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Article

Variations in Fine-Root Traits of Pseudotsuga sinensis Across Different Rocky-Desertification Gradients

by
Wangjun Li
1,
Shun Zou
1,
Dongpeng Lv
2,
Bin He
1 and
Xiaolong Bai
1,*
1
College of Ecological Engineering, Guizhou University of Engineering Science, Bijie 551700, China
2
Key Laboratory of Ecological Microbial Remediation Technology of Yunnan Higher Education Institutes, Dali University, Dali 671003, China
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(8), 533; https://doi.org/10.3390/d17080533
Submission received: 19 June 2025 / Revised: 26 July 2025 / Accepted: 27 July 2025 / Published: 29 July 2025
(This article belongs to the Section Plant Diversity)
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Abstract

Plant functional traits serve as vital tools for understanding vegetation adaptation mechanisms in changing environments. As the primary organs for nutrient acquisition from soil, fine roots are highly sensitive to environmental variations. However, current research on fine-root adaptation strategies predominantly focuses on tropical, subtropical, and temperate forests, leaving a significant gap in comprehensive knowledge regarding fine-root responses in rocky-desertification habitats. This study investigates the fine roots of Pseudotsuga sinensis across varying degrees of rocky desertification (mild, moderate, severe, and extremely severe). By analyzing fine-root morphological and nutrient traits, we aim to elucidate the trait differences and correlations under different desertification intensities. The results indicate that root dry matter content increases significantly with escalating desertification severity. Fine roots in mild and extremely severe desertification exhibit notably higher root C, K, and Mg concentrations compared to those in moderate and severe desertification, while root Ca concentration shows an inverse trend. Our correlation analyses reveal a highly significant positive relationship between specific root length and specific root area, whereas root dry matter content demonstrates a significant negative correlation with elemental concentrations. The principal component analysis (PCA) further indicates that the trait associations adopted by the forest in mild- and extremely severe-desertification environments are different from those in moderate- and severe-desertification environments. This study did not account for soil nutrient dynamics, microbial diversity, or enzymatic activity—key factors influencing fine-root adaptation. Future research should integrate root traits with soil properties to holistically assess resource strategies in rocky-desertification ecosystems. This study can serve as a theoretical reference for research on root characteristics and adaptation strategies of plants in rocky-desertification habitats.

1. Introduction

Plant functional traits are morphological, physiological, and phenological characteristics that reflect plant adaptations to changing environments [1]. These functional traits encompass traits of leaves, stems, and roots [2]. Over the past two decades, extensive research has been conducted on the functional traits of plant leaves and stems and their adaptive strategies to environmental changes [3,4,5]. However, studies on root systems remain limited due to challenges in sample collection and trait measurement [6,7]. Furthermore, while research on plant fine-root functional traits has primarily focused on tropical, subtropical, and temperate forests [8,9,10], investigations in karst rocky-desertification regions are exceptionally scarce [11]. Consequently, our understanding of how root systems adapt to arid and nutrient-poor soil environments in karst rocky-desertification habitats is lacking, as well.
Fine roots (diameters ≤ 2 mm) are critical organs for water and nutrient acquisition, exhibiting high sensitivity to soil environmental changes [12,13]. Their functional traits can be categorized into morphological traits (root diameter, RD; root volume, RV; root length, RL; specific root length, SRL; and specific root area, SRA) and physiological traits (root tissue density, RTD; root dry matter content, RDMC; and elemental concentrations, including carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg)) [2,14]. These traits collectively form the root economics spectrum (RES), which represents the trade-off between acquisitive strategies (prioritizing rapid resource uptake) and conservative strategies (emphasizing resource preservation and stress tolerance) [15,16,17]. Morphologically, traits like SRL (root length per unit biomass) and SRA (surface area per unit mass) reflect resource acquisition efficiency, with higher values indicating greater nutrient uptake capacity per biomass investment [18,19,20,21,22]. Conversely, RTD (mass per unit volume) and RDMC (dry matter content) signify structural investment and defense, often increasing in nutrient-poor soils [23,24,25]. Chemically, elemental stoichiometry (e.g., C:N:P ratios) reveals biochemical trade-offs: N and P drive metabolic processes (e.g., enzyme synthesis, ATP production), while K, Ca, and Mg regulate physiological functions (e.g., stomatal control, cell wall integrity) [26,27,28,29,30,31,32,33]. Within the RES, acquisitive-strategy roots are characterized by smaller diameters, high SRL/SRA, and elevated N concentrations, favoring rapid resource exploitation [19,21]. In contrast, conservative-strategy roots exhibit larger diameters, high RTD/RDMC, and lower N concentrations, enhancing durability and stress resistance [23,25,34]. While this spectrum is well-established in tropical/subtropical/temperate forests, its applicability to karst rocky-desertification regions remains unclear.
Karst rocky desertification represents a distinctive geological landscape characterized by low vegetation cover, a high bedrock exposure rate, and shallow impoverished soils [35]. As one of the most fragile ecosystems, it is particularly vulnerable to degradation triggered by both natural and anthropogenic factors, resulting in a significant decline in vegetation productivity [36,37]. Guizhou Province constitutes the center of rocky desertification in southwestern China, featuring an extensive and contiguous distribution of affected areas [38]. In recent decades, inappropriate land use practices and indiscriminate deforestation have exacerbated both the severity and spatial extent of rocky desertification [39]. Consequently, the development of effective strategies for protection, governance and ecological restoration of rocky-desertification vegetation has emerged as a critical challenge in regional environmental governance.
Pseudotsuga sinensis, a member of the Pinaceae family, is widely distributed in Guizhou Province in southwestern China, with forests covering an area of 373.2 hectares [40]. As a relict species from the Tertiary period, it is recognized as an endangered and endemic plant in China. Its close relative, the genus Pseudotsuga, serves as a classic model for studying the adaptive evolution of conifers, offering particularly valuable insights into drought tolerance and mechanisms of adaptation to karst habitats [41]. This conifer plays a crucial ecological role, including climate regulation, soil and water conservation, and biodiversity maintenance [42]. Notably, its predominant habitat—fragile karst rocky-desertification areas—renders it highly vulnerable to degradation or even extinction under the pressures of global warming and anthropogenic disturbances [43]. The extensive distribution and genetic diversity studies of Pseudotsuga species provide a comparative framework for analyzing the ecological adaptability of Pseudotsuga sinensis [44]. Therefore, investigating the physiological mechanisms by which Pseudotsuga sinensis adapts to karst rocky-desertification habitats through functional trait research can not only reveal its unique survival strategies but also offer cross-species conservation insights for other endangered species within the genus.
In this study, we investigated the fine-root traits of Pseudotsuga sinensis across different rocky-desertification gradients (mild, moderate, severe, and extremely severe) to understand the trait variation correlations in its characteristics. We hypothesize that (1) significant differences exist in fine-root morphological traits among Pseudotsuga sinensis trees growing under varying rocky-desertification intensities, and (2) in mild and moderate rocky-desertification forests, as well as in severe and extremely severe rocky-desertification forests, Pseudotsuga sinensis adopts similar trait combinations to adapt to the environment.

