Species contribution in carbon storage

Across both sanctuaries, Anogeissus pendula was the dominant species, accounting for 47% of tree density and 66% of basal area in Nahargarh, compared with 58 and 33%, respectively, in Jamwa Ramgarh. The maximum tree biomass carbon was found for Anogeissus pendula (775.71 Mg C/ha) in Nahargarh, whereas in Jamwa Ramgarh it was for Acacia catechu (285.55 Mg C/ha), contributing 29 and 40%, respectively (Supplementary Table 2). Other tree species with substantial contributions included Boswellia serrata (21%) and Sterculia urens (6%) in Nahargarh and Acacia tortilis and Acacia senegal (8%) in Jamwa Ramgarh.

In forest stands S1 and S2 of Nahargarh sanctuary, the highest tree density (47, 58%), basal area (66, 33%), and tree biomass carbon (51, 26%) were contributed by Anogeissus pendula. Likewise, for the Jamwa Ramgarh sanctuary, the maximum contribution of tree density was reported for Anogeissus pendula at S3 (62%) and S5 (38%), whereas it was Prosopis julifora (18.53%) at S5. However, the contribution of tree basal area and tree biomass carbon was highest for Boswellia serrata at S3 (38, 40%) and S4 (30, 37%). At stand S5, Acacia catechu reported the highest tree biomass carbon (53%) despite lower tree density (2%) and basal area (0.5%).

3.3 Forest stand structure

The highest total tree density (trees/ha) was recorded in stand S2 (1720), followed by S3 (1197), S1 (1060), S5 (897), and S4 (875). Tree density, basal area, and biomass across different diameter classes are presented in Figures 2A–E. Across diameter classes, tree density peaked in the 11–20 cm class, accounting for 16.34, 42.58, 18.90, 24.28, and 18.80% of total density in stands S1 through S5, respectively. The proportion of large trees (diameter > 70 cm) was estimated at 14.73, 7.93, 21.39, 28.57 and 23.68% in S1, S2, S3, S4, and S5, respectively. Overall, the diameter class-based tree distribution in Jamwa Ramgarh Sanctuary was more widely distributed, with relatively higher density in diameter classes ≥ 300 cm.

**(A–E)** Distribution of tree density (TD), basal area (BA), and tree biomass (TB) in different diameter classes (DBH) at selected forest stands, i.e., S1, S2, S3, S4, and S5 across Nahargarh and Jamwa Ramgarh wildlife sanctuaries.
Figure 2. (A–E) Distribution of tree density (TD), basal area (BA), and tree biomass (TB) in different diameter classes (DBH) at selected forest stands, i.e., S1, S2, S3, S4, and S5 across Nahargarh and Jamwa Ramgarh wildlife sanctuaries.

Basal areas across forest stands ranged from 18.77 to 35.67 m2/ha, with mean values of 27.27 m2/ha. The highest basal area was recorded at stand S3 (35.67 m2/ha), followed by S5 (30.29 m2/ha), S4 (29.33 m2/ha), S1 (22.31 m2/ha), and S2 (18.77 m2/ha). Among diameter classes, basal area values peaked at 51–60 cm in S1 (3.56 m2/ha) and S2 (2.54 m2/ha), 101–110 cm in S3 (3.01 m2/ha), 121–130 cm in S4 (3.11 m2/ha), and 71–80 cm in S5 (2.93 m2/ha). The contribution of large trees (diameter > 70 cm) to the total basal area in S1 was 44.26, 48.05% in S2, 71.3% in S3, 79.19% in S4, and 71.03% in S5. Across the forest sanctuaries, the contribution was highest for the diameter class 51–60 cm at Nahargarh (6.10 m2/ha) and in the 101–110 cm class at Jamwa Ramgarh (7.97 m2/ha). Total basal area did not differ significantly among the sanctuaries (p > 0.05).

Maximum tree biomass was recorded for diameter class 51–60 cm in Nahargarh (241.62 Mg/ha) and 111–120 cm in Jamwa Ramgarh (918.41 Mg/ha). Overall, the contribution of large trees was between 40 and 80% among the forest stands, with a comparatively higher proportion at Jamwa Ramgarh than at Nahargarh wildlife sanctuary.

The Shannon-weiner index (Shannon and Weaver, 1963) for trees was highest for S5 forest stand (2.57) than S4 (2.41), S1 (2.01), S2 (1.89), and S3 (1.64), while for shrubs the diversity was highest in S3 (2.12) followed by S4 (2.09), S1 (2.07), S5 (1.92), and S2 (1.81).

