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Article

Effects of Tree Species on Moso Bamboo (Phyllostachys edulis (Carriere) J. Houzeau) Fine Root Morphology, Biomass, and Soil Properties in Bamboo–Broadleaf Mixed Forests

by
Yang Zhou
1,2,
Fengying Guan
1,2,3,*,
Zhen Li
1,
Yaxiong Zheng
1,
Xiao Zhou
1 and
Xuan Zhang
1
1
International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing 100102, China
2
The Bamboo Forest Eco-System Research Station of Fujian Yongan, Yongan 366000, China
3
National Location Observation and Research Station of the Bamboo Forest Ecosystem in Yixing, National Forestry and Grassland Administration, Yixing 214200, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(11), 1834; https://doi.org/10.3390/f13111834
Submission received: 19 September 2022 / Revised: 1 November 2022 / Accepted: 1 November 2022 / Published: 3 November 2022
(This article belongs to the Special Issue Soil Biology and Biochemistry of Forests)
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Abstract

:
Understanding fine root characteristics in relation to soil properties of bamboo–broadleaf mixed forests may help optimize belowground production management and ecological functions in mixed-forest ecosystems. In this study, we compared four different bamboo–broadleaf mixed forests: Castanopsis chinensis (Sprengel) Hance with moso bamboo (CCB), Alniphyllum fortunei (Hemsl.) Makino with moso bamboo (AFB), Choerospondias axillaris (Roxb.) Burtt and Hill with moso bamboo (CAB), and Castanopsis fargesii Franch with moso bamboo (CFB), and analyzed their effects on the traits of fine roots of moso bamboo, soil nutrient contents, and enzyme activities. In January 2022, fine root and soil samples from four different mixed bamboo–broadleaf forests were collected from a subtropical region of Fujian Province, China. Results showed that CAB significantly increased fine root biomass (FRB) and root length density (RLD); however, specific root length (SRL) was only in the 0–20 cm soil layer. Specific surface area (SSA) was significantly reduced in the CCB in the 0–20 cm and 20–40 cm soil layers. The total phosphorous (TP) and total potassium (TK) contents of AFB and CAB were significantly increased (p < 0.05), and the alkali-hydrolyzable nitrogen (AN) content was significantly increased by CCB in the 0–20 cm soil layer (p < 0.05). Additionally, CFB increased the activities of acid phosphatase (ACP) and catalase (CAT) but decreased the activity of sucrase (SC). Principal component analysis showed that fine root traits (FRB, RLD, SRL, and SSA) were not only positively associated with soil organic carbon (SOC), total nitrogen (TN) and available potassium (AK) but also associated with urease (UE) and CAT. Therefore, belowground interactions between different species have a significant impact on the characteristics of fine roots and soil in bamboo–broadleaf mixed forests.

1. Introduction

Mixed forests are composed of two or more species, which can positively affect above- and belowground ecosystem properties and processes and have been proposed as a forest management strategy to resist and adapt to environmental changes compared to far-from-nature monocultures [1,2]. It emphasizes the interactions of ecological features of species as a condition for achieving maximum ecological advantages [3]. At present, there is a hypothesis that explains the advantages of mixed forests: the complementarity effect, which refers to positive interspecific interactions such as reduced competition, easier resource capture, and better growth conditions [4]. However, our current understanding of the effects of species composition and interspecific interactions in mixed forests is primarily based on aboveground component research [5,6,7]. Belowground factors and their interactions have received far less attention [8,9].
Fine roots (diameter ≤ 2.0 mm) are crucial vegetative organs of plants that enable material exchange and energy flow in the aboveground environment [10]. Plants can detect and distinguish their neighbors through fine roots and adjust their nutrient exploitation strategies accordingly [11,12], especially when confronted with belowground competition for soil resources between species [13]. The capacity of plants to absorb nutrients is determined by fine root traits, such as acclimatization by altering fine root biomass allocation and morphology, which are key indicators of plant growth to ensure survival [14]. For example, the production of fine roots significantly contributes to carbon and nutrient cycling in forest ecosystems [15]. Specific root length (SRL) is generally regarded as the efficiency of roots to proliferate in soil per unit of carbon invested in prospective nutrient uptake ability [16,17], and root tissue density (RTD) is also used to evaluate the absorptive potential and turnover of fine roots [18]. In addition, the activity of soil enzymes may help understand plastic fine root responses to resource availability for plants and microbes to degrade complex organic substances and make nutrients available for root uptake [19,20]. Previous findings have demonstrated that fine root traits may follow a similar conservation/acquisition trade-off [21]. Resource-acquisitive fine roots have high SRL and low RTD, whereas resource-conservative roots have high RTD and low turnover [21]. However, it is unclear whether different strategies for the exploitation of belowground resources exist between tree species and other plant guilds, such as moso bamboo.
China has about 6.41 million hectares of bamboo forest, which is home to over 534 species in 34 genera, and accounts for 20% of the world’s bamboo forest area [22]. Moso bamboo (Phyllostachys edulis (Carrière) J.Houz.) has a unique feature in that it grows 30–100 cm each day during its peak growth stages after sprouting and consequently has become a valuable bamboo species [23,24]. Although the mixing of broad-leaved forest tree species with moso bamboo has been widely used in China, mixed species need to be matched appropriately to improve the belowground stability and productivity of the ecosystem [25,26,27]. Thus far, less attention has been paid to belowground processes between species than to their aboveground counterparts in bamboo–broadleaf mixed forests [28].
Understanding how interspecies interactions alter underground ecological processes can not only elucidate the factors that drive productivity changes in mixed-species stands but may also contribute to a broader understanding of community ecology. In a broader sense, this knowledge is critical for the future development of sustainable production systems. Research on the adaptability of mixed stands with different species compositions should assist in regulating forest ecosystems. The morphological distribution patterns of underground fine roots under diverse growth environmental conditions should clarify the regulatory mechanisms of roots in stands, and they should be useful in guiding management plans and boosting forest production. Thus, this study was conducted in four different mixed broadleaf tree species and moso bamboo in the Tianbaoyan Nature Reserve to (1) reveal the plasticity response and characteristics of moso bamboo fine roots and (2) explore the influence of tree species differences on the fine root and soil characteristics of moso bamboo–broadleaf mixed forests.

