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Article

The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations

by
Jing Tu
1,
Zhongwen Zhao
2 and
Zongzheng Chai
1,*
1
College of Forestry, Guizhou University, Guiyang 550025, China
2
Guizhou Province Zhijin County State-Owned Guihua Forest Farm, Bijie 552100, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(1), 183; https://doi.org/10.3390/f16010183
Submission received: 3 December 2024 / Revised: 11 January 2025 / Accepted: 17 January 2025 / Published: 19 January 2025
(This article belongs to the Special Issue Growth and Stand Dynamics of Unmanaged and Managed Forests Under Global Change)
Browse Figures
Figure 1
<p>Geographical location of and sample block distribution in the study area.</p> "> Figure 2
<p>Unit circle method stand-state evaluation of <span class="html-italic">Masson pine</span> mid-aged plantations. The differently colored sectors represent various stand state indicators. The area of the sector that is colored in represents the magnitude of each stand state indicator value, with a larger area signifying a better indicator. The symbol ω in the upper right corner represents the value of the comprehensive evaluation of each forest stand state.</p> "> Figure 3
<p>Principal component analysis-based stand-state evaluation of <span class="html-italic">Masson pine</span> mid-aged plantations. (<b>a</b>) Based on the principal component analysis, the load values of stand state indicators; Different colored boxes represent different logging intensity sample areas; (<b>b</b>) based on the principal component analysis, the ranking of stand state indicator weights.</p> "> Figure 4
<p>Correlation analysis of unit circle method versus principal component analysis stand-state evaluation results.</p> ">
Versions Notes

Abstract

:
By assessing the short-term impact that various logging intensities have on stand state in middle-aged P. massoniana plantations, this investigation aimed to establish a theoretical foundation to support the judicious management of Pinus massoniana plantations. Five distinct logging intensity categories were delineated (0%, 10%, 20%, 30%, 40%). To construct a robust stand-state evaluation framework, nine representative indicators across the three dimensions of structure, vitality, and diversity were selected. We scrutinized the short-term impacts of logging intensity by employing the unit circle method. The findings revealed that (1) four indicators—stand density, tree health, species composition, and species diversity—exhibited pronounced sensitivity to logging intensity. These four exhibited significant improvements in the short-term post-logging (p < 0.05). Conversely, the indicators of species evenness, diameter distribution, height distribution, tree dominance, and stand growth exhibited a more subdued response to logging intensity. These five necessitated an extended period to begin to improve. (2) The comprehensive evaluation values measuring the stand state of middle-aged P. massoniana plantations initially ascended but then subsequently descended as logging intensity escalated. The stand-state zenith was pinpointed at an approximate 30% logging intensity. (3) A highly significant linear correlation emerged between the unit circle method results and the principal component analysis results in evaluating stand state (R2 = 0.909, p < 0.001), and the unit circle method proved to be more intuitive and responsive. In summation, logging intensity exerted a substantial influence on stand state in middle-aged P. massoniana plantations, with moderate logging (circa 30% logging intensity) enhancing stand state the most. The unit circle method proficiently and effectively illuminated the short-term effects of logging intensity on the stand dynamics of middle-aged P. massoniana plantations, so it thereby may provide invaluable guidance for the formulation of specific forest management strategies.

1. Introduction

In light of escalating global environmental change and the intensification of human activity, the stewardship of plantation forests has become a particularly paramount issue. Plantation forests particularly are pivotal for mitigating climate change [1], protecting biodiversity [2,3,4], and providing timber and other forest products [5]. They play an essential role in maintaining ecological balance and fostering sustainable development through their robust growth. However, contemporary plantation forest management plans often inadequately account for the influence of stand state on forest ecosystem functions, thereby unintentionally exacerbating challenges such as excessive density [6], suboptimal growth [7], monospecific structure [8], and diminished ecological service function [9]. An in-depth examination of the specific impacts of forest management practices on plantation stand states can not only elucidate the dynamic processes underpinning forest ecosystems but also establish a scientific foundation for the sustainable management of plantation forests in the future. In recent years, a multitude of researchers have investigated many methods of assessing stand states within a forest, which has thus resulted in the formulation of various theoretical frameworks and practical guidelines. Nonetheless, practitioners continue to grapple with issues such as the absence of unified evaluation standards, an insufficiently comprehensive consideration of affecting factors, and limited adaptability to diverse regions and forest types. These challenges complicate the potential for the sustainable management and development of plantation forests.
Masson pine (Pinus massoniana Lamb.), a staple timber species and a pioneer useful in afforestation [10,11,12], is highly adaptable, economically significant [13], and extensively distributed throughout southern China [12], making it an integral component of local forestry production and forest ecosystems [14]. However, a prolonged monoculture methodology has led to problems such as homogeneous species composition, high stand density, and low species diversity in P. massoniana plantations. These factors further contribute to reduced productivity, degraded soil [15], and heightened susceptibility to pest or disease infestation [16,17,18]. These issues profoundly affect tree development and reproductive environments, thus posing formidable threats to the sustainable development of plantation lands. Logging intensity, a critical technical component of forest management, exerts a direct influence on the structure, function, and dynamics of P. massoniana plantations [19]. Previous studies have demonstrated that different degrees of logging intensity can exert different complex and multifaceted effects on forest ecosystems. Moderate logging can improve a standing environment, bolstering tree regeneration and growth while also sustaining and enhancing ecosystem function [20,21,22,23]. Conversely, excessive logging can precipitate habitat destruction, biodiversity loss, and soil erosion. Despite an extensive archive of related research, there remains a significant gap in our understanding of how varying logging intensity specifically affects plantation forests. This knowledge gap is particularly evident given the heterogeneous responses observed across different geographical regions, tree species, developmental stages, and environmental conditions [24,25,26]. Understanding the growth dynamics of forest stands, including their responses to different logging intensities, is therefore crucial for maintaining ecosystem stability and implementing scientific forest management practices [26,27,28].
This study targeted middle-aged P. massoniana plantations. A stand-state evaluation index system was constructed based on typical indicators of structure, vitality, and diversity. The objective was to systematically assess the impacts of various logging intensities on stand states in P. massoniana plantations to unravel the ecological response mechanisms and thus provide a scientific basis for optimizing forest management strategies. Through this investigation, we aspired to gain a profound understanding of the regulatory impacts of logging intensity on forest ecosystems, thereby offering novel theoretical support and practical guidance for enhancing forest quality and promoting its sustainable management.