2. Materials and Methods

2.1. Study Sites

This study was conducted in the Pseudotsuga sinensis Nature Reserve of Weining County, Bijie City, located in the southwestern Guizhou Province of China (103.93° E–104.26° E, 26.54° N–26.76° N). The research area features a humid subtropical monsoon climate characterized by abundant precipitation, with an average annual rainfall of 1000 mm. The mean annual temperature is 10.5 °C, exhibiting minor seasonal variations but significant diurnal temperature fluctuations. The frost-free period extends for 182 days [45]. The area’s unique geographical combination of low latitude (26°), high elevation (1800 m to 2450 m), and plateau topography results in an exceptionally long sunshine duration, with yearly solar exposure averaging 1812 h [46]. The soil types in the region predominantly consist of brown limestone soil, yellow-brown earth, and dark red-brown loam [47].
The study area is predominantly characterized by karst rocky-desertification landforms, with extensive carbonate rock distributions, making it a representative site for rocky-desertification research [39]. Statistical data indicate that karst landscapes cover 4400 km2 within the county, including 1167.06 km2 affected by rocky desertification, of which moderate-to-severe desertification accounts for 30.54% of the total affected area [48]. The irrational utilization of land resources coupled with excessive deforestation has resulted in a significant decline in forest coverage. This ecological degradation has exacerbated soil erosion, leading to severe rocky desertification in karst regions [49]. The dominant vegetation consists of coniferous forests and deciduous oak species, primarily including Pinus yunnanensis, various Quercus species, Pinus armandii, and Keteleeria fortune [47,50].

2.2. Sample Plot Establishment

According to the rocky-desertification classification system established by Li et al. [41,51], four rocky-desertification levels were identified: mild, moderate, severe, and extremely severe. The classification was based on the following ecological indicators: mild (30–50% rock exposure rate, 50–70% vegetation cover, 30–50 cm average soil thickness), moderate (50–70% rock exposure rate, 30–50% vegetation cover, 20–40 cm average soil thickness), severe (70–90% rock exposure rate, 10–30% vegetation cover, <20 cm average soil thickness), and extremely severe (>90% rock exposure rate, <10% vegetation cover, <10 cm average soil thickness) (Table 1). In 2022, one representative 400 m2 (20 × 20 m) sample plots for each rocky-desertification degree were established within the Pseudotsuga sinensis protected area, and each plot was further divided into four 10 × 10 m subplots to ensure standardized and scientific sampling (Figure 1).

2.3. Root Sample Collection and Preparation

In 2022, sampling was conducted from the four 10 × 10 m quadrats established for each degree of rocky desertification, with the permission of the Weining County Forestry Bureau, Bijie City, Guizhou Province. Near the center of each 10 × 10 m quadrat, two Pseudotsuga sinensis trees with a diameter at breast-height (DBH) of approximately 30 cm were selected, totaling eight trees per rocky-desertification level. For each sampled tree, surface soil and litter were cleared at a distance of 50 cm from the main stem in the four cardinal directions (north, south, east, west). Fine roots (diameter ≤ 2 mm) from the 0–20 cm soil layer were excavated using the digging method, with three intact fine-root samples collected from each direction. For each rocky-desertification level, a total of 96 samples were collected and immediately placed in sealed bags, refrigerated, and transported to the laboratory for subsequent analysis.

2.4. Trait Measurements

Fine-root samples were thoroughly washed with tap water to remove attached soils and organic residues, followed by three rinses with distilled water to ensure complete cleaning. After surface water was removed to ensure complete cleaning using absorbent paper, fresh weight was determined using a precision electronic balance (0.0001 g). To prepare for scanning, fine roots were carefully arranged in a transparent tray filled with a thin layer of distilled water, using fine-tipped forceps to separate overlapping roots and minimize tangling. The roots were then scanned at a 1200 dpi resolution using a root scanner (Microtek ScanMaker i850, Delhi, India) [52], with care taken to avoid shadows or reflections during imaging. Following scanning, samples were oven-dried at 65 °C for 72 h to determine their dry weight (Figure 2).
The root length (RL, cm), diameter (RD, mm), volume (RV, cm3), and surface area (cm2) of the root systems were analyzed by scanning each root image using DJ-GX02 image analysis software (Dianjiang Technology, Shanghai, China). The specific root length (SRL, cm g−1) was by dividing the root length by the dry weight, while the specific root area (SRA, cm2 g−1) was derived by dividing the surface area divided by the dry weight. The root tissue density (RTD, g cm−3) was determined by dividing the dry weight by the volume, and the root dry matter content (RDMC, g g−1) was computed as the ratio of dry weight to fresh weight.
For elemental analysis, dried roots were ground using a cup mill and sieved through a 60-mesh screen (2 mm). Carbon (C) and nitrogen (N) concentrations were quantified using Dumas-type combustion (Vario MAX CN analyzer, Elementar, Gurgaon, India), while phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations were measured by ICP-AES (iCAP 7400, Thermo Fisher, Waltham, MA, USA). The N:P ratio, an indicator of nutrient limitation, was calculated following Wang et al. (2019) [53].