2. Materials and Methods

2.1. Site Description

This study was conducted at the Tianbaoyan National Nature Reserve (25°50′51″–26°01′20″ N, 117°28′03″–117°35′28″ E, Fujian Province, China). (Figure 1). The area is characterized by a mid-subtropical marine monsoon climate, with an annual average temperature of 15 °C (min. and max. are −11 °C and 40 °C, respectively) and precipitation of 2039 mm, with an annual average relative humidity >80% and an annual average frost-free period of approximately 290 days. This region is dominated by middle and low mountains at 580–1604.8 m a.s.l., where the main type of soil is subsoil accumulation of humus and/or oxides [29,30]. Moso bamboo is a fast-growing shade-tolerant species with high economic value in this region. The main evergreen tree species with Moso bamboo were Castanopsis chinensis (Sprengel) Hance and Castanopsis fargesii Franch., and the deciduous tree species were Alniphyllum fortunei (Hemsl.) Makino, and Choerospondias axillaris (Roxb.) Burtt et Hill. In 1996, the Chinese government designated the study area as a nature reserve; therefore, managers only harvested moso bamboo that had matured for 5–6 years in mixed bamboo–broadleaf forests.

2.2. Experiment Design

In January 2022, field experiments were conducted in accordance with the overall exploration of mixed forests in protected areas. A mixed forest of bamboo and broadleaf trees was selected under the same site conditions (Table 1). We used typical sampling methods to set up four different bamboo-broadleaf mixed forests: Castanopsis chinensis with moso bamboo (CCB), Alniphyllum fortunei with moso bamboo (AFB), Choerospondias axillaris with moso bamboo (CAB), and Castanopsis fargesii with moso bamboo (CFB), and took a broadleaf tree as the center with a radius of 9 m (Figure 2). In total, 12 sample stands were established. To reduce spatial autocorrelation, each stand type was replicated in three spatially interspersed stands at distances > 300 m [31]. The stand’s boundary was 100 m from the forest edge bordering either agricultural land or roads, ensuring that there were no other broadleaf trees in the circular sample stand apart from moso bamboo.

2.3. Fine Root Sampling and Analysis

We used soil coring (50 mm diameter, 20 cm height) to collect fine roots of Moso bamboo in early January 2022, when growth was slow [32]. In the sample stand, nine sampling points were randomly selected and separated into three layers: 0–20, 20–40, and 40–60 cm, comprised almost entirely of fine roots [33]. The samples were brought back to the laboratory, and the roots and soil were placed in a soil sieve (aperture size = 2.0 mm) to separate impurities, such as soil, gravel, and dead leaves. The separated roots were then placed in a sieve with a pore diameter of 0.85 mm. The sediment attached to the fine roots was washed with running water, the washed root was laid flat on the surface of the evaporating dish, and the roots of the Moso bamboo were carefully selected and graded with tweezers, Vernier calipers, and a magnifying glass (diameter ≤ 2 mm). Live and dead roots were identified according to their configuration, color, and elasticity. In this study, a root with a diameter ≤ 2 mm was defined as a fine root [33]. Fresh roots were weighed using an electronic scale (0.0001 g) and scanned using a ScanMaker i800 scanner, and photos were saved as JPEG files (300 dpi). The root analysis system (WSeen’s Root Analysis System, model_LA-S, China) was used to analyze root length (RL, cm) and surface area (RSA, cm2). The roots were then oven-dried at 65 °C to a constant weight to calculate root dry mass (RDM, g). Specific root length (SRL, cm·g−1) and specific surface area (SSA, cm2·g−1) reflect morphological and physiological functions. Root length density (RLD, m·m3) is a significant indicator of fine root nutrient and water absorption capacity. Fine root biomass (FRB, g·m3), SRL (m·g−3), and RLD (m·m−3) were calculated using the following equations [33]:
SRL   =   RL / RDM
SSA   =   RSA / RDM
FRB = RDM × 10 6 / π d / 2 2 × h
RLD = RL × 10 6 / π d / 2 2 × h
where d is the diameter (cm) and h is the height (cm) of the core.

2.4. Soil Sampling and Analysis

In January 2022, nine soil cores were sequentially extracted from each stand by soil coring. The sampling depths were 0–20 cm, 20–40 cm, and 40–60 cm. The corings from the same layer were combined to form a composite sample. After removing coarse roots from the mixed samples using a 2 cm sieve, the chemical characteristics of the soil were measured after air-drying. An elemental analyzer (Costech ECS 4024 CHNSO, Picarro, Italy) was used to determine the soil organic carbon (SOC) and total nitrogen (TN) content. Using an automatic chemical analyzer, the total phosphorus (TP) content of the soil was determined using the molybdenum–antimony resistance colorimetric method (concentrated H2SO4–HClO4) (Smartchem 300, AMS, Corsico, Italy). The total potassium (TK) content of the soil was determined using a flame photometer (M410; Sherwood, UK). The alkali-hydrolyzable diffusion method was used to determine the alkali-hydrolyzable nitrogen content (AN) of the soil. Available soil phosphorus (AP) content was determined using a continuous flow analyzer (AA3, Seal, Norderstedt, Germany). Flare atomic absorption spectrometry was used to determine the amount of available potassium (AK). Soil sucrase (SC) was determined by the 3,5-dinitrosalicylic acid colorimetric method. Urease (UE) activity was determined using the phenol-sodium hypochlorite colorimetric method, acid phosphatase (ACP) was determined using the phenyldisodium phosphate colorimetric method, and catalase (CAT) was determined using the potassium permanganate titration method.