2. Materials and Methods

2.1. Study Area

The study area was situated within the state-owned Guihua Forest Farm, located in Zhijin County, Guizhou Province (26°38′21″–26°39′18″ N, 105°44′55″–105°45′5″ E) (Figure 1), at an average elevation of 1310 m above sea level. The annual precipitation there ranges from 1200 to 1500 mm, complemented by approximately 1170 h of sunshine per annum. The frost-free period spans from 240 to 290 days, with an annual mean temperature oscillating between 11.6 and 15 °C. This locale falls under the southeastern monsoon climate zone, characterized by a well-developed karst landscape. Prominent geomorphological features include mountains and hills, with notable topographical variations such as peak clusters, trough valleys, and interspersed depressions. The soil parent material predominantly consists of limestone, with calcareous soil, yellow–brown soil, and yellow soil being the primary soil types.
The forest expanse encompasses 439,490 hectares, an area which is dominated by P. massoniana forests. These forests primarily include artificially-established plantations that are extensive and broadly distributed. This forest farm has garnered recognition as one of the “Pilot Forest Farms for the GEF National Forest Project”, “National Long-Term Scientific Experimental Base for P. massoniana forest”, and a “Teaching and Research Practice Base for Guizhou University”. It boasts a relatively extensive history in sustainable forest management and has well-established forest management monitoring blocks and ecological service monitoring systems, rendering it an exemplary site for scientific inquiry.
This study specifically focused on the Machang Work Area within the Guihua Forest Farm, where a substantial aggregation of middle-aged P. massoniana plantations can be found. These stands were planted in 2004 with an initial spacing of 1.5 × 1.5 m. Since their establishment, the stands have undergone zero silvicultural interventions and minimal anthropogenic disturbance. However, these middle-aged P. massoniana plantations are generally plagued by issues such as excessive stand density, suboptimal stem form, monospecific composition, inadequate regeneration, and low productivity. A crop tree selective thinning operation system was proposed to rectify these issues. This system aims to gradually bring about a mixed-species, uneven-aged, multi-layered forest structure. The overarching objective is to expedite community succession, enhance stand productivity, and facilitate the utilization of large-diameter timber.

2.2. Block Setup and Survey

In April 2021, after a thorough reconnaissance of middle-aged P. massoniana plantations, we selected blocks with consistent forest stand states and established a demonstration area for our close-to-nature management technology experiment. The demonstration area included four sample blocks, each containing five square blocks with an area of 32 × 32 m each. These corresponded to five different logging intensity treatments: CK (control group), T1 (approximately 10% logging intensity), T2 (approximately 20% logging intensity), T3 (approximately 30% logging intensity), and T4 (approximately 40% logging intensity) (Figure 1). Before logging, all trees within the blocks with a diameter at breast height (DBH) of ≥5 cm were individually numbered with a permanent iron tag, and key factors such as DBH (diameter at the breast height), tree height, crown width, and height to the first live branch were measured and recorded. In addition to sampling, an undergrowth vegetation and soil survey was conducted concurrently.
The logging intensity was determined based on the total basal area of each block. The “target tree selection + whole-stand management” operation method was used. Trees that interfered with the growth of the crop trees were removed, as were trees with poor form, weak vitality, or zero cultivation potential. Eleven crop trees were selected within each block. Logging targeted P. massoniana individuals mainly, which means that other well-growing and potentially valuable broadleaf species were retained and left to grow. In July 2023, the experimental blocks were re-measured, using the same survey factors as before logging (Table 1).

2.3. Evaluation Index Measuring Forest Stand State

This study established a stand state indicator system based on three fundamental principles: rationality, comprehensiveness, and operational feasibility [29,30]. The rationality principle ensures that the indicators accurately and objectively reflect specific stand attributes so they can demonstrate high precision, sensitivity, comparability, and discriminatory capacity. The comprehensiveness principle guarantees that the system encompasses multiple aspects of stand states. The operational feasibility principle stipulates that all indicators must be clearly defined, with straightforward data collection and calculation methods, which facilitates practical implementation and management decision-making. Guided by these principles and informed by past research on stand-state evaluation [31,32], our method also integrates a stand-state indicator system proposed in previous studies [32,33]. This study developed a framework that assesses stand state through three primary dimensions: structure, vitality, and diversity. Each dimension encompasses three specific indicators, thereby creating a nine-indicator system that enables comprehensive stand-state assessment. The indicators of stand structure were diameter at breast height distribution (H), tree height distribution (V), and stand density (K); the indicators of vitality were stand dominance (U), stand growth (B), and tree health (Q); the indicators of diversity were tree species composition (Z), tree species diversity (D), and tree species evenness (P). The specific definitions and formulas are shown in Table 2.
For the above nine indicators, except for the intermediate indicators of diameter at breast height distribution (H) and stand density (K), a higher value is better than a lower value, with the values ranging from 0 to 1. The two intermediate indicators of diameter at breast height distribution (H) and stand density (K) were standardized using formula (1) [39] to fit within the same from 0 to 1 range as the other indicators. Again, the larger the value, the better the indicator.
f x = 0.1                                                                                                                     X ≤ L , X ≥ U 0.1 + 0.9 X − L / O 1 − L                                 L < X < O 1 1.0                                                                                                                     O 1 ≤ X ≤ O 2 0.1 + 0.9 X − O 2 / U − O 2                         O 2 < X < U
In the formula, X represents the observed value of the indicator, f(X) represents the score function value, U represents the upper limit value of the indicator (the U value of diameter at breast height distribution (H) is 2.7, and the U value of stand density is 1.0), L represents the lower limit value of the indicator (the U values of diameter at breast height distribution (H) and stand density (K) are both 0), and O1 and O2 represent the optimal values of the indicator (the O1 and O2 values of diameter at breast height distribution (H) are 1.2 and 1.7, respectively; the O1 and O2 values of stand density (K) are 0.6 and 0.8, respectively).

2.4. Comprehensive Evaluation of Stand State

The unit circle method was used to evaluate the stand states of middle-aged P. massoniana plantations under different logging intensities. The calculation formula was as follows [33,40]:
ω = ∑ k = 1 m π R k 2 m m R 2 = 1 m ∑ k = 1 m R k 2
In the formula, ω represents the comprehensive evaluation value of forest stand state, m represents the number of forest stand state indicators, and Rk represents the k-th forest stand state indicator.

