Assessing the dynamics of soil microbial biomass carbon and labile carbon pools across different forest types of Mizoram, India

Assessing the dynamics of soil microbial biomass carbon and labile carbon pools across different forest types of Mizoram, 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 Assessing the dynamics of soil microbial biomass carbon and labile carbon pools across different forest types of Mizoram, India Debaaditya Mukhopadhyay, Gaurav Mishra, Rosa Francaviglia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7130549/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract This study investigated soil organic carbon (SOC) pools and soil microbial biomass carbon (SMBC) across three forest types in Mizoram, India: Secondary Moist Bamboo Brakes, East Himalayan Moist Mixed Deciduous Forest, and Cachar Tropical Semi-Evergreen Forest. SOC is crucial for global biogeochemical cycles, influencing nutrient availability and ecosystem resilience. The study highlights the impact of forest type on SOC dynamics and microbial activity. The Secondary Moist Bamboo Brakes exhibited an Active Pool (VLC + LC) of 1.21% and a Passive Pool (LLC + NLC) of 0.78%, with SOC of 1.58% and SMBC of 340.72 mg/kg, indicating a balanced microbial presence. The East Himalayan Moist Mixed Deciduous Forest had relatively higher SOC (2.49%) and SMBC (352.31 mg/kg), suggesting increased microbial activity and faster carbon turnover. The Cachar Tropical Semi-Evergreen Forest recorded the highest SOC (2.51%) and significant SMBC (348.73 mg/kg), with the highest Very Labile Carbon (VLC) at 1.06%. Dehydrogenase activity (DHA), a key indicator of microbial metabolic activity, was highest in the East Himalayan Moist Mixed Deciduous Forest (6.42 µg TPF/g/h) and lowest in the Secondary Moist Bamboo Brakes (5.62 µg TPF/g/h). Correlations between SMBC and SOC pools were weak, with VLC and LC showing significant positive relationships with TOC (r = 0.636 and 0.693). These findings underscore the importance of forest type in shaping SOC and microbial dynamics, with implications for sustainable land management and carbon sequestration strategies in tropical forests. Soil organic carbon soil microbial biomass carbon carbon pools forest types sustainable land management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION In the terrestrial biosphere, most of the organic carbon is sequestered within soil systems, as underlined by several researchers (Scharlemann et al. 2014 ; Trivedi et al. 2018 ; Bargali et al 2019 ; Manral et al 2020 ; Fartyal et al 2025 ). Soil organic carbon plays a pivotal role in global biogeochemical cycles, contributing significantly to the availability of various nutrients (Stevenson et al. 2016 ; Wang et al. 2020 ). Soil organic carbon (SOC) storage is a dynamic process influenced by a feedback loop involving carbon input and output through microbial activities such as of organic matter decomposition, mineralization, and greenhouse gases release (McClean et al. 2015 ; Beillouin et al. 2022 ; Pandey et al 2024 ). Land-use exert a substantial impact on SOC stocks and carbon cycling. Available research suggests that factors like the adoption of no-till practices, the maintenance of straw residues, and environmental conditions, including droughts and soil depth, are primary drivers influencing carbon dynamics in agricultural soils (Spohn et al. 2016 ; Xue et al. 2017 ). The microbial biomass is a vigorous component that plays a crucial role in the dynamics of nutrients in the soil (Jenkinson and Ladd 1981 , Manral et al 2020 ), however, the earnings of microbial biomass carbon is fundamentally due to the reduced lifespan of microorganisms (Costa et al. 2014 ; Padalia et al 2018 ). Soil microbial biomass carbon and microbial activity are key components of soil organic matter and serve as sensitive indicators of soil quality, offering valuable insights for assessment (Benintende et al., 2015 ; Sun et al., 2018 ). These biological indicators exhibit rapid responses to environmental shifts, especially following land-use changes. Reduced tillage practices have been shown to enhance microbial biomass and enzyme activity over time, consequently decreasing the rate of soil carbon and nitrogen mineralization. This effect is influenced by microbial turnover rates and the carbon-to-nitrogen (C:N) ratio (Frazão et al., 2010 ; Silva et al., 2014 ; Kabiri et al., 2016 ). Furthermore, these indicators are crucial for monitoring essential ecosystem services, including water and nutrient cycling, plant-pathogen suppression, and the breakdown of organic residues. Given the critical role of soil quality in sustainable land management for food, wood, and fiber production, evaluating soil biological quality presents a promising method for measuring functional resilience to disturbances. This approach can inform decisions regarding the suitability of various soils for agricultural or conservation purposes. Research on SMBC across different land use types in Mizoram has been documented. A study found that converting natural forests to agroforestry systems did not significantly alter soil organic carbon content within the first year. The importance of soil organic carbon and microbial biomass carbon in forest soils has been emphasized due to their contributions to the carbon cycle and their influence on overall soil quality and health. Land use and land cover alterations are significant factors that have a pronounced impact on SOC storage. This is primarily due to changes in the rate of organic matter input, such as plant litter, and the rate of output, such as SOC mineralization, as influenced by shifts in plant communities and land management practices (Dawson and Smith 2007 ; Poeplau and Don 2013 ). In tropical regions, the status of forests plays a crucial role in sequestering or emitting carbon, exerting a substantial influence on atmospheric carbon concentrations (Wei et al. 2013 ). The conversion of tropical forests into alternative land uses like plantations and croplands can contribute to carbon emissions (Fan et al. 2016 ), leading to alterations in soil characteristics and processes. The conversion of natural forests into croplands disrupts soil structure, thereby enhancing organic matter mineralization by microorganisms and subsequently resulting in SOC depletion (Golchin and Asgari 2008 ). In the Northeastern Himalayan Region (NEHR), SOC concentrations ranging from 0.85 to 3.56% have been documented, with SOC stocks reported at 20–40 Mg ha − 1 and the top 0–30 cm soil depth showing the highest values (Choudhury et al. 2013 ). To understand the dynamics of SOC storage and depletion in soils, SOC stocks can be categorized into distinct functional pools based on their varying residence times, namely labile and non-labile pools. The labile pool, or active pool, constitutes a highly responsive fraction present in relatively small proportions, susceptible to fluctuations in environmental conditions. It undergoes rapid decomposition and oxidation in response to alterations in land use practices (Haynes 2005 ). Conversely, the non-labile pool, known as the passive pool, represents a more stable and recalcitrant SOC fraction, forming organic-mineral complexes with soil minerals and undergoing a slower microbial decomposition (Weisenberg et al. 2010). Consequently, the labile SOC pool serves as a sensitive indicator of soil quality, reflecting variations induced by land use changes (Vieira et al. 2007 ), while the non-labile SOC pool contributes to the overall organic carbon stocks (Chan et al., 2001 ). Land-use changes significantly influence soil organic carbon (SOC) dynamics and microbial biomass carbon (SMBC) across different forest types in Mizoram, with variations in SOC pools (labile and non-labile) and microbial activity reflecting the impact of these changes on soil quality and carbon sequestration potential. The present study aimed to (i) investigate Soil Microbial Biomass Carbon and activity values across different forest types and (ii) quantify various SOC fractions (very labile, labile, less labile and non-labile) and their relative proportions. MATERIALS AND METHODS Study area The present study was conducted in Mizoram state, Northeast India (Fig. 1 ). Geographically, Mizoram is located between 21˚58’ N to 24˚35’ N, and 91˚15’ E to 93˚29’ E and its total area is 21081 km 2 . The state shares southern international borders with Myanmar and Bangladesh and northern domestic borders with Manipur, Assam and Tripura Indian states. In summer, the temperature varies between 20–29 ˚C and sometimes peaks to 30 ˚C; in winter, the temperature is in the range 7–22 ˚C. The state experiences short winters and long summers. The onset of the monsoon season is from May until September, with an average annual rainfall of 2450 mm (Government of India 2024). A total of 33 sites were selected from 3 different districts of Mizoram, namely – Kolasib, Aizawl and Mamit, covering 3 forest types, namely– Secondary Moist Bamboo Brakes, East Himalayan Moist Mixed Deciduous Forest and Cachar Tropical Semi-Evergreen Forest. Soil sampling and sample preparation A random sampling technique was adopted for soil sample collection, to avoid biasedness, in 2023 from the three major forest types dominant in Mizoram (Bahuguna et al. 2016 ). Five quadrats of 10 × 10 m were randomly laid down in each forest type and soil samples from 0–30 cm were collected, as this layer experiences maximum changes due to above ground natural and anthropogenic activities (Hauser and Pulleman, 2023), from the four corners and the center to obtain a composite sample. Moreover it was observed that in Mizoram majority of the land has accessible soil depths of 0-30cm if the entire state is considered in totality. A total of 165 soil samples were gathered, covering three forest types across 11 sites per forest type, with five replicates at each site. All samples were then transported to the laboratory for subsequent processing and analysis. At each sampling location, a steel core with a volume of 50 cm³ was driven vertically into the soil to obtain an undisturbed sample for determining bulk density (BD). Soil BD was calculated after oven drying of samples at 105°C for 72 h (Blake and Hartge 1986). The soil samples were air-dried, lightly ground, and passed through a 2 mm sieve. To assess soil microbial biomass carbon (SMBC) and dehydrogenase activity (DHA), separate portions were taken from the same composite samples collected from a depth of 0–30 cm across the various forest types at the selected sites. These samples were immediately transported to the laboratory in sealed polythene bags. Any stones, roots, and other debris were removed, and the samples were then stored at low temperatures. Soil pH was measured using a pH meter and soil textural class was identified following International Society of Soil Science classification system (ISSS 1927). The general soil properties are reported in Table 1 . Table 1 General information and soil parameters for the different forest types Forest Types BD (g/cm3) Moisture (%) pH Soil Textural Class Soil Organic Carbon (%) Secondary Moist Bamboo Brakes 1.09 ± 0.02 8.54 ± 0.87 4.75 ± 0.18 Sandy Loam 1.58 ± 0.11 East Himalayan Moist Mixed Deciduous Forest 1.11 ± 0.03 6.54 ± 1.19 4.67 ± 0.17 Sandy Loam 1.83 ± 0.17 CacharTropical Semi-Evergreen Forest 1.11 ± 0.02 8.30 ± 0.82 4.88 ± 0.13 Sandy Loam 2.09 ± 0.08 Analysis of carbon fractions and pools The modified Walkley and Black method, as reported by Chan et al. ( 2001 ), was used to determine the different SOC pools, whereas the Yeomans and Bremnermethod (Yeomans and Bremner 1988 ) was used to calculate Total Organic Carbon (TOC) (%). 0.5 g of soil samples were put into 250 mL Erlenmeyer flasks, and 10 mL of 1 N K 2 Cr 2 O 7 (i.e. 0.167 mol/l) was poured into each. Following that, 2, 5, 10, and 20 mL of concentrated H 2 SO 4 (98%, sp. gr. 1.84) were added to the corresponding flasks, yielding 6 N, 12 N, 18 N, and 24 N H 2 SO 4 , or 3, 6, 9, and 12 mol/L of H 2 SO 4 , respectively, in the final oxidizing solution. The flasks were stored and protected from any external heat source behind an insulating layer. Following 30 minutes of oxidation, 200 mL of distilled water were added to the flasks, and phenanthroline was used as an indicator to titrate the contents using freshly made 0.5 N Fe (NH 4 ) 2 (SO 4 ) 2 •6H 2 O. The same method was used to titrate the equivalent acidic dichromate solutions with ferrous ammonium sulphate in a blank (soil-free) control. The methodology for calculating the different labile SOC pools cited in Mishra and Sarkar ( 2020 ) was followed, using the following equations: VLC (Fraction 1) ∶ Organic C oxidizable under 12 N H 2 SO 4 Eq. 1 LC (Fraction 2) ∶ Organic C oxidizable under 18 N − 12 N H 2 SO 4 Eq. 2 LLC (Fraction 3) ∶ Organic C oxidizable under 24 N − 18 N H 2 SO 4 Eq. 3 NLC (Fraction 4) ∶ Organic C oxidizable under TOC − 24 N H 2 SO 4 Eq. 4 ACP ∶ VLC (Fraction 1) + LC (Fraction 2) Eq. 5 PCP ∶ LLC (Fraction 3) + NLC (Fraction 4) Eq. 6 Where VLC is Very Labile Carbon, LC is Labile Carbon, LLC is Less Labile Carbon, NLC is Non Labile Carbon, ACP is Active Carbon Pool and PCP is Passive Carbon Pool (Fig. 2 ). Analysis of soil biological properties Soil microbial biomass carbon (SMBC) was quantified using the chloroform fumigation-extraction method as outlined by Vance et al. ( 1987 ), expressed in µg per gram of dry soil. $$\:SMBC=\:\frac{TOC\left(F\right)-TOC\left(NF\right)}{Kc}$$ Where F is the fumigated soil, NF is the non fumigated soil, Kc = 0.45 (Jenkinson and Ladd, 1981 ). Dehydrogenase activity (DHA) was assessed following the reduction of 2, 3, 5-triphenyl tetrazolium chloride according to the method described by Casida et al. ( 1964 ). The activity of DHA was determined based on a standard curve of triphenylformazan (TPF) in methanol and expressed in µg of TPF per gram of soil per hour. Statistical Analysis General soil characteristics, labile SOC pools, and total SOC stocks were summarized and their mean and standard deviations determined. An ANOVA one-way test was used to compare soil properties, followed by Tukey's post-hoc test (p < 0.05). All statistical analyses were carried out using SPSS version 23.0. RESULTS Soil organic carbon concentrations and microbial biomass in different diverse forest types in Mizoram provided insightful findings. The analysis revealed distinct carbon pool variations among the forest types (Table 2 ). In the Secondary Moist Bamboo Brakes, the Very Labile Carbon (VLC) fraction was found to be 0.81%, Labile Carbon (LC) 0.40%, Less Labile Carbon (LLC) 0.37%, and Non Labile Carbon (NLC) 0.41% (Fig. 3 ). The Active Pool, which encompasses VLC and LC, amounted to 1.21%, while the Passive Pool, comprising LLC and NLC, was 0.78% (Fig. 