3.4 Relationship between forest structural attributes with biomass and carbon storage

The Pearson correlation matrix revealed strong relationships between biomass, carbon pools, and stand structural variables. Aboveground biomass, belowground biomass, tree biomass, and tree carbon were correlated strongly and positively (r > 0.90). Large tree structural attributes, including large tree density, large tree basal area, and large tree biomass, also showed strong positive correlations with biomass and carbon variables (r > 0.75), suggesting their substantial contribution to total carbon stock (Figure 3).

Relationship between forest structural and diversity parameters with biomass and carbon components in Nahargarh and Jamwa Ramgarh wildlife sanctuaries, Rajasthan. TD, Tree density; SRS, Species richness shrubs; SB, Shrub biomass; H, Shannon diversity index; HB, Herb biomass; SD, Shrub density; LTD, Larger tree density; TBA, Tree basal area; LTBA, Large tree basal area; LB, Litter biomass; SRT, Species richness of trees; LTB, Large tree biomass; BGB, Belowground biomass; AGB, Aboveground biomass; TB, Tree biomass; TC, Tree carbon.
Figure 3. Relationship between forest structural and diversity parameters with biomass and carbon components in Nahargarh and Jamwa Ramgarh wildlife sanctuaries, Rajasthan. TD, Tree density; SRS, Species richness shrubs; SB, Shrub biomass; H, Shannon diversity index; HB, Herb biomass; SD, Shrub density; LTD, Larger tree density; TBA, Tree basal area; LTBA, Large tree basal area; LB, Litter biomass; SRT, Species richness of trees; LTB, Large tree biomass; BGB, Belowground biomass; AGB, Aboveground biomass; TB, Tree biomass; TC, Tree carbon.

Principal component analysis further indicated the associations among structural parameters and biomass components across forest stands (S1 to S5). The first two principal components explained 76% of the total variance, with PC1 and PC2 accounting for 52.1 and 23.9%, respectively. Positive and strong loadings of aboveground biomass, belowground biomass, tree biomass, tree biomass carbon, large tree density, large tree basal area, and large tree biomass on PC1 indicate that this axis represents a gradient of forest stand maturity and carbon-storage potential. In contrast to PC1, significant loadings of shrub biomass, herb biomass, litter biomass, and shrub species richness on PC2 suggested an association of this axis with understory characteristics. Stands S1 and S2 grouped closely together and were strongly associated with high tree density, whereas S3 and S4 occupied intermediate positions in the ordination space. S5 was positioned toward biomass and carbon variables, indicating the highest carbon storage and dominance of large trees (Figure 4).

Principal Component Analysis with biplot indicating the ordination of forest sites and forest structural, diversity, and biomass parameters. TD, Tree density; SRS, Species richness shrubs; SB, Shrub biomass; H, Shannon diversity index; HB, Herb biomass; SD, Shrub density; LTD, Larger tree density; TBA, Tree basal area; LTBA, Large tree basal area; LB, Litter biomass; SRT, Species richness of trees; LTB, Large tree biomass; BGB, Belowground biomass; AGB, Aboveground biomass; TB, Tree biomass; TC, Tree carbon.
Figure 4. Principal Component Analysis with biplot indicating the ordination of forest sites and forest structural, diversity, and biomass parameters. TD, Tree density; SRS, Species richness shrubs; SB, Shrub biomass; H, Shannon diversity index; HB, Herb biomass; SD, Shrub density; LTD, Larger tree density; TBA, Tree basal area; LTBA, Large tree basal area; LB, Litter biomass; SRT, Species richness of trees; LTB, Large tree biomass; BGB, Belowground biomass; AGB, Aboveground biomass; TB, Tree biomass; TC, Tree carbon.

4 Discussion

Tropical dry deciduous forests contribute substantially to the global carbon stock; however, variation in species composition and site characteristics influence their sequestration potential.