2.5. Statistical Analysis

All statistical analyses were performed using the R programming language (version: 4.3.1). All datasets were tested for normality using Shapiro–Wilk tests. Analysis of variance was employed to examine the disparities in stand state indicators between blocks and logging measures, and Tukey’s test was used to test for differences in treatment means among different logging intensity levels at p ≤ 0.05. Furthermore, we employed the unit circle method and principal component analysis (PCA) to elucidate the impact of logging intensities on forest stand state. Linear regressions were conducted between the unit circle method results and the principal component analysis results. Significant differences and effects were established at p ≤ 0.05.

3. Results

3.1. Impact of Various Logging Intensities on Stand State Indicators in Middle-Aged P. massoniana Plantations

Analysis of variance was used to examine the influences of block and logging on the Masson pine plantation stand states. According to the results, significant differences were observed among different blocks in stand dominance (U) and in species composition (Z) (p < 0.05). Moreover, logging measures significantly impacted stand density (K), tree health (Q), species composition (Z), species diversity (D), species evenness (P), and comprehensive evaluation (ω) (p < 0.05). Further analysis unveiled that blocks had a very small impact on the stand state of Masson pine plantations. The main difference was caused by the variation in logging intensity (Table 3).
In middle-aged P. massoniana plantations subjected to various logging intensities (Table 4), four stand state indicators were found to be critical—stand density (K), species composition (Z), species diversity (D), and species evenness (P). These four initially exhibited an increase and then subsequently decreased as logging intensity escalated, with the apex values observed at a logging intensity of 30% (T3). Analysis of variance revealed that both stand density (K) and species diversity (D) were markedly lower in the control blocks (CKs) than in any blocks subjected to logging treatments (p < 0.05). The species composition (Z) was significantly more pronounced in the T1, T2, and T3 blocks than in the control blocks (p < 0.05), whereas the T4 blocks show no significant deviation from the control blocks (p > 0.05). Moreover, species evenness (P) was substantially higher in the T2 and T3 blocks than in the control blocks (p < 0.05). Tree health (Q) improved as logging intensity increased, registering significantly and increasingly superior levels in the treatment blocks than in the control blocks (p < 0.05). Conversely, tree height distribution (V), DBH distribution (H), tree dominance (U), and stand growth (B) differed between the control and treatment blocks, but not significantly (p > 0.05).

3.2. Comprehensive Evaluation of Stand States in Middle-Aged P. massoniana Plantations Using the Unit Circle Method

By employing the unit circle method to evaluate stand states, an integrative assessment could be performed on middle-aged P. massoniana plantations under differing logging intensities. The comprehensive evaluation values (ω) oscillated between 0.151 and 0.196 for the control blocks (CKs), between 0.288 and 0.344 for the T1 blocks, between 0.292 and 0.348 for the T2 blocks, between 0.301 and 0.386 for the T3 blocks, and between 0.270 and 0.377 for the T4 blocks. This holistic examination revealed that the stand state initially improved but then subsequently deteriorated as logging intensity increased, with the zenith occurring at a 30% logging intensity (T3). Furthermore, the stand states of all logging treatments (T1–T4) surpassed those of the control blocks, underscoring that moderate logging enhances the stand state. The 30% logging intensity exerted the most salient positive impact, culminating in the best stand state observed in this study (Figure 2).

3.3. Stand-State Evaluation of Middle-Aged P. massoniana Plantations Using Principal Component Analysis

Principal component analysis (PCA) was conducted on stand state indicators of stands in middle-aged P. massoniana plantations that had been subjected to various logging intensities (Table 4; Figure 2). The sample data successfully passed the KMO and Bartlett’s sphericity tests, affirming their suitability for PCA. The first two principal components accounted for a cumulative contribution rate of 87.546% and were thus selected for comprehensive evaluation. The first principal component’s variance contribution rate was 56.677%, with high loadings on species diversity (D), species composition (Z), and species evenness (P), the three diversity attributes. Consequently, the first principal component can represent diversity status in comprehensive evaluations. The second principal component’s variance contribution rate was 30.869%, with high loadings on tree dominance (U), stand density (K), tree health (Q), stand growth (B), DBH distribution (H), and tree height distribution (V), which were the vitality and structural characteristics. Thus, the second principal component can represent growth status in comprehensive evaluations. Notably, significant disparities existed between the control blocks (CKs) and the logging blocks (T1, T2, T3, T4), which illustrates that logging markedly improved the stand state of middle-aged P. massoniana plantations. However, the stand states were mostly similar across different logging intensities, suggesting that the few observable differences were not strikingly pronounced (Figure 3a).
Based on the principal component analysis, the ranking of trait weights is (Figure 3b) stand density (K, 56.411%) > species composition (Z, 30.543%) > species diversity (D, 9.744%) > tree health (Q, 2.591%) > stand dominance (U, 0.556%) > species evenness (P, 0.091%) > diameter distribution (H, 0.050%) > height distribution (V, 0.011%) > stand vigor (B, 0.003%). The four indicators of stand density (K, 56.411%), species composition (Z, 30.543%), species diversity (D, 9.744%), and tree health (Q, 2.591%) account for 87.146% of the weight, and each indicator has a weight greater than 1%, making them key indicators for characterizing the stand state of middle-aged Masson pine forests. In addition, there were no significant differences in the comprehensive evaluation of the stand state of mid-aged Masson pine forests under various logging intensities (T1, T2, T3, T4), but all were significantly greater than in the control blocks CK (Table 5).

3.4. Comparative Analysis of Stand-State Evaluations Using the Unit Circle Method Versus Principal Component Analysis

The evaluative outcomes derived from the unit circle method and principal component analysis exhibit a highly significant linear correlation (R2 = 0.909, p < 0.001) (Figure 4), which attests to the high consistency of overall trends across both methods. Nevertheless, multiple comparative analyses revealed that the unit circle method demonstrated a relatively higher sensitivity, thus enabling a more precise differentiation of subtle distinctions among the various logging treatments (Figure 4).