4 ). These findings indicate relatively balanced carbon distribution in the different lability pools within this forest type. The Active Pool (1.38%) in the East Himalayan Moist Mixed Deciduous Forest is derived from the sum of Very Labile Carbon (VLC, 0.94%) and Labile Carbon (LC, 0.44%), representing the rapidly decomposable SOC fraction. Similarly, the Passive Pool (1.11%) consists of Less Labile Carbon (LLC, 0.45%) and Non-Labile Carbon (NLC, 0.66%), indicating the more stable and recalcitrant carbon fraction, which aligns with established SOC fractionation methods. Notably, this forest type exhibited a higher proportion of Labile Carbon compared to the Secondary Moist Bamboo Brakes, indicating a potentially faster carbon turnover. In the Cachar Tropical Semi-Evergreen Forest, VLC was 1.06%, LC 0.54%, LLC 0.49%, and NLC 0.43%. The Active Pool in this forest type was 1.60%, and the Passive Pool was 0.92%. Notably, this forest type displayed the highest values across all carbon pools, signifying a potentially greater carbon stock. Significant differences (p < 0.05) were observed in the carbon pool values within each forest type. These variations emphasize the importance of considering the specific forest type when assessing soil organic carbon lability and distribution. Table 2 Soil organic carbon concentration (%) of varying lability and pools in different forest types of Mizoram. Forest Types Very Labile Carbon (VLC) Labile Carbon (LC) Less Labile Carbon (LLC) Non Labile Carbon (NLC) Active Pool Passive Pool Secondary Moist Bamboo Brakes 0.81 ± 0.05 a 0.40 ± 0.04 a 0.37 ± 0.03 a 0.41 ± 0.04 a 1.21 ± 0.08 a 0.78 ± 0.04 a East Himalayan Moist Mixed Deciduous Forest 0.94 ± 0.09 ab 0.44 ± 0.07 a 0.45 ± 0.04 ab 0.66 ± 0.15 a 1.38 ± 0.15 ab 1.11 ± 0.13 ab Cachar Tropical Semi-Evergreen Forest 1.06 ± 0.04 b 0.54 ± 0.03 a 0.49 ± 0.03 b 0.43 ± 0.02 a 1.60 ± 0.06 b 0.92 ± 0.05 b ± indicates standard error of mean. Values in same column followed by different letters are significantly different (p < 0.05). The relations among TOC, SOC, SMBC and Dehydrogenase concentrations were also studied under the different forest types (Table 3 ). In the Secondary Moist Bamboo Brakes, the TOC concentration was 1.99%, with SOC accounting for a substantial portion (1.58%). The SMBC concentration in this forest type was 340.72 mg/kg, indicating an active microbial community. The Dehydrogenase (DHA) activity was 5.62 µg TPF/g/h soil (Fig. 6 ). These findings suggest a relatively stable carbon pool in this forest type, with a notable presence of soil organic carbon and an active microbial biomass. In the East Himalayan Moist Mixed Deciduous Forest, the TOC concentration was 2.49%, higher thanthe Secondary Moist Bamboo Brakes, and the SOC fraction accounted for 1.83%. The SMBC concentration in this forest type was 352.31 mg/Kg, indicating a slightly higher microbial biomass compared to the Secondary Moist Bamboo Brakes. The DHA activity was 6.42 µg TPF/g/h soil. These results suggest that this forest type exhibits a higher carbon content, particularly in the TOC pool, than the previous one. In the Cachar Tropical Semi-Evergreen Forest, the TOC concentration was also relatively high (2.51%), with a significant portion (2.09%) being SOC. The SMBC concentration was 348.73 mg/kg, indicating a robust microbial biomass (Fig. 5 ). However, the DHA activity in this forest type was slightly lower (5.94 µg TPF/g/h soil) than the previous forest type. These findings suggest that this forest type holds a substantial carbon storage, with a particularly pronounced presence of SOC, signifying its potential importance in carbon sequestration. Table 3 Total Organic Carbon (%), Soil Organic Carbon (%), Soil Microbial Carbon (mg/Kg) and Dehydrogenase (ug TPF/g/h soil) concentrations under different forest types. Forest Types Total Organic Carbon (TOC) Soil Organic Carbon (SOC) Soil Microbial Biomass Carbon (SMBC) Dehydrogenase (DHA) Secondary Moist Bamboo Brakes 1.99 ± 0.09 a 1.58 ± 0.11 a 340.72 ± 1.42 a 5.62 ± 0.09 a East Himalayan Moist Mixed Deciduous Forest 2.49 ± 0.08 b 1.83 ± 0.17 ab 352.31 ± 4.28 ab 6.42 ± 0.08 a Cachar Tropical Semi-Evergreen Forest 2.51 ± 0.10 b 2.09 ± 0.08 b 348.73 ± 2.60 b 5.94 ± 0.11 b ± indicates standard error of mean. Values in same column followed by different letters are significantly different (p < 0.05). Table 4 presents Pearson's correlation coefficients (r) elucidating the associations among the various organic carbon pools and soil microbial biomass carbon (SMBC) within soils at 0–30 cm depths across the different forest types. Important findings emerge from these correlations. Total Organic Carbon (TOC) shows a robust positive correlation with Very Labile Carbon (VLC), presenting a coefficient of 0.636. Similarly, TOC exhibits a significant positive relationship with Labile Carbon (LC) (r = 0.693), while LC and Very Labile Carbon (VLC) reveal a notably strong positive association (r = 0.773). The Less Labile Carbon (LLC) also correlates significantly positively with TOC (r = 0.745). In contrast, Non-Labile Carbon (NLC) shows no substantial correlation with TOC or LC, with respective coefficients of 0.121 and − 0.607. Furthermore, the correlation coefficients between SMBC and the carbon pools range from 0.054 to 0.233, indicating relatively weak associations. Table 4 Correlation coefficient (Pearson’s) between different organic carbon pools and soil microbial biomass carbon in soils (0–30 cm soil depth) under different forest types in Mizoram. Variables TOC VLC LC LLC NLC SMBC TOC 1 VLC .636 ** 1 LC .693 ** .773 ** 1 LLC .745 ** .643 ** .743 ** 1 NLC .121 − .607 ** − .527 ** − .318 1 SMBC .233 .123 .054 .180 .102 1 **. Correlation is significant at the 0.01 level (2-tailed). DISCUSSION The present study shed light on the complex relationships among different carbon fractions and SMBC within the soils of different forest ecosystems in Mizoram. While some carbon pools, such as VLC, LC, LLC, and TOC exhibit strong positive correlations, SMBC appears to have comparatively weaker connections with these carbon fractions (Table 4 ). It is imperative to delve deeper into the underlying ecological mechanisms governing these associations, which can be instrumental in understanding the complex dynamics of carbon cycling and microbial biomass in forest soils, ultimately contributing to our understanding of ecosystem functioning in this region. Variability in carbon pools across forest types Forest carbon stocks play a crucial role in regulating carbon cycles, with soil being the major carbon pool in tropical dry deciduous forests and plantations (Kothandaraman et al. 2023 ). All the sampling sites differ markedly in bio-chemical properties. In general, the physical, biological, and chemical characteristics of soil exhibit spatial and temporal variation due to differences in terrain, climatic conditions, weathering intensity, vegetation cover, and microbial processes (Paudel and Sah, 2003 ; Manral et al., 2023 ), along with a range of other biotic and abiotic influences (Bargali et al., 2019 ; Pandey et al., 2024 ). In landscapes with significant dissection, rapid shifts in bioclimatic factors occur over short distances, leading to marked heterogeneity in soil’s physical and chemical attributes (Bäumler, 2015 ; Fartyal et al., 2025 ). Conversion of natural forests to agroforestry systems in Mizoram can affect SOC content, with different tree-crop combinations leading to variations in carbon stocks (Kenye et al. 2019 ). The stock of forest soil carbon when converted to other land uses, like rubber plantations, is found to have the potential for long-term carbon storage (Mishra et al. 2020 ). Additionally, SOC content and stock among different forest categories are higher within subsoil layers, that are recognized as better carbon sinks (Sreekanth et al. 2013 ). The analysis of carbon pools within the present studied forest types revealed notable variations, providing a detailed understanding of the nuanced carbon dynamics within ecosystems. In relation to Very Labile Carbon (VLC), the Cachar Tropical Semi-Evergreen Forest showed a significantly higher value (1.06%) compared to the Secondary Moist Bamboo Brakes (0.81%) and the East Himalayan Moist Mixed Deciduous Forest (0.94%). The variability is further pronounced in Labile Carbon (LC), with the Cachar Tropical Semi-Evergreen Forest showing the maximum (0.54), compared with the Secondary Moist Bamboo Brakes (0.40%) and the East Himalayan Moist Mixed Deciduous Forest (0.44%). Similar trends of variability were observed in Less Labile Carbon (LLC) and Non-Labile Carbon (NLC), with distinct values for each forest type (Table 2 ). The Active Pool and Passive Pool components revealed notable distinctions (Fig. 4 ). The Cachar Tropical Semi-Evergreen Forest displayed a substantial Active Pool (1.60%), higher than the Secondary Moist Bamboo Brakes (1.21%) and the East Himalayan Moist Mixed Deciduous Forest (1.38%). Conversely, the East Himalayan Moist Mixed Deciduous Forest showed the highest Passive Pool (1.11%), followed by the Cachar Tropical Semi-Evergreen Forest (0.92%) and the Secondary Moist Bamboo Brakes (0.78%). The careful examination of carbon pools within different forest types elucidate complex variations, underscoring the importance of considering the ecosystem-specific dynamics in carbon sequestration research (Stringer et al., 2012 ; Anderegg et al., 2020 ; Zimmerman et al., 2024 ). Interplay between carbon pools and soil microbial biomass The interplay between carbon pools and soil microbial biomass is crucial in understanding soil carbon dynamics. Soil microbial biomass is strongly linked and positively correlated to soil carbon concentrations (Beugnon et al. 2023 ). Tree productivity and functional traits, such as root diameter and litterfall C:N content, influence soil carbon concentrations and microbial biomass (Hagerty et al. 2022 ). Additionally, the decomposition of plant and microbial dead biomass by soil microorganisms contributes to carbon flow and turnover, with different microbial communities and their corresponding carbohydrate-active enzymes playing a role in the degradation of different biomass components (Ren et al. 2021 ). The allocation of carbon by the microbial community, represented by carbon use efficiency (CUE), affects soil carbon pool sizes, and models that consider dynamic allocation schemes provide a more accurate representation of microbial carbon partitioning (Finstad et al. 2023 ). Understanding the relationship between carbon pools and soil microbial biomass is essential for understanding soil carbon dynamics and the effects of tree diversity, micro-environmental conditions, and afforestation on carbon sequestration (Wei et al. 2023 ). In the present study the correlations among various organic carbon pools and SMBC provided important insights into their interrelations. Total Organic Carbon (TOC) demonstrated a robust positive correlation with Very Labile Carbon (VLC), Labile Carbon (LC), and Less Labile Carbon (LLC) (Choudhury et al., 2021). These findings suggest that a higher total carbon content is associated with a greater proportion of labile and less labile carbon fractions (Benbi et al., 2015 ). It is important to note that labile carbon fractions are typically more responsive to environmental changes and microbial activity. Thus, a higher total carbon content may indicate a greater availability of substrates for microbial utilization. The strong positive association between LC and VLC underscores the significance of readily decomposable carbon fractions contributing to microbial biomass and activity (Cookson et al., 2005 ; Condron et al., 2010 ; Khatoon et al., 2017 ). On the other hand, non-Labile Carbon (NLC) exhibited no substantial correlation with TOC or LC, indicating its resistance to change with variations in total or labile carbon (Figuerêdo et al., 2020 ). This resistance is characteristic of recalcitrant carbon fractions, which take more time to decompose and are less susceptible to microbial action (Zhou et al., 2023 ). It is essential to recognize from this study the contribution of non-labile carbon pools to long-term carbon storage, even though they may not exhibit immediate responses to environmental changes. The correlation coefficients between SMBC and the various carbon pools were relatively weak. This implies that soil microbial biomass carbon did not vary strongly with different carbon fractions in the studied forest types (Bargali et al., 2018 ). The weak associations may be due to complex interactions between microbial communities, environmental conditions, and the availability of carbon substrates (Allison 2014 ). Additionally, microbial biomass carbon is influenced by many factors beyond carbon availability, such as soil temperature, moisture, and nutrient status. Implications for soil carbon dynamics and ecosystem function Soil organic carbon (SOC) is a crucial component of soil organic matter (SOM) and significantly influences soil productivity, ecosystem stability, and carbon sequestration potential (Jackson et al. 2017 ). Variations in SOC and its associated fractions across different forest types as observed demonstrates their role in carbon cycling and microbial activity. The Cachar Tropical Semi-Evergreen Forest exhibited the highest SOC (2.09 ± 0.08b) among the studied forests, reinforcing its potential for carbon sequestration and its role in maintaining ecosystem stability. This aligns with previous findings that suggest forests play a critical role in carbon storage and mitigating climate change through microbial-mediated SOC dynamics (Chen et al. 2021). The observed strong correlations between Total Organic Carbon (TOC) and various SOC fractions, particularly labile carbon (LC, r = 0.693**), less labile carbon (LLC, r = 0.745**), and very labile carbon (VLC, r = 0.636**), indicate that labile pools significantly contribute to overall SOC turnover. The presence of higher labile fractions, especially in the Cachar Tropical Semi-Evergreen Forest, suggests a potentially increased turnover rate, increasing the likelihood of SOC mineralization into CO₂ (Jilkova et al. 2019 ). Furthermore, soil microbial biomass carbon (SMBC) values across the forest types were also notable, with the East Himalayan Moist Mixed Deciduous Forest (352.31 ± 4.28ab) displaying the highest SMBC, suggesting greater microbial activity and carbon cycling potential (Lalnunmawia et al. 2020). Although TOC and SMBC exhibited a weak correlation (r = 0.233), SMBC remains an important biological indicator of soil health and quality (Manna et al. 2021). Dehydrogenase activity (DHA), which reflects microbial metabolic activity, also varied across forests, with the highest values observed in the East Himalayan Moist Mixed Deciduous