4.1 Vegetation biomass and carbon

The average total vegetation biomass (1258.74 Mg/ha) and carbon (597.90 Mg C/ha) estimated for the studied tropical dry deciduous forests fall well within the range reported for other forests in previous studies on the Himalayas (566.2–1280.8 Mg/ha and 358.2–577.8 Mg C/ha, Kaushal and Baishya, 2021; 185.59–1572.1 Mg/ha and 82.64–700.06 Mg C/ha, Tiwari et al., 2025), Western Ghats (Kothandaraman et al., 2020), and moist deciduous forest of Assam (Kalita and Yumnam, 2024). The values are higher than values reported for the Garhwal Himalaya (354.18–729.78 Mg/ha and 177.06–364.89 Mg C/ha; Tiwari et al., 2023; 262.05–715.35 Mg/ha and 131–357.7 Mg C/ha; Kumar et al., 2025; 256.6–692.1 Mg/ha and 115.5–311.4 Mg C/ha; Bisht et al., 2023; 107.9–274.2 Mg C/ha; 227.23–577.16 Mg/ha and 107.93–274.15 Mg C/ha; Joshi et al., 2021) and tropical moist deciduous forests in Bangladesh (204.29–452.09 Mg/ha and 102.15–226.05 Mg C/ha; Akter et al., 2025). Variations in factors including climatic (precipitation and temperature), stand structure (girth class), topographic (aspect, elevation, and slope), soil characteristics (nutrient availability and soil moisture), and forest type influence carbon stock in tropical forests (Joshi and Garkoti, 2025). The present study clearly reported substantial carbon storage in the tropical dry forests of India in vegetation biomass.

4.1.1 Understory biomass and carbon

Carbon storage in understory biomass is influenced by the forest’s type and species composition (Haq et al., 2024). In the present study, average understory biomass and carbon stock were estimated as 1.35 Mg C/ha and 5.33 Mg/ha, respectively. These values were higher than those in Northwest Himalaya (2.10–4.4 Mg/ha; Kumar et al., 2025) but were lower than the tropical dry deciduous forests of Haryana (8.4–16.2 Mg/ha, Yadav et al., 2022). The study reported a relatively small contribution of understory biomass to total biomass in Nahargarh (0.78%) and Jamwa Ramgarh (0.26%) sanctuaries, consistent with previous studies (Baishya and Barik, 2011; Dar and Sundarapandian, 2015). This may be attributed to anthropogenic disturbances and grazing, limiting shrub growth, especially in higher tree density forests (Dar and Sundarapandian, 2015).

Forest floor litter biomass is a crucial functional reservoir in the forest carbon cycle (Meena et al., 2019). Litter quantity and accumulation are governed primarily by climatic factors, management strategies (Tang et al., 2018), and vegetation growth and influence the carbon stocks. In the present study, litter biomass and litter biomass carbon were estimated as 0.69 Mg/ha and 0.34 Mg C/ha, respectively. Multiple factors, including litter quantity and quality, tree age and density (Yue et al., 2018), species diversity (Zhang et al., 2020), soil nutrients (Zhang and Wang, 2010), decomposition rate of organic matter (Thakur et al., 2022), anthropogenic disturbances, and land-use history, govern litter carbon storage.

4.2 Stand characteristics and carbon storage

4.2.1 Tree density and basal area

Forest biomass and carbon stocks are influenced by tree density, basal area, and species composition (Das and Mondal, 2025). Tree density and basal area in forests with similar species composition can explain variations in biomass estimates, as they reflect competition for light, nutrients, and water (Zhang and Wang, 2010; Kaushal and Baishya, 2021; Salunkhe et al., 2023). Higher tree biomass carbon in Jamwa Ramgarh than in Nahargarh could be explained by increased total tree density and mean basal area. Also, higher values of tree carbon from forest stands in Ramgarh (S3) and Digota (S5) can be justified by the significantly higher mean basal area, indicating size of the tree as a critical factor for tree carbon storage.

The distribution of tree carbon among diameter classes is crucial for the estimation of forest carbon sequestration at the regional level. Several studies have highlighted the critical role of large trees (>70 cm dbh) in carbon sequestration within woody ecosystems (Gandhi and Sundarapandian, 2017; Mansingh et al., 2025), while medium and small trees (<70 cm dbh) play a major role in tropical dry forests (Borah et al., 2013). Trees with larger diameters are the primary drivers of aboveground biomass and aboveground biomass carbon storage in tropical forests (Slik et al., 2013), and basal areas influence the distribution of large trees spatially (Panzou et al., 2018). The present study revealed a substantially higher contribution of a few large trees to total vegetation carbon stock and their strong association with elevated basal area and aboveground biomass, suggesting their critical importance in maintaining forest carbon stock. Further, the increased tree density in lower diameter classes is significant in maintaining carbon storage, biomass accumulation, and ecosystem stability (Joshi et al., 2021). In both forest sanctuaries, there is a considerable contribution of forest carbon storage from trees belonging to lower or intermediate diameters due to their high density and basal area. However, the large-diameter trees stored substantially more carbon, individually accounting for a greater proportion of biomass accumulation and carbon storage relative to their abundance among forest stands. Heterogeneity in carbon stock among forest stands could be explained by variation in the abundance of trees and basal areas in distinct diameter classes. Tree biomass and tree biomass carbon correlated positively with the basal area (Borah et al., 2015).