4. Discussion

A comparative analysis of stand states under varying logging intensities, using the unit circle method, elucidates that selective logging can significantly improve stand state in middle-aged P. massoniana stands characterized by excessive density and high canopy closure. Notably, a logging intensity approximating 30% was observed to yield the most optimal stand state. Logging can alleviate inter-tree competition by liberating growth and nutrient space, thereby fostering tree growth [41,42,43]. A judicious logging intensity thus exerts beneficial effects on forest structural optimization [44] by fine-tuning the forest structure to its optimal state through the removal of a specific number of trees, thus maximizing ecological function [45,46]. Chang et al. discerned that moderate logging (30.4% logging intensity) had the most pronounced impact on the stand state of P. taiwanensis plantations, enhancing spatial structure and stability [47]. Logging can engender new ecological niches within forest communities, mitigate competitive intensity among trees, promote ecological succession, and augment forest health [24]. Consequently, logging, as a scientific and efficacious forest management technique, diverges from traditional clear-cutting; its essence lies in retaining superior species and robust, vigorous trees, thereby maintaining forest productivity and stability through rational structural adjustments. Only healthy and stable forest ecosystems can fully utilize their ecological, social, and economic functions [48].
This study assessed the ramifications of logging intensity on stand states in middle-aged P. massoniana plantations by employing the unit circle method alongside principal component analysis. The findings revealed that the parameters of stand density, tree health, species composition, and species diversity were most responsive to logging. Two years after the logging event, these indicators exhibited the most improvement. This improvement can be chiefly attributed to the excision of tightly packed, underperforming trees devoid of future cultivation potential, as well as those afflicted by pests and diseases [49]. Furthermore, the predominantly single-species nature and high degree of monospecificity in mid-aged P. massoniana plantations highlights that the logging process successfully removed P. massoniana while bypassing other thriving broad-leaved tree species. Thereby, to some extent, this altered the species composition and stand density of the P. massoniana plantations while bolstering the health and vigor of any remaining trees. Logging can promote stand composition and vertical structural characteristics while potentially accelerating natural regeneration [50,51], optimizing stand structure, and increasing the growth rate of residual trees when inferior trees are removed [52]. Diameter distribution, height distribution, tree dominance, stand growth, and species evenness all showed relatively poor responsiveness to logging; however, short-term logging efforts have always struggled to enhance these latter indicators. This shortcoming may be attributed to the brevity of the logging period, which is not long enough to allow for substantial changes in tree diameter and height growth. Moreover, the responses of trees and stands to logging can vary temporally [53,54], with uneven size and growth distribution changes manifesting differently at varying logging intervals [53,55]. Previous research has demonstrated that there is a temporal lag in tree and stand growth responses to logging disturbance. For example, no significant changes in growth patterns have been observed during the initial post-logging period (approximately two years) [56,57]. In a notable study, Zeng et al. investigated how growth in P. massoniana plantations responded to varying logging intensities in southwestern Guangxi. Their analysis of tree height and diameter growth data across different post-logging intervals revealed no significant changes within the first one to three years following logging [19]. As such, with relatively short logging intervals and limited long-term monitoring data being the norm, our current understanding of tree growth patterns throughout the post-logging recovery period remains incomplete. Further research is needed to assess potential significant changes in various stand-level indicators during an extended recovery process. Thus, prolonged observation and more nuanced management strategies might be needed to improve the indicators that showed poor responsiveness in this study.
The unit circle method, which was utilized in this study to evaluate stand states, enabled a scientific assessment that mirrors actual stand states and provided robust guidance for formulating specific forest management measures. Compared to other traditional evaluation methods, the unit circle method boasts at least three advantages: (1) it is a more scientific evaluation system involving typical indicators of structure, vitality, and diversity. These indicators have a unified value range, thereby rendering the biological significance of the results easier to interpret. (2) It offers a more intuitive data presentation, with the unit circle result presentation method markedly enhancing the comprehension and acceptance of comprehensive stand-state evaluation information. (3) It results in more precise evaluation results; our study demonstrates that the unit circle method and principal component analysis both yield highly congruent overall stand-state evaluation results, but that the unit circle method is more sensitive to logging treatments due to its geometric properties, making it easier for this method to detect trend shifts post-data transformation. In the unit circle method, each sector independently represents a separate evaluation index, which facilitates contrasting how each index contributes to the overall evaluation result. Together, the sectors offer a summary of the overall stand performance [32]. Additionally, the geometric transformation process of the unit circle method effectively diminishes noise interference and augments data resolution. Although principal component analysis excels in dimensionality reduction and data simplification, it relies excessively on certain principal components during different treatment contrast analyses, and this results in its insufficient sensitivity to subtle differences [58,59]. Therefore, in scenarios demanding high-precision differentiation of disparate treatment effects, the unit circle method is more advantageous because it delivers more accurate stand-state evaluation results and can strongly indicate forest quality enhancement.

5. Conclusions

Comprehending the impact of logging on stand structure and condition is vital for managing forest resources scientifically. The results of this study underscore that moderate logging intensity substantially influences stand state in mid-aged P. massoniana plantation stands with excessive density and high canopy closure. The unit circle method scientifically and effectively reflects the short-term impact of logging intensity on mid-aged P. massoniana plantation stand states and strongly indicates forest quality enhancement. This method holds potential for widespread adoption across various forestry production practices as well as for guiding forest management activities. Furthermore, our short-term research demonstrates that implementing an appropriate logging intensity effectively optimizes stand structure and maintains ecosystem stability. However, logging operations can also negatively impact forests, such as through soil compaction or nutrient loss. Thus, it is always necessary to conduct continuous monitoring and ongoing research to clarify the comprehensive impacts of logging on the forest ecosystem.

