Introduction
Tropical dry forests (TDFs) are the most widely distributed landcover type in the tropics (Jaramillo et al. Reference Jaramillo, Kauffman, Rentería-Rodríguez, Cummings and Ellingson2003). TDFs have received less attention from the research community than tropical moist or wet forests. TDFs are distinguished from tropical moist and rain forests by lower annual rainfall and more acute dry seasons (Murphy and Lugo Reference Murphy and Lugo1986). These highly seasonal rainfall regimes contribute to the large diversity of leaf habits present in TDF tree species (Eamus Reference Eamus1999) and the broad variation in canopy phenology ranging from forests dominated by evergreen trees to those dominated by deciduous trees (Vico et al. Reference Vico, Dralle, Feng, Thompson and Manzoni2017). These forests achieve the highest rate of net primary production (NPP) globally (Hengl et al. Reference Hengl, Leenaars, Shepherd, Walsh, Heuvelink, Mamo, Tilahun, Berkhout, Cooper, Fegraus and Wheeler2017). In the tropics, more than one-third of NPP is allocated to fine roots (Cordeiro et al. Reference Cordeiro, Norby, Andersen, Valverde-Barrantes, Fuchslueger, Oblitas, Hartley, Iversen, Gonçalves, Takeshi and Lapola2020; Huaraca Huasco et al. Reference Huaraca Huasco, Riutta, Girardin, Hancco Pacha, Puma Vilca, Moore, Rifai, del Aguila-Pasquel, Araujo Murakami, Freitag and Morel2021). Fine roots are a dynamic connection between the soil and the plants and thus can help to control the nutrient and water cycle within the ecosystem (McCormack et al. Reference McCormack, Dickie, Eissenstat, Fahey, Fernandez, Guo, Helmisaari, Hobbie, Iversen, Jackson and Leppälammi-Kujansuu2015). Thus, understanding potential changes in root dynamics and traits with ongoing change in TDFs is of global significance.
Fine roots, typically defined as roots with less than 2 mm in diameter, are essential components of forest ecosystems, serving as primary channels for water and nutrient acquisition, contributing organic matter through turnover, and mediating belowground carbon and nutrient cycling (Eissenstat and Yanai Reference Eissenstat and Yanai1997; Guo et al. Reference Guo, Mitchell, Withington, Fan and Hendricks2008; Sardar et al. Reference Sardar, Younas, Farooqi and Li2023). They are metabolically active, characterized by rapid growth and decomposition rates, and show high sensitivity to environmental changes and human-induced pressures (Freschet et al. Reference Freschet, Valverde‐Barrantes, Tucker, Craine, McCormack, Violle, Fort, Blackwood, Urban‐Mead, Iversen and Bonis2017; Gill and Jackson Reference Gill and Jackson2000; Menezes et al. Reference Menezes, Lins, Silva, Tabarelli and Filgueiras2024). Their dynamic nature positions them as key indicators of ecosystem functioning, particularly in tropical forests, where spatial and temporal variability in biotic and abiotic conditions is pronounced. Quantifying fine root biomass and nutrient composition variability is critical for predicting forest ecosystem responses to global change drivers such as climate extremes, altered land-use patterns, and evolving forest management strategies (Freschet et al. Reference Freschet, Valverde‐Barrantes, Tucker, Craine, McCormack, Violle, Fort, Blackwood, Urban‐Mead, Iversen and Bonis2017; Pregitzer et al. Reference Pregitzer, Burton, King and Zak2008; Sakashita et al. Reference Sakashita, Hosoi, Asakura and Makita2024).
Fine roots have long been treated as a homogeneous ecosystem component; however, evidence shows a wide variability in terms of root longevity in fine roots even in narrow diameter classes (Joslin et al. Reference Joslin, Gaudinski, Torn, Riley and Hanson2006; Tierney and Fahey Reference Tierney and Fahey2002; Wells and Eissenstat Reference Wells and Eissenstat2001). This variability emphasizes the need to distinguish between different root populations and classify roots into narrower diameter classes (Guo et al. Reference Guo, Mitchell, Withington, Fan and Hendricks2008). Thus, despite certain drawbacks of such a method, diameter-based classification remains one of the most effective approaches for distinguishing fine root populations (Gill and Jackson Reference Gill and Jackson2000; Pregitzer et al. Reference Pregitzer, DeForest, Burton, Allen, Ruess and Hendrick2002). Fine roots can therefore be further subdivided into narrower diameter classes (e.g., <1 and >1–5 mm, Singh Reference Singh1990) to capture functional heterogeneity within the root system. As per this classification, thinner roots (<1 mm) primarily represent absorptive functions, while thicker roots (>1–5 mm) contribute to transport and storage (Guo et al. Reference Guo, Mitchell, Withington, Fan and Hendricks2008; McCormack et al. Reference McCormack, Dickie, Eissenstat, Fahey, Fernandez, Guo, Helmisaari, Hobbie, Iversen, Jackson and Leppälammi-Kujansuu2015).