Forest (6.42 ± 0.08a), followed by the Cachar Tropical Semi-Evergreen Forest (5.94 ± 0.11b), indicating that microbial-driven carbon transformation processes differ with forest type and soil conditions. Negative correlations between non-labile carbon (NLC) and labile fractions (VLC, r = -0.607**; LC, r = -0.527**) suggest a trade-off between stable and more decomposable carbon pools, emphasizing the complexity of soil carbon stabilization mechanisms (Vieira et al. 2007 ). Given these variations, adopting agroforestry practices and sustainable land management strategies tailored to specific forest types could help balance SOC dynamics, enhance sequestration, and improve soil fertility while mitigating climate change (Lenka et al. 2020 ). Conclusion The present study revealed significant carbon pool variations among forest types, with potential implications for carbon turnover and storage. The balance between labile and non-labile carbon fractions in the Secondary Moist Bamboo Brakes can suggest a stable carbon pool, while the East Himalayan Moist Mixed Deciduous Forest showed a higher proportion of labile carbon, indicating a faster turnover rate. The Cachar Tropical Semi-Evergreen Forest stands out with the highest carbon values, emphasizing its potential for carbon sequestration. The correlations between carbon pools and soil microbial biomass carbon highlighted the intricate interplay among these factors. Total carbon content was positively associated with labile carbon fractions, indicating the availability of substrates for microbial utilization. Non-labile carbon, on the other hand, showed the resistance to change, emphasizing its role in long-term carbon storage. While the relationships between microbial biomass and carbon pools were relatively weak, they underscore the complexity of microbial responses to environmental conditions and substrate availability. Sustainable forest management requires strategies that enhance soil organic carbon (SOC) storage and microbial biomass carbon (SMBC) to maintain ecosystem resilience and productivity. Land-use changes, such as deforestation and agricultural expansion, significantly impact SOC pools, influencing carbon sequestration potential and soil health. Practices like reduced tillage, agroforestry, and maintaining forest cover can mitigate SOC depletion by enhancing microbial activity and organic matter stability. Monitoring SOC fractions provides valuable insights into soil quality and functional resilience, informing land-use decisions that balance carbon conservation with economic and ecological sustainability. These findings have significant implications for soil carbon dynamics and ecosystem functioning. The presence of labile carbon fractions can suggest the potential for increased carbon turnover and greenhouse gas emissions, while the substantial soil organic carbon and microbial biomass in the Cachar Tropical Semi-Evergreen Forest shows its role in carbon sequestration and ecosystem stability. This study underscores the need to consider the various carbon pools and their interactions when assessing the impact of environmental factors on soil carbon storage and microbial activity, contributing to the understanding ecosystem functioning in this region. Declarations Statement of Competing Interests The authors state that none of their known financial or personal conflicts could have affected the research described in this paper. Consent to Participate declaration not applicable Consent to Publish declaration not applicable Ethics declaration not applicable Funding: Not applicable Author Contribution First Author: Conceptualization, Field and lab work, writing & original draft preparationSecond Author & Third Author: extensive editing of first original draft. All the authors have read the manuscript and approved it for submission and have no competing interests. Acknowledgement Authors are thankful to the editor and reviewers for the timely review and comment to improve the quality of the manuscript drastically. Data availability options : Data will be made available on reasonable request from the first author. References Allison, S. D. (2014). Modeling adaptation of carbon use efficiency in microbial communities. Frontiers in Microbiology, 5 , 571. Anderegg, W. R., Trugman, A. T., Badgley, G., Anderson, C. M., Bartuska, A., Ciais, P., & Randerson, J. T. (2020). Climate-driven risks to the climate mitigation potential of forests. Science, 368 (6497), eaaz7005. Bahuguna, V. K., Swaminath, M. H., Tripathi, S., Singh, T. P., Rawat, V. R. S., & Rawat, R. S. (2016). Revisiting forest types of India. International Forestry Review, 18 (2), 135–145. Bargali, K., Manral, V., Padalia, K., Bargali, S. S., & Upadhyay, V. P. (2018). Effect of vegetation type and season on microbial biomass carbon in Central Himalayan forest soils, India. Catena, 171 , 125-135. Bargali, S. S., Padalia, K., & Bargali, K. (2019). Effects of tree fostering on soil health and microbial biomass under different land use systems in Central Himalaya. Land Degradation & Development, 30 (16), 1984–1998. https://doi.org/10.1002/ldr.3394 Bäumler, R. (2015). Soils. In Miehe, G., & Pendry, C. A. (Eds.), Nepal: An Introduction to the Natural History, Ecology and Human Environment in the Himalayas - A Companion to the Flora of Nepal (1st ed., pp. 125–134). The Royal Botanical Garden Edinburgh. Beillouin, D., Cardinael, R., Berre, D., Boyer, A., Corbeels, M., et al. (2022). A global overview of studies about land management, land-use change, and climate change effects on soil organic carbon. Global Change Biology, 28 (4), 1690–702. doi:10.1111/gcb.15998 Benbi, D. K., Brar, K., Toor, A. S., & Singh, P. (2015). Total and labile pools of soil organic carbon in cultivated and undisturbed soils in northern India. Geoderma, 237 , 149-158. Benintende, S., Benintende, M., Sterren, M., Saluzzio, M., & Barbagelata, P. (2015). Biological variables as soil quality indicators: effect of sampling time and ability to classify soils by their suitability. Ecological Indicators, 52 , 147-152. Beugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., et al. (2023). Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration. Ecological Monographs, 93 (2). Blake, G. R., & Hartage, K. H. (1986). Soil bulk density and particle density determination. In A. Klute (Ed.), Methods of Soil Analysis (2nd ed.). Madison: American Society of Agronomy. Casida Jr., L. E., Klein, D. A., & Santoro, T. (1964). Soil dehydrogenase activity. Soil Science, 98 (6), 371-376. Chan, K. Y., Bowman, A., & Oates, A. (2001). Oxidizable organic carbon fractions and soil quality changes in an oxicpaleustalf under different pasture leys. Soil Science, 166 (1), 61-67. Chen, J., & Sinsabaugh, R. L. (2021). Linking microbial functional gene abundance and soil extracellular enzyme activity: implications for soil carbon dynamics. Global Change Biology, 27 (7), 1322-1325. Choudhary, R., Naresh, R. K., Chandra, M. S., Chandra, N., Giri, K. L., & Tiwari, H. (2021). Conservation tillage and integrated nutrient management impact on soil organic carbon fractions, labile soil organic matter pools and microbial community under rice-wheat cropping system: A review. The Pharma Innovation Journal, 10 , 739-756. Choudhury, B. U., Mohapatra, K. P., Das, A., Das, P. T., Nongkhlaw, L., Fiyaz, R. A., et al. (2013). Spatial variability in the distribution of organic carbon stocks in the soils of North East India. Current Science, 104 , 604-614. Condron, L., Stark, C., O’Callaghan, M., Clinton, P., & Huang, Z. (2010). The role of microbial communities in the formation and decomposition of soil organic matter. In Soil microbiology and sustainable crop production (pp. 81-118). Cookson, W. R., Abaye, D. A., Marschner, P., Murphy, D. V., Stockdale, E. A., & Goulding, K. W. (2005). The contribution of soil organic matter fractions to carbon and nitrogen mineralization and microbial community size and structure. Soil Biology and Biochemistry, 37 (9), 1726-1737. Costa, S. E. V. G. A., Souza, E. D., Anghinoni, I., & Carvalho, P. C. F., et al. (2014). Impact of an integrated no-till crop–livestock system on phosphorus distribution, availability, and stock. Agriculture, Ecosystems & Environment, 90 , 43–51. https://doi.org/10.1016/j.agee.2013.12.001 Dawson, J. J., & Smith, P. (2007). Carbon losses from soil and its consequences for land-use management. Science of the Total Environment, 382 (2-3), 165-190. Fan, Y., Wu, J., Xia, Y., & Liu, J. Y. (2016). How will a nationwide carbon market affect regional economies and efficiency of CO2 emission reduction in China? China Economic Review, 38 , 151-166. Fartyal, A., Bargali, S. S., Bargali, K., & Negi, B. (2025). Changes in soil properties, organic carbon, and nutrient stocks after land-use change from forests to grasslands in Kumaun Himalaya, India. Land Degradation & Development. https://doi.org/10.1007/s11258-024-01487-6 Figuerêdo, K. S., Pereira, M. T., Nick, C., Silva, I. R., & Oliveira, T. S. (2020). Long-term changes in organic matter stocks and quality in an Oxisol under intensive vegetable cultivation. Catena, 188 , 104442. Finstad, K. M., Nuccio, E. E., Grant, K. E., Broek, T. A., Pett-Ridge, J., & McFarlane, K. J. (2023). Radiocarbon analysis of soil microbial biomass via direct chloroform extraction. Radiocarbon , 1-9. Frazão, L. A., Piccolo, M. C., Feigl, B. J., Cerri, C. C., & Cerri, C. E. P. (2010). Inorganic nitrogen, microbial biomass and microbial activity of a sandy Brazilian Cerrado soil under different land uses. Agriculture, Ecosystems & Environment, 135 (3), 161-167. doi:10.1016/j.agee.2009.09.003 Golchin, A., & Asgari, H. (2008). Land use effects on soil quality indicators in north-eastern Iran. Soil Research, 46 (1), 27-36. Government of Mizoram (2024) Mizoram Forests. Environment, Forests & Climate Change Department. https://forest.mizoram.gov.in/page/mizoram-forest-at-a-glance. Hagerty, S. B., Allison, S. D., & Schimel, J. P. (2022). Testing microbial models with data from a 14C glucose tracer experiment. Soil Biology and Biochemistry, 172 , 108781. Hauser S, Saito K, Pulleman M (2023) Soil sampling (disturbed and undisturbed), handling and storage for soil chemical, biological and physical properties, v1. Standard Operating Procedure 003. In: Saito K, Johnson J-M, Hauser S, Corbeels M, Devkota M, Casimero M (eds) Guideline for measuring agronomic gain key performance indicators in on-farm trials, v. 1. Excellence in Agronomy for Sustainable Intensification and Climate Change Adaptation Initiative. Haynes, R. J. (2005). Labile organic matter fractions as central components of the quality of agricultural soils: an overview. Advances in Agronomy, 85 , 221-268. International Society of Soil Science (ISSS). (1929). Minutes of the first commission meetings. International Congress of Soil Science , Washington. Jackson, R. B., Lajtha, K., Crow, S. E., Hugelius, G., Kramer, M. G., & Piñeiro, G. (2017). The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annual Review of Ecology, Evolution, and Systematics, 48 , 419-445. Jenkinson, D. S., & Ladd, J. N. (1981). Microbial biomass in soil: Measurement and turnover. In E. A. Paul, & J. N. Ladd (Eds.), Soil Biochemistry (Vol. 5) (pp. 415–471). New York: Marcel Dekker. Jilkova, V., Jandová, K., Sim, A., Thornton, B., & Paterson, E. (2019). Soil organic matter decomposition and carbon sequestration in temperate coniferous forest soils affected by soluble and insoluble spruce needle fractions. Soil Biology and Biochemistry, 138 , 107595. Kabiri, V., Raiesi, F., & Ghazavi, M. A. (2016). Tillage effects on soil microbial biomass, SOM mineralization and enzyme activity in a semi-arid Calcixerepts. Agriculture, Ecosystems & Environment, 232 , 73-84. Kaschuk, G., Alberton, O., & Hungria, M. (2010). Three decades of soil microbial biomass studies in Brazilian ecosystems: Lessons learned about soil quality and indications for improving sustainability. Soil Biology and Biochemistry, 42 (1), 1-13. doi:10.1016/j.soilbio.2009.08.020 Kenye, A., Sahoo, U., Singh, S., & Gogoi, A. (2019). Soil organic carbon stock of different land uses of Mizoram, Northeast India. AIMS Journal, 5 , 25-40. doi:10.3934/geosci.2019.1.25 Khatoon, H., Solanki, P., Narayan, M., Tewari, L., Rai, J., & Hina Khatoon, C. (2017). Role of microbes in organic carbon decomposition and maintenance of soil ecosystem. International Journal of Chemical Studies, 5 (6), 1648-1656. Kothandaraman, S., Dar, J., Sundarapandian, S., & Khan, M. (2023). Variation in ecosystem carbon allocation patterns among different vegetation types in Western Ghats, India. EGU23-3944. Copernicus Meetings . Lalnunmawia, Vanlalhluna, P., & Tawnenga. (2020). Assessment of soil organic carbon (SOC) under different agroforestry systems in Mizoram, Northeast India. Journal of Emerging Technologies and Innovative Research, 7 (8), 1878-1882. Lenka, S., Lenka, N. K., Mohanty, M., Saha, J. K., & Patra, A. K. (2020). Potentials and limitations of soil carbon modelling: implications in Indian conditions. In Carbon Management in Tropical and Sub-Tropical Terrestrial Systems (pp. 417-438). Manna, M. C., & Ghosh, A. (2021). Soil organic carbon dynamics, stabilization, and environmental implication. In Soil Science: Fundamentals to Recent Advances (pp. 13-33). Manral, V., Bargali, K., Bargali, S. S., & Shahi, C. (2020). Changes in soil biochemical properties following replacement of Banj oak forest with Chir pine in Central Himalaya, India. Ecological Processes, 9 , 30. https://doi.org/10.1186/s13717-020-00235-8 Manral, V., Bargali, K., Bargali, S. S., Karki, H., & Chaturvedi, R. K. (2023). Seasonal dynamics of soil microbial biomass C, N, and P along an altitudinal gradient in Central Himalaya, India. Sustainability, 15 , 1651. https://doi.org/10.3390/su15021651 McClean, G. J., Rowe, R. L., Heal, K. V., Cross, A., Bending, G. D., et al. (2015). An empirical model approach for assessing soil organic carbon stock changes following biomass crop establishment in Britain. Biomass and Bioenergy, 83 , 141-151. doi:10.1016/j.biombioe.2015.09.005 Mishra, G., & Sarkar, A. (2020). Studying the relationship between total organic carbon and soil carbon pools under different land management systems of Garo hills, Meghalaya. Journal of Environmental Management, 257 , 110002. Mishra, G., Sarkar, A., & Das, J. (2020). Changes in soil organic carbon lability along the altitudinal variations in three land use types of Meghalaya, India. Environmental Sustainability, 3 , 325-331. Padalia, K., Bargali, S. S., Bargali, K., & Khulbe, K. (2018). Microbial biomass carbon and nitrogen in relation to cropping systems in Central Himalaya, India. Current Science, 115 (9), 1741–1750. Pandey, R., Bargali, S. S., Bargali, K., Karki, H., & Chaturvedi, R. K. (2024). Dynamics of nitrogen mineralization and fine root decomposition in sub-tropical Shorea robusta Gaertner f. forests of Central Himalaya, India. Science of The Total Environment, 921 , 170896. https://doi.org/10.1016/j.scitotenv.2024.170896 Paudel, S., & Sah, J. P. (2003). Physico-chemical characters of soil in tropical Shorea robusta Gaertn. forests in eastern Nepal. Himalayan Journal of Science, 1 (2), 107–110. Poeplau, C., & Don, A. (2013). Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. Geoderma, 192 , 189-201. Prout, J. M., Shepherd, K. D., McGrath, S. P., Kirk, G. J. D., Hassall, K. L., et al. (2022). Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time. Scientific Reports, 12 (1), 1-14. doi:10.1038/s41598-022-09101-3 Ren, C., Zhang, X., Zhang, S., Wang, J., Xu, M., Guo, Y., et al. (2021). Altered microbial CAZyme families indicated dead biomass decomposition following afforestation. Soil Biology and Biochemistry, 160 , 108362. Sahoo, U. K., Ahirwal, J., Giri, K., Mishra, G., & Francaviglia, R. (2023). Modeling land use and climate change effects on soil organic carbon storage under different plantation systems in Mizoram, Northeast India. Agriculture , 13(7), 1332. doi:10.3390/agriculture13071332 Scharlemann, J. P., Tanner, E. V., Hiederer, R., & Kapos, V. (2014). Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Management, 5 (1), 81-91. Silva, A. P. da, Babujia, L. C., Franchini, J. C., Ralisch, R., Hungria, M., et al. (2014). Soil structure and its influence on microbial biomass in different soil and crop management systems. Soil & Tillage Research, 142 , 42-53. doi:10.1016/j.still.2014.04.006 Silva, R. C. S., Almeida, J. C. R., Batista, G. T., & Fortes Neto, P. (2011). Os indicadores físicos, químicos e biológicos da qualidade do solo e da sustentabilidade dos ambientes naturais [Physical, chemical and biological indicators of soil quality and sustainability of natural environments]. Repositório Eletrônico Ciências Agrárias, Coleção Ciências Ambientais. 1-11. Retrieved from http://www.agro.unitau.br/dspace Spohn, M., Klaus, K., Wanek, W., & Richter, A. (2016). Microbial carbon use efficiency and biomass turnover times depending on soil depth – Implications for carbon cycling. Soil Biology and Biochemistry, 96 , 74-81. doi:10.1016/j.soilbio.2016.01.016 Sreekanth, N. P., Prabha, V. S., Babu, P., & Thomas, A. P. (2013). Soil carbon alterations of selected forest types as an environmental feedback to climate change. International Journal of Environmental Science, 3 (5), 1516-1530. Stevenson, B. A., Sarmah, A. K., Smernik, R., Hunter, D. W. F., & Fraser, S. (2016). Soil carbon suitability. Ecological Indicators, 52 , 147-152. doi:10.1016/j.ecolind.2014.11.030 Stringer, L. C., Dougill, A. J., Thomas, A. D., Spracklen, D. V., Chesterman, S., Speranza, C. I., & Kopolo, G. (2012). Challenges and opportunities in linking carbon sequestration, livelihoods and ecosystem service provision in drylands. Environmental Science & Policy, 19 , 121-135. Sun, J., Zou, L., Li, W., Wang, Y., Xia, Q., et al. (2018). Soil microbial and chemical properties influenced by continuous cropping of banana. Scientia Agricola, 75 (5), 420-425. doi:10.1590/1678-992x-2016-0503 Trivedi, P., Wallenstein, M. D., Delgado-Baquerizo, M., & Singh, B. K. (2018). Microbial modulators and mechanisms of soil carbon storage. In Soil carbon storage: modulators, mechanisms and modeling (pp. 73-115). Elsevier. Vance, E. D., Brookes, P. C., & Jenkinson, D. S. (1987). An extraction method for measuring soil microbial biomass C. Soil Biology and Biochemistry, 19 (6), 703-707. Vieira, F. C. B., Bayer, C., Zanatta, J. A., Dieckow, J., Mielniczuk, J., & He, Z. L. (2007). Carbon management index based on physical fractionation of soil organic matter in an Acrisol under long-term no-till cropping systems. Soil & Tillage Research, 96 (1-2), 195-204. Wang, C. H., Wu, L., Id, Z. W., Alabady, M. S., Parson, D. (2020). Characterizing changes in soil microbiome abundance and diversity due to different cover crop techniques. PLoS One, 15 (5). doi:10.1371/journal.pone.0232453 Wei, X., Shao, M., Gale, W. J., Zhang, X., & Li, L. (2013). Dynamics of aggregate-associated organic carbon following conversion of forest to cropland. Soil Biology and Biochemistry, 57 , 876-883. Wei, X., Van Meerbeek, K., Yue, K., Ni, X., Desie, E., Heděnec, P. (2023). Responses of soil C pools to combined warming and altered precipitation regimes: A meta-analysis. Global Ecology and Biogeography, 32 (9), 1660-1675. Wiesenberg, G. L., Dorodnikov, M., & Kuzyakov, Y. (2010). Source determination of lipids in bulk soil and soil density fractions after four years of wheat cropping. Geoderma, 156 (3-4), 267-277. Xue, S., Yang, X., Liu, G., Gai, L., Zhang, C., et al. (2017). Effects of elevated CO2 and drought on the microbial biomass and enzymatic activities in the rhizospheres of two grass species in Chinese loess soil. Geoderma, 286 , 25-34. doi:10.1016/j.geoderma.2016.10.025 Yeomans, J. C., & Bremner, J. M. (1988). A rapid and precise method for routine determination of organic carbon in soil. Communications in Soil Science and Plant Analysis, 19 (13), 1467-1476. Zhou, S., Lin, J., Wang, P., Zhu, P., & Zhu, B. (2023). Resistant soil organic carbon is more vulnerable to priming by root exudate fractions than relatively active soil organic carbon. Plant and Soil, 488 (1), 71-82. Zimmerman, A. E., Graham, E. B., McDermott, J., & Hofmockel, K. S. (2024). Estimating the Importance of Viral Contributions to Soil Carbon Dynamics. Global Change Biology, 30 (10), e17524. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7130549","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493430907,"identity":"3bb50991-7952-49b3-aae7-f48c91316664","order_by":0,"name":"Debaaditya Mukhopadhyay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDCCAzxAwsBGzr69AcSwIFZLQZqxAc8BkBYJYrV8OJy4QSIBxCVCC9/x3oMffhgcTtwu+fzqhh8FEgz87d0JeLVInjmXLNljkG68c3ZO2c0eoMMkzpzdgFeLwY0cAwkeA2vZhts5aTd4gFoMJHIJaLn/xvjnHwNmxoabZ9Ju/iFKyw0eM2keA2fFDTfYj90myhbJM3lp1jIGacaSPTlst2WAjiToF77jZw/ffPPHRo6f/fgzCKO9F78WJMBjACaJVQ4C7A9IUT0KRsEoGAUjCAAAeiFM/w0TiYAAAAAASUVORK5CYII=","orcid":"","institution":"ICFRE-Rain Forest Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Debaaditya","middleName":"","lastName":"Mukhopadhyay","suffix":""},{"id":493430908,"identity":"9720e7a4-1e20-4df8-8920-875c7f1c2c85","order_by":1,"name":"Gaurav Mishra","email":"","orcid":"","institution":"Indian Council of Forestry Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Mishra","suffix":""},{"id":493430909,"identity":"a2387b72-01f1-41cf-a72b-6b9edab54609","order_by":2,"name":"Rosa Francaviglia","email":"","orcid":"","institution":"Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"","lastName":"Francaviglia","suffix":""}],"badges":[],"createdAt":"2025-07-15 12:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7130549/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7130549/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87981298,"identity":"671e33af-35f5-4953-b130-b2c231eecb17","added_by":"auto","created_at":"2025-07-31 06:08:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485946,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area with study points\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/2ad4c363c8b193ef03b20cca.png"},{"id":87981323,"identity":"9eaf8e2f-1071-46f8-997f-fd119729d79a","added_by":"auto","created_at":"2025-07-31 06:08:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210980,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of active and passive soil carbon at three forest types.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/dac2a03d4e34d435bdabe483.png"},{"id":87981273,"identity":"b57706dc-e422-4513-a589-0c2230c50ae7","added_by":"auto","created_at":"2025-07-31 06:08:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52131,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of forest types on relative proportion of various C fractions in SOC pools. SOC: Soil organic carbon; VLC: Very labile carbon; LC: Labile carbon; LLC: Less labile carbon; NLC: Non-labile carbon.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/6561e680f8f6622eb4ea10bc.png"},{"id":87981366,"identity":"5f0455dd-90e9-4c8f-9720-64b158ff69f7","added_by":"auto","created_at":"2025-07-31 06:08:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24959,"visible":true,"origin":"","legend":"\u003cp\u003ePercent contribution of ACP and PCP to TOC under different forest types.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/aacba2843d93033ee9177468.png"},{"id":87981342,"identity":"4c61e64d-e89d-4e15-8afd-07d032558514","added_by":"auto","created_at":"2025-07-31 06:08:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26940,"visible":true,"origin":"","legend":"\u003cp\u003eSoil Microbial Biomass Carbon values under different forest types. Means followed by the same letter do not differ with Tukey’s HSD test at 5% probability.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/a5ff95bf9bb70087e9bc5b1d.png"},{"id":87981279,"identity":"4b3c3ed9-ac4a-4b7f-9816-51dcd1344cdd","added_by":"auto","created_at":"2025-07-31 06:08:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33255,"visible":true,"origin":"","legend":"\u003cp\u003eDehydrogenase values under different forest types. Means followed by the same letter do not differ with Tukey’s HSD test at 5% probability.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/b76885b55195e2a91f4c83cc.png"},{"id":87981799,"identity":"1bda1760-072f-46b9-9234-4d5c0b36a1b4","added_by":"auto","created_at":"2025-07-31 06:16:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1835853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7130549/v1/c4facd3c-50d0-4c97-a351-b46962137df9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the dynamics of soil microbial biomass carbon and labile carbon pools across different forest types of Mizoram, India","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn the terrestrial biosphere, most of the organic carbon is sequestered within soil systems, as underlined by several researchers (Scharlemann et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bargali et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manral et al \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fartyal et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Soil organic carbon plays a pivotal role in global biogeochemical cycles, contributing significantly to the availability of various nutrients (Stevenson et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Soil organic carbon (SOC) storage is a dynamic process influenced by a feedback loop involving carbon input and output through microbial activities such as of organic matter decomposition, mineralization, and greenhouse gases release (McClean et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Beillouin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pandey et al \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Land-use exert a substantial impact on SOC stocks and carbon cycling. Available research suggests that factors like the adoption of no-till practices, the maintenance of straw residues, and environmental conditions, including droughts and soil depth, are primary drivers influencing carbon dynamics in agricultural soils (Spohn et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe microbial biomass is a vigorous component that plays a crucial role in the dynamics of nutrients in the soil (Jenkinson and Ladd \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1981\u003c/span\u003e, Manral et al \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however, the earnings of microbial biomass carbon is fundamentally due to the reduced lifespan of microorganisms (Costa et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Padalia et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Soil microbial biomass carbon and microbial activity are key components of soil organic matter and serve as sensitive indicators of soil quality, offering valuable insights for assessment (Benintende et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These biological indicators exhibit rapid responses to environmental shifts, especially following land-use changes. Reduced tillage practices have been shown to enhance microbial biomass and enzyme activity over time, consequently decreasing the rate of soil carbon and nitrogen mineralization. This effect is influenced by microbial turnover rates and the carbon-to-nitrogen (C:N) ratio (Fraz\u0026atilde;o et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kabiri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, these indicators are crucial for monitoring essential ecosystem services, including water and nutrient cycling, plant-pathogen suppression, and the breakdown of organic residues. Given the critical role of soil quality in sustainable land management for food, wood, and fiber production, evaluating soil biological quality presents a promising method for measuring functional resilience to disturbances. This approach can inform decisions regarding the suitability of various soils for agricultural or conservation purposes. Research on SMBC across different land use types in Mizoram has been documented. A study found that converting natural forests to agroforestry systems did not significantly alter soil organic carbon content within the first year. The importance of soil organic carbon and microbial biomass carbon in forest soils has been emphasized due to their contributions to the carbon cycle and their influence on overall soil quality and health.