2.5. Data Analyses

We analyzed the fine-root traits across different rocky-desertification gradients using averaged data from 8 Pseudotsuga sinensis trees per 400 m2 plot. Prior to the statistical analysis, all datasets were log10-transformed to improve normality. Variations in fine-root traits were examined using one-way ANOVA, while trait interrelationships were assessed through Pearson correlation analysis. Principal component analysis (PCA) was employed to evaluate multivariate trait associations. We used PERMANOVA to test whether the differences between groups in multivariate space were significant. All statistical computations were performed using R statistical software (version 4.4.0, R Core Team 2024).

3. Results

Figure 3 demonstrates significant differences (p < 0.05) in RDMC among varying intensities of rocky desertification. Notably, the fine roots of Pseudotsuga sinensis in mild-desertification forests showed significantly lower RDMC values compared to those in moderate-, severe-, and extremely severe-desertification forests (Figure 3E; Table 2). However, we did not find significant variations (p > 0.05) in RD, RV, SRL, SRA, or RTD across the four desertification gradients (Figure 3A–D,F). Regarding fine-root nutrient concentrations, the C, K, and Mg concentrations in fine roots were significantly higher in both mild- and extremely severe-desertification forests compared to moderately and severely rocky-desertification forests (Figure 4A,D,F). Conversely, Ca concentration showed an inverse pattern, being significantly lower in mild- and extremely severe-desertification forests (Figure 4E). No significant differences were found in either the N and P concentrations of fine roots and their stoichiometric ratios (C:N, N:P, and C:P) among different rocky-desertification forests (Figure 4B,C,G–I).
The correlation analysis of fine-root traits demonstrated significant relationships among various parameters. Root diameter (RD) correlated positively with RV and SRL (p < 0.05), but negatively with SRA (Figure 5A–C). Specific root area (SRA) showed an inverse relationship with SRL (p < 0.05) while displaying a positive correlation with RTD (Figure 5D,E). Regarding chemical composition, fine-root C concentration was negatively associated with SRA but positively correlated with RTD (p < 0.05; Figure 6A,B). Root tissue density (RTD) exhibited positive elemental correlations with root Ca concentration but negative associations with root K and Mg concentrations (all p < 0.05; Figure 6C–E). Fine-root C concentration showed strong positive relationships with root K and Mg (p < 0.01), contrasting with its negative correlation with root Ca concentration (p < 0.05; Figure 7A,B). Among the mineral elements, fine-root Ca concentration demonstrated negative correlations with both root K and Mg concentrations (p < 0.05; Figure 5E,F), whereas root K and Mg concentrations were strongly positively correlated (p < 0.01; Figure 5D).
Principal component analysis (PCA) of five morphological and nine chemical traits in fine roots revealed differences in nutrient acquisition strategies across varying degrees of rocky desertification (Figure 8). The first principal axis (PC1) accounted for 75.04% of the total variance, with root Mg and K concentrations, SRA, and SRL loading negatively, corresponding to moderate- and severe-rocky-desertification habitats, reflecting a resource-conservative strategy. In contrast, RTD and Ca concentration loaded positively, associated with mild- and extremely severe rocky-desertification habitats, indicating a similar conservative resource-use strategy. Conversely, the second axis (PC2) contributed only 7.58% of the variation. Spatial convergence was evident in the species loading plot, with overlapping distributions between two pairs: mild- and extremely severe-desertification sites, as well as moderate- and-severe desertification forests. This clustering implies similar resource acquisition strategies within each paired desertification category (Figure 8).

4. Discussion

The observed differences in RDMC and root C, K, Mg, and Ca concentrations across rocky-desertification gradients provide partial support for the first hypothesis, confirming that edaphic stress influences belowground trait expression. However, the lack of significant variation in RV, RD, SRL, SRA, or RTD contrasts with studies demonstrating strong environmental sensitivity in these traits [54,55,56]. This discrepancy may stem from phylogenetic constraints in Pseudotsuga sinensis, as root morphological traits in some species exhibit evolutionary conservatism despite environmental variation [10]. Alternatively, the absence of detectable differences could reflect methodological limitations, such as a restricted sample size or unaccounted-for biotic interactions (e.g., mycorrhizal associations) that modulate root plasticity [26,52]. The convergence in root traits between mild- and extremely severe-desertification sites challenges the second hypothesis and aligns with emerging evidence of trait convergence under divergent stress regimes [57]. For instance, similar patterns have been reported in drought and nutrient-scarce environments, where plants adopt analogous morphological adaptations—such as reduced fine-root branching and increased tissue density—to cope with contrasting stressors [1,57,58]. This parallels findings in Mediterranean shrubs, where high abiotic filtering led to comparable root architectures in both high- and low-resource habitats [22]. By contrast, moderate- and severe-desertification sites exhibited intermediate trait values, suggesting that moderate stress may permit greater trait variability, whereas extreme conditions impose stronger selective pressures, narrowing the range of viable phenotypes.
The elevated C, K, and Mg concentrations in fine roots under both mild and extreme desertification highlight responses to stress [30,32,59]. While mild-desertification sites likely benefit from higher nutrient availability, the retention of these elements in extreme desertification aligns with studies where increased C allocation to structural compounds (e.g., lignin, cellulose) [60] is linked with K/Mg accumulation to osmotic regulation under duress [32,61]. Notably, this contrasts with observations in non-karst ecosystems, where severe nutrient depletion typically reduces tissue Mg and K concentrations [62,63]. The retention of these nutrients in Pseudotsuga sinensis may reflect karst-specific adaptations, such as calcium-rich soils altering cation exchange dynamics.
According to the root economics spectrum theory [22], diverse relationships exist among functional traits within or between plant organs, arising from functional trade-offs that reflect ecological adaptation strategies to environmental changes [64,65]. Higher SRA and SRL show positive correlations with the proportion of absorptive roots, indicating that a greater root–soil contact area enhances water and nutrient acquisition capacity [62]. A strong negative correlation exists between RL and RD, reflecting trade-offs in root resource acquisition strategies (e.g., lifespan vs. acquisition efficiency, and acquisition vs. protection) [66,67]. Root tissue density (RTD) decreases with increasing nutrient availability, demonstrating higher density in nutrient-poor soils [24,25]. This may explain why RTD shows significant negative correlations with fine-root C, K, and Mg concentrations.
The high variance explanation rate (75.04%) of the first principal component (PC1) highlights its dominant role in distinguishing strategies. These results align with studies highlighting how environmental stress drives trait covariation and resource-use trade-offs [16,19]. In moderate/severe-rocky-desertification plots, the negative loadings of specific root area (SRA) and specific root length (SRL) on PC1 reflect typical slow-growth traits of plants adapting to barren or stressed soils [20,68]. In contrast, the positive loadings of root tissue density (RTD) and calcium (Ca) on PC1 in mild/extremely severe plots reveal parallel yet differentiated adaptive strategies: high RTD and Ca may represent adaptations to physical barriers (e.g., rock fragmentation) or drought, as dense tissues enhance water retention and mechanical strength [69]. Notably, the roles of Ca in cell wall stabilization [31] could explain its association with extreme desertification, where structural integrity is critical. The clustering of mild/extremely severe and moderate/severe sites on PC2 implies shared strategies within paired degradation levels. This convergence may reflect environmental filtering: similar abiotic stresses (e.g., water scarcity, nutrient heterogeneity) select for analogous trait syndromes [57]. Future work could integrate soil properties (e.g., rock cover, moisture) to clarify drivers of trait divergence. Additionally, trait covariation (e.g., Mg-K synergy) warrants investigation into nutrient interactions under desertification [70].
Unlike leaf traits, fine-root functional traits exhibit less pronounced inter-trait relationships due to multiple interacting factors, including soil resource heterogeneity and variations in mycorrhizal colonization type/intensity. Consequently, a single acquisition-conservation axis inadequately captures the complexity of fine-root trade-offs [34,66]. Furthermore, the scope of this study (limited to a single species and geographical range) may constrain the detection of universal patterns in fine-root trait trade-offs [16,34,71]. In addition, our study relied on fine-root trait data from a single year (2022), which may not account for interannual variability influenced by climatic fluctuations or long-term environmental changes. Fine-root traits can exhibit seasonal dynamics, such as variations in production, mortality, and functional adaptations to resource availability [62,72]. Our findings elucidate the differential nutrient requirements of Pseudotsuga sinensis root systems across varying degrees of rocky desertification. This discovery will enable us to develop tailored nutrient supply strategies for Pseudotsuga sinensis communities in different desertification stages, thereby providing crucial data support for the cultivation and management of these communities in rocky-desertification habitats.