Root size class further influences functional variation among fine roots, with smaller roots generally exhibiting higher nutrient concentrations and faster metabolic turnover compared to thicker roots (Freschet et al. Reference Freschet, Valverde‐Barrantes, Tucker, Craine, McCormack, Violle, Fort, Blackwood, Urban‐Mead, Iversen and Bonis2017; Kembel and Cahill Reference Kembel and Cahill2011; Sakashita et al. Reference Sakashita, Hosoi, Asakura and Makita2024). Environmental pressures such as seasonal drought or disturbance intensity can differentially affect these root classes, shifting belowground nutrient cycling patterns.
Disturbance—both natural and human-induced—remains a dominant driver of changes in fine root structure and function. Natural events such as windstorms, wildfires, and drought, alongside anthropogenic impacts like logging, agricultural conversion, and soil compaction, modify root environments through tissue damage, shifts in soil physical properties, and microbial community rearrangements (Brunner et al. Reference Brunner, Herzog, Dawes, Arend and Sperisen2015; Hertel et al. Reference Hertel, Harteveld and Leuschner2009; Seitz et al. Reference Seitz, Schütte and Drown2021). These changes alter root production, mortality, and nutrient uptake efficiency (Kotowska et al. Reference Kotowska, Samhita, Hertel, Triadiati, Beyer, Allen, Link and Leuschner2023; Philippot et al. Reference Philippot, Griffiths and Langenheder2021). While many studies report declines in fine root biomass following severe disturbance linked to impaired nutrient acquisition (Pregitzer et al. Reference Pregitzer, Burton, King and Zak2008; Vogt et al. Reference Vogt, Vogt and Bloomfield1998), other findings highlight contexts where disturbance enhances root proliferation, especially under nutrient flushes from decomposing biomass and reduced competition (Gill and Jackson Reference Gill and Jackson2000; Kotowska et al. Reference Kotowska, Samhita, Hertel, Triadiati, Beyer, Allen, Link and Leuschner2023). These contrasting outcomes emphasize the importance of accounting for disturbance type, intensity, and timing in root system studies.
Seasonal variability further complicates fine root dynamics in tropical forests with distinct wet–dry cycles. Root activity often peaks during wet seasons due to elevated soil moisture and temperature promoting elongation, branching, and nutrient uptake, whereas dry periods tend to suppress growth, induce dormancy, and increase mortality (Allen et al. Reference Allen, Dupuy, Gei, Hulshof, Medvigy, Pizano, Salgado-Negret, Smith, Trierweiler, Van Bloem and Waring2017; Chen et al. Reference Chen, Zhang, Zhou and Zhou2024; Cusack et al. Reference Cusack, Addo-Danso, Agee, Andersen, Arnaud, Batterman, Brearley, Ciochina, Cordeiro, Dallstream and Diaz-Toribio2021). Seasonal shifts in fine root biomass and nutrient flow strongly influence soil nutrient availability, organic matter deposition, and microbial functioning (Freschet et al. Reference Freschet, Pagès, Iversen, Comas, Rewald, Roumet, Klimešová, Zadworny, Poorter, Postma and Adams2021; Kotowska et al. Reference Kotowska, Samhita, Hertel, Triadiati, Beyer, Allen, Link and Leuschner2023; Maslova et al. Reference Maslova, Tabalenkova, Malyshev and Golovko2013; Silver and Miya Reference Silver and Miya2001). Monitoring these patterns is therefore critical for accurate biogeochemical modeling in tropical ecosystems.
Soil depth plays a defining role in fine root biomass and nutrient distribution. Roots are generally concentrated in upper soil layers rich in organic matter, moisture, and nutrients (Jackson et al. Reference Jackson, Mooney and Schulze1997; Pregitzer et al. Reference Pregitzer, Burton, King and Zak2008), but vertical profiles vary with soil texture, compaction, and hydrological conditions, often reflecting historical disturbance legacies (Legay et al. Reference Legay, Baxendale, Grigulis, Krainer, Kastl, Schloter, Bardgett, Arnoldi, Bahn, Dumont and Poly2014; Sakashita et al. Reference Sakashita, Hosoi, Asakura and Makita2024). Deeper layers, though less root-dense, may host specialized roots adapted to drought resilience, deep water uptake, or long-term carbohydrate storage (Kramer and Chadwick Reference Kramer and Chadwick2018; Sardar et al. Reference Sardar, Younas, Farooqi and Li2023). Variability in root nutrient traits, such as carbon-to-nitrogen (C:N) ratios, across depths reflects complex interactions among plant physiology, microbial processes, and nutrient gradients (de Graaff et al. Reference de Graaff, Jastrow, Gillette, Johns and Wullschleger2014; Jiang et al. Reference Jiang, Gong, Yang, He and Liu2021).
Fine root C:N ratios serve as sensitive indicators of biochemical quality and decomposition potential. High ratios generally correspond to slow decomposition and nutrient release, while low ratios signal nitrogen-rich tissues with rapid turnover potential (Hobbie Reference Hobbie2015; Kramer and Chadwick Reference Kramer and Chadwick2018; Saha et al. Reference Saha, Huang, Khoso, Wu, Han, Ma, Poudel, Li, Zhu, Lan and Sakib2023). Spatiotemporal variation in root C:N ratios is driven by soil fertility, moisture conditions, and plant physiological status, with disturbance and seasonality modulating these traits via changes in nutrient acquisition and metabolic rates (Freschet et al. Reference Freschet, Pagès, Iversen, Comas, Rewald, Roumet, Klimešová, Zadworny, Poorter, Postma and Adams2021).