\u003c/p\u003e\u003cp\u003eLand use and land cover alterations are significant factors that have a pronounced impact on SOC storage. This is primarily due to changes in the rate of organic matter input, such as plant litter, and the rate of output, such as SOC mineralization, as influenced by shifts in plant communities and land management practices (Dawson and Smith \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Poeplau and Don \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In tropical regions, the status of forests plays a crucial role in sequestering or emitting carbon, exerting a substantial influence on atmospheric carbon concentrations (Wei et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The conversion of tropical forests into alternative land uses like plantations and croplands can contribute to carbon emissions (Fan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), leading to alterations in soil characteristics and processes. The conversion of natural forests into croplands disrupts soil structure, thereby enhancing organic matter mineralization by microorganisms and subsequently resulting in SOC depletion (Golchin and Asgari \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the Northeastern Himalayan Region (NEHR), SOC concentrations ranging from 0.85 to 3.56% have been documented, with SOC stocks reported at 20\u0026ndash;40 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the top 0\u0026ndash;30 cm soil depth showing the highest values (Choudhury et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo understand the dynamics of SOC storage and depletion in soils, SOC stocks can be categorized into distinct functional pools based on their varying residence times, namely labile and non-labile pools. The labile pool, or active pool, constitutes a highly responsive fraction present in relatively small proportions, susceptible to fluctuations in environmental conditions. It undergoes rapid decomposition and oxidation in response to alterations in land use practices (Haynes \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Conversely, the non-labile pool, known as the passive pool, represents a more stable and recalcitrant SOC fraction, forming organic-mineral complexes with soil minerals and undergoing a slower microbial decomposition (Weisenberg et al. 2010). Consequently, the labile SOC pool serves as a sensitive indicator of soil quality, reflecting variations induced by land use changes (Vieira et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while the non-labile SOC pool contributes to the overall organic carbon stocks (Chan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Land-use changes significantly influence soil organic carbon (SOC) dynamics and microbial biomass carbon (SMBC) across different forest types in Mizoram, with variations in SOC pools (labile and non-labile) and microbial activity reflecting the impact of these changes on soil quality and carbon sequestration potential.\u003c/p\u003e\u003cp\u003eThe present study aimed to (i) investigate Soil Microbial Biomass Carbon and activity values across different forest types and (ii) quantify various SOC fractions (very labile, labile, less labile and non-labile) and their relative proportions.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe present study was conducted in Mizoram state, Northeast India (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geographically, Mizoram is located between 21˚58\u0026rsquo; N to 24˚35\u0026rsquo; N, and 91˚15\u0026rsquo; E to 93˚29\u0026rsquo; E and its total area is 21081 km\u003csup\u003e2\u003c/sup\u003e. The state shares southern international borders with Myanmar and Bangladesh and northern domestic borders with Manipur, Assam and Tripura Indian states. In summer, the temperature varies between 20\u0026ndash;29 ˚C and sometimes peaks to 30 ˚C; in winter, the temperature is in the range 7\u0026ndash;22 ˚C. The state experiences short winters and long summers. The onset of the monsoon season is from May until September, with an average annual rainfall of 2450 mm (Government of India 2024). A total of 33 sites were selected from 3 different districts of Mizoram, namely \u0026ndash; Kolasib, Aizawl and Mamit, covering 3 forest types, namely\u0026ndash; Secondary Moist Bamboo Brakes, East Himalayan Moist Mixed Deciduous Forest and Cachar Tropical Semi-Evergreen Forest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSoil sampling and sample preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA random sampling technique was adopted for soil sample collection, to avoid biasedness, in 2023 from the three major forest types dominant in Mizoram (Bahuguna et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Five quadrats of 10 \u0026times; 10 m were randomly laid down in each forest type and soil samples from 0\u0026ndash;30 cm were collected, as this layer experiences maximum changes due to above ground natural and anthropogenic activities (Hauser and Pulleman, 2023), from the four corners and the center to obtain a composite sample. Moreover it was observed that in Mizoram majority of the land has accessible soil depths of 0-30cm if the entire state is considered in totality. A total of 165 soil samples were gathered, covering three forest types across 11 sites per forest type, with five replicates at each site. All samples were then transported to the laboratory for subsequent processing and analysis. At each sampling location, a steel core with a volume of 50 cm\u0026sup3; was driven vertically into the soil to obtain an undisturbed sample for determining bulk density (BD). Soil BD was calculated after oven drying of samples at 105\u0026deg;C for 72 h (Blake and Hartge 1986). The soil samples were air-dried, lightly ground, and passed through a 2 mm sieve. To assess soil microbial biomass carbon (SMBC) and dehydrogenase activity (DHA), separate portions were taken from the same composite samples collected from a depth of 0\u0026ndash;30 cm across the various forest types at the selected sites. These samples were immediately transported to the laboratory in sealed polythene bags. Any stones, roots, and other debris were removed, and the samples were then stored at low temperatures. Soil pH was measured using a pH meter and soil textural class was identified following International Society of Soil Science classification system (ISSS 1927). The general soil properties are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\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\u003eGeneral information and soil parameters for the different forest types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBD (g/cm3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMoisture (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoil Textural Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSoil Organic Carbon (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary Moist Bamboo Brakes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEast Himalayan Moist Mixed Deciduous Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCacharTropical Semi-Evergreen Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\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\u003eAnalysis of carbon fractions and pools\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe modified Walkley and Black method, as reported by Chan et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), was used to determine the different SOC pools, whereas the Yeomans and Bremnermethod (Yeomans and Bremner \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) was used to calculate Total Organic Carbon (TOC) (%). 0.5 g of soil samples were put into 250 mL Erlenmeyer flasks, and 10 mL of 1 N K\u003csub\u003e2\u003c/sub\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e (i.e. 0.167 mol/l) was poured into each. Following that, 2, 5, 10, and 20 mL of concentrated H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (98%, sp. gr. 1.84) were added to the corresponding flasks, yielding 6 N, 12 N, 18 N, and 24 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, or 3, 6, 9, and 12 mol/L of H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, respectively, in the final oxidizing solution. The flasks were stored and protected from any external heat source behind an insulating layer. Following 30 minutes of oxidation, 200 mL of distilled water were added to the flasks, and phenanthroline was used as an indicator to titrate the contents using freshly made 0.5 N Fe (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e(SO\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e\u0026bull;6H\u003csub\u003e2\u003c/sub\u003eO. The same method was used to titrate the equivalent acidic dichromate solutions with ferrous ammonium sulphate in a blank (soil-free) control. The methodology for calculating the different labile SOC pools cited in Mishra and Sarkar (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was followed, using the following equations:\u003c/p\u003e\u003cp\u003eVLC (Fraction 1) ∶ Organic C oxidizable under 12 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003eLC (Fraction 2) ∶ Organic C oxidizable under 18 N\u0026thinsp;\u0026minus;\u0026thinsp;12 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e Eq.\u0026nbsp;2\u003c/p\u003e\u003cp\u003eLLC (Fraction 3) ∶ Organic C oxidizable under 24 N\u0026thinsp;\u0026minus;\u0026thinsp;18 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e Eq.\u0026nbsp;3\u003c/p\u003e\u003cp\u003eNLC (Fraction 4) ∶ Organic C oxidizable under TOC\u0026thinsp;\u0026minus;\u0026thinsp;24 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e Eq.\u0026nbsp;4\u003c/p\u003e\u003cp\u003eACP ∶ VLC (Fraction 1)\u0026thinsp;+\u0026thinsp;LC (Fraction 2) Eq.\u0026nbsp;5\u003c/p\u003e\u003cp\u003ePCP ∶ LLC (Fraction 3)\u0026thinsp;+\u0026thinsp;NLC (Fraction 4) Eq.\u0026nbsp;6\u003c/p\u003e\u003cp\u003eWhere VLC is Very Labile Carbon, LC is Labile Carbon, LLC is Less Labile Carbon, NLC is Non Labile Carbon, ACP is Active Carbon Pool and PCP is Passive Carbon Pool (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of soil biological properties\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSoil microbial biomass carbon (SMBC) was quantified using the chloroform fumigation-extraction method as outlined by Vance et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), expressed in \u0026micro;g per gram of dry soil.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SMBC=\\:\\frac{TOC\\left(F\\right)-TOC\\left(NF\\right)}{Kc}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere F is the fumigated soil, NF is the non fumigated soil, Kc\u0026thinsp;=\u0026thinsp;0.45 (Jenkinson and Ladd, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDehydrogenase activity (DHA) was assessed following the reduction of 2, 3, 5-triphenyl tetrazolium chloride according to the method described by Casida et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). The activity of DHA was determined based on a standard curve of triphenylformazan (TPF) in methanol and expressed in \u0026micro;g of TPF per gram of soil per hour.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eGeneral soil characteristics, labile SOC pools, and total SOC stocks were summarized and their mean and standard deviations determined. An ANOVA one-way test was used to compare soil properties, followed by Tukey's post-hoc test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All statistical analyses were carried out using SPSS version 23.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eSoil organic carbon concentrations and microbial biomass in different diverse forest types in Mizoram provided insightful findings. The analysis revealed distinct carbon pool variations among the forest types (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the Secondary Moist Bamboo Brakes, the Very Labile Carbon (VLC) fraction was found to be 0.81%, Labile Carbon (LC) 0.40%, Less Labile Carbon (LLC) 0.37%, and Non Labile Carbon (NLC) 0.41% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Active Pool, which encompasses VLC and LC, amounted to 1.21%, while the Passive Pool, comprising LLC and NLC, was 0.78% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings indicate relatively balanced carbon distribution in the different lability pools within this forest type. The Active Pool (1.38%) in the East Himalayan Moist Mixed Deciduous Forest is derived from the sum of Very Labile Carbon (VLC, 0.94%) and Labile Carbon (LC, 0.44%), representing the rapidly decomposable SOC fraction. Similarly, the Passive Pool (1.11%) consists of Less Labile Carbon (LLC, 0.45%) and Non-Labile Carbon (NLC, 0.66%), indicating the more stable and recalcitrant carbon fraction, which aligns with established SOC fractionation methods. Notably, this forest type exhibited a higher proportion of Labile Carbon compared to the Secondary Moist Bamboo Brakes, indicating a potentially faster carbon turnover. In the Cachar Tropical Semi-Evergreen Forest, VLC was 1.06%, LC 0.54%, LLC 0.49%, and NLC 0.43%. The Active Pool in this forest type was 1.60%, and the Passive Pool was 0.92%. Notably, this forest type displayed the highest values across all carbon pools, signifying a potentially greater carbon stock. Significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in the carbon pool values within each forest type. These variations emphasize the importance of considering the specific forest type when assessing soil organic carbon lability and distribution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\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\u003eSoil organic carbon concentration (%) of varying lability and pools in different forest types of Mizoram.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Labile Carbon (VLC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLabile Carbon (LC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLess Labile Carbon (LLC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon Labile Carbon (NLC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eActive Pool\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePassive Pool\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary Moist Bamboo Brakes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEast Himalayan Moist Mixed Deciduous Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCachar Tropical Semi-Evergreen Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\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\u003cp\u003e\u0026plusmn; indicates standard error of mean. Values in same column followed by different letters are significantly different (p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThe relations among TOC, SOC, SMBC and Dehydrogenase concentrations were also studied under the different forest types (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the Secondary Moist Bamboo Brakes, the TOC concentration was 1.99%, with SOC accounting for a substantial portion (1.58%). The SMBC concentration in this forest type was 340.72 mg/kg, indicating an active microbial community. The Dehydrogenase (DHA) activity was 5.62 \u0026micro;g TPF/g/h soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings suggest a relatively stable carbon pool in this forest type, with a notable presence of soil organic carbon and an active microbial biomass. In the East Himalayan Moist Mixed Deciduous Forest, the TOC concentration was 2.49%, higher thanthe Secondary Moist Bamboo Brakes, and the SOC fraction accounted for 1.83%. The SMBC concentration in this forest type was 352.31 mg/Kg, indicating a slightly higher microbial biomass compared to the Secondary Moist Bamboo Brakes. The DHA activity was 6.42 \u0026micro;g TPF/g/h soil. These results suggest that this forest type exhibits a higher carbon content, particularly in the TOC pool, than the previous one. In the Cachar Tropical Semi-Evergreen Forest, the TOC concentration was also relatively high (2.51%), with a significant portion (2.09%) being SOC. The SMBC concentration was 348.73 mg/kg, indicating a robust microbial biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the DHA activity in this forest type was slightly lower (5.94 \u0026micro;g TPF/g/h soil) than the previous forest type. These findings suggest that this forest type holds a substantial carbon storage, with a particularly pronounced presence of SOC, signifying its potential importance in carbon sequestration.