Author Contributions

Conceptualization, Z.C.; data curation, J.T. and Z.C.; investigation, J.T., Z.Z. and Z.C.; methodology, J.T., Z.Z. and Z.C.; writing—original draft, J.T.; writing—review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the following grants: Guizhou Province Forestry Science Project, Grant Number: QLKH [2022]38; National Natural Science Foundation of China, Grant Number: 32001314.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We sincerely thank the editor and reviewers for their valuable comments on this manuscript. We are also deeply grateful to the College of Forestry of Guizhou University for the support in this work.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Dong, L.; Lin, X.; Bettinger, P.; Liu, Z. The contributions of stand characteristics on carbon sequestration potential are triple that of climate variables for Larix spp. plantations in northeast China. Sci. Total Environ. 2024, 911, 168726. [Google Scholar] [CrossRef] [PubMed]
  2. Stephens, S.S.; Wagner, M.R. Forest Plantations and Biodiversity: A Fresh Perspective. J. For. 2007, 105, 307–313. [Google Scholar] [CrossRef]
  3. Pawson, S.M.; Brockerhoff, E.G.; Meenken, E.D.; Didham, R.K. Non-native plantation forests as alternative habitat for native forest beetles in a heavily modified landscape. Biodivers. Conserv. 2008, 17, 1127–1148. [Google Scholar] [CrossRef]
  4. Coote, L.; French, L.J.; Moore, K.M.; Mitchell, F.J.G.; Kelly, D.L. Can plantation forests support plant species and communities of semi-natural woodland? For. Ecol. Manag. 2012, 283, 86–95. [Google Scholar] [CrossRef]
  5. Pawson, S.M.; Brin, A.; Brockerhoff, E.G.; Lamb, D.; Payn, T.W.; Paquette, A.; Parrotta, J.A. Plantation forests, climate change and biodiversity. Biodivers. Conserv. 2013, 22, 1203–1227. [Google Scholar] [CrossRef]
  6. Jiménez, M.N.; Spotswood, E.N.; Cañadas, E.M.; Navarro, F.B. Stand management to reduce fire risk promotes understorey plant diversity and biomass in a semi-arid Pinus halepensis plantation. Appl. Veg. Sci. 2015, 18, 467–480. [Google Scholar] [CrossRef]
  7. Muñoz Flores, H.J.; Sáenz Reyes, J.T.; García Sánchez, J.J.; Hernández Máximo, E.; Anguiano Contreras, J. Áreas Potenciales Para Establecer Plantaciones Forestales Comerciales de Pinus pseudostrobus Lindl. y Pinus greggii Engelm. en Michoacán. Rev. Mex. Cienc. For. 2011, 2, 29–44. [Google Scholar]
  8. Haughian, S.R.; Frego, K.A. Short-term effects of three commercial thinning treatments on diversity of understory vascular plants in white spruce plantations of northern New Brunswick. For. Ecol. Manag. 2016, 370, 45–55. [Google Scholar] [CrossRef]
  9. Ming, A.; Jia, H.; Zhao, J.; Tao, Y.; Li, Y. Above- and Below-Ground Carbon Stocks in an Indigenous Tree (Mytilaria laosensis) Plantation Chronosequence in Subtropical China. PLoS ONE 2014, 9, e109730. [Google Scholar] [CrossRef]
  10. Zheng, Y.; Yang, Q.; Xu, M.; Chi, Y.; Shen, R.; Li, P.; Dai, H. Responses of Pinus massoniana and Pinus taeda to freezing in temperate forests in central China. Scand. J. For. Res. 2012, 27, 520–531. [Google Scholar] [CrossRef]
  11. Zhang, Z.; Huang, X.; Zhou, Y. Factors influencing the evolution of human-driven rocky desertification in karst areas. Land Degrad. Dev. 2021, 32, 817–829. [Google Scholar] [CrossRef]
  12. Wang, Y.; Qian, J.; Zhang, C.; Zhang, Z. Effects of Cadmium Stress on Seed Germination and Physiology and Biochemistry during Early Seedling Growth of Masson Pine (Pinus massoniana Lamb.). For. Sci. 2024, 70, 242–249. [Google Scholar] [CrossRef]
  13. Yang, Z.; Xia, H.; Tan, J.; Feng, Y.; Huang, Y. Selection of superior families of Pinus massoniana in southern China for large-diameter construction timber. J. For. Res. 2020, 31, 475–484. [Google Scholar] [CrossRef]
  14. Fu, L.; Lei, X.; Hu, Z.; Zeng, W.; Tang, S.; Marshall, P.; Cao, L.; Song, X.; Yu, L.; Liang, J. Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Ann. For. Sci. 2017, 74, 42. [Google Scholar] [CrossRef]
  15. Zhang, Y.; Zhang, D.; Li, X.; Zhang, J. Contribution of soil fauna to the degradation of recalcitrant components in Cinnamomum camphora foliar litter in different-sized gaps in Pinus massoniana plantations. J. For. Res. 2019, 30, 931–941. [Google Scholar] [CrossRef]
  16. Li, M.; Du, M.; Nie, C. Research Advances in Soil Improvement of Pinus massoniana Plantation. World For. Res. 2014, 27, 31–36. [Google Scholar]
  17. You, Y.; Huang, X.; Zhu, H.; Liu, S.; Liang, H.; Wen, Y.; Wang, H.; Cai, D.; Ye, D. Positive interactions between Pinus massoniana and Castanopsis hystrix species in the uneven-aged mixed plantations can produce more ecosystem carbon in subtropical China. For. Ecol. Manag. 2018, 410, 193–200. [Google Scholar] [CrossRef]
  18. Liu, S.; Yang, Y.; Wang, H. Development strategy and management countermeasures of planted forests in China: Transforming from timber-centered single objective management towards multi-purpose management for enhancing quality and benefits of ecosystem services. Acta Ecol. Sin. 2018, 38, 1–10. [Google Scholar]
  19. Zeng, J.; Lei, Y.; Jia, H.; Cai, D.; Tang, J. Dynamic Growth Response of Pinus massoniana Plantation on Intensive Thinning in Southwestern Guangxi, China. For. Res. 2017, 30, 335–341. [Google Scholar]
  20. Olajuyigbe, S.; Tobin, B.; Saunders, M.; Nieuwenhuis, M. Forest thinning and soil respiration in a Sitka spruce forest in Ireland. Agric. For. Meteorol. 2012, 157, 86–95. [Google Scholar] [CrossRef]
  21. Wic Baena, C.; Andrés-Abellán, M.; Lucas-Borja, M.E.; Martínez-García, E.; García-Morote, F.A.; Rubio, E.; López-Serrano, F.R. Thinning and recovery effects on soil properties in two sites of a Mediterranean forest, in Cuenca Mountain (South-eastern of Spain). For. Ecol. Manag. 2013, 308, 223–230. [Google Scholar] [CrossRef]