2.5. Data Analysis

One-way analysis of variance (ANOVA), LSD, and Duncan’s test were used to detect significant differences (α = 0.05) between fine root traits, biomass, and soil properties in the four stand types. Homogeneous variance and assumptions of normality were examined using the Leven test and Shapiro–Wilk test, respectively. Principal component analysis (PCA) was used to examine the associations between fine root traits and soil properties. The PCA was calculated with the ‘FactoMineR’ package. Graphs were drawn with the ‘ggplot2′ package. All statistical analyses were performed using SPSS 25.0 and collated using Excel 2019, and all figures were created using OriginPro 2017C SR2b9.4.2.380 (Northampton, MA, USA) and RStudio (Version 1.3.1093).

3. Results

3.1. Fine Root Traits

FRB and RLD significantly decreased with increasing soil depth in the four mixed bamboo–broadleaf forests (Figure 3, p < 0.05). CAB significantly increased FRB and RLD compared with CCB and CFB in the 0–20 cm and 40–60 cm soil layers, but for SRL, it was only in the 20–40 cm soil layer (p < 0.05). However, there was no significant difference between AFB and CFB in SRL and RLD. CCB significantly decreased SSA in the 0–20 cm and 20–40 cm soil layers (p < 0.05).

3.2. Soil Properties

SOC, AN, and AP content significantly decreased with increasing soil depth in the four stand types (Table 2, p < 0.05). Compared with CCB and CFB, AFB and CAB significantly increased the content of TP and TK in the 0–20 cm and 20–40 cm soil layers (p < 0.05). Meanwhile, the content of SOC, TN, and AK was significantly increased by CAB in the 0–20 cm and 20–40 cm soil layers. The AN content of CCB significantly increased in the 0–20 cm soil layer (p < 0.05).

3.3. Soil Enzyme Activity

The activity of CAT significantly decreased with increasing soil depth in CCB and CFB (Figure 4, p < 0.05). AFB and CAB significantly increased the activity of SC and reduced the activity of ACP in the 20–40 cm soil layer. AFB significantly increased UE activity in the 0–20 cm soil layer. The CAT activity of CFB was significantly increased in the three soil layers (p < 0.05).

3.4. Associations between Stand Fine Root Traits and Soil Properties

A PCA of fine root traits and soil properties explained 74.1% of the first two principal axes (Figure 5). The first PCA axis explained 59.3% of the variance and was mainly represented by RLD (10.74%), AN (10.42%), and SRL (9.89%), whereas the second PCA axis explained an additional 14.8% of the variance and was associated primarily with ACP (29.51%), TK (26.89%), and UE (11.17%) (Table 3). The fine root traits were significantly positively correlated with soil enzymes (i.e., UE and CAT) and SOC, TN, and AK, but did not show correlations with TK. ACP was negatively correlated with TN and TK, and no significant correlations were seen with fine root traits.