Overall, higher biomass in Jamwa Ramgarh in comparison to Nahargarh sanctuary could also be due to a greater abundance of large trees, substantially enhancing the total aboveground biomass (Haile et al., 2024). Previous studies have reported that large trees (diameter > 70 cm) provide up to 50% of total aboveground biomass (Bradford and Murphy, 2019; Meena et al., 2019; Chaudhury et al., 2022; Ahmed et al., 2023; Nyamukuru et al., 2023).

4.2.2 Species composition

In the present study, we recorded 60 tree species (26 families), 19 shrub species (12 families), and 78 herb species (27 families). In Nahargarh, the Combretaceae family accounted for 40% of the total tree biomass and carbon stock; the remaining families were Fabaceae (32%), Malvaceae (6%), and Apocynaceae (4%). However, 41% of the contribution to Jamwa Ramgarh derived from the Fabaceae family, followed by the Burseraceae (21%), Moraceae (9%), and Combretaceae (7%). Among both the sanctuaries, Malvaceae was reported to be the dominant family contributing to shrub biomass and carbon stock in Nahargarh and Jamwa Ramgarh (54 and 52%), respectively. The highest tree biomass and carbon density was estimated for Anogeissus pendula, reported as the dominant species with highest density and basal area among tropical dry deciduous forests of Aravalli’s in Rajasthan (Kumar et al., 2024b). The high tree carbon density for Boswellia serrata at Ramgarh (S3) and Raisar (S4), and Acacia catechu at Digota (S5) could be due to their substantial contribution in basal area, suggesting that tree size and stand structural attributes are significant determinants of forest carbon storage. Similarly, the other tree species, such as Holoptelea integrifolia, Butea monosperma, Lannea coromandelica, Boswellia serrata, Acacia senegal, Acacia tortilis, Maytenus emarginata, Prosopis cineraria, Prosopis juliflora, and Sterculia urens, despite having low density, also contributed significantly to the total biomass among the two forest sanctuaries. The high Shannon diversity index in Jamwa Ramgarh compared to Nahargarh further indicates that species diversity affects the productivity of the forest (Kaushal and Baishya, 2021). Moreover, differences in carbon density among forest types may also result in variations in tree density (Meena et al., 2019).

4.3 Influence of forest structure on carbon storage

Carbon accumulation in forest ecosystems is strongly influenced by structural variables. The present study highlighted the role of large trees and vegetation components in carbon accumulation in tropical forests (Chaudhury et al., 2022). The weak correlation between tree density and biomass and carbon parameters suggests a structural pattern that supports the concept that forests may transition from high-density, low-carbon early successional phases to lower-density, high-carbon mature states as competition favors the selection and survival of larger individuals (Zhang and Wang, 2010; Pardos et al., 2025; Dou and Tang, 2025). Furthermore, insignificant correlation among understory parameters (shrubs, herbs, and litter) with carbon and biomass pools indicates minimal contribution to the overall ecosystem carbon stock. These findings are consistent with previous studies reporting that understory biomass accounts for less than 3% of total carbon stock in tropical and semi-arid ecosystems, mainly due to canopy shading and grazing pressures (Dar and Sundarapandian, 2015; Haq et al., 2024).

The PCA analysis highlighted large tree density and basal area as the critical factors influencing tree biomass and carbon storage among the studied forest stands. The principal component PC1 reflects the gradient for biomass and carbon storage and differentiates the forest stands on the basis of biomass and carbon accumulation. The strong association of stand S5 (Digota) in Jamwa Ramgarh Sanctuary with large tree attributes, aboveground biomass, belowground biomass, and Shannon diversity demonstrates that this stand represents a higher proportion of trees with large diameters, which consequently contributed disproportionately to total forest carbon stock. This pattern is consistent with earlier findings demonstrating that large-diameter trees function as major carbon reservoirs in tropical forests, even when present in relatively low densities (Bradford and Murphy, 2019). Therefore, management and conservation of large trees are essential for maintaining the future carbon sequestration potential of these forests.