Despite growing recognition of fine root trait importance, significant gaps persist in understanding how ecological drivers—disturbance, seasonality, soil depth, and root size class—interact synergistically in tropical forests. Much past research has addressed these factors in isolation (Dietrich et al. Reference Dietrich Hertel, Harteveld and Leuschner2009; Legay et al. Reference Legay, Baxendale, Grigulis, Krainer, Kastl, Schloter, Bardgett, Arnoldi, Bahn, Dumont and Poly2014), restricting integrative modeling of belowground processes and limiting predictions of forest resilience under mounting disturbance frequencies and climatic extremes. Addressing these gaps requires a holistic approach examining interactive effects on biomass, nutrient content, and stoichiometry.
This study aims to address these gaps by examining the interactive effects of disturbance intensity, seasonal variation, soil depth, and root size class on fine root biomass, carbon and nitrogen content, and their C:N ratios in a tropical forest. Specifically, the study seeks to (i) quantify variations in live and dead fine root biomass across a disturbance gradient during different seasons, (ii) evaluate the influence of soil depth on fine root biomass and carbon and nitrogen concentrations under varying levels of disturbance, (iii) assess differences in carbon and nitrogen concentrations and C:N ratios among different root size classes in relation to disturbance, seasonal changes, and soil depth, and (iv) analyze the interactive effects of these four factors on root stoichiometric traits using multivariate statistical approaches.
The study posits several hypotheses regarding fine root biomass and nutrient content: (i) increased disturbance intensity leads to a decrease in biomass and lower carbon and nitrogen levels due to physical damage and impaired nutrient absorption, (ii) seasonal changes will significantly affect root biomass and nutrient content, with higher levels observed during the rainy season and reductions during dry periods, (iii) fine root biomass and nutrient concentrations will be greater in the upper soil layers compared to deeper layers, attributed to increased resource availability near the surface, (iv) smaller root size classes should show greater biomass and nutrient content than larger fine roots due to their key role in nutrient absorption, and (v) the interactions among disturbance, seasonality, soil depth, and root size class will increase C:N ratios under high disturbance in the dry season, particularly in deeper soils (10–20 cm) and larger roots (1–5 mm), while lower ratios are expected under low disturbance in the rainy season in surface soils (0–10 cm) and finer roots (<1 mm).
Materials and methods
Study sites
The study was conducted in the Pali Forest Range of the Katghora Forest Division, Chhattisgarh, India (Figure 1). The selected sites are located at 22°22.45′ N latitude and 82°19.54′ E longitude, at an elevation of 303 m.a.s.l.. The region is predominantly covered with tropical dry forests, where Sal (Shorea robusta) and its associated species form the dominant vegetation (Baghel et al. Reference Baghel, Singh, Jhariya, Singh, Singh and Chaturvedi2025). The region has a tropical climate, receiving an average annual rainfall of 1751 mm, with almost 80% of the precipitation occurring from June to September. Mean monthly temperature varies between 17.5°C (Jan.) and 30.70°C (May), and the annual temperature averages 24.5°C in this region (Figure S1). Lateritic (Entisol) is the dominant soil type, which is sandy in texture with 60–85% sand content. The soil has high organic matter content, which contributes to its diverse properties. Its pH varies between 5 and 8. The soil possesses good water-holding capacity, high cation exchange capacity, and a well-developed profile, with its texture ranging from loamy to sandy loam types. Naturally occurring clods generally exhibit granular or crumb-like structures (Pawar et al. Reference Pawar, Singh, Jhariya and Sahu2014). Soil in the area has high nutrient content, especially within plantation zones, and supports a diverse range of microfauna and microflora (Jhariya and Singh Reference Jhariya and Singh2021).
Location map of the study sites (Source: Google Earth and Range Forest Office, Pali, Chhattisgarh).
Figure 1. Long description
The image contains three elements: one map of India, one map of the state of Chhattisgarh, and one satellite image of the study sites. The map of India highlights the state of Chhattisgarh in red. The map of Chhattisgarh highlights the district of Pali. The satellite image shows specific study sites within Pali, labeled as OA 579, P 151, and P 153, with Mungadevi and Oneprachhatna marked as well.
Disturbance factors
During the study, grazing, lopping, and the collection of fuelwood, fodder, and non-timber forest products were considered direct indicators of anthropogenic disturbance, while the presence of stumps, illicit tree felling, animal excreta, and surface burning served as indirect indicators, evaluated during sampling. Disturbance levels were confirmed through ground truthing based on disturbance frequency, assessing anthropogenic intensity at the study sites (Baghel et al. Reference Baghel, Singh, Jhariya, Singh, Singh and Chaturvedi2025; Kumar and Ram Reference Kumar and Ram2005; Oraon et al. Reference Oraon, Singh and Jhariya2018).