4. Discussion

Fine root traits represent essential trade-offs between resource acquisition and costs [34]. In this study, CAB significantly increased the FRB of moso bamboo (Figure 3, p < 0.05), but the biomass decreased significantly with soil depth, which is consistent with the results of Yuan and Miao [35,36]. The change in biomass allocation when clonal plants (Moso bamboo) adapt to the environment is an important biological characteristic of plant-coordinated growth [37]. Plants can adjust their rooting depths and grow fine roots in soil layers with more resources or less root competition [38]. The possible reasons for the increase in FRB are as follows: first, Choerospondias axillaris is a deep-rooted plant, and moso bamboo is a shallow-rooted plant that avoids direct competition for soil nutrients or water [39], so the bamboo spreads its fine roots into the more nutrient-rich topsoil; and second, the abundant litter of Choerospondias axillaris may be brought into the soil by leaching, which promotes the ability of the fine roots to obtain nutrients from the soil surface. Various studies have concluded that a reduction in SRL can help plants adapt to competitive pressures. For example, Lai et al. [40] found that wheat increased RLD and decreased SRL in a walnut–wheat agroforestry system. Zamora et al. [41] also found that SRL was significantly reduced in competition between Gossypium hirsutum L. and Carya illinoinensis (Wangenheim) K. Koch. However, some scholars have proposed that an increase in SRL is conducive to enhancing fine-root competitiveness [42]. Curt and Prévosto [43] found the SRL of Quercus glauca Thunb. improved root competition with Pinus sylvestris and Betula pendula Roth. In our study, we found that CAB significantly increased SRL and RLD in the 0–20 cm soil layer, and CCB significantly decreased SSA in the 0–20 cm and 20–40 cm soil layers (Figure 3, p < 0.05). This may be because, to obtain more nutrient resources and gain an advantage in the competition, allelopathic substances in root exudates have intentional or harmful allelopathic effects on other plants, such as affecting nutrient availability in soil or microbial activities and affecting root morphology and growth. Using principal component analysis, we found that fine root biomass and morphological characteristics (SRL, RLD, and SSA) were strongly positively correlated with SOC, TN and AK (Figure 5), which is consistent with the results of Zheng [44]. This suggests that soil properties can significantly influence root characteristics. Therefore, we can speculate that a higher level of soil nutrients induces root extension to a greater soil depth, which in turn stimulates root growth and proliferation.
Different tree species have a strong influence on the chemistry of forest soils [45]. We conclude that soil nutrients were affected by different mixed tree species, and SOC content was significantly higher than in other stand types in CAB (Table 2, p < 0.05). Miller H G. [46] reported that differences in species growth rate and litter characteristics also affect the rate of litter decomposition, leading to the accumulation of organic matter. Wang [47] has suggested that the presence of N-fixing species may accelerate litter decomposition to increase soil organic C. Both are likely to explain why the rate of litter decomposition of Choerospondias axillaris was faster than other species, resulting in a greater accumulation of soil organic matter in the stands [48]. In addition, AFB and CAB significantly increased the content of TP and TK in the 0–20 cm and 20–40 cm soil layers, and the AN content of CCB was significantly increased in the 0–20 cm soil layer (Table 2, p < 0.05). Zhang [49] suggested that different canopy proportions in the bamboo–broadleaf mixed forest could influence the soil’s physical and chemical properties of the forest. The difference in canopy structure or light transmittance of broadleaf trees results in differences in soil temperature and humidity in the forest, affecting soil microbial activities and changing the conversion rate of available soil nutrients. Thus, different tree species may result in variations in microbial community composition, which may affect the local carbon and nutrient pools and fluxes.
Soil enzymes play important roles in nutrient cycle processes and the maintenance of soil health [50]. The composition of various plant life forms helps increase enzyme activity in the soil [51,52]. Avazpoor Z. and Masayuki Ushio [53,54] showed that the soil enzyme activities varied with the different species, with better soil aeration and higher litter input by trees at the soil surface and topsoil layers compared to the subsoil [55]. The fact that the sources of soil enzymes are primarily animal and plant residues, microbial activities, and plant root exudates, whereas the surface soil has many roots, good aeration, rich accumulation of various substances, and numerous biological species and quantities [52]. This may explain why the activity of CAT gradually decreased with increasing soil depth in CCB and CFB (Figure 4, p < 0.05). Generally, deciduous broad-leaved trees generate more litter than evergreen broad-leaved trees. Microorganisms in deciduous tree with moso bamboo mixed forests allocate more resources to soil enzyme production and improve soil fertility to meet the demands of plant and microbe growth [56]. These factors may contribute to the high SC and UE activity observed in AFB and CAB (Figure 4, p < 0.05). Likewise, an increase in soil catalase activity may alter soil solution chemistry and redox conditions. These changes in the soil solution can dissociate metal–metal connections and complex organic matter bonds, altering soil carbon storage [47]. CFB had a much higher CAT activity than the other stand types in all three soil layers (Figure 4, p < 0.05). Therefore, we speculated that the high value of CAT activity in CFB indicated that Castanopsis fargesii played a significant role in changing soil redox, resulting in improved soil saprophytic intensity and organic matter accumulation, increased soil removal ability for harmful substances and enhanced tree resistance. Moreover, further studies should be conducted on the composition of root exudates, understory vegetation diversity, and light environments to confirm these speculations.

5. Conclusions

Our research indicates that the fine root morphological plasticity of moso bamboo and the enzyme and chemical properties of the soil in bamboo–broadleaf mixed forests differ as the mixed tree species changes in a stand. AFB and CAB significantly stimulated root growth and morphological changes of Moso bamboo for more rapid soil exploration. AFB significantly increased the content of TP and TK in the 0–20 cm soil layer but reduced the activity of ACP in the 20–40 cm soil layer. ACB not only increased FRB and RLD in the 0–20 cm soil layer but also increased SC activity in the 0–20 cm soil layer. There is a correlation between fine root traits (FRB, RLD, SRL, and SSA) and enzymes (UE and CAT) and nutrients (SOC, TN, and AK) in soil. The changes in fine root traits may be caused by increasing litter quality, soil conditions and biological activities. Further research should address these points. Although our study did not exclude the errors associated with other environmental factors, it can provide a theoretical basis for the local, sustainable management of bamboo–broadleaf mixed forests.

Author Contributions

F.G. designed this study and improved the English language and grammar. Y.Z. (Yang Zhou) wrote the first draft of the manuscript and performed data analysis. Y.Z. (Yang Zhou) and Z.L. performed fieldwork. Y.Z. (Yaxiong Zheng), X.Z. (Xiao Zhou) and X.Z. (Xuan Zhang) provided guidance and methodological advice. All coauthors contributed to the discussion, revision, and improvement of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by basic scientific research funding from the International Center for Bamboo and Rattan (Grant No. 1632022004).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data in this article was provided by the instructor and this statement does not apply.

Acknowledgments

We are extremely grateful to Shaohui Fan for the improvements to the introduction and discussion sections of the manuscript.

Conflicts of Interest

The authors declare that there are no conflict of interest.