\u003c/p\u003e\u003cp\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\u003eTotal Organic Carbon (%), Soil Organic Carbon (%), Soil Microbial Carbon (mg/Kg) and Dehydrogenase (ug TPF/g/h soil) concentrations under different forest types.\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Organic Carbon (TOC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil Organic Carbon (SOC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoil Microbial Biomass Carbon (SMBC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDehydrogenase (DHA)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary Moist Bamboo Brakes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e340.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEast Himalayan Moist Mixed Deciduous Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e352.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCachar Tropical Semi-Evergreen Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003eb\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\u003cp\u003e\u0026plusmn; indicates standard error of mean. Values in same column followed by different letters are significantly different (p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents Pearson's correlation coefficients (r) elucidating the associations among the various organic carbon pools and soil microbial biomass carbon (SMBC) within soils at 0\u0026ndash;30 cm depths across the different forest types. Important findings emerge from these correlations. Total Organic Carbon (TOC) shows a robust positive correlation with Very Labile Carbon (VLC), presenting a coefficient of 0.636. Similarly, TOC exhibits a significant positive relationship with Labile Carbon (LC) (r\u0026thinsp;=\u0026thinsp;0.693), while LC and Very Labile Carbon (VLC) reveal a notably strong positive association (r\u0026thinsp;=\u0026thinsp;0.773). The Less Labile Carbon (LLC) also correlates significantly positively with TOC (r\u0026thinsp;=\u0026thinsp;0.745). In contrast, Non-Labile Carbon (NLC) shows no substantial correlation with TOC or LC, with respective coefficients of 0.121 and \u0026minus;\u0026thinsp;0.607. Furthermore, the correlation coefficients between SMBC and the carbon pools range from 0.054 to 0.233, indicating relatively weak associations.\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\u003eCorrelation coefficient (Pearson\u0026rsquo;s) between different organic carbon pools and soil microbial biomass carbon in soils (0\u0026ndash;30 cm soil depth) under different forest types in Mizoram.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTOC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVLC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLLC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNLC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSMBC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVLC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.636\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.693\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.773\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLLC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.745\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.643\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.743\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.607\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.527\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSMBC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\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**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study shed light on the complex relationships among different carbon fractions and SMBC within the soils of different forest ecosystems in Mizoram. While some carbon pools, such as VLC, LC, LLC, and TOC exhibit strong positive correlations, SMBC appears to have comparatively weaker connections with these carbon fractions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is imperative to delve deeper into the underlying ecological mechanisms governing these associations, which can be instrumental in understanding the complex dynamics of carbon cycling and microbial biomass in forest soils, ultimately contributing to our understanding of ecosystem functioning in this region.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariability in carbon pools across forest types\u003c/b\u003e\u003c/p\u003e\u003cp\u003eForest carbon stocks play a crucial role in regulating carbon cycles, with soil being the major carbon pool in tropical dry deciduous forests and plantations (Kothandaraman et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All the sampling sites differ markedly in bio-chemical properties. In general, the physical, biological, and chemical characteristics of soil exhibit spatial and temporal variation due to differences in terrain, climatic conditions, weathering intensity, vegetation cover, and microbial processes (Paudel and Sah, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Manral et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), along with a range of other biotic and abiotic influences (Bargali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In landscapes with significant dissection, rapid shifts in bioclimatic factors occur over short distances, leading to marked heterogeneity in soil\u0026rsquo;s physical and chemical attributes (B\u0026auml;umler, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fartyal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversion of natural forests to agroforestry systems in Mizoram can affect SOC content, with different tree-crop combinations leading to variations in carbon stocks (Kenye et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The stock of forest soil carbon when converted to other land uses, like rubber plantations, is found to have the potential for long-term carbon storage (Mishra et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, SOC content and stock among different forest categories are higher within subsoil layers, that are recognized as better carbon sinks (Sreekanth et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The analysis of carbon pools within the present studied forest types revealed notable variations, providing a detailed understanding of the nuanced carbon dynamics within ecosystems. In relation to Very Labile Carbon (VLC), the Cachar Tropical Semi-Evergreen Forest showed a significantly higher value (1.06%) compared to the Secondary Moist Bamboo Brakes (0.81%) and the East Himalayan Moist Mixed Deciduous Forest (0.94%). The variability is further pronounced in Labile Carbon (LC), with the Cachar Tropical Semi-Evergreen Forest showing the maximum (0.54), compared with the Secondary Moist Bamboo Brakes (0.40%) and the East Himalayan Moist Mixed Deciduous Forest (0.44%). Similar trends of variability were observed in Less Labile Carbon (LLC) and Non-Labile Carbon (NLC), with distinct values for each forest type (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Active Pool and Passive Pool components revealed notable distinctions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Cachar Tropical Semi-Evergreen Forest displayed a substantial Active Pool (1.60%), higher than the Secondary Moist Bamboo Brakes (1.21%) and the East Himalayan Moist Mixed Deciduous Forest (1.38%). Conversely, the East Himalayan Moist Mixed Deciduous Forest showed the highest Passive Pool (1.11%), followed by the Cachar Tropical Semi-Evergreen Forest (0.92%) and the Secondary Moist Bamboo Brakes (0.78%). The careful examination of carbon pools within different forest types elucidate complex variations, underscoring the importance of considering the ecosystem-specific dynamics in carbon sequestration research (Stringer et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Anderegg et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zimmerman et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInterplay between carbon pools and soil microbial biomass\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe interplay between carbon pools and soil microbial biomass is crucial in understanding soil carbon dynamics. Soil microbial biomass is strongly linked and positively correlated to soil carbon concentrations (Beugnon et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Tree productivity and functional traits, such as root diameter and litterfall C:N content, influence soil carbon concentrations and microbial biomass (Hagerty et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the decomposition of plant and microbial dead biomass by soil microorganisms contributes to carbon flow and turnover, with different microbial communities and their corresponding carbohydrate-active enzymes playing a role in the degradation of different biomass components (Ren et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The allocation of carbon by the microbial community, represented by carbon use efficiency (CUE), affects soil carbon pool sizes, and models that consider dynamic allocation schemes provide a more accurate representation of microbial carbon partitioning (Finstad et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding the relationship between carbon pools and soil microbial biomass is essential for understanding soil carbon dynamics and the effects of tree diversity, micro-environmental conditions, and afforestation on carbon sequestration (Wei et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study the correlations among various organic carbon pools and SMBC provided important insights into their interrelations. Total Organic Carbon (TOC) demonstrated a robust positive correlation with Very Labile Carbon (VLC), Labile Carbon (LC), and Less Labile Carbon (LLC) (Choudhury et al., 2021). These findings suggest that a higher total carbon content is associated with a greater proportion of labile and less labile carbon fractions (Benbi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is important to note that labile carbon fractions are typically more responsive to environmental changes and microbial activity. Thus, a higher total carbon content may indicate a greater availability of substrates for microbial utilization. The strong positive association between LC and VLC underscores the significance of readily decomposable carbon fractions contributing to microbial biomass and activity (Cookson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Condron et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Khatoon et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, non-Labile Carbon (NLC) exhibited no substantial correlation with TOC or LC, indicating its resistance to change with variations in total or labile carbon (Figuer\u0026ecirc;do et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This resistance is characteristic of recalcitrant carbon fractions, which take more time to decompose and are less susceptible to microbial action (Zhou et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is essential to recognize from this study the contribution of non-labile carbon pools to long-term carbon storage, even though they may not exhibit immediate responses to environmental changes. The correlation coefficients between SMBC and the various carbon pools were relatively weak. This implies that soil microbial biomass carbon did not vary strongly with different carbon fractions in the studied forest types (Bargali et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The weak associations may be due to complex interactions between microbial communities, environmental conditions, and the availability of carbon substrates (Allison \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, microbial biomass carbon is influenced by many factors beyond carbon availability, such as soil temperature, moisture, and nutrient status.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for soil carbon dynamics and ecosystem function\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSoil organic carbon (SOC) is a crucial component of soil organic matter (SOM) and significantly influences soil productivity, ecosystem stability, and carbon sequestration potential (Jackson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Variations in SOC and its associated fractions across different forest types as observed demonstrates their role in carbon cycling and microbial activity. The Cachar Tropical Semi-Evergreen Forest exhibited the highest SOC (2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b) among the studied forests, reinforcing its potential for carbon sequestration and its role in maintaining ecosystem stability. This aligns with previous findings that suggest forests play a critical role in carbon storage and mitigating climate change through microbial-mediated SOC dynamics (Chen et al. 2021). The observed strong correlations between Total Organic Carbon (TOC) and various SOC fractions, particularly labile carbon (LC, r\u0026thinsp;=\u0026thinsp;0.693**), less labile carbon (LLC, r\u0026thinsp;=\u0026thinsp;0.745**), and very labile carbon (VLC, r\u0026thinsp;=\u0026thinsp;0.636**), indicate that labile pools significantly contribute to overall SOC turnover. The presence of higher labile fractions, especially in the Cachar Tropical Semi-Evergreen Forest, suggests a potentially increased turnover rate, increasing the likelihood of SOC mineralization into CO₂ (Jilkova et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, soil microbial biomass carbon (SMBC) values across the forest types were also notable, with the East Himalayan Moist Mixed Deciduous Forest (352.