In contrast, the principal component PC2 demonstrates variation in biomass allocation among the vegetation layer and forest structural attributes. Positive loadings of herb, shrub, and litter biomass signify a greater contribution of forest floor litter and understory biomass. The negative loadings of stand density, total basal area, large tree density, and large tree basal area suggested an association with mature stands represented by large trees. Forest stands S1 and S2 in Nahargarh sanctuary ordinated positively with PC2 suggests a relatively higher contribution from understory vegetation biomass as compared with stands dominated by large trees. These trends highlight that variation in biomass and carbon allocation is governed by forest structural attributes. Despite the significant role of understory vegetation in ecosystem functioning and biodiversity maintenance, its contribution to total carbon accumulation is much lower than that of mature trees (Thakrey et al., 2024). This is evident from the fact that the stands dominated by understory vegetation contribute substantially less carbon storage than mature stands characterized by large trees. The management of these stands should focus on the conservation of large trees and the maintenance of species diversity that improves the carbon storage potential. Chaudhury et al. (2022) studied early successional or disturbed forests and documented that high density does not translate into high carbon storage due to the absence of large-diameter trees. The carbon storage study among the tropical dry forests of Delhi by Meena et al. (2019) reported a higher density of trees in lower diameter classes reduces the carbon stock potential in these forests. These findings indicate that the conservation of large trees rather than tree density alone strongly influences the carbon storage. Therefore, protection of mature trees should be prioritized in management interventions along with strategies to improve species diversity to enhance long-term carbon sequestration in tropical dry forest ecosystems.

A limitation of this study is its exclusive focus on vegetation carbon pools (aboveground and belowground biomass), omitting soil organic carbon and its contribution to total ecosystem carbon storage. Since the soil carbon pool is recognized as an important pool as per the IPCC, future studies should incorporate this component to provide a more comprehensive assessment of forest carbon stock.

5 Conclusion

Tropical dry forests play a significant role in global carbon storage and in mitigating climate change by storing an enormous amount of carbon across various vegetation pools. Quantifying vegetation carbon stock from these ecosystems would help forest managers to produce effective management strategies for conservation. The following conclusions can be drawn from this study:

  • The dominance of young trees belonging to lower diameter classes have the highest tree density, basal area, and biomass and have a substantial role in carbon allocation. However, the presence of mature trees in higher diameter classes and their carbon storage potential, with as much as 23% allocation, is the strongest determinant of carbon storage capacity, outweighing the contribution from understory biomass or stem density.
  • Biomass and carbon distribution also varied with species composition, site characteristics, structural attributes (primarily tree density and basal area), and diversity components, indicating that these factors exert a synergistic influence on regional carbon storage.
  • Comparisons with previous studies on tropical forests of India indicate the carbon richness in tropical dry deciduous forests.
  • The findings emphasize the ecological significance of protecting mature forest stands and preventing their selective removal, given their significant contribution to long-term carbon sequestration. Identification and monitoring of large trees will ensure their conservation.

To enhance the productivity and carbon storage of forests, an integrated approach focusing on all vegetation compartments is required. Future research should also examine the effect of anthropogenic activities on biomass accumulation and carbon sequestration in these forests.

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Supplementary material

The Supplementary material for this article can be found online at https://www.frontiersin.org/articles/10.3389/ffgc.2026.1815587/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Indu K. Murthy, Center for Study of Science, Technology and Policy - CSTEP, India

Reviewed by: Priyanka Kalita, Cotton University, India

Divya Khatri, Indian Council of Forestry Research and Education (ICFRE), India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The authors are thankful to the Department of Botany, University of Rajasthan, and RUSA 2.0 Theme 3, for providing funds and laboratory facilities for the conduction of the research work. They also acknowledge the Council of Scientific and Industrial Research (CSIR) for providing a research fellowship to NK via letter no. 17/12/2017/(ii) EU-V.

Data availability

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

NK: Resources, Visualization, Data curation, Methodology, Formal analysis, Project administration, Software, Writing – review & editing, Investigation, Writing – original draft, Funding acquisition, Conceptualization. AM: Investigation, Project administration, Writing – review & editing, Supervision, Writing – original draft, Validation, Software, Conceptualization, Visualization, Methodology.

Acknowledgements

The authors are grateful to the forest officials of Jaipur division, Rajasthan, for allowing and providing facilities to pursue the study, and also to the forest staff of Nahargarh Wildlife Sanctuary, especially Gaurav Kumar, Range Forest Officer, and Rajaram Meena (Asst. Forester), for their support and cooperation. The authors are thankful to RUSA 2.0 for providing research facilities and support.

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Citation

Kumar, N. & Meena, A. (2026). Large tree and forest structural attributes drive biomass and carbon distribution in tropical dry deciduous forests of Rajasthan, India. Frontiers in Forests and Global Change, Volume 9. https://doi.org/
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9 - 2026

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Copyright © 2026 Kumar and Meena.

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*Correspondence: Archana Meena

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