References

  1. Río, M.; Pretzsch, H.; Alberdi, I.; Bielak, K.; Bravo, F.; Brunner, A.; Condés, S.; Ducey, M.J.; Fonseca, T.; von Lüpke, N.; et al. Characterization of mixed forests. In Dynamics, Silviculture and Management of Mixed Forests; Springer: Cham, Switzerland, 2018; pp. 27–71. [Google Scholar]
  2. Williams, L.J.; Paquette, A.; Cavender-Bares, J.; Messier, C.; Reich, P.B. Spatial complementarity in tree crowns explains overyielding in species mixtures. Nat. Ecol. Evol. 2017, 1, 1–7. [Google Scholar] [CrossRef]
  3. Jactel, H.; Bauhus, J.; Boberg, J.; Bonal, D.; Castagneyrol, B.; Gardiner, B.; Gonzalez-Olabarria, J.R.; Koricheva, J.; Meurisse, N.; Brockerhoff, E.G. Tree diversity drives forest stand resistance to natural disturbances. Curr. For. Rep. 2017, 3, 223–243. [Google Scholar] [CrossRef]
  4. Forrester, D.I. The spatial and temporal dynamics of species interactions in mixed-species forests: From pattern to process. For. Ecol. Manag. 2014, 312, 282–292. [Google Scholar] [CrossRef]
  5. Pretzsch, H.; Steckel, M.; Heym, M.; Biber, P.; Ammer, C.; Ehbrecht, M.; Bielak, K.; Bravo, F.; Ordóñez, C.; Collet, C.; et al. Stand growth and structure of mixed-species and monospecific stands of Scots pine (Pinus sylvestris L.) and oak (Q. robur L., Quercus petraea (Matt.) Liebl.) analysed along a productivity gradient through Europe. Eur. J. For. Res. 2020, 139, 349–367. [Google Scholar] [CrossRef] [Green Version]
  6. Primicia, I.; Camarero, J.J.; Imbert, J.B.; Castillo, F.J. Effects of thinning and canopy type on growth dynamics of Pinus sylvestris: Inter-annual variations and intra-annual interactions with microclimate. Eur. J. For. Res. 2013, 132, 121–135. [Google Scholar] [CrossRef]
  7. Sterck, F.J.; Duursma, R.A.; Pearcy, R.W.; Valladares, F.; Cieslak, M.; Weemstra, M. Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey. J. Ecol. 2013, 101, 971–980. [Google Scholar] [CrossRef] [Green Version]
  8. Valverde-Barrantes, O.J.; Raich, J.W.; Russell, A.E. Fine-root mass, growth and nitrogen content for six tropical tree species. Plant Soil 2007, 290, 357–370. [Google Scholar] [CrossRef]
  9. Bardgett, R.D.; Mommer, L.; de Vries, F.T. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 2014, 29, 692–699. [Google Scholar] [CrossRef]
  10. Hallett, P.D.; Marin, M.; Bending, G.D.; George, T.S.; Collins, C.D.; Otten, W. Building soil sustainability from root–soil interface traits. Trends Plant Sci. 2022, 27, 688–698. [Google Scholar] [CrossRef]
  11. Casper, B.B.; Jackson, R.B. Plant competition underground. Annu. Rev. Ecol. Evol. Syst. 1997, 545–570. [Google Scholar] [CrossRef]
  12. de Kroon, H. How do roots interact? Science 2007, 318, 1562–1563. [Google Scholar] [CrossRef] [PubMed]
  13. Messier, C.; Coll, L.; Poitras-Larivière, A.; Bélanger, N.; Brisson, J. Resource and non-resource root competition effects of grasses on early-versus late-successional trees. J. Ecol. 2009, 97, 548–554. [Google Scholar] [CrossRef] [Green Version]
  14. Harper, J.L.; Jones, M.; Hamilton, N.R. The Evolution of Roots and The Problems of Analyzing Their Behavior, Plant Root Growth, An Ecological Perspective. Spec. Publ. Br. Ecol. Soc. 1991, 10, 3–22. [Google Scholar]
  15. Børja, I.; De Wit, H.A.; Steffenrem, A.; Majdi, H. Stand age and fine root biomass, distribution and morphology in a Norway spruce chronosequence in southeast Norway. Tree Physiol. 2008, 28, 773–784. [Google Scholar] [CrossRef]
  16. Persson, H.; Ahlström, K. Fine-root response to nitrogen supply in nitrogen manipulated Norway spruce catchment areas. For. Ecol. Manag. 2002, 168, 29–41. [Google Scholar] [CrossRef]
  17. 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]
  18. 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]
  19. Zhang, X.; Dippold, M.A.; Kuzyakov, Y.; Razavi, B.S. Spatial pattern of enzyme activities depends on root exudate composition. Soil Biol. Biochem. 2019, 133, 83–93. [Google Scholar] [CrossRef]
  20. Ni, H.; Su, W.; Fan, S.; Chu, H. Effects of intensive management practices on rhizosphere soil properties, root growth, and nutrient uptake in Moso bamboo plantations in subtropical China. For. Ecol. Manag. 2021, 493, 119083. [Google Scholar] [CrossRef]
  21. Roumet, C.; Birouste, M.; Picon-Cochard, C.; Ghestem, M.; Osman, N.; Vrignon-Brenas, S.; Cao, K.-F.; Stokes, A. Root structure–function relationships in 74 species: Evidence of a root economics spectrum related to carbon economy. New Phytol. 2016, 210, 815–826. [Google Scholar] [CrossRef]
  22. Bahamondez, C.; Álvarez, O.; Itzelcoaut, M. Global Forest Resources Assessment 2010, Main Report; FAO: Rome, Italy, 2010. [Google Scholar]
  23. Jiang, Z.H. Bamboo and Rattan in the World; China Forest Publishing House: Beijng, China, 2007. [Google Scholar]
