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Article

Dynamic Interactions of Stand Characteristics and Site on Quercus spp. Volume in China Under Climate Change

by
Cheng-Rui Liao
*,
Jun-Xiang Ouyang
*,
Yu-Hao Li
,
Hong-Bo Jiang
and
Yin-Yin Wang
Academy of Forestry Investigation and Planning of National Forestry and Grassland Administration, Beijing 100714, China
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(12), 1769; https://doi.org/10.3390/f16121769
Submission received: 30 October 2025 / Revised: 21 November 2025 / Accepted: 22 November 2025 / Published: 25 November 2025
(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems: 3rd Edition)
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Abstract

The impacts of global warming on species’ habitat suitability and consequent potential range shifts have attracted increasing scholarly attention. As keystone species in China’s climax communities, Quercus spp. are widely distributed across the country and play vital roles in ecological conservation, economic development, and recreational services. Current research primarily focuses on variations in biomass at regional/watershed scales or employs distribution modeling to predict population responses to climate change. This study investigates nationwide trends in stand volume of Quercus spp. across three elevation gradients, analyzing the impacts of forest age, origin, and temporal dynamics by integrating historical National Forest Inventory (NFI) datasets with meteorological records spanning 1948–2021. Our findings demonstrate a persistent warming trend throughout China from 1948 to 2021, exhibiting significant seasonal divergence in temperature variability patterns. The stand volume of Quercus spp. showed non-significant elevational variation (p > 0.05), but exhibited marked differences across temporal gradients and origins. Notably, natural forests demonstrated higher stand volume than plantations (p < 0.01). Moreover, significant interactive effects were observed among elevation, origin, and forest age (p < 0.05), particularly for natural Quercus spp. Their stand volume exhibited distinct age-dependent trajectories: (1) high-elevation stands (>3000 m) displayed a “decline-recovery” fluctuation during 41–80 years, (2) mid-elevation stands (500–3000 m) maintained steady increases, and (3) low-elevation stands (<500 m) followed parabolic patterns peaking at 61–80 years. Our work further validates differential migration patterns of Quercus spp. under global warming, providing novel mechanistic insights into their climate-responsive dynamics.

1. Introduction

Global warming is affecting trees and forests through multiple pathways, with typical responses including shifts in phenology (both advances and delays) [1,2], adjustments in growth patterns as indicated by changes in tree-ring density and width [3,4], alterations in species distribution ranges through upward and poleward migrations [5,6], and ultimately profound impacts on ecosystem structure and biodiversity [7]. The “fingerprints” of global warming on wildlife have been analyzed by an early study in Nature, revealing accelerated shifts in species distributions and phenological mismatches (e.g., altered migration timing) due to rising temperatures [8]. So far, the significant impact of global warming on animal and plant populations is beyond doubt. China, a country with vast latitudinal and longitudinal spans and high species richness, is also among the most vulnerable to the impacts of climate change. Consequently, it has conducted extensive research on the distribution and population dynamics of flora and fauna. Liu et al. found that the secondary broad-leaf mixed forest in northern China showed an unfavorable degradation trend under the influence of climate change, perhaps due to the positive response of broad-leaf tree species to northern climate warming, leading to the encroachment of the living space of coniferous tree species in the original secondary broad-leaf mixed forest by broad-leaf tree species [7]. Fu et al. predicted the potential changes in above-ground biomass, dominant tree species composition, and distribution in the forest region of northeast China and noted that most temperate broad-leaved tree species are expected to demonstrate a northward migration [9]. However, a nationwide analysis systematically examining the response of stand volume (a primary growth indicator) to climate change along elevational gradients remains unreported.
Quercus L. have significant ecological, economic, and aesthetic importance all over the world, and play a critical role in maintaining the health and function of forest ecosystems by providing essential habitats and food for multiple species [10]. Currently, the species distributions of Quercus spp. have been independently studied in different regions [11]. Mirhashemi et al. indicated that the potential habitat of Brant’s oak will decline in the future under climate change scenarios and across all three geographical extents compared to the current habitat [12]. Vigren et al. forecast widespread future growth changes in oak stands across Europe, revealing that some southern European regions exhibit increased oak stand growth compared to historical levels [13]. As Quercus L. are dominant species spanning both northern and southern China across a wide latitudinal range, numerous studies have investigated their responses to climate change. Xu et al. identified drought-tolerant species like Quercus variabilis as being climate-resilient, whereas moisture-sensitive species (e.g., Quercus acutissima) face contraction risks [14]. Zhang et al. projected significant range contractions for woody plant species (including oaks) in Yunnan Province under extreme climate scenarios, highlighting the particular vulnerability of moisture-sensitive species like Q. acutissima [15]. Additionally, using the MaxEnt model, Sun et al. predicted potential distribution shifts of 35 Quercus species across China under future climate scenarios [11]. Their results revealed that mountainous areas—especially the Hengduan and Qinling Mountains—would persist as biodiversity hotspots despite climatic changes. However, holistic trends in Quercus spp. dynamics across China—particularly elevational-, origin-, and age-dependent variations in stand volume under climate change—remain underexplored, constraining the development of comprehensive management and conservation strategies.
Therefore, this study approaches the topic from the perspective of dynamics in the stand volume of Quercus spp. in response to climate change across different elevations, analyzing the dynamic trends in stand volume and the interaction effects of elevation, forest age, and origin of Quercus spp. in response to climate change from southern to northern China. Based on the prevailing research consensus, we propose two key hypotheses: (1) The stand volume of Quercus spp. exhibits significant spatiotemporal dynamics under climate warming. (2) The interactive effects of forest origin and stand age modulate the elevational response. These hypotheses aim to bridge the current knowledge gap at a macro-scale regarding how Quercus spp.—particularly their growth and stand volume dynamics—respond to climate change across low-to-high elevation gradients, thereby providing a scientific basis for developing more comprehensive conservation and