5. Conclusions

This study investigated the morphological and nutrient traits of Pseudotsuga sinensis fine roots across varying degrees of rocky desertification. The results revealed that among the root morphological traits, only RDMC exhibited significant differences. Regarding nutrient traits, fine roots in mild- and extremely severe-desertification forests showed significantly higher concentrations of fine-root C, K, and Mg but lower Ca concentrations compared to those in moderate- and severe-desertification forests. Significant correlations were observed between fine-root morphological and nutrient traits. Principal component analysis indicated that Pseudotsuga sinensis in mild- and extremely severe-desertification forests shared similar adaptation mechanisms, while those in moderate- and severe-desertification forests exhibited analogous adaptation strategies. The observed similarity in trends between mild/extremely severe and moderate/severe desertification levels could not be fully explained due to the unaccounted-for influence of soil factors in this study. Therefore, future research should integrate fine-root morphology, anatomy, nutrient dynamics, and soil properties to comprehensively elucidate the adaptation mechanisms of Pseudotsuga sinensis to rocky-desertification gradients. The findings of this study provide a reference for studies on plant root systems in rocky-desertification regions.

Author Contributions

Conceptualization, W.L., B.H. and X.B.; investigation, W.L., D.L. and X.B.; methodology, X.B., D.L. and S.Z.; formal analysis: X.B., S.Z. and B.H.; writing—original draft preparation, W.L. and X.B.; writing—review and editing, W.L. and X.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Guizhou Provincial Science and Technology Project (qiankehejichu[2024]Youth031, the Guizhou Provincial Science and Technology Project (qiankehejichu[2024]key077), the Bijie Science and Technology Project (bikelianhe[2023]10), the Bijie Science and Technology Project (bikelianhe[2023]22), and the Bijie Science and Technology Project (bikelianhe[2023]23).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