  24. Yang, C.; Ni, H.; Zhong, Z.; Zhang, X.; Bian, F. Changes in soil carbon pools and components induced by replacing secondary evergreen broadleaf forest with Moso bamboo plantations in subtropical China. Catena 2019, 180, 309–319. [Google Scholar] [CrossRef]
  25. Li, W.; Bai, Z.; Jin, C.; Zhang, X.; Guan, D.; Wang, A.; Yuan, F.; Wu, J. The influence of tree species on small scale spatial heterogeneity of soil respiration in a temperate mixed forest. Sci. Total Environ. 2017, 590, 242–248. [Google Scholar] [CrossRef] [PubMed]
  26. Li, W.; Tan, R.; Yang, Y.; Wang, J. Plant diversity as a good indicator of vegetation stability in a typical plateau wetland. J. Mt. Sci. Engl. 2014, 11, 464–474. [Google Scholar] [CrossRef]
  27. Li, Y.; Zhang, J.; Chang, S.-X.; Jiang, P.; Zhou, G.; Fu, S.; Yan, E.; Wu, J.; Lin, L. Long-term intensive management effects on soil organic carbon pools and chemical composition in Moso bamboo (Phyllostachys pubescens) forests in subtropical China. For. Ecol. Manag. 2013, 303, 121–130. [Google Scholar] [CrossRef]
  28. Zhang, M.-M.; Fan, S.-H.; Yan, X.-R.; Zhou, Y.-Q.; Guan, F.-Y. Relationships between stand spatial structure characteristics and influencing factors of bamboo and broadleaf mixed forest. J. For. Res. Jpn. 2020, 25, 1–9. [Google Scholar]
  29. IUSS Working Group WRB 2014. World Reference Base for Soil Resources 2014. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; FAO: Rome, Italy, 2014. [Google Scholar]
  30. Schad, P. The international soil classification system WRB 2014. In Novel Methods for Monitoring and Managing Land and Water Resources in Siberia; Springer: Cham, Switzerland, 2016; pp. 563–571. [Google Scholar]
  31. Legendre, P.; Legendre, L. Numerical Ecology, 2nd ed.; Elsevier: New York, NY, USA, 2012. [Google Scholar]
  32. Shaohui, F.; Fuming, X.; Silong, W.; Wenhui, S.; Xiaojun, Y.; Zhengqi, S. Fine Root Biomass and Turnover in Moso Bamboo Plantation in Huitong Forest Station, Hunan Province. Sci. Silvae Sin. 2009, 45, 1–6. [Google Scholar]
  33. Liu, G.; Fan, S.; Cai, C.; Liu, X.; Li, Y.; Luo, T. Fine Root Biomass Distribution of Moso Bamboo at Different Ages. J. Trop. Subtrop. Bot. 2017, 25, 472–479. [Google Scholar]
  34. Zhao, L.; Wang, J. Research on Relations Between Growth Effect and Soil Enzyme Activities and Soil Nutrient Factors in Mixed Poplar and Black Locust Plantations. J. Beijing For. Univ. 1995, 17, 1–8. [Google Scholar]
  35. Yuan, W.; Li, X.; Zhang, J.; Rong, L. Preliminary studies on carbon reserves of litterfall and fine root in an age series of Eucalyptus grandis plantation. Sci. Silvae Sin. 2009, 22, 385–389. [Google Scholar]
  36. Miao, Y.; Chen, Y.-L.; Li, X.W.; Fan, C.; Liu, Y.-K.; Yang, Z.-J.; Zhang, J.; Cai, X.-L. Effects of fertilization on Alnus formosana fine root morphological characteristics, biomass and issue content of C N under A. formosana-Hemarthria compressa compound mode. Chin. J. Plant Ecol. 2013, 37, 674–683. [Google Scholar]
  37. Wijesinghe, D.K.; John, E.A.; Beurskens, S.; Hutchings, M.J. Root system size and precision in nutrient foraging: Responses to spatial pattern of nutrient supply in six herbaceous species. J. Ecol. 2001, 89, 972–983. [Google Scholar] [CrossRef]
  38. Ma, Z.; Chen, H.Y.H. Effects of species diversity on fine root productivity increase with stand development and associated mechanisms in a boreal forest. J. Ecol. 2017, 105, 237–245. [Google Scholar] [CrossRef]
  39. Zhou, Y.; Guan, F.; Fan, S. Niche characteristics of Phyllostachys edulis and its associated tree species in Tianbaoyan bamboo and broadleaved mixed forest. J. Beijing For. Univ. 2017, 39, 46–53. [Google Scholar]
  40. Wang, L.; Zhong, C.; Cai, J. Spatial distribution and morphological variations of the fine root in walnut-wheat intercropping agroforestry ecosystem. J. Northwest A F Univ. Nat. Sci. Ed. 2011, 39, 64–70. [Google Scholar]
  41. Zamora, D.S.; Jose, S.; Nair, P.K.R. Morphological plasticity of cotton root in response to interspecific competition with pecan in an alleycropping system in the southern United States. Agrofor. Syst. 2007, 69, 107–116. [Google Scholar] [CrossRef]
  42. Davis, J.P.; Haines, B.; Coleman, D.; Hendrick, R. Fine root dynamics along an elevational gradient in the southern Appalachian Mountains, USA. For. Ecol. Manag. 2004, 187, 19–33. [Google Scholar] [CrossRef]
  43. Curt, T.; Prévosto, B. Rooting strategy of naturally regenerated beech in Silver birch and Scots pine woodlands. In Roots: The Dynamic Interface between Plants and the Earth; Springer: Dordrecht, The Netherlands, 2003; pp. 265–279. [Google Scholar]
  44. Zheng, Y.; Guan, F.; Fan, S.; Zhou, Y.; Jing, X. Functional trait responses to strip clearcutting in a Moso bamboo forest. Forests 2021, 12, 793. [Google Scholar] [CrossRef]