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28ab) displaying the highest SMBC, suggesting greater microbial activity and carbon cycling potential (Lalnunmawia et al. 2020). Although TOC and SMBC exhibited a weak correlation (r\u0026thinsp;=\u0026thinsp;0.233), SMBC remains an important biological indicator of soil health and quality (Manna et al. 2021). Dehydrogenase activity (DHA), which reflects microbial metabolic activity, also varied across forests, with the highest values observed in the East Himalayan Moist Mixed Deciduous Forest (6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a), followed by the Cachar Tropical Semi-Evergreen Forest (5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11b), indicating that microbial-driven carbon transformation processes differ with forest type and soil conditions. Negative correlations between non-labile carbon (NLC) and labile fractions (VLC, r = -0.607**; LC, r = -0.527**) suggest a trade-off between stable and more decomposable carbon pools, emphasizing the complexity of soil carbon stabilization mechanisms (Vieira et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Given these variations, adopting agroforestry practices and sustainable land management strategies tailored to specific forest types could help balance SOC dynamics, enhance sequestration, and improve soil fertility while mitigating climate change (Lenka et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study revealed significant carbon pool variations among forest types, with potential implications for carbon turnover and storage. The balance between labile and non-labile carbon fractions in the Secondary Moist Bamboo Brakes can suggest a stable carbon pool, while the East Himalayan Moist Mixed Deciduous Forest showed a higher proportion of labile carbon, indicating a faster turnover rate. The Cachar Tropical Semi-Evergreen Forest stands out with the highest carbon values, emphasizing its potential for carbon sequestration. The correlations between carbon pools and soil microbial biomass carbon highlighted the intricate interplay among these factors. Total carbon content was positively associated with labile carbon fractions, indicating the availability of substrates for microbial utilization. Non-labile carbon, on the other hand, showed the resistance to change, emphasizing its role in long-term carbon storage. While the relationships between microbial biomass and carbon pools were relatively weak, they underscore the complexity of microbial responses to environmental conditions and substrate availability. Sustainable forest management requires strategies that enhance soil organic carbon (SOC) storage and microbial biomass carbon (SMBC) to maintain ecosystem resilience and productivity. Land-use changes, such as deforestation and agricultural expansion, significantly impact SOC pools, influencing carbon sequestration potential and soil health. Practices like reduced tillage, agroforestry, and maintaining forest cover can mitigate SOC depletion by enhancing microbial activity and organic matter stability. Monitoring SOC fractions provides valuable insights into soil quality and functional resilience, informing land-use decisions that balance carbon conservation with economic and ecological sustainability. These findings have significant implications for soil carbon dynamics and ecosystem functioning. The presence of labile carbon fractions can suggest the potential for increased carbon turnover and greenhouse gas emissions, while the substantial soil organic carbon and microbial biomass in the Cachar Tropical Semi-Evergreen Forest shows its role in carbon sequestration and ecosystem stability. This study underscores the need to consider the various carbon pools and their interactions when assessing the impact of environmental factors on soil carbon storage and microbial activity, contributing to the understanding ecosystem functioning in this region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Competing Interests\u003c/strong\u003e\u003cp\u003eThe authors state that none of their known financial or personal conflicts could have affected the research described in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to Participate declaration\u003c/h2\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFirst Author: Conceptualization, Field and lab work, writing \u0026amp; original draft preparationSecond Author \u0026amp; Third Author: extensive editing of first original draft. All the authors have read the manuscript and approved it for submission and have no competing interests.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAuthors are thankful to the editor and reviewers for the timely review and comment to improve the quality of the manuscript drastically.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003e\u003cb\u003eoptions\u003c/b\u003e: Data will be made available on reasonable request from the first author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllison, S. D. (2014). Modeling adaptation of carbon use efficiency in microbial communities. \u003cem\u003eFrontiers in Microbiology, 5\u003c/em\u003e, 571.\u003c/li\u003e\n\u003cli\u003eAnderegg, W. R., Trugman, A. T., Badgley, G., Anderson, C. M., Bartuska, A., Ciais, P., \u0026amp; Randerson, J. T. (2020). Climate-driven risks to the climate mitigation potential of forests. \u003cem\u003eScience, 368\u003c/em\u003e(6497), eaaz7005.\u003c/li\u003e\n\u003cli\u003eBahuguna, V. K., Swaminath, M. H., Tripathi, S., Singh, T. P., Rawat, V. R. S., \u0026amp; Rawat, R. S. (2016). Revisiting forest types of India. \u003cem\u003eInternational Forestry Review, 18\u003c/em\u003e(2), 135\u0026ndash;145.\u003c/li\u003e\n\u003cli\u003eBargali, K., Manral, V., Padalia, K., Bargali, S. S., \u0026amp; Upadhyay, V. P. (2018). Effect of vegetation type and season on microbial biomass carbon in Central Himalayan forest soils, India. \u003cem\u003eCatena, 171\u003c/em\u003e, 125-135.\u003c/li\u003e\n\u003cli\u003eBargali, S. S., Padalia, K., \u0026amp; Bargali, K. (2019). Effects of tree fostering on soil health and microbial biomass under different land use systems in Central Himalaya. \u003cem\u003eLand Degradation \u0026amp; Development, 30\u003c/em\u003e(16), 1984\u0026ndash;1998. https://doi.org/10.1002/ldr.3394\u003c/li\u003e\n\u003cli\u003eB\u0026auml;umler, R. (2015). Soils. In Miehe, G., \u0026amp; Pendry, C. A. (Eds.), \u003cem\u003eNepal: An Introduction to the Natural History, Ecology and Human Environment in the Himalayas - A Companion to the Flora of Nepal\u003c/em\u003e (1st ed., pp. 125\u0026ndash;134). The Royal Botanical Garden Edinburgh.\u003c/li\u003e\n\u003cli\u003eBeillouin, D., Cardinael, R., Berre, D., Boyer, A., Corbeels, M., et al. (2022). A global overview of studies about land management, land-use change, and climate change effects on soil organic carbon. \u003cem\u003eGlobal Change Biology, 28\u003c/em\u003e(4), 1690\u0026ndash;702. doi:10.1111/gcb.15998\u003c/li\u003e\n\u003cli\u003eBenbi, D. K., Brar, K., Toor, A. S., \u0026amp; Singh, P. (2015). Total and labile pools of soil organic carbon in cultivated and undisturbed soils in northern India. \u003cem\u003eGeoderma, 237\u003c/em\u003e, 149-158.\u003c/li\u003e\n\u003cli\u003eBenintende, S., Benintende, M., Sterren, M., Saluzzio, M., \u0026amp; Barbagelata, P. (2015). Biological variables as soil quality indicators: effect of sampling time and ability to classify soils by their suitability. \u003cem\u003eEcological Indicators, 52\u003c/em\u003e, 147-152.\u003c/li\u003e\n\u003cli\u003eBeugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., et al. (2023). Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration. \u003cem\u003eEcological Monographs, 93\u003c/em\u003e(2).\u003c/li\u003e\n\u003cli\u003eBlake, G. R., \u0026amp; Hartage, K. H. (1986). Soil bulk density and particle density determination. In A. Klute (Ed.), \u003cem\u003eMethods of Soil Analysis\u003c/em\u003e (2nd ed.). Madison: American Society of Agronomy.\u003c/li\u003e\n\u003cli\u003eCasida Jr., L. E., Klein, D. A., \u0026amp; Santoro, T. (1964). Soil dehydrogenase activity. \u003cem\u003eSoil Science, 98\u003c/em\u003e(6), 371-376.\u003c/li\u003e\n\u003cli\u003eChan, K. Y., Bowman, A., \u0026amp; Oates, A. (2001). Oxidizable organic carbon fractions and soil quality changes in an oxicpaleustalf under different pasture leys. \u003cem\u003eSoil Science, 166\u003c/em\u003e(1), 61-67.\u003c/li\u003e\n\u003cli\u003eChen, J., \u0026amp; Sinsabaugh, R. L. (2021). Linking microbial functional gene abundance and soil extracellular enzyme activity: implications for soil carbon dynamics. \u003cem\u003eGlobal Change Biology, 27\u003c/em\u003e(7), 1322-1325. \u003c/li\u003e\n\u003cli\u003eChoudhary, R., Naresh, R. K., Chandra, M. S., Chandra, N., Giri, K. L., \u0026amp; Tiwari, H. (2021). Conservation tillage and integrated nutrient management impact on soil organic carbon fractions, labile soil organic matter pools and microbial community under rice-wheat cropping system: A review. \u003cem\u003eThe Pharma Innovation Journal, 10\u003c/em\u003e, 739-756.\u003c/li\u003e\n\u003cli\u003eChoudhury, B. U., Mohapatra, K. P., Das, A., Das, P. T., Nongkhlaw, L., Fiyaz, R. A., et al. (2013). Spatial variability in the distribution of organic carbon stocks in the soils of North East India. \u003cem\u003eCurrent Science, 104\u003c/em\u003e, 604-614.\u003c/li\u003e\n\u003cli\u003eCondron, L., Stark, C., O\u0026rsquo;Callaghan, M., Clinton, P., \u0026amp; Huang, Z. (2010). The role of microbial communities in the formation and decomposition of soil organic matter. In \u003cem\u003eSoil microbiology and sustainable crop production\u003c/em\u003e (pp. 81-118).\u003c/li\u003e\n\u003cli\u003eCookson, W. R., Abaye, D. A., Marschner, P., Murphy, D. V., Stockdale, E. A., \u0026amp; Goulding, K. W. (2005). The contribution of soil organic matter fractions to carbon and nitrogen mineralization and microbial community size and structure. \u003cem\u003eSoil Biology and Biochemistry, 37\u003c/em\u003e(9), 1726-1737. \u003c/li\u003e\n\u003cli\u003eCosta, S. E. V. G. A., Souza, E. D., Anghinoni, I., \u0026amp; Carvalho, P. C. F., et al. (2014). Impact of an integrated no-till crop\u0026ndash;livestock system on phosphorus distribution, availability, and stock. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 90\u003c/em\u003e, 43\u0026ndash;51. https://doi.org/10.1016/j.agee.2013.12.001\u003c/li\u003e\n\u003cli\u003eDawson, J. J., \u0026amp; Smith, P. (2007). Carbon losses from soil and its consequences for land-use management. \u003cem\u003eScience of the Total Environment, 382\u003c/em\u003e(2-3), 165-190.\u003c/li\u003e\n\u003cli\u003eFan, Y., Wu, J., Xia, Y., \u0026amp; Liu, J. Y. (2016). How will a nationwide carbon market affect regional economies and efficiency of CO2 emission reduction in China? \u003cem\u003eChina Economic Review, 38\u003c/em\u003e, 151-166.\u003c/li\u003e\n\u003cli\u003eFartyal, A., Bargali, S. S., Bargali, K., \u0026amp; Negi, B. (2025). Changes in soil properties, organic carbon, and nutrient stocks after land-use change from forests to grasslands in Kumaun Himalaya, India. \u003cem\u003eLand Degradation \u0026amp; Development.\u003c/em\u003e https://doi.org/10.1007/s11258-024-01487-6\u003c/li\u003e\n\u003cli\u003eFiguer\u0026ecirc;do, K. S., Pereira, M. T., Nick, C., Silva, I. R., \u0026amp; Oliveira, T. S. (2020). Long-term changes in organic matter stocks and quality in an Oxisol under intensive vegetable cultivation. \u003cem\u003eCatena, 188\u003c/em\u003e, 104442.\u003c/li\u003e\n\u003cli\u003eFinstad, K. M., Nuccio, E. E., Grant, K. E., Broek, T. A., Pett-Ridge, J., \u0026amp; McFarlane, K. J. (2023). Radiocarbon analysis of soil microbial biomass via direct chloroform extraction. \u003cem\u003eRadiocarbon\u003c/em\u003e, 1-9.\u003c/li\u003e\n\u003cli\u003eFraz\u0026atilde;o, L. A., Piccolo, M. C., Feigl, B. J., Cerri, C. C., \u0026amp; Cerri, C. E. P. (2010). Inorganic nitrogen, microbial biomass and microbial activity of a sandy Brazilian Cerrado soil under different land uses. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 135\u003c/em\u003e(3), 161-167. doi:10.1016/j.agee.2009.09.003\u003c/li\u003e\n\u003cli\u003eGolchin, A., \u0026amp; Asgari, H. (2008). Land use effects on soil quality indicators in north-eastern Iran. \u003cem\u003eSoil Research, 46\u003c/em\u003e(1), 27-36.\u003c/li\u003e\n\u003cli\u003eGovernment of Mizoram (2024) Mizoram Forests. Environment, Forests \u0026amp; Climate Change Department. https://forest.mizoram.gov.in/page/mizoram-forest-at-a-glance.\u003c/li\u003e\n\u003cli\u003eHagerty, S. B., Allison, S. D., \u0026amp; Schimel, J. P. (2022). Testing microbial models with data from a 14C glucose tracer experiment. \u003cem\u003eSoil Biology and Biochemistry, 172\u003c/em\u003e, 108781. \u003c/li\u003e\n\u003cli\u003eHauser S, Saito K, Pulleman M (2023) Soil sampling (disturbed and undisturbed), handling and storage for soil chemical, biological and physical properties, v1. Standard Operating Procedure 003. In: Saito K, Johnson J-M, Hauser S, Corbeels M, Devkota M, Casimero M (eds) Guideline for measuring agronomic gain key performance indicators in on-farm trials, v. 1. Excellence in Agronomy for Sustainable Intensification and Climate Change Adaptation Initiative.\u003c/li\u003e\n\u003cli\u003eHaynes, R. J. (2005). Labile organic matter fractions as central components of the quality of agricultural soils: an overview. \u003cem\u003eAdvances in Agronomy, 85\u003c/em\u003e, 221-268.\u003c/li\u003e\n\u003cli\u003eInternational Society of Soil Science (ISSS). (1929). Minutes of the first commission meetings. \u003cem\u003eInternational Congress of Soil Science\u003c/em\u003e, Washington.\u003c/li\u003e\n\u003cli\u003eJackson, R. B., Lajtha, K., Crow, S. E., Hugelius, G., Kramer, M. G., \u0026amp; Pi\u0026ntilde;eiro, G. (2017). The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. \u003cem\u003eAnnual Review of Ecology, Evolution, and Systematics, 48\u003c/em\u003e, 419-445.\u003c/li\u003e\n\u003cli\u003eJenkinson, D. S., \u0026amp; Ladd, J. N. (1981). Microbial biomass in soil: Measurement and turnover. In E. A. Paul, \u0026amp; J. N. Ladd (Eds.), Soil Biochemistry (Vol. 5) (pp. 415\u0026ndash;471). New York: Marcel Dekker.\u003c/li\u003e\n\u003cli\u003eJilkova, V., Jandov\u0026aacute;, K., Sim, A., Thornton, B., \u0026amp; Paterson, E. (2019). Soil organic matter decomposition and carbon sequestration in temperate coniferous forest soils affected by soluble and insoluble spruce needle fractions. \u003cem\u003eSoil Biology and Biochemistry, 138\u003c/em\u003e, 107595.\u003c/li\u003e\n\u003cli\u003eKabiri, V., Raiesi, F., \u0026amp; Ghazavi, M. A. (2016). Tillage effects on soil microbial biomass, SOM mineralization and enzyme activity in a semi-arid Calcixerepts. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 232\u003c/em\u003e, 73-84.\u003c/li\u003e\n\u003cli\u003eKaschuk, G., Alberton, O., \u0026amp; Hungria, M. (2010). Three decades of soil microbial biomass studies in Brazilian ecosystems: Lessons learned about soil quality and indications for improving sustainability. \u003cem\u003eSoil Biology and Biochemistry, 42\u003c/em\u003e(1), 1-13. doi:10.1016/j.soilbio.2009.08.020\u003c/li\u003e\n\u003cli\u003eKenye, A., Sahoo, U., Singh, S., \u0026amp; Gogoi, A. (2019). Soil organic carbon stock of different land uses of Mizoram, Northeast India. \u003cem\u003eAIMS Journal, 5\u003c/em\u003e, 25-40. doi:10.3934/geosci.2019.1.25\u003c/li\u003e\n\u003cli\u003eKhatoon, H., Solanki, P., Narayan, M., Tewari, L., Rai, J., \u0026amp; Hina Khatoon, C. (2017). Role of microbes in organic carbon decomposition and maintenance of soil ecosystem. \u003cem\u003eInternational Journal of Chemical Studies, 5\u003c/em\u003e(6), 1648-1656.