Direct disturbances included frequent grazing by livestock such as goats and cows, which browsed on herbaceous plants and seedlings, especially in medium- and high-disturbance forest areas. Local villagers regularly collected fuelwood and medicinal bark, and illicit tree felling of species like Diospyros melanoxylon and Shorea robusta was observed, mainly for income generation. Indirect indicators included high stump density and animal droppings, which increased with disturbance levels. Some stumps were regenerating through coppice shoots while others decayed. Additionally, girdling, debarking, and understory burning were reported, impacting smaller vegetation layers without causing tree mortality. Based on the intensity and frequency of these disturbances, sites were classified into low, medium, and high disturbance forests, reflecting escalating human impact (Baghel et al. Reference Baghel, Singh, Jhariya, Singh, Singh and Chaturvedi2025; Kumar and Ram Reference Kumar and Ram2005; Oraon et al. Reference Oraon, Singh and Jhariya2018).
Ground verification was conducted through repeated field visits and direct observations across all study sites before sampling. Disturbance indicators were quantified using visual assessments of grazing intensity, evidence of tree cutting, stump counts, frequency of fuelwood collection, animal droppings, and signs of understory burning within representative sampling areas. Forest sites were classified into least disturbed forest (LDF), moderately disturbed forest (MDF), and highly disturbed forest (HDF). Human presence was absent in LDF, but some cattle were noted occasionally. In MDF, villagers collected wood, bark, and logs; illegal tree cuttings and debarking occurred. Villagers collected fuelwood frequently in HDF; moreover, they burned the lower layer before the monsoon season, thereby burning bushes and young saplings. Based on indirect anthropogenic impacts on forests, a distinct gradient emerged among different types of anthropogenic disturbance, like stump density, which increased from 11 per hectare in LDF to 46 per hectare in MDF and 57 per hectare in HDF and animal excreta from 7 per hectare in LDF to 23 per hectare in MDF and 13 per hectare in HDF (Baghel et al. Reference Baghel, Singh, Jhariya, Singh, Singh and Chaturvedi2025).
Methods
Three forest stands representing contrasting disturbance conditions were selected for comparative analysis: low (LDF), medium (MDF), and high (HDF) disturbance. The LDF covered an area of 281.403 ha, dominated by more than 96% Shorea robusta trees, with a canopy density ranging between 0.6 and 0.8. The MDF represented mixed forest, occupying 229.045 ha with a canopy density of 0.4–0.5, and the HDF comprised 62.738 ha with a canopy density of 0.2–0.4. The classification of these forests followed the criteria of the Forest Survey of India: areas with a canopy density of 0.1 or less are considered blanks, designated mainly for plantation programs; canopy density between >0.1 and 0.4 is categorized as open forest, generally targeted for rehabilitation; canopy density between >0.4 and 0.6 is termed moderately dense forest, where assisted natural regeneration is promoted by the forest department; forests with >0.6–0.8 canopy density are classified as dense forest; and those with canopy density above 0.8 fall under the very dense forest category.
Fine roots were sampled in five replications from designated forest sites subjected to three levels of disturbance intensity, categorized as low, medium, and high, and across three distinct seasons: winter, dry, and rainy. Although tropical dry forests are commonly characterized by dry and rainy seasons, the present study retained a three-season framework (winter, dry summer, and rainy season) because winter in central India represents a distinct ecological phase characterized by lower temperature, reduced microbial activity, and altered root physiological processes compared to the hotter summer dry season.
To capture vertical variation in root traits, soil samples were collected using a cylindrical soil corer of known diameter to a depth of 20 cm, and each core was separated into 0–10 cm and 10–20 cm sections immediately after collection. Sampling was restricted to the upper 20 cm soil layer because previous studies in tropical dry forests have shown that the majority of fine root biomass and nutrient activity are concentrated within surface soils. Additionally, compact lateritic subsoil conditions limited deeper coring during field sampling. Fine roots were carefully sorted into two diameter-based size classes (<1 and 1–5 mm) to examine how morphological characteristics influence biomass and nutrient content (Baghel et al. Reference Baghel, Singh, Jhariya, Singh, Singh and Chaturvedi2025; McCormack et al. Reference McCormack, Dickie, Eissenstat, Fahey, Fernandez, Guo, Helmisaari, Hobbie, Iversen, Jackson and Leppälammi-Kujansuu2015). Fine roots are commonly defined as roots <2 mm diameter; however, to better capture functional heterogeneity, the present study further separated roots into <1 mm and 1–5 mm diameter classes following Singh (Reference Singh1990). Live roots were identified based on lighter color, intact stele, elasticity, and presence of cortex tissues, whereas dead roots were darker, brittle, and lacked cohesive internal tissues, following Singh and Singh (Reference Singh and Singh1993). Although visual separation is widely used in field-based root ecology studies, some uncertainty in distinguishing partially decomposed roots may remain, particularly in intermediate decomposition stages. Finally, each fine root class was converted into live fine roots and dead fine roots using live and dead fine root proportions (Singh Reference Singh1990). Samples were dried at 80°C to constant weight and weighed.
For each resulting subsample, we measured root biomass (g per core, standardized to g m−2), carbon content (mg C per g dry root), and nitrogen content (mg N per g dry root). Samples of fine roots from all three study sites were pooled and oven-dried at 80°C. The oven-dried samples were ground and stored for chemical analysis. C and N contents in the fine roots were calculated by multiplying root biomass by the respective C and N concentrations, and the carbon-to-nitrogen (C:N) ratio was calculated following quality-control procedures (N > 0).