  45. Kooch, Y.; Tarighat, F.S.; Hosseini, S.M. Tree species effects on soil chemical, biochemical and biological features in mixed Caspian lowland forests. Trees 2017, 31, 863–872. [Google Scholar] [CrossRef]
  46. Miller, H.G. Dynamics of nutrient cycling in plantation ecosystems. In Nutrition of Plantation Forests; Bowen, G.D., Nambiar, E.K.S., Eds.; FAO: Rome, Italy, 1984. [Google Scholar]
  47. Wang, B.; Xue, S.; Liu, G.B.; Zhang, G.H.; Li, G.; Ren, Z.P. Changes in soil nutrient and enzyme activities under different vegetations in the Loess Plateau area, Northwest China. Catena 2012, 92, 186–195. [Google Scholar] [CrossRef]
  48. Hao, C.; Dong, S.; Lei, L.; Mo, C.; Zhang, T.; Zhu, X.; Mo, J. Effects of Experimental Nitrogen and Phosphorus Addition on Litter Decomposition in an Old-Growth Tropical Forest. PLoS ONE 2013, 8, e84101. [Google Scholar]
  49. Zhang, M.; Fan, S.; Guan, F.; Yan, X.; Yin, Z. Soil bacterial community structure of mixed bamboo and broad-leaved forest based on tree crown width ratio. Sci. Rep. 2020, 10, 6522. [Google Scholar]
  50. Moghimian, N.; Hosseini, S.M.; Kooch, Y.; Darki, B.Z. Impacts of changes in land use/cover on soil microbial and enzyme activities. Catena 2017, 157, 407–414. [Google Scholar] [CrossRef]
  51. Rong, L.; Shou-Jian, L.I.; Xian-Wei, L.I. Soil enzyme dynamics during fine root (including grass root) decomposition in different farmland-to-forest/grassland conversions in the rainy zone of western China. Chin. J. Plant Ecol. 2010, 34, 642–650. [Google Scholar]
  52. Dick, R.P. Soil enzyme activities as indicators of soil quality. Soil Sci. Soc. Am. J. 1994, 58, 107–124. [Google Scholar]
  53. Avazpoor, Z.; Moradi, M.; Basiri, R.; Mirzaei, J.; Taghizadeh-Mehrjardi, R.; Kerry, R. Soil enzyme activity variations in riparian forests in relation to plant species and soil depth. Arab. J. Geosci. 2019, 12, 708. [Google Scholar] [CrossRef]
  54. Ushio, M.; Kitayama, K.; Balser, T.C. Tree species effects on soil enzyme activities through effects on soil physicochemical and microbial properties in a tropical montane forest on Mt. Kinabalu, Borneo. Pedobiologia 2010, 53, 227–233. [Google Scholar] [CrossRef]
  55. Behbahani, S.M.; Moradi, M.; Basiri, R.; Mirzaei, J. Sand mining disturbances and their effects on the diversity of arbuscular mycorrhizal fungi in a riparian forest of Iran. J. Arid Land 2017, 9, 837–849. [Google Scholar] [CrossRef] [Green Version]
  56. Sardans, J.; Peñuelas, J. Plant-soil interactions in Mediterranean forest and shrublands: Impacts of climatic change. Plant Soil 2013, 365, 1–33. [Google Scholar] [CrossRef]
Forests 13 01834 g001 550
Figure 1. The geographic location of the study site.
Figure 1. The geographic location of the study site.
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Figure 2. Schematic diagram of stands.
Figure 2. Schematic diagram of stands.
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Figure 3. (a) Fine root biomass, (b) specific root length, (c) root length density and (d) specific surface area in mixed forest of bamboo–broadleaved trees. Error bars indicate the standard deviation (n = 3). Capital letters indicate the difference in fine root characteristics between different soil layers under the same stand (p < 0.05); lowercase letters indicate differences in fine root characteristics between different stands in the same soil layer (p < 0.05).
Figure 3. (a) Fine root biomass, (b) specific root length, (c) root length density and (d) specific surface area in mixed forest of bamboo–broadleaved trees. Error bars indicate the standard deviation (n = 3). Capital letters indicate the difference in fine root characteristics between different soil layers under the same stand (p < 0.05); lowercase letters indicate differences in fine root characteristics between different stands in the same soil layer (p < 0.05).
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Forests 13 01834 g004 550
Figure 4. (a) Sucrase activity, (b) urease activity, (c) acid phosphatase activity and (d) catalase activity in mixed forest of different bamboo and broad-leaved trees.
Figure 4. (a) Sucrase activity, (b) urease activity, (c) acid phosphatase activity and (d) catalase activity in mixed forest of different bamboo and broad-leaved trees.
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Forests 13 01834 g005 550
Figure 5. Principal component analysis of variables. The arrow line of the variables is plotted as the correlation coefficients between them and the first two principal components in the unit circle; the length of the arrow line indicates the contribution of the variable.