We are grateful to the Weining County Forestry Bureau for providing support during sampling and data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Violle, C.; Navas, M.L.; Vile, D.; Kazakou, E.; Fortunel, C.; Hummel, I.; Garnier, E. Let the concept of trait be functional! Oikos 2007, 116, 882–892. [Google Scholar] [CrossRef]
  2. Perez-Harguindeguy, N.; Diaz, S.; Garnier, E.; Lavorel, S.; Poorter, H.; Jaureguiberry, P.; Bret-Harte, M.S.; Cornwell, W.K.; Craine, J.M.; Gurvich, D.E.; et al. Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 2016, 64, 715–716. [Google Scholar] [CrossRef]
  3. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.C.; Diemer, M.; et al. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef]
  4. Chave, J.; Coomes, D.; Jansen, S.; Lewis, S.L.; Swenson, N.G.; Zanne, A.E. Towards a worldwide wood economics spectrum. Ecol. Lett. 2009, 12, 351–366. [Google Scholar] [CrossRef] [PubMed]
  5. Wang, J.; Defrenne, C.; McCormack, M.L.; Yang, L.; Tian, D.; Luo, Y.; Hou, E.; Yan, T.; Li, Z.; Bu, W.; et al. Fine-root functional trait responses to experimental warming: A global meta-analysis. New Phytol. 2021, 230, 1856–1867. [Google Scholar] [CrossRef] [PubMed]
  6. Laliberté, E. Below-ground frontiers in trait-based plant ecology. New Phytol. 2017, 213, 1597–1603. [Google Scholar] [CrossRef] [PubMed]
  7. Freschet, G.T.; Roumet, C.; Comas, L.H.; Weemstra, M.; Bengough, A.G.; Rewald, B.; Bardgett, R.D.; De Deyn, G.B.; Johnson, D.; Klimešová, J.; et al. Root traits as drivers of plant and ecosystem functioning: Current understanding, pitfalls and future research needs. New Phytol. 2021, 232, 1123–1158. [Google Scholar] [CrossRef]
  8. Ma, Z.; Guo, D.; Xu, X.; Lu, M.; Bardgett, R.D.; Eissenstat, D.M.; McCormack, M.L.; Hedin, L.O. Evolutionary history resolves global organization of root functional traits. Nature 2018, 555, 94–97. [Google Scholar] [CrossRef]
  9. Cusack, D.F.; Addo-Danso, S.D.; Agee, E.A.; Andersen, K.M.; Arnaud, M.; Batterman, S.A.; Brearley, F.Q.; Ciochina, M.I.; Cordeiro, A.L.; Dallstream, C.; et al. Tradeoffs and synergies in tropical forest root traits and dynamics for nutrient and water acquisition: Field and modeling advances. Front. For. Glob. Change 2021, 4, 704469. [Google Scholar] [CrossRef]
  10. Sun, L.; Ataka, M.; Han, M.; Han, Y.; Gan, D.; Xu, T.; Guo, Y.; Zhu, B. Root exudation as a major competitive fine-root functional trait of 18 coexisting species in a subtropical forest. New Phytol. 2021, 229, 259–271. [Google Scholar] [CrossRef]
  11. Wang, Y.; Wang, J.; Wang, Y.; Wang, X.; Jin, B.; Chen, C.; Zhao, X. Contribution of litter and root to soil nutrients in different rocky desertification grasslands in a karst area. Plants 2024, 13, 2329. [Google Scholar] [CrossRef]
  12. Burton, A.J.; Pregitzer, K.S.; Hendrick, R.L. Relationships between fine root dynamics and nitrogen availability in Michigan northern hardwood forests. Oecologia 2000, 125, 389–399. [Google Scholar] [CrossRef]
  13. Addo-Danso, S.D.; Defrenne, C.E.; McCormack, M.L.; Ostonen, I.; Addo-Danso, A.; Foli, E.G.; Borden, K.A.; Isaac, M.E.; Prescott, C.E. Fine-root morphological trait variation in tropical forest ecosystems: An evidence synthesis. Plant Ecol. 2020, 221, 1–13. [Google Scholar] [CrossRef]
  14. Guerrero-Ramírez, N.R.; Mommer, L.; Freschet, G.T.; Iversen, C.M.; McCormack, M.L.; Kattge, J.; Poorter, H.; van der Plas, F.; Bergmann, J.; Kuyper, T.W.; et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 2021, 30, 25–37. [Google Scholar] [CrossRef]
  15. Pinno, B.D.; Wilson, S.D. Fine root response to soil resource heterogeneity differs between grassland and forest. Plant Ecol. 2013, 214, 821–829. [Google Scholar] [CrossRef]
  16. Weemstra, M.; Mommer, L.; Visser, E.J.; van Ruijven, J.; Kuyper, T.W.; Mohren, G.M.; Sterck, F.J. Towards a multidimensional root trait framework: A tree root review. New Phytol. 2016, 211, 1159–1169. [Google Scholar] [CrossRef] [PubMed]
  17. Miyatani, K.; Tanikawa, T.; Makita, N.; Hirano, Y. Relationships between specific root length and respiration rate of fine roots across stands and seasons in Chamaecyparis obtusa. Plant Soil 2018, 423, 215–227. [Google Scholar] [CrossRef]
  18. Mommer, L.; Weemstra, M. The role of roots in the resource economics spectrum. New Phytol. 2012, 195, 725–727. [Google Scholar] [CrossRef]
  19. Reich, P.B.; Luo, Y.; Bradford, J.B.; Poorter, H.; Perry, C.H.; Oleksyn, J. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl. Acad. Sci. USA 2014, 111, 13721–13726. [Google Scholar] [CrossRef]
  20. Ostonen, I.; Püttsepp, Ü.; Biel, C.; Alberton, O.; Bakker, M.R.; Lõhmus, K.; Majdi, H.; Metcalfe, D.; Olsthoorn, A.F.M.; Pronk, A.; et al. Specific root length as an indicator of environmental change. Plant Biosyst. 2007, 141, 426–442. [Google Scholar] [CrossRef]
  21. Lõhmus, K.; Oja, T.; Lasn, R. Specific root area: A soil characteristic. Plant Soil 1989, 119, 245–249. [Google Scholar] [CrossRef]
  22. de la Riva, E.G.; Querejeta, J.I.; Villar, R.; Pérez-Ramos, I.M.; Marañón, T.; Galán Díaz, J.; Marín, S.T.; Prieto, I. The economics spectrum drives root trait strategies in Mediterranean vegetation. Front. Plant Sci. 2021, 12, 773118. [Google Scholar] [CrossRef] [PubMed]
  23. Craine, J.M.; Froehle, J.; Tilman, D.G.; Wedin, D.A.; Chapin, F.S., III. The relationships among root and leaf traits of 76 grassland species and relative abundance along fertility and disturbance gradients. Oikos 2001, 93, 274–285. [Google Scholar] [CrossRef]
  24. Kramer-Walter, K.R.; Bellingham, P.J.; Millar, T.R.; Smissen, R.D.; Richardson, S.J.; Laughlin, D.C. Root traits are multidimensional: Specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 2016, 104, 1299–1310. [Google Scholar] [CrossRef]