Replicate-level data were managed and analyzed in R (R Core Team Reference Team2025). Disturbance, season, depth, and root size were coded as ordered factors. Data transformation to long format was implemented using tidyverse packages, facilitating multi-factor exploratory data analysis and visualizations. Boxplots with overlaid jittered data points (representing true replication within each category) were used for all response variables, subdivided by physiological (live/dead), size, and soil depth categories.
Statistical analysis
Factorial ANOVAs were conducted separately on live and dead fractions to test main and interactive effects of disturbance, season, depth, and root size. Each model treated the plot (nested within disturbance level) as a random effect to reflect spatial replication and non-independence. Model assumptions were checked via diagnostic plots (Quinn and Keough Reference Quinn and Keough2002). Significant main or interaction effects were further examined with Tukey’s HSD post-hoc tests; compact letter displays were generated using multcompView (Graves et al. Reference Graves, Piepho and Selzer2024) for visual group differentiation.
Principal component analysis (PCA) was conducted on standardized variables using FactoMineR (Lê et al. Reference Lê, Josse and Husson2008). Biplots illustrated gradients among root traits, with samples colored by season and disturbance for clarity (Jolliffe and Cadima Reference Jolliffe and Cadima2016), and visualized using ggplot2 and ggrepel for improved legibility. Graphical outputs were generated using ggplot2 (Wickham Reference Wickham and Wickham2016) and enhanced with ggrepel (Slowikowski et al. Reference Slowikowski, Schep, Hughes and Dang2023) to improve label legibility in PCA biplots.
Results
This study examined the effects of disturbance intensity, seasonality, soil depth, and root size class on fine root biomass, carbon and nitrogen content, and their stoichiometric ratios in a tropical forest ecosystem. Across all samples, clear patterns emerged in both live and dead root fractions, revealing complex interactions between environmental factors and root physiological status. Descriptive statistics across all treatment combinations showed that low disturbance and rainy season conditions favored higher biomass and nutrient content in live roots, while high disturbance and winter conditions were associated with reduced biomass, lower nutrient content, and elevated C:N ratios.
Fine root biomass
Fine root biomass of both live and dead fractions was shaped by main and interactive effects among disturbance, season, soil depth, and root size class. In live roots, the overall effect of disturbance was significant (F2,87 = 18.3, p < 0.001), with mean biomass highest under low disturbance (1.75 ± 0.42 g m−2), intermediate under medium disturbance (1.32 ± 0.40 g m−2), and lowest under high disturbance (1.10 ± 0.35 g m−2). However, the magnitude of this effect was contingent on season: it was most pronounced in the rainy season (live biomass: 2.10 ± 0.41 g m−2 under low disturbance vs. 1.20 ± 0.32 g m−2 under high disturbance; Tukey HSD p < 0.01), while in other seasons, differences were less marked or limited to low vs. high disturbance comparisons (e.g., winter, p = 0.04, dry, n.s.).
Dead root biomass followed a parallel but reduced pattern, with disturbance remaining a significant factor (F2,87 = 9.8, p < 0.001), though dead root values were markedly lower (low disturbance: 0.45 ± 0.15 g m−2; high disturbance: 0.20 ± 0.07 g m−2). Seasonal variation in dead roots was less pronounced than in live roots (F2,87 = 4.2, p = 0.019), but declines during the rainy and winter seasons under higher disturbance suggested possible cumulative stress or incomplete decomposition.
Soil depth influenced fine root biomass in both fractions, with higher values in the upper 0–10 cm than in 10–20 cm (mean difference: 0.4 g, p < 0.05), consistent across disturbance and seasonal contexts. Fine root size class (<1 mm vs. 1–5 mm) was also significant (F size, p < 0.001); the majority of biomass was in the smallest roots regardless of environmental condition, reinforcing the disproportionate role of absorptive roots in ecosystem function.
Post-hoc Tukey HSD tests (Figure 2) revealed distinct groupings for disturbance levels within each season. For example, during the rainy season, live root biomass under low disturbance was significantly higher than both medium and high disturbance groups (p < 0.01), whereas in winter, only low versus high disturbance differed significantly (p = 0.04). These statistical distinctions were annotated in Figure 2 with compact letter displays, aiding visualization of treatment effects.
Boxplots showing the distribution of fine root biomass (g/m2) separated by live and dead roots across different seasons (winter, dry, and rainy), root sizes (<1 mm, 1–5 mm), and soil depths (0–10 cm, 10–20 cm) under varying disturbance levels (low, medium, high). Significant differences among disturbance treatments within each season and status are indicated by Tukey’s HSD letters.
Carbon and nitrogen content
Patterns in live root carbon (C) content mirrored those for biomass but manifested nuanced interactions with season and disturbance. Carbon levels ranged from 400 ± 50 mg m−2 under low disturbance to 250 ± 35 mg m−2 in high disturbance plots (F2,87 = 16.7, p < 0.001), with a marked increase seen during the rainy season (430 ± 45 mg m−2) compared to the lowest values in winter (270 ± 30 mg m−2 ANOVA F2,87 = 10.9, p < 0.001). The disturbance × season interaction was significant (F4,87 = 2.9, p = 0.027), with differences in carbon most evident in the rainy season and less so in others, as shown in Figure S2.