  22. Trentini, C.P.; Campanello, P.I.; Villagra, M.; Ritter, L.; Ares, A.; Goldstein, G. Thinning of loblolly pine plantations in subtropical Argentina: Impact on microclimate and understory vegetation. For. Ecol. Manag. 2017, 384, 236–247. [Google Scholar] [CrossRef]
  23. Zhao, B.; Cao, J.; Geng, Y.; Zhao, X.; von Gadow, K. Inconsistent responses of soil respiration and its components to thinning intensity in a Pinus tabuliformis plantation in northern China. Agric. For. Meteorol. 2019, 265, 370–380. [Google Scholar] [CrossRef]
  24. Ye, S.; Zheng, Z.; Diao, Z.; Ding, G.; Bao, Y.; Liu, Y.; Gao, G. Effects of Thinning on the Spatial Structure of Larix principis-rupprechtii Plantation. Sustainability 2018, 10, 1250. [Google Scholar] [CrossRef]
  25. Zhang, T.; Dong, X.; Guan, H.; Meng, Y.; Ruan, J.; Wang, Z. Effect of Thinning on the Spatial Structure of a Larix gmelinii Rupr. Secondary Forest in the Greater Khingan Mountains. Forests 2018, 9, 720. [Google Scholar] [CrossRef]
  26. Xu, L.; Liu, Y.; Yang, L. Impact of Selective Logging of Secondary Forest of Tilia Amurensis on Stand Structure After 20 Years. Pak. J. Agric. Sci. 2021, 58, 411–420. [Google Scholar]
  27. Yücesan, Z.; Özçelik, S.; Oktan, E. Effects of thinning on stand structure and tree stability in an afforested oriental beech (Fagus orientalis Lipsky) stand in northeast Turkey. J. For. Res. 2015, 26, 123–129. [Google Scholar] [CrossRef]
  28. Wei, L.X.; Geng, Y.; Cui, K.D.; Qiao, X.T.; Yue, Q.M.; Fan, C.Y.; Zhang, C.Y.; Zhao, X.H. Responses of tree growth to harvesting intensity among forest strata and growth stages in abroadleaved Korean pine forest. Chin. J. Plant Ecol. 2022, 46, 642–655. [Google Scholar] [CrossRef]
  29. Hui, G.; Zhang, G.; Zhao, Z.; Yang, A. Methods of Forest Structure Research: A Review. For. Manag. 2019, 5, 142–154. [Google Scholar] [CrossRef]
  30. Wei, H.; Zhang, Y.; Dong, L.; Liu, Z.; Chen, Y. Comprehensive evaluation of spatial structure state for the main forest types in Maoershan mountain. J. Cent. South Univ. For. Technol. 2021, 41, 131–139. [Google Scholar]
  31. Zhao, Z.; Hui, G.; Liu, W.; Hu, Y.; Zhang, G. A Novel Method for Calculating Stand Structural Diversity Based on the Relationship of Adjacent Trees. Forests 2022, 13, 343. [Google Scholar] [CrossRef]
  32. Zhang, G.; Hui, G.; Zhang, G.; Hu, Y.; Zhao, Z. A Novel Comprehensive Evaluation Method of Forest State Based on Unit Circle. Forests 2019, 10, 5. [Google Scholar] [CrossRef]
  33. Hui, G.; Zhang, G.; Zhao, Z.; Hu, Y.; Liu, W.; Zhang, S.; Bai, C. A New Rule of π Value of Natural Mixed Forest Optimal Stand State. Sci. Silvae Sin. 2016, 52, 1–8. [Google Scholar]
  34. Zhao, Z.; Hui, G. Forest Naturalness Evaluation Method Based on Stand State Characters: A Case Study of Gansu Xiaolongshan Forests. Sci. Silvae Sin. 2011, 47, 9–16. [Google Scholar]
  35. Pukkala, T.; Gadow, K. Continuous Cover Forestry; Springer: Dordrecht, The Netherlands, 2012. [Google Scholar]
  36. Hui, G.; Zhang, L.; Hu, Y.; Wang, H.; Zhang, G. Stand crowding degree and its application. J. Beijing For. Univ. 2016, 38, 1–6. [Google Scholar]
  37. Zhao, Z.; Hui, G.; Hu, Y.; Li, Y.; Wang, H. Method and application of stand spatial advantage degree based on the neighborhood comparison. J. Beijing For. Univ. 2014, 36, 78–82. [Google Scholar]
  38. Tang, M.; Tang, S.; Li, X.; Li, Z. Tree Species Composition Index and Its Application. For. Grassl. Resour. Res. 2003, 33–36. [Google Scholar] [CrossRef]
  39. Cabello-Solorzano, K.; Ortigosa De Araujo, I.; Peña, M.; Correia, L.; Tallón-Ballesteros, A.J. The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis. In Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, Salamanca, Spain, 5–7 September 2023; Springer Nature: Cham, Switzerland, 2023. [Google Scholar]
  40. Hui, G.; Zhao, Z.; Zhang, G. Priority of management measures for natural forests based on the stand state. J. Beijing For. Univ. 2016, 38, 1–10. [Google Scholar]
  41. Mäkinen, H.; Isomäki, A. Thinning intensity and growth of Norway spruce stands in Finland. Forestry 2004, 77, 349–364. [Google Scholar] [CrossRef]
  42. Štefančík, I. Development of target (crop) trees in beech (Fagus sylvatica L.) stand with delayed initial tending and managed by different thinning methods. J. For. Sci. 2013, 59, 253–259. [Google Scholar] [CrossRef]
  43. He, H.; Zhang, Z.; Zhang, C.; Hao, M.; Yao, J.; Xie, Z.; Gao, H.; Zhao, X. Short-Term Effects of Thinning Intensity on Stand Growth and Species Diversity of Mixed Coniferous and Broad-Leaved Forest in Northeastern China. Sci. Silvae Sin. 2019, 55, 1–12. [Google Scholar]
  44. Li, C.; Dong, L.; Liu, Z. Effect of Tending Cutting Intensity on the Stand Structure and Accumulation of Natural Larch Forest. J. Northeast For. Univ. 2021, 49, 1–5. [Google Scholar]
  45. Ghalandarayeshi, S.; Nord-Larsen, T.; Johannsen, V.K.; Larsen, J.B. Spatial patterns of tree species in Suserup Skov—A semi-natural forest in Denmark. For. Ecol. Manag. 2017, 406, 391–401. [Google Scholar] [CrossRef]
  46. Hui, G.; Hu, Y.; Liu, R. Methods of Analyzing Stand Spatial Dominance in Forest Observational Studies. J. Temp. For. Res. 2019, 2, 1–6+12. [Google Scholar]
  47. Chang, C.; Lü, K.; Zhou, M.; Chen, X.; Zhen, T.; Yan, D. Effects of Thinning on the Spatial Structure and Stability of Pinus taiwanensis Plantations. J. Northwest For. Univ. 2022, 37, 138–144. [Google Scholar]
  48. Yen, T.; Lee, J.; Li, C.; Chen, Y. Aboveground biomass and vertical distribution of crown for Taiwan red cypress 20 years after thinning. Dendrobiology 2013, 70, 109–116. [Google Scholar] [CrossRef]