  25. Díaz, S.; Kattge, J.; Cornelissen, J.H.; Wright, I.J.; Lavorel, S.; Dray, S.; Reu, B.; Kleyer, M.; Wirth, C.; Prentice, I.C.; et al. The global spectrum of plant form and function. Nature 2016, 529, 167–171. [Google Scholar] [CrossRef] [PubMed]
  26. Lambers, H.; Chapin, F.S., III; Pons, T.L. Plant Physiological Ecology; Springer Science & Business Media: Heidelberg, Germany, 2008. [Google Scholar]
  27. Guo, D.; Xia, M.; Wei, X.; Chang, W.; Liu, Y.; Wang, Z. Anatomical traits associated with absorption and mycorrhizal colonization are linked to root branch order in twenty-three Chinese temperate tree species. New Phytol. 2008, 180, 673–683. [Google Scholar] [CrossRef]
  28. Ruffel, S.; Krouk, G.; Ristova, D.; Shasha, D.; Birnbaum, K.D.; Coruzzi, G.M. Nitrogen economics of root foraging: Transitive closure of the nitrate–cytokinin relay and distinct systemic signaling for N supply vs. demand. Proc. Natl. Acad. Sci. USA 2011, 108, 18524–18529. [Google Scholar] [CrossRef]
  29. Tränkner, M.; Tavakol, E.; Jákli, B. Functioning of potassium and magnesium in photosynthesis, photosynthate translocation and photoprotection. Physiol. Plant. 2018, 163, 414–431. [Google Scholar] [CrossRef]
  30. Sardans, J.; Peñuelas, J. Potassium control of plant functions: Ecological and agricultural implications. Plants 2021, 10, 419. [Google Scholar] [CrossRef]
  31. White, P.J.; Broadley, M.R. Calcium in plants. Ann. Bot. 2003, 92, 487–511. [Google Scholar] [CrossRef]
  32. Verbruggen, N.; Hermans, C. Physiological and molecular responses to magnesium nutritional imbalance in plants. Plant Soil 2013, 368, 87–99. [Google Scholar] [CrossRef]
  33. Ågren, G.I. The C: N: P stoichiometry of autotrophs–theory and observations. Ecol. Lett. 2004, 7, 185–191. [Google Scholar] [CrossRef]
  34. Kong, D.; Wang, J.; Wu, H.; Valverde-Barrantes, O.J.; Wang, R.; Zeng, H.; Kardol, P.; Zhang, H.; Feng, Y. Nonlinearity of root trait relationships and the root economics spectrum. Nat. Commun. 2019, 10, 2203. [Google Scholar] [CrossRef] [PubMed]
  35. Jiang, Z.; Lian, Y.; Qin, X. Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth Sci. Rev. 2014, 132, 1–12. [Google Scholar] [CrossRef]
  36. Wang, D.; Zhu, S.; Huang, B. Changes of vegetation features of rocky desertification process in karst area of Guizhou. J. Nanjing For. Univ. 2003, 27, 26–30. [Google Scholar]
  37. Liu, J.; Guo, Y.; Wang, L.; Han, D. Current status and control measures of ecological restoration in the karst rocky desertification areas of Guizhou. J. Anhui Agric. Sci. 2011, 39, 11684–11686. [Google Scholar]
  38. Cao, J.; Yuan, D.; Zhang, C.; Jiang, Z. Karst ecosystem constrained by geological conditions in southwest China. Earth Environ. 2004, 32, 1–8. [Google Scholar]
  39. Li, L.; Cai, Z.; Chen, Z.; Li, Q.; Sun, J. Extraction and analysis of temporal and spatial evolution of rocky desertification: Acase study of Weining County. J. Geomat. 2022, 47, 115–118. [Google Scholar]
  40. Xiong, B.; Wang, Z.; Tian, K.; Zhang, E.; Li, Z.; Li, S.; Peng, Z. Coenological characteristics of Pseudotsuga sinensis forests in Qizimei Mountains Nature Reserve. Guihaia 2017, 37, 434–441. [Google Scholar]
  41. Li, W.; Zhang, W.; Feng, T.; Lv, D.; Zou, S.; He, B.; Bai, X. Variations in leaf functional traits of Pseudotsuga sinensis across forests with varying levels of rocky desertification. Ecol. Evol. 2025, 15, e70916. [Google Scholar] [CrossRef]
  42. Li, M.; Xie, S. Study on community and population structure of Pseudotsuga sinensis forest in karst mountainous region of Guizhou Province. Tianjin Agric. Sci. 2015, 21, 150–153. [Google Scholar]
  43. Nong, X.; Yu, H.; Xiang, Y.; Yang, P.; Zhang, Q. Analysis of potential suitable habitat change of Pseudotsuga sinensis based on MaxEnt mode. J. Guangxi Norm. Univ. 2024, 42, 215–225. [Google Scholar]
  44. Li, W.J.; Feng, T.; Li, J.; He, B.; Zou, S.; Liu, P.J. The complete chloroplast genome of Pseudotsuga sinensis, a China endemic species. Mitochondrial DNA Part B 2023, 8, 23–25. [Google Scholar] [CrossRef] [PubMed]
  45. Cai, J.; Yang, C.; Ni, J.; Zhang, Y.; Yu, L. Analysis and forecast method for maximum and minimum temperature in towns of Weining. Mid-Low Latit. Mt. Meteorol. 2019, 43, 29–35. [Google Scholar]
  46. Linghu, X.; Teng, M.; Wang, J. Lang cover changes and transfer characteristics in the Weining Mountains based on topographic gradients: A case study on Weining County, Guizhou Province. Geograph. Environ. Sci. 2023, 41, 74–81. [Google Scholar]
  47. Zang, Y.; Wei, X.; Wei, X.; Shu, Z.; Liu, T. Analysis of mechanical composition of three typical soil types in Weining County, Guizhou Province. J. Green Sci. Technol. 2020, 17–22. [Google Scholar]
  48. Chen, Q. Analysis of monitoring results and control measure for stony desertification in Weining County. J. Green Sci. Technol. 2023, 25, 64–67. [Google Scholar]
  49. Wang, K.; Yue, Y.; Chen, H.; Wu, X.; Xiao, J.; Zhang, W.; Du, H. The comprehensive treatment of karst rocky desertification and its regional restoration effects. Acta Ecol. Sin. 2019, 39, 7432–7440. [Google Scholar] [CrossRef]
  50. Wang, Z.; Ren, J.; Dai, Y.; Yang, K. Temporal-spatial variation of vegetation productivity in Guizhou: A case study in Weining County. Environ. Sci. Technol. 2018, 41, 200–205. [Google Scholar]
  51. Li, S.; Dong, Y.X.; Wang, J.H. Re-discussion on the concept and classification of rocky desertification. Carsol. Sin. 2007, 26, 279–284. [Google Scholar]
  52. Gao, Y.; Yuan, Y.; Li, Q.; Kou, L.; Fu, X.; Dai, X.; Wang, H. Mycorrhizal type governs foliar and root multi-elemental stoichiometries of trees mainly via root traits. Plant Soil 2021, 460, 229–246. [Google Scholar] [CrossRef]
  53. Wang, Z.; Yu, K.; Lv, S.; Niklas, K.J.; Mipam, T.D.; Crowther, T.W.; Umaña, M.N.; Zhao, Q.; Huang, H.; Reich, P.B. The scaling of fine root nitrogen versus phosphorus in terrestrial plants: A global synthesis. Funct. Ecol. 2019, 33, 2081–2094. [Google Scholar] [CrossRef]