Nitrogen (N) content in live roots decreased with increasing disturbance (F2,87 = 14.2, p < 0.001), with the highest values in low disturbance plots (35.0 ± 5.2 mg m−2) and the lowest in high disturbance (22.5 ± 4.1 mg m−2). Seasonal effects were weaker but statistically significant (F2,87 = 6.5, p = 0.003), with slightly elevated N during dry compared to other times. Soil depth was a significant factor for N (p = 0.02), as the 0–10 cm layer had on average 15% more N than 10–20 cm, as illustrated in Figure S3.
Dead root carbon and nitrogen contents were significantly lower than in live roots across all conditions. Carbon ranged from 150 ± 25 mg m−2 to 90 ± 20 mg m−2, while nitrogen ranged from 10 ± 3 mg m−2 to 6 ± 2 mg m−2 across disturbance gradients, with disturbance effects significant for both elements (p < 0.01). These trends are summarized numerically in Table S1.
C:N ratio
The carbon-to-nitrogen ratio (C:N) in fine roots is an important indicator of root quality and nutrient cycling potential. In live roots, the mean C:N ratio increased with disturbance intensity, from 12.5 ± 1.8 under low disturbance to 14.8 ± 2.2 under high disturbance (F2,87 = 7.4, p = 0.001). Seasonality affected C:N ratios as well, with the lowest ratios in dry (11.9 ± 1.5) and the highest in winter (15.2 ± 2.1) (F2,87 = 9.0, p < 0.001). Dead roots showed higher and more variable C:N ratios (range: 18–25), but disturbance and season effects were less consistent, likely reflecting decomposition processes.
Post-hoc analysis (Figure 3) indicated that live roots under low disturbance consistently had significantly lower C:N ratios compared to medium and high disturbance, particularly during the dry season (p < 0.05). This pattern suggests reduced nitrogen limitation or enhanced nutrient cycling in less disturbed environments.
Boxplots of C:N ratios in live and dead fine roots across seasons, soil sizes, and depths under varying disturbance intensities. Tukey’s HSD letters highlight statistically significant differences among disturbance levels within each season and root status.
Figure 3. Long description
The image contains four sets of box plots, each representing C:N ratios in live and dead fine roots across different seasons, soil sizes, and depths under varying disturbance intensities. Each set is divided into two rows: the top row for live roots and the bottom row for dead roots. The x-axis represents the seasons (Winter, Dry, Rainy), and the y-axis represents the C:N ratio. The disturbance levels are categorized as Low, Medium, and High, indicated by different colors (red, blue, green). Tukey’s HSD letters (a, b, ab) highlight statistically significant differences among disturbance levels within each season and root status. The box plots show the distribution of C:N ratios, with the central line indicating the median, the box representing the interquartile range, and the whiskers showing the range of the data. Outliers are depicted as individual points. The plots reveal variations in C:N ratios across different conditions, with notable differences between live and dead roots and among disturbance levels.
Interactions among factors
Significant four-way interactions among disturbance, season, soil depth, and root size class were detected in several response variables, particularly for biomass and nitrogen content in live roots (e.g., disturbance × season × soil depth × size: F4,87 = 2.8, p = 0.035). This underscores the necessity of considering multiple environmental gradients simultaneously to understand fine root dynamics comprehensively. Such complex interactions are clearly illustrated by the divergence of live and dead fractions across gradients in Figures 2 and 3 and Figure S2 and S3.
Multivariate analysis
PCA revealed that the first two principal components explained 62.3% of the total variance in the combined dataset of biomass, carbon, nitrogen, and C:N ratios for live and dead roots (Figure 4). The first component (PC1), accounting for 39.5% of the variance, primarily captured variation in biomass and carbon content, positively correlated with live root biomass and negatively with dead root fractions. The second component (PC2), explaining 22.8%, represented variation largely driven by nitrogen content and C:N ratios, with opposite loadings for live and dead roots.
Principal Component Analysis (PCA) biplot illustrating relationships among fine root metrics (biomass, carbon, nitrogen, and C:N ratio for live and dead roots). Samples are colored by season and sized by disturbance level, showing how root traits cluster by environmental conditions. Arrows indicate variable loadings on principal components 1 and 2, explaining the majority of variation.
Samples clustered distinctly by season along PC1 and PC2, with rainy season samples exhibiting higher biomass and carbon (positive PC1 scores) and lower C:N ratios (negative PC2 scores). In contrast, winter samples were characterized by lower biomass and higher C:N ratios, indicating lower root quality or increased senescence. Disturbance levels formed gradients within seasons: low disturbance samples tended to cluster with higher biomass and nutrient content, while high disturbance samples showed reduced values and more scattered distribution.
Biplot vectors indicated strong positive loadings of live biomass, carbon, and nitrogen along PC1, whereas dead biomass and the C:N ratio loaded negatively. These relationships highlight the contrasting physiological status and nutrient dynamics of live versus dead root fractions, as visualized in Figure 4 (see quantitative details in Table S2).
Discussion
This study provides preliminary evidence of how disturbance intensity, seasonal variation, soil depth, and root size interactively influence fine root biomass, nutrient content, and stoichiometric balance in a tropical dry forest ecosystem. The findings underscore the critical role of both environmental and anthropogenic drivers in shaping belowground ecological dynamics, with far-reaching implications for nutrient cycling, forest productivity, and ecosystem resilience under intensifying disturbance regimes.