Figure 5. Principal component analysis of variables. The arrow line of the variables is plotted as the correlation coefficients between them and the first two principal components in the unit circle; the length of the arrow line indicates the contribution of the variable.
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Table
Table 1. Stand characteristics of bamboo–broadleaf mixed forests.
Table 1. Stand characteristics of bamboo–broadleaf mixed forests.
Stand TypeAge of Broadleaf Tree (a)DBH of Broadleaf Tree (cm)Average DBH of Bamboo (cm)Culm Density (culm ha−1)Altitude (m)Slope (°)Aspect
CCB5340.828.89282979620adret
AFB5549.119.15271277025adret
CAB4441.369.48286877822adret
CFB5653.559.37298778923adret
Note: Bamboo–broadleaf mixed forests: Castanopsis chinensis (Sprengel) Hance with moso bamboo (CCB), Alniphyllum fortune (Hemsl.) Makino with moso bamboo (AFB), Choerospondias axillaris (Roxb.) Burtt and Hill with moso bamboo (CAB), and Castanopsis fargesii Franch with moso bamboo (CFB).
Table
Table 2. Soil characteristics for different bamboo–broadleaf mixed forests.
Table 2. Soil characteristics for different bamboo–broadleaf mixed forests.
Stand TypeSoil LayerSOCTNTPTKANAPAK
cm(g/kg)(g/kg)(g/kg)(g/kg)(mg/kg)(mg/kg)(mg/kg)
0–2030.38 ± 1.03 Ab2.12 ± 0.10 Ac0.33 ± 0.01 Ab12.99 ± 2.39 Ac144.91 ± 2.34 Aa1.69 ± 0.17 Ad32.77 ± 2.68 Ab
CCB20–4023.43 ± 2.78 Bab1.87 ± 0.05 Bb0.30 ± 0.01A Bc13.25 ± 0.82 Ad114.89 ± 5.45 Ba0.88 ± 0.05 Bc21.97 ± 4.32 Bb
40–6014.93 ± 0.83 Ca1.84 ± 0.12 Ba0.29 ± 0.03 Ba12.67 ± 1.53 Ad101.73 ± 3.19 Ca0.13 ± 0.02 Cab18.44 ± 3.70 Bb
0–2034.66 ± 2.52 Ab2.49 ± 0.24 Ab0.39 ± 0.01 Aa36.27 ± 4.56 Aa127.99 ± 1.16 Ab3.71 ± 0.10 Aa45.97 ± 5.88 Aab
AFB20–4024.30 ± 2.51 Bab1.93 ± 0.15 Bab0.39 ± 0.03 Aa37.08 ± 3.53 Aa105.05 ± 2.76 Bab1.24 ± 0.09 Ba36.18 ± 2.22 Ba
40–6017.10 ± 1.30 Ca1.84 ± 0.07 Ba0.30 ± 0.02 Ba37.48 ± 4.41 Aa85.61 ± 1.20 Cc0.15 ± 0.01 Cab17.69 ± 1.23 Cb
0–2046.71 ± 7.53 Aa2.81 ± 0.11 Aa0.39 ± 0.01 Aa29.13 ± 3.27 Ab127.93 ± 1.45 Ab3.44 ± 0.09 Ab54.39 ± 12.25 Aa
CAB20–4027.19 ± 3.25 Ba2.17 ± 0.21 Ba0.36 ± 0.01 Bb28.54 ± 2.79 Ab105.09 ± 6.98 Bab1.11 ± 0.14 Bab43.27 ± 4.29 ABa
40–6017.29 ± 1.86 Ca1.83 ± 0.17 Ca0.27 ± 0.01 Cab29.29 ± 3.46 Ab90.92 ± 2.59 Cb0.17 ± 0.03 Ca28.9 ± 3.35 Ba
0–2030.23 ± 1.77 Ab2.26 ± 0.18 Abc0.28 ± 0.01 Ac19.88 ± 4.31 Ac104.25 ± 7.09 Ac2.07 ± 0.05 Ac35.66 ± 0.62 Ab
CFB20–4020.05 ± 1.30 Bb1.95 ± 0.08 Bab0.27 ± 0.01 Ac21.12 ± 4.21 Ac94.82 ± 6.81 Bb1.03 ± 0.04 Bbc24.17 ± 6.24 Bb
40–6016.06 ± 0.61 Ca1.80 ± 0.08 Ba0.24 ± 0.02 Bb20.96 ± 4.00 Ac82.07 ± 1.89 Cc0.12 ± 0.02 Cb29.93 ± 1.62 ABa
Values are presented as the mean ± standard deviation (n = 3). Different lowercase letters indicate significant differences between different stands in the same soil layer; different capital letters indicate significant differences between different soil layers in the same stand (one-way ANOVA and LSD test, p < 0.05). SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: alkali-hydrolyzable nitrogen; AP: available phosphorus; AK: available potassium.
Table
Table 3. Principal component analysis contribution rate of variables.
Table 3. Principal component analysis contribution rate of variables.
FRBSRLRLDSSASOCTNTPTKANAPAKSCUEACPCAT
Dim. 19.89089.267310.07387.76639.77359.06095.78110.821310.42415.65348.14695.30795.79670.27911.9562
Dim. 22.54462.68832.45920.11510.04100.00676.618526.88630.33732.23512.49345.598111.166729.51477.2946
Note: SRL, specific root length; SSA, specific surface area; RLD, root length density; FRB, fine root biomass; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; TK, total potassium; AN, alkali-hydrolyzable nitrogen; AP, available phosphorus; AK, available potassium; SC, sucrase; UE, urease; ACP, acid phosphatase; CAT, catalase.
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Zhou, Y.; Guan, F.; Li, Z.; Zheng, Y.; Zhou, X.; Zhang, X. Effects of Tree Species on Moso Bamboo (Phyllostachys edulis (Carriere) J. Houzeau) Fine Root Morphology, Biomass, and Soil Properties in Bamboo–Broadleaf Mixed Forests. Forests 2022, 13, 1834. https://doi.org/10.3390/f13111834

AMA Style

Zhou Y, Guan F, Li Z, Zheng Y, Zhou X, Zhang X. Effects of Tree Species on Moso Bamboo (Phyllostachys edulis (Carriere) J. Houzeau) Fine Root Morphology, Biomass, and Soil Properties in Bamboo–Broadleaf Mixed Forests. Forests. 2022; 13(11):1834. https://doi.org/10.3390/f13111834

Chicago/Turabian Style

Zhou, Yang, Fengying Guan, Zhen Li, Yaxiong Zheng, Xiao Zhou, and Xuan Zhang. 2022. "Effects of Tree Species on Moso Bamboo (Phyllostachys edulis (Carriere) J. Houzeau) Fine Root Morphology, Biomass, and Soil Properties in Bamboo–Broadleaf Mixed Forests" Forests 13, no. 11: 1834. https://doi.org/10.3390/f13111834

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