  49. Wang, Z.; Li, Y.; Meng, Y.; Li, C.; Zhang, Z. Thinning Effects on Stand Structure and Carbon Content of Secondary Forests. Forests 2022, 13, 512. [Google Scholar] [CrossRef]
  50. Bradford, J.B.; Palik, B.J. A comparison of thinning methods in red pine: Consequences for stand-level growth and tree diameter. Can. J. For. Res. 2009, 39, 489–496. [Google Scholar] [CrossRef]
  51. Swinfield, T.; Afriandi, R.; Antoni, F.; Harrison, R.D. Accelerating tropical forest restoration through the selective removal of pioneer species. For. Ecol. Manag. 2016, 381, 209–216. [Google Scholar] [CrossRef]
  52. Deng, C.; Zhang, S.; Lu, Y.; Froese, R.E.; Ming, A.; Li, Q. Thinning Effects on the Tree Height–Diameter Allometry of Masson Pine (Pinus massoniana Lamb.). Forests 2019, 10, 1129. [Google Scholar] [CrossRef]
  53. Lemire, C.; Bédard, S.; Guillemette, F.; Pothier, D. Changes in growth dominance after partial cuts in even- and uneven-aged northern hardwood stands. For. Ecol. Manag. 2020, 466, 118115. [Google Scholar] [CrossRef]
  54. Hu, J.; Herbohn, J.; Chazdon, R.L.; Baynes, J.; Vanclay, J.K. Long-term growth responses of three Flindersia species to different thinning intensities after selective logging of a tropical rainforest. For. Ecol. Manag. 2020, 476, 118442. [Google Scholar] [CrossRef]
  55. Britwum Acquah, S.; Marshall, P.L.; Eskelson, B.N.I.; Moss, I.; Barbeito, I. Changes in size inequality and growth dominance in thinned and unthinned uneven-aged interior Douglas-fir dominated stands. For. Ecol. Manag. 2022, 524, 120517. [Google Scholar] [CrossRef]
  56. Thorpe, H.C.; Thomas, S.C.; Caspersen, J.P. Residual-tree growth responses to partial stand harvest in the black spruce (Picea mariana) boreal forest. Can. J. For. Res. 2007, 37, 1563–1571. [Google Scholar] [CrossRef]
  57. Yue, Q.; He, H.; Zhang, C.; Zhao, X.; Hao, M. Responses of tree growth and stand productivity to harvesting disturbance in Korean pine-broadleaved forests. Acta Ecol. Sin. 2024, 44, 2019–2028. [Google Scholar]
  58. Jolliffe, I.T. Principal Component Analysis and Factor Analysis. In Principal Component Analysis; Springer: New York, NY, USA, 2002; pp. 150–166. [Google Scholar]
  59. Härdle, W.K.; Simar, L.; Fengler, M.R. Principal Component Analysis. In Applied Multivariate Statistical Analysis; Springer International Publishing: Cham, Switzerland, 2024; pp. 309–345. [Google Scholar]
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Figure 1. Geographical location of and sample block distribution in the study area.
Figure 1. Geographical location of and sample block distribution in the study area.
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Figure 2. Unit circle method stand-state evaluation of Masson pine mid-aged plantations. The differently colored sectors represent various stand state indicators. The area of the sector that is colored in represents the magnitude of each stand state indicator value, with a larger area signifying a better indicator. The symbol ω in the upper right corner represents the value of the comprehensive evaluation of each forest stand state.
Figure 2. Unit circle method stand-state evaluation of Masson pine mid-aged plantations. The differently colored sectors represent various stand state indicators. The area of the sector that is colored in represents the magnitude of each stand state indicator value, with a larger area signifying a better indicator. The symbol ω in the upper right corner represents the value of the comprehensive evaluation of each forest stand state.
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Figure 3. Principal component analysis-based stand-state evaluation of Masson pine mid-aged plantations. (a) Based on the principal component analysis, the load values of stand state indicators; Different colored boxes represent different logging intensity sample areas; (b) based on the principal component analysis, the ranking of stand state indicator weights.
Figure 3. Principal component analysis-based stand-state evaluation of Masson pine mid-aged plantations. (a) Based on the principal component analysis, the load values of stand state indicators; Different colored boxes represent different logging intensity sample areas; (b) based on the principal component analysis, the ranking of stand state indicator weights.
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Figure 4. Correlation analysis of unit circle method versus principal component analysis stand-state evaluation results.
Figure 4. Correlation analysis of unit circle method versus principal component analysis stand-state evaluation results.
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Table 1. Stand state in study area two years after logging at various intensities.
Table 1. Stand state in study area two years after logging at various intensities.
ItemCKT1T2T3T4
Mean DBH
(cm)		13.5 ± 0.7514.2 ± 0.9913.5 ± 1.8014.3 ± 1.4314.6 ± 1.43
Mean Height
(m)		11.5 ± 0.4911.4 ± 0.5110.88 ± 1.4912.1 ± 2.1312.4 ± 2.01
Tree density
(No. of trees·hm−2)		1651 ± 2161331 ± 1881319 ± 1951028 ± 184801 ± 159
Canopy cover0.88 ± 0.0250.84 ± 0.0250.79 ± 0.0250.69 ± 0.0250.57 ± 0.025
Basal area
(m2·hm−2)		27.49 ± 3.6423.93 ± 3.6521.50 ± 2.8218.17 ± 1.3815.15 ± 0.93
Volume
(m3·hm−2)		186.1 ± 21.2159.1 ± 31.6138.4 ± 26.2126.9 ± 24.3106.5 ± 9.8
Table 2. Indicators and corresponding formulas for evaluating forest stand state.
Table 2. Indicators and corresponding formulas for evaluating forest stand state.
ItemStand
Characteristics		IndicatorsFormula or Definition
StructureDBH distribution (H)q valueThe DBH of trees is divided into classes with a width of 2 cm, and N = k e − a d is represented by Meyer’s negative exponential distribution, where N is the number of trees; e is the base of the natu-ral logarithm; d is the DBH; a is a negative expo-nential distribution structure constant and k is a constant. q = e a h , where h indicates that the diameter step is 2 cm. If the q value falls between 1.2 and 1.7, it means that it is in the reasonable distribution range of different age forest diameters [34].