\u003c/li\u003e\n\u003cli\u003eKothandaraman, S., Dar, J., Sundarapandian, S., \u0026amp; Khan, M. (2023). Variation in ecosystem carbon allocation patterns among different vegetation types in Western Ghats, India. \u003cem\u003eEGU23-3944. Copernicus Meetings\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLalnunmawia, Vanlalhluna, P., \u0026amp; Tawnenga. (2020). Assessment of soil organic carbon (SOC) under different agroforestry systems in Mizoram, Northeast India. \u003cem\u003eJournal of Emerging Technologies and Innovative Research, 7\u003c/em\u003e(8), 1878-1882.\u003c/li\u003e\n\u003cli\u003eLenka, S., Lenka, N. K., Mohanty, M., Saha, J. K., \u0026amp; Patra, A. K. (2020). Potentials and limitations of soil carbon modelling: implications in Indian conditions. In \u003cem\u003eCarbon Management in Tropical and Sub-Tropical Terrestrial Systems\u003c/em\u003e (pp. 417-438).\u003c/li\u003e\n\u003cli\u003eManna, M. C., \u0026amp; Ghosh, A. (2021). Soil organic carbon dynamics, stabilization, and environmental implication. In \u003cem\u003eSoil Science: Fundamentals to Recent Advances\u003c/em\u003e (pp. 13-33).\u003c/li\u003e\n\u003cli\u003eManral, V., Bargali, K., Bargali, S. S., \u0026amp; Shahi, C. (2020). Changes in soil biochemical properties following replacement of Banj oak forest with Chir pine in Central Himalaya, India. \u003cem\u003eEcological Processes, 9\u003c/em\u003e, 30. https://doi.org/10.1186/s13717-020-00235-8\u003c/li\u003e\n\u003cli\u003eManral, V., Bargali, K., Bargali, S. S., Karki, H., \u0026amp; Chaturvedi, R. K. (2023). Seasonal dynamics of soil microbial biomass C, N, and P along an altitudinal gradient in Central Himalaya, India. \u003cem\u003eSustainability, 15\u003c/em\u003e, 1651. https://doi.org/10.3390/su15021651\u003c/li\u003e\n\u003cli\u003eMcClean, G. J., Rowe, R. L., Heal, K. V., Cross, A., Bending, G. D., et al. (2015). An empirical model approach for assessing soil organic carbon stock changes following biomass crop establishment in Britain. \u003cem\u003eBiomass and Bioenergy, 83\u003c/em\u003e, 141-151. doi:10.1016/j.biombioe.2015.09.005 \u003c/li\u003e\n\u003cli\u003eMishra, G., \u0026amp; Sarkar, A. (2020). Studying the relationship between total organic carbon and soil carbon pools under different land management systems of Garo hills, Meghalaya. \u003cem\u003eJournal of Environmental Management, 257\u003c/em\u003e, 110002.\u003c/li\u003e\n\u003cli\u003eMishra, G., Sarkar, A., \u0026amp; Das, J. (2020). Changes in soil organic carbon lability along the altitudinal variations in three land use types of Meghalaya, India. \u003cem\u003eEnvironmental Sustainability, 3\u003c/em\u003e, 325-331.\u003c/li\u003e\n\u003cli\u003ePadalia, K., Bargali, S. S., Bargali, K., \u0026amp; Khulbe, K. (2018). Microbial biomass carbon and nitrogen in relation to cropping systems in Central Himalaya, India. \u003cem\u003eCurrent Science, 115\u003c/em\u003e(9), 1741\u0026ndash;1750.\u003c/li\u003e\n\u003cli\u003ePandey, R., Bargali, S. S., Bargali, K., Karki, H., \u0026amp; Chaturvedi, R. K. (2024). Dynamics of nitrogen mineralization and fine root decomposition in sub-tropical \u003cem\u003eShorea robusta\u003c/em\u003e Gaertner f. forests of Central Himalaya, India. \u003cem\u003eScience of The Total Environment, 921\u003c/em\u003e, 170896. https://doi.org/10.1016/j.scitotenv.2024.170896\u003c/li\u003e\n\u003cli\u003ePaudel, S., \u0026amp; Sah, J. P. (2003). Physico-chemical characters of soil in tropical Shorea robusta Gaertn. forests in eastern Nepal. \u003cem\u003eHimalayan Journal of Science, 1\u003c/em\u003e(2), 107\u0026ndash;110.\u003c/li\u003e\n\u003cli\u003ePoeplau, C., \u0026amp; Don, A. (2013). Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. \u003cem\u003eGeoderma, 192\u003c/em\u003e, 189-201.\u003c/li\u003e\n\u003cli\u003eProut, J. M., Shepherd, K. D., McGrath, S. P., Kirk, G. J. D., Hassall, K. L., et al. (2022). Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time. \u003cem\u003eScientific Reports, 12\u003c/em\u003e(1), 1-14. doi:10.1038/s41598-022-09101-3 \u003c/li\u003e\n\u003cli\u003eRen, C., Zhang, X., Zhang, S., Wang, J., Xu, M., Guo, Y., et al. (2021). Altered microbial CAZyme families indicated dead biomass decomposition following afforestation. \u003cem\u003eSoil Biology and Biochemistry, 160\u003c/em\u003e, 108362.\u003c/li\u003e\n\u003cli\u003eSahoo, U. K., Ahirwal, J., Giri, K., Mishra, G., \u0026amp; Francaviglia, R. (2023). Modeling land use and climate change effects on soil organic carbon storage under different plantation systems in Mizoram, Northeast India. \u003cem\u003eAgriculture\u003c/em\u003e, 13(7), 1332. doi:10.3390/agriculture13071332\u003c/li\u003e\n\u003cli\u003eScharlemann, J. P., Tanner, E. V., Hiederer, R., \u0026amp; Kapos, V. (2014). Global soil carbon: understanding and managing the largest terrestrial carbon pool. \u003cem\u003eCarbon Management, 5\u003c/em\u003e(1), 81-91.\u003c/li\u003e\n\u003cli\u003eSilva, A. P. da, Babujia, L. C., Franchini, J. C., Ralisch, R., Hungria, M., et al. (2014). Soil structure and its influence on microbial biomass in different soil and crop management systems. \u003cem\u003eSoil \u0026amp; Tillage Research, 142\u003c/em\u003e, 42-53. doi:10.1016/j.still.2014.04.006\u003c/li\u003e\n\u003cli\u003eSilva, R. C. S., Almeida, J. C. R., Batista, G. T., \u0026amp; Fortes Neto, P. (2011). Os indicadores f\u0026iacute;sicos, qu\u0026iacute;micos e biol\u0026oacute;gicos da qualidade do solo e da sustentabilidade dos ambientes naturais [Physical, chemical and biological indicators of soil quality and sustainability of natural environments]. Reposit\u0026oacute;rio Eletr\u0026ocirc;nico Ci\u0026ecirc;ncias Agr\u0026aacute;rias, Cole\u0026ccedil;\u0026atilde;o Ci\u0026ecirc;ncias Ambientais. 1-11. Retrieved from http://www.agro.unitau.br/dspace\u003c/li\u003e\n\u003cli\u003eSpohn, M., Klaus, K., Wanek, W., \u0026amp; Richter, A. (2016). Microbial carbon use efficiency and biomass turnover times depending on soil depth \u0026ndash; Implications for carbon cycling. \u003cem\u003eSoil Biology and Biochemistry, 96\u003c/em\u003e, 74-81. doi:10.1016/j.soilbio.2016.01.016\u003c/li\u003e\n\u003cli\u003eSreekanth, N. P., Prabha, V. S., Babu, P., \u0026amp; Thomas, A. P. (2013). Soil carbon alterations of selected forest types as an environmental feedback to climate change. \u003cem\u003eInternational Journal of Environmental Science, 3\u003c/em\u003e(5), 1516-1530.\u003c/li\u003e\n\u003cli\u003eStevenson, B. A., Sarmah, A. K., Smernik, R., Hunter, D. W. F., \u0026amp; Fraser, S. (2016). Soil carbon suitability. \u003cem\u003eEcological Indicators, 52\u003c/em\u003e, 147-152. doi:10.1016/j.ecolind.2014.11.030\u003c/li\u003e\n\u003cli\u003eStringer, L. C., Dougill, A. J., Thomas, A. D., Spracklen, D. V., Chesterman, S., Speranza, C. I., \u0026amp; Kopolo, G. (2012). Challenges and opportunities in linking carbon sequestration, livelihoods and ecosystem service provision in drylands. \u003cem\u003eEnvironmental Science \u0026amp; Policy, 19\u003c/em\u003e, 121-135.\u003c/li\u003e\n\u003cli\u003eSun, J., Zou, L., Li, W., Wang, Y., Xia, Q., et al. (2018). Soil microbial and chemical properties influenced by continuous cropping of banana. \u003cem\u003eScientia Agricola, 75\u003c/em\u003e(5), 420-425. doi:10.1590/1678-992x-2016-0503\u003c/li\u003e\n\u003cli\u003eTrivedi, P., Wallenstein, M. D., Delgado-Baquerizo, M., \u0026amp; Singh, B. K. (2018). Microbial modulators and mechanisms of soil carbon storage. In \u003cem\u003eSoil carbon storage: modulators, mechanisms and modeling\u003c/em\u003e (pp. 73-115). Elsevier.\u003c/li\u003e\n\u003cli\u003eVance, E. D., Brookes, P. C., \u0026amp; Jenkinson, D. S. (1987). An extraction method for measuring soil microbial biomass C. \u003cem\u003eSoil Biology and Biochemistry, 19\u003c/em\u003e(6), 703-707.\u003c/li\u003e\n\u003cli\u003eVieira, F. C. B., Bayer, C., Zanatta, J. A., Dieckow, J., Mielniczuk, J., \u0026amp; He, Z. L. (2007). Carbon management index based on physical fractionation of soil organic matter in an Acrisol under long-term no-till cropping systems. \u003cem\u003eSoil \u0026amp; Tillage Research, 96\u003c/em\u003e(1-2), 195-204.\u003c/li\u003e\n\u003cli\u003eWang, C. H., Wu, L., Id, Z. W., Alabady, M. S., Parson, D. (2020). Characterizing changes in soil microbiome abundance and diversity due to different cover crop techniques. \u003cem\u003ePLoS One, 15\u003c/em\u003e(5). doi:10.1371/journal.pone.0232453\u003c/li\u003e\n\u003cli\u003eWei, X., Shao, M., Gale, W. J., Zhang, X., \u0026amp; Li, L. (2013). Dynamics of aggregate-associated organic carbon following conversion of forest to cropland. \u003cem\u003eSoil Biology and Biochemistry, 57\u003c/em\u003e, 876-883.\u003c/li\u003e\n\u003cli\u003eWei, X., Van Meerbeek, K., Yue, K., Ni, X., Desie, E., Heděnec, P. (2023). Responses of soil C pools to combined warming and altered precipitation regimes: A meta-analysis. \u003cem\u003eGlobal Ecology and Biogeography, 32\u003c/em\u003e(9), 1660-1675.\u003c/li\u003e\n\u003cli\u003eWiesenberg, G. L., Dorodnikov, M., \u0026amp; Kuzyakov, Y. (2010). Source determination of lipids in bulk soil and soil density fractions after four years of wheat cropping. \u003cem\u003eGeoderma, 156\u003c/em\u003e(3-4), 267-277.\u003c/li\u003e\n\u003cli\u003eXue, S., Yang, X., Liu, G., Gai, L., Zhang, C., et al. (2017). Effects of elevated CO2 and drought on the microbial biomass and enzymatic activities in the rhizospheres of two grass species in Chinese loess soil. \u003cem\u003eGeoderma, 286\u003c/em\u003e, 25-34. doi:10.1016/j.geoderma.2016.10.025\u003c/li\u003e\n\u003cli\u003eYeomans, J. C., \u0026amp; Bremner, J. M. (1988). A rapid and precise method for routine determination of organic carbon in soil. \u003cem\u003eCommunications in Soil Science and Plant Analysis, 19\u003c/em\u003e(13), 1467-1476.\u003c/li\u003e\n\u003cli\u003eZhou, S., Lin, J., Wang, P., Zhu, P., \u0026amp; Zhu, B. (2023). Resistant soil organic carbon is more vulnerable to priming by root exudate fractions than relatively active soil organic carbon. \u003cem\u003ePlant and Soil, 488\u003c/em\u003e(1), 71-82.\u003c/li\u003e\n\u003cli\u003eZimmerman, A. E., Graham, E. B., McDermott, J., \u0026amp; Hofmockel, K. S. (2024). Estimating the Importance of Viral Contributions to Soil Carbon Dynamics. \u003cem\u003eGlobal Change Biology, 30\u003c/em\u003e(10), e17524.\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":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soil organic carbon, soil microbial biomass carbon, carbon pools, forest types, sustainable land management","lastPublishedDoi":"10.21203/rs.3.rs-7130549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7130549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated soil organic carbon (SOC) pools and soil microbial biomass carbon (SMBC) across three forest types in Mizoram, India: Secondary Moist Bamboo Brakes, East Himalayan Moist Mixed Deciduous Forest, and Cachar Tropical Semi-Evergreen Forest. SOC is crucial for global biogeochemical cycles, influencing nutrient availability and ecosystem resilience. The study highlights the impact of forest type on SOC dynamics and microbial activity. The Secondary Moist Bamboo Brakes exhibited an Active Pool (VLC\u0026thinsp;+\u0026thinsp;LC) of 1.21% and a Passive Pool (LLC\u0026thinsp;+\u0026thinsp;NLC) of 0.78%, with SOC of 1.58% and SMBC of 340.72 mg/kg, indicating a balanced microbial presence. The East Himalayan Moist Mixed Deciduous Forest had relatively higher SOC (2.49%) and SMBC (352.31 mg/kg), suggesting increased microbial activity and faster carbon turnover. The Cachar Tropical Semi-Evergreen Forest recorded the highest SOC (2.51%) and significant SMBC (348.73 mg/kg), with the highest Very Labile Carbon (VLC) at 1.06%. Dehydrogenase activity (DHA), a key indicator of microbial metabolic activity, was highest in the East Himalayan Moist Mixed Deciduous Forest (6.42 \u0026micro;g TPF/g/h) and lowest in the Secondary Moist Bamboo Brakes (5.62 \u0026micro;g TPF/g/h). Correlations between SMBC and SOC pools were weak, with VLC and LC showing significant positive relationships with TOC (r\u0026thinsp;=\u0026thinsp;0.636 and 0.693). These findings underscore the importance of forest type in shaping SOC and microbial dynamics, with implications for sustainable land management and carbon sequestration strategies in tropical forests.\u003c/p\u003e","manuscriptTitle":"Assessing the dynamics of soil microbial biomass carbon and labile carbon pools across different forest types of Mizoram, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 06:08:15","doi":"10.21203/rs.3.rs-7130549/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-01T08:20:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T10:53:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205016710176305642153871005374278679855","date":"2025-09-25T04:41:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T06:08:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T06:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328879289636302111717446757539209067986","date":"2025-09-22T05:40:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144575621949588083239334252717553010303","date":"2025-09-19T11:05:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141721184532063470927596501536652655657","date":"2025-09-19T02:16:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160724343240460364163115815760219355103","date":"2025-09-18T05:58:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216024093828700994260268664053384444187","date":"2025-09-17T11:42:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195429893253570322503436185587396370131","date":"2025-09-17T10:42:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T09:26:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302455232975829917251866338592012465625","date":"2025-07-31T00:19:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-27T12:05:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T14:57:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T13:53:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Soil","date":"2025-07-24T13:50:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffb5d555-0c14-4f84-87c3-d8694bec2fbd","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T14:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 06:08:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7130549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7130549","identity":"rs-7130549","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}