  54. Comas, L.H.; Mueller, K.E.; Taylor, L.L.; Midford, P.E.; Callahan, H.S.; Beerling, D.J. Evolutionary patterns and biogeochemical significance of angiosperm root traits. Int. J. Plant Sci. 2012, 173, 584–595. [Google Scholar] [CrossRef]
  55. Aritsara, A.N.A.; Wang, S.; Li, B.N.; Jiang, X.; Qie, Y.D.; Tan, F.S.; Zhang, Q.W.; Cao, K.F. Divergent leaf and fine root “pressure–volume relationships” across habitats with varying water availability. Plant Physiol. 2022, 190, 2246–2259. [Google Scholar] [CrossRef] [PubMed]
  56. Gao, Y.; Wang, H.; Yang, F.; Dai, X.; Meng, S.; Hu, M.; Kou, L.; Fu, X. Relationships between root exudation and root morphological and architectural traits vary with growing season. Tree Physiol. 2024, 44, tpad118. [Google Scholar] [CrossRef]
  57. Kraft, N.J.; Adler, P.B.; Godoy, O.; James, E.C.; Fuller, S.; Levine, J.M. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 2015, 29, 592–599. [Google Scholar] [CrossRef]
  58. Chapin, F.S., III; Autumn, K.; Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 1993, 142, S78–S92. [Google Scholar] [CrossRef]
  59. Sterner, R.W.; Elser, J.J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere; Princeton University Press: Princeton, NJ, USA, 2002; pp. 1–20. [Google Scholar]
  60. Poorter, H.; Niklas, K.J.; Reich, P.B.; Oleksyn, J.; Poot, P.; Mommer, L. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 2012, 193, 30–50. [Google Scholar] [CrossRef]
  61. Liu, X.; Ma, K. Plant functional traits—Concepts, applications and future directions. Sci. Sin. Vitae 2015, 45, 325–339. [Google Scholar] [CrossRef]
  62. Hu, Q.; Sheng, M.; Yin, J.; Bai, Y. Stoichiometric characteristics of fine roots and rhizosphere soil of Broussonetia papyrifera adapted to the karst rocky desertification environment in southwest China. Chin. J. Plant Ecol. 2020, 44, 962–972. [Google Scholar] [CrossRef]
  63. Gondal, A.H.; Hussain, I.; Ijaz, A.B.; Zafar, A.; Ch, B.I.; Zafar, H.; Sohail, M.D.; Niazi, H.; Touseef, M.; Khan, A.A.; et al. Influence of soil pH and microbes on mineral solubility and plant nutrition: A review. Int. J. Agric. Biol. Sci. 2021, 5, 71–81. [Google Scholar]
  64. Wang, F.; Yan, L.; Liu, L. Research progress of plant functional traits in Karst areas. Guizhou Fore. Sci. Technol. 2025, 53, 80–86. [Google Scholar]
  65. Sell, M.; Rohula-Okunev, G.; Kupper, P.; Ostonen, I. Adapting to climate change: Responses of fine root traits and C exudation in five tree species with different light-use strategy. Front. Plant Sci. 2024, 15, 1389569. [Google Scholar] [CrossRef] [PubMed]
  66. Bergmann, J.; Weigelt, A.; van Der Plas, F.; Laughlin, D.C.; Kuyper, T.W.; Guerrero-Ramirez, N.; Valverde-Barrantes, O.J.; Bruelheide, H.; Freschet, G.T.; Iversen, C.M.; et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 2020, 6, eaba3756. [Google Scholar] [CrossRef]
  67. Holdaway, R.J.; Richardson, S.J.; Dickie, I.A.; Peltzer, D.A.; Coomes, D.A. Species-and community-level patterns in fine root traits along a 120 000-year soil chronosequence in temperate rain forest. J. Ecol. 2011, 99, 954–963. [Google Scholar] [CrossRef]
  68. Sun, J.H.; Shi, H.L.; Chen, K.Y.; Ji, B.M.; Zhang, J. Research advances on trade-off relationships of plant fine root functional traits. Chin. J. Plant Ecol. 2023, 47, 1055. [Google Scholar] [CrossRef]
  69. Fort, F.; Jouany, C.; Cruz, P. Root and leaf functional trait relations in Poaceae species: Implications of differing resource-acquisition strategies. J. Plant Ecol. 2013, 6, 211–219. [Google Scholar] [CrossRef]
  70. Zemunik, G.; Turner, B.L.; Lambers, H.; Laliberté, E. Diversity of plant nutrient-acquisition strategies increases during long-term ecosystem development. Nat. Plants 2015, 1, 15050. [Google Scholar] [CrossRef]
  71. Zheng, J.; Freschet, G.T.; Tedersoo, L.; Li, S.; Yan, H.; Jiang, L.; Wang, H.; Ma, N.; Dai, X.; Fu, X.; et al. A trait-based root acquisition-defence-decomposition framework in angiosperm tree species. Nat. Commun. 2024, 15, 5311. [Google Scholar] [CrossRef]
  72. Hendricks, J.J.; Hendrick, R.L.; Wilson, C.A.; Mitchell, R.J.; Pecot, S.D.; Guo, D. Assessing the patterns and controls of fine root dynamics: An empirical test and methodological review. J. Ecol. 2006, 94, 40–57. [Google Scholar] [CrossRef]
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Figure 1. Examples of four plots with differing intensities of rocky desertification.
Figure 1. Examples of four plots with differing intensities of rocky desertification.
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Figure 2. Representative photographs of fine roots from different degrees of rocky desertification areas.
Figure 2. Representative photographs of fine roots from different degrees of rocky desertification areas.
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Figure 3. The differences in fine-root morphological traits of Pseudotsuga sinensis in forests with different degrees of rocky desertification. RD, root diameter; RV, root volume; SRL, specific root length; SRA, specific root area; RDMC, root dry matter content; RTD, root tissue density. (A–F) indicate the serial numbers of the figures. ns, p > 0.05; *, p < 0.05; **, p < 0.01.
Figure 3. The differences in fine-root morphological traits of Pseudotsuga sinensis in forests with different degrees of rocky desertification. RD, root diameter; RV, root volume; SRL, specific root length; SRA, specific root area; RDMC, root dry matter content; RTD, root tissue density. (A–F) indicate the serial numbers of the figures. ns, p > 0.05; *, p < 0.05; **, p < 0.01.