Tree height distribution (V)Gini coefficient In   the   equation   H = 1 − ∑ 2 Q i − w i / n , where n represents the number of individual trees, Wi represents the proportion of the height of the i-th individual tree to the total tree height, and Qi represents the cumulative value of Wi [35].
Stand density (K)Crowding degree of forest In   the   equation   K = 100 N 0.5 / c w ¯ , where N represents the number of trees per hectare, represents the average crown width. If the value of K falls within the range of 0.6 to 0.8, it indicates a reasonable stand density [36].
VitalityStand dominance (U)Dominant tree species proportionThe proportion of count of dominant canopy trees to count of total trees in a forest.
Stand growth (B)Potential density In   the   equation   B = G ¯ G m a x ,   where   G ¯ represents the cross-sectional area of the forest stand and Gmax represents the potential maximum cross-sectional area of the forest stand. Here, Gmax is defined as the product of the average cross-sectional area of the 50% largest individuals in the forest stand and the total tree count [32,37].
Tree health (Q)Proportion of healthy treesThe proportion of healthy trees (without pests and diseases, and without deformities such as broken branches, bending, hollow trunks, etc.) to the total number of trees in a forest stand [32,33].
DiversitySpecies composition (Z) Index of tree species composition In   the   equation   Z = − ∑ i = 1 s P i ln P i , where Pi represents the volume proportion of the i-th tree species, and S represents the number of tree species in the sample block. Note: The composition of tree species is expressed in decimal notation [38].
Species diversity (D) Simpson index In   the   equation   D = 1 − ∑ i = 1 s P i 2 , where Pi represents the proportion of individuals of the i-th tree species, and S represents the number of tree species present in the block [34].
Species evenness (P) Pielou index In   the   equation   p = − ∑ i = 1 s P i ln P i / ln s , where S represents the number of tree species present in the block [34].
Table 3. Analysis of variance of block and logging on stand state in Masson pine plantations.
Table 3. Analysis of variance of block and logging on stand state in Masson pine plantations.
IndicatorsBlockLogging
Degree of FreedomF RationDegree of FreedomF Ration
DBH distribution (H)31.53440.570
Tree height distribution (V)31.56940.854
Stand density (K)30.55241.259 × 1030 ***
Stand dominance (U)36.690 **40.794
Stand growth (B)31.88640.173
Tree health (Q)31.491411.187 ***
Species composition (Z)34.094 *44.539 *
Species diversity (D)33.13843.939 *
Species evenness (P)32.51243.252
Comprehensive evaluation (ω)315.638444.042 ***
Note: * means that there is a significant effect between them (p < 0.05); ** means that there is a very significant effect between them (p < 0.01); *** means that there is a highly significant effect between them (p < 0.001).
Table 4. The impact of logging intensity on stand state indicators of Masson pine plantations.
Table 4. The impact of logging intensity on stand state indicators of Masson pine plantations.
IndicatorsCKT1T2T3T4
DBH distribution (H)0.219 ± 0.03 a0.201 ± 0.024 a0.198 ± 0.009 a0.222 ± 0.042 a0.206 ± 0.032 a
Tree height distribution (V)0.119 ± 0.037 a0.135 ± 0.025 a0.117 ± 0.014 a0.13 ± 0.04 a0.097 ± 0.044 a
Stand density (K)0.55 ± 0 c1 ± 0 a1 ± 0 a1 ± 0 a0.85 ± 0 b
Stand dominance (U)0.046 ± 0.048 a0.091 ± 0.173 a0.028 ± 0.05 a0.17 ± 0.333 a0.182 ± 0.348 a
Stand growth (B)0.639 ± 0.033 a0.658 ± 0.03 a0.642 ± 0.023 a0.641 ± 0.045 a0.648 ± 0.055 a
Tree health (Q)0.788 ± 0.038 d0.868 ± 0.07 c0.9 ± 0.071 bc0.96 ± 0.045 ab1 ± 0 a
Species composition (Z)0.187 ± 0.122 b0.422 ± 0.128 a0.475 ± 0.105 a0.494 ± 0.211 a0.401 ± 0.144 ab
Species diversity (D)0.214 ± 0.144 b0.452 ± 0.147 a0.512 ± 0.097 a0.537 ± 0.218 a0.455 ± 0.143 a
Species evenness (P)0.138 ± 0.079 b0.219 ± 0.061 ab0.235 ± 0.038 a0.269 ± 0.072 a0.224 ± 0.043 ab
Note: Different lowercase letters indicate a significant difference between different logging intensities (p < 0.05).
Table 5. Stand-state evaluation of mid-aged Masson pine plantations using the unit circle method and principal component analysis.
Table 5. Stand-state evaluation of mid-aged Masson pine plantations using the unit circle method and principal component analysis.
MethodCKT1T2T3T4
Unit circle method0.170 ± 0.020 c0.305 ± 0.026 b0.316 ± 0.024 ab0.355 ± 0.039 a0.308 ± 0.033 b
Principal component analysis0.194 ± 0.149 b0.695 ± 0.124 a0.749 ± 0.101 a0.810 ± 0.184 a0.648 ± 0.134 a
Note: Different lowercase letters indicate a significant difference between different logging intensities (p< 0.05).
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Tu, J.; Zhao, Z.; Chai, Z. The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations. Forests 2025, 16, 183. https://doi.org/10.3390/f16010183

AMA Style

Tu J, Zhao Z, Chai Z. The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations. Forests. 2025; 16(1):183. https://doi.org/10.3390/f16010183

Chicago/Turabian Style

Tu, Jing, Zhongwen Zhao, and Zongzheng Chai. 2025. "The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations" Forests 16, no. 1: 183. https://doi.org/10.3390/f16010183

APA Style

Tu, J., Zhao, Z., & Chai, Z. (2025). The Short-Term Impact of Logging Intensity on the Stand State of Middle-Aged Masson Pine (Pinus massoniana Lamb.) Plantations. Forests, 16(1), 183. https://doi.org/10.3390/f16010183

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