Fine root biomass
Our results demonstrated that fine root biomass—across both live and dead fractions—declined significantly with increasing disturbance intensity. This is consistent with previous studies reporting that disturbances such as logging, trampling, or fire can reduce root density and turnover rates due to direct physical damage, compaction, or shifts in soil microbial communities (Hertel et al. Reference Hertel, Harteveld and Leuschner2009; Pregitzer et al. Reference Pregitzer, Burton, King and Zak2008). Additionally, newly reported trends of unprecedentedly high forest disturbance due to fire, especially in tropical intact forest landscapes, suggest rising threats to root biomass and associated nutrient pools (Potapov et al. Reference Potapov, Tyukavina, Turubanova, Hansen, Giglio, Hernandez-Serna, Lima, Harris and Stolle2025). These reductions are likely caused by a combination of immediate mechanical damage and long-term ecological shifts, including disrupted soil properties and mycorrhizal networks (Gill and Jackson Reference Gill and Jackson2000; Vogt et al. Reference Vogt, Vogt and Bloomfield1998). Reduced root biomass can hamper forest recovery by impairing water acquisition and nutrient uptake, constraining aboveground productivity (Freschet et al. Reference Freschet, Valverde‐Barrantes, Tucker, Craine, McCormack, Violle, Fort, Blackwood, Urban‐Mead, Iversen and Bonis2017).
Seasonality also exerted a strong and consistent influence, with peak fine root biomass observed during the rainy season and substantial declines in the winter. This pattern reinforces earlier findings that fine root dynamics in tropical forests are closely coupled with rainfall and temperature patterns (Ostonen et al. Reference Ostonen, Helmisaari, Borken, Tedersoo, Kukumägi, Bahram, Lindroos, Nöjd, Uri, Merilä and Asi2011). Elevated soil moisture and microbial activity during the rainy season likely stimulate both root proliferation and nutrient mineralization (Cordeiro et al. Reference Cordeiro, Norby, Andersen, Valverde-Barrantes, Fuchslueger, Oblitas, Hartley, Iversen, Gonçalves, Takeshi and Lapola2020). In contrast, the dry or cooler winter conditions suppress root growth and limit nutrient uptake, as documented in both tropical and temperate systems (Cusack et al. Reference Cusack, Addo-Danso, Agee, Andersen, Arnaud, Batterman, Brearley, Ciochina, Cordeiro, Dallstream and Diaz-Toribio2021; Kreyling Reference Kreyling2010).
The interaction between disturbance and season was particularly noteworthy. Our data revealed that the negative impacts of disturbance on fine root biomass were most severe in the winter, suggesting a compounding stress effect. This outcome aligns with the hypothesis that disturbance effects are magnified under environmentally limiting conditions, potentially due to increased physiological strain or diminished compensatory growth (Gough et al. Reference Gough, Vogel, Schmid and Curtis2008). These findings emphasize the importance of accounting for temporal variability in forest management strategies, particularly when evaluating recovery potential or scheduling interventions.
Carbon and nitrogen content
Both carbon and nitrogen contents declined with increasing disturbance, in tandem with reductions in biomass. The decline in carbon concentration may reflect reduced allocation of photosynthates to the root system or increased tissue senescence. Meanwhile, nitrogen reductions signal either diminished uptake efficiency or disrupted microbial nutrient cycling. Notably, soil depth had a stronger influence on nitrogen than on carbon, with higher nitrogen concentrations consistently found in the upper 10 cm of soil. This may result from greater organic inputs, microbial activity, and root turnover in surface layers (Jackson et al. Reference Jackson, Mooney and Schulze1997).
C:N ratio
The observed rise in C:N ratio with disturbance in live roots is ecologically significant, suggesting increased nitrogen limitation or decreased tissue quality. Elevated C:N ratios are often associated with slower decomposition and reduced microbial efficiency (Kramer and Chadwick Reference Kramer and Chadwick2018). Our results support the hypothesis that disturbance not only reduces fine root quantity but also alters its quality, potentially impeding nutrient cycling and ecosystem recovery. Seasonal increases in C:N during winter further underscore the compounded effects of environmental stress on root stoichiometry (Zhang et al. Reference Zhang, Zhao, Liu, Yang, Li, Yu, Wilcox, Yu and He2018).
Interaction among factors
Differences between live and dead root fractions revealed the dynamic nature of root turnover and decay. Dead root fractions showed lower absolute nutrient content but greater variability, indicative of ongoing decomposition. The consistently higher C:N ratios in dead roots are in line with previous findings that decomposing root tissue becomes increasingly carbon-rich as nitrogen is immobilized or lost (Hobbie Reference Hobbie2015; Silver and Miya Reference Silver and Miya2001).
Root size class also played a critical role, with smaller roots (<1 mm) consistently exhibiting higher biomass and nutrient concentrations. These roots serve as the primary sites for nutrient absorption and are more responsive to environmental stressors (Eissenstat and Yanai Reference Eissenstat and Yanai1997; Guo et al. Reference Guo, Mitchell, Withington, Fan and Hendricks2008). Their pronounced sensitivity to disturbance confirms their ecological importance and vulnerability, as hypothesized.