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Figure 4. The differences in fine-root nutrient concentrations and their stoichiometric ratios of Pseudotsuga sinensis in forests with different degrees of rocky desertification. C, carbon concentration; N, nitrogen concentration; P, phosphorus concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration; C:N, carbon-to-nitrogen ratio; C:P, carbon-to-phosphorus ratio; N:P, nitrogen-to-phosphorus ratio. (A–I) indicate the serial numbers of the figures. ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 4. The differences in fine-root nutrient concentrations and their stoichiometric ratios of Pseudotsuga sinensis in forests with different degrees of rocky desertification. C, carbon concentration; N, nitrogen concentration; P, phosphorus concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration; C:N, carbon-to-nitrogen ratio; C:P, carbon-to-phosphorus ratio; N:P, nitrogen-to-phosphorus ratio. (A–I) indicate the serial numbers of the figures. ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
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Figure 5. Pearson’s correlations of fine-root morphological traits of Pseudotsuga sinensis. RV, root volume; SRL, specific root length; SRA, specific root area; RTD, root tissue density. The gray scale represents the 95% confidence interval. (A–E) indicate the serial numbers of the figures.
Figure 5. Pearson’s correlations of fine-root morphological traits of Pseudotsuga sinensis. RV, root volume; SRL, specific root length; SRA, specific root area; RTD, root tissue density. The gray scale represents the 95% confidence interval. (A–E) indicate the serial numbers of the figures.
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Figure 6. Pearson’s correlations between fine-root morphological traits and nutrient concentrations of Pseudotsuga sinensis. SRA, specific root area; RTD, root tissue density; C, carbon concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration. The gray scale represents the 95% confidence interval. (A–E) indicate the serial numbers of the figures.
Figure 6. Pearson’s correlations between fine-root morphological traits and nutrient concentrations of Pseudotsuga sinensis. SRA, specific root area; RTD, root tissue density; C, carbon concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration. The gray scale represents the 95% confidence interval. (A–E) indicate the serial numbers of the figures.
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Figure 7. Pearson’s correlations of nutrients concentrations of Pseudotsuga sinensis. C, carbon concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration. The gray scale represents the 95% confidence interval. (A–F) indicate the serial numbers of the figures.
Figure 7. Pearson’s correlations of nutrients concentrations of Pseudotsuga sinensis. C, carbon concentration; K, potassium concentration; Ca, calcium concentration; Mg, magnesium concentration. The gray scale represents the 95% confidence interval. (A–F) indicate the serial numbers of the figures.
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Figure 8. A biplot of the first two axes of the principal component analysis (PCA) for the relationships of fine-root traits and the loadings of the forests with different degrees of rocky desertification. See the text for trait abbreviations. All leaf traits were log10-transformed before the analysis.
Figure 8. A biplot of the first two axes of the principal component analysis (PCA) for the relationships of fine-root traits and the loadings of the forests with different degrees of rocky desertification. See the text for trait abbreviations. All leaf traits were log10-transformed before the analysis.
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Table 1. Classification characteristics of rocky-desertification degrees.
Table 1. Classification characteristics of rocky-desertification degrees.
Degree of Rocky DesertificationRock Expose Rate (%)Vegetation Coverage (%)Average Soil Thickness (cm)
Mild30–5050–7030–50
Moderate50–7030–5020–40
Severe70–9010–30<20
Extremely severe>90<10<10
Table 2. One-way ANOVA results for differences among four rocky-desertification levels.
Table 2. One-way ANOVA results for differences among four rocky-desertification levels.
TraitsdfF-Value95% Confidence Intervalp-Value
RD(3, 28)0.143[0.93, 1.06]0.934
RV(3, 28)0.982[0.68, 0.95]0.416
SRL(3, 28)1.12[2.52, 3.67]0.358
SRA(3, 28)1.660[67.16, 80.18]0.198
RDMC(3, 28)4.010[0.46, 0.52]0.017
RTD(3, 28)2.408[0.52, 0.59]0.088
C(3, 28)22.74[490.40, 504.04]<0.001
N(3, 28)1.465[3.37, 3.89]0.245
P(3, 28)1.266[0.49, 0.61]0.305
K(3, 28)19.552[1.51, 2.74]<0.001
Ca(3, 28)21.569[1.18, 1.94]<0.001
Mg(3, 28)85.295[2.04, 4.01]<0.001
C/N(3, 28)2.144[129.87, 159.04]0.117
C/P(3, 28)1.084[882.69, 1071.72]0.372
N/P(3, 28)0.298[6.28, 7.77]0.872
Note: df, degrees of freedom (between groups, within groups). Confidence intervals (CI) are for mean differences between groups. Abbreviations of traits are shown in text.
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Li, W.; Zou, S.; Lv, D.; He, B.; Bai, X. Variations in Fine-Root Traits of Pseudotsuga sinensis Across Different Rocky-Desertification Gradients. Diversity 2025, 17, 533. https://doi.org/10.3390/d17080533

AMA Style

Li W, Zou S, Lv D, He B, Bai X. Variations in Fine-Root Traits of Pseudotsuga sinensis Across Different Rocky-Desertification Gradients. Diversity. 2025; 17(8):533. https://doi.org/10.3390/d17080533

Chicago/Turabian Style

Li, Wangjun, Shun Zou, Dongpeng Lv, Bin He, and Xiaolong Bai. 2025. "Variations in Fine-Root Traits of Pseudotsuga sinensis Across Different Rocky-Desertification Gradients" Diversity 17, no. 8: 533. https://doi.org/10.3390/d17080533

APA Style

Li, W., Zou, S., Lv, D., He, B., & Bai, X. (2025). Variations in Fine-Root Traits of Pseudotsuga sinensis Across Different Rocky-Desertification Gradients. Diversity, 17(8), 533. https://doi.org/10.3390/d17080533

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