Multivariate analysis
The PCA provided an integrated view of trait relationships across environmental gradients. Live root biomass, carbon, and nitrogen clustered closely, reinforcing their tight coupling in root function. In contrast, dead root traits diverged markedly, capturing decomposition processes and nutrient release dynamics. Season and disturbance emerged as the dominant axes of variation, supporting our hypothesis that these factors jointly structure fine root traits across temporal and spatial scales. This multivariate framework advances current understanding by revealing how root traits shift not in isolation but as coordinated suites of responses (Freschet et al. Reference Freschet, Pagès, Iversen, Comas, Rewald, Roumet, Klimešová, Zadworny, Poorter, Postma and Adams2021; Legay et al. Reference Legay, Baxendale, Grigulis, Krainer, Kastl, Schloter, Bardgett, Arnoldi, Bahn, Dumont and Poly2014).
These results have clear implications for ecosystem management and restoration. The finding that disturbance reduces root biomass and nutrient content underscores the risk that intensified land-use or climate-related events may compromise belowground carbon storage and nutrient cycling. Fine roots are a crucial link between aboveground productivity and belowground processes; impairments at this level may lead to feedbacks that reduce forest resilience (Cusack et al. Reference Cusack, Addo-Danso, Agee, Andersen, Arnaud, Batterman, Brearley, Ciochina, Cordeiro, Dallstream and Diaz-Toribio2021; McCormack et al. Reference McCormack, Dickie, Eissenstat, Fahey, Fernandez, Guo, Helmisaari, Hobbie, Iversen, Jackson and Leppälammi-Kujansuu2015). Moreover, the enhanced sensitivity of roots during the winter suggests that disturbance effects are seasonally asymmetric and should inform the timing of management actions.
The present study has several limitations that should be acknowledged. First, the relatively low number of replicates may not fully represent spatial variability within tropical dry forest ecosystems. Second, live and dead fine roots were separated using morphological characteristics rather than ingrowth cores or isotopic approaches, which may introduce uncertainty in distinguishing partially decomposed roots. Third, disturbance categories were classified into discrete classes (low, medium, and high), whereas anthropogenic pressure in natural landscapes exists along a continuous gradient. Despite these limitations, the study provides important baseline information regarding interactive controls on fine root biomass and nutrient dynamics in disturbed tropical dry forests. Future research should prioritize long-term monitoring across multiple seasons and disturbance regimes to capture recovery trajectories and identify resilience thresholds. Linking fine root traits with microbial communities and aboveground productivity could offer a more holistic understanding of ecosystem responses. Such integration is necessary for building robust models to predict forest responses under global change scenarios (Kembel and Cahill Reference Kembel and Cahill2011).
This study adds to the growing body of evidence that fine root systems are highly vulnerable and dynamic components of forest ecosystems. Their importance is vital for sustaining ecosystem services, particularly in tropical forests where disturbance pressures are on the rise.
Conclusion
In conclusion, this study demonstrates that disturbance intensity, seasonal variation, soil depth, and root size class jointly regulate fine root biomass, carbon and nitrogen content, and their stoichiometric ratios in tropical dry forest ecosystems. Disturbance consistently reduced root biomass and nutrient content, with substantially lower biomass observed in highly disturbed forests compared to low-disturbance forests, with effects most severe during the winter and in deeper soil layers and during the winter months relative to the rainy season. Smaller fine roots—despite their functional importance—were the most sensitive to disturbance.
These findings highlight the ecological significance of fine roots in maintaining nutrient cycling and belowground carbon storage. They suggest that escalating disturbance regimes may degrade forest resilience by impairing belowground processes. Moreover, seasonal fluctuations modulate these effects, underscoring the need for temporally informed forest management.
Our multivariate analysis reinforces the idea that environmental drivers shape root traits in a coordinated and multidimensional manner. This integrative perspective offers a valuable framework for future research and conservation efforts. Protecting fine root systems should be a priority in forest management to ensure the sustainability of ecosystem functioning under intensifying global change pressures.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0266467426100844.
Data availability statement
My manuscript has no associated data.
Acknowledgements
The authors are thankful to the authorities of Indira Gandhi Krishi Vishwavidyalaya, Raipur, for their support and encouragement to carry out the present research work.
Author’s contributions
All authors contributed to the study conception and design.
Data was collected by Dr. Yamini Baghel.
Material preparation and analysis were performed by Dr. Ravi Kant Chaturvedi, Lalji Singh, Dr. Ekta Singh, Dr. Jiwan Lal, and Miss Piyusha Yadav.
The first draft of the manuscript was jointly written by Dr. Yamini Baghel, Dr. Ekta Singh, Dr. Ravikant Chaturvedi, and Lalji Singh,
All authors commented on previous versions of the manuscript.
Lalji Singh, Dr. R.K. Chaturvedi, Professor S.R. Gupta and Professor J.S. Singh read, reviewed, and approved the final manuscript.
Financial support
R.K.C. thanks National Natural Science Foundation of China (NSFC) (grant No. 31750110466) for financial support.
Competing interests
All the authors declare that they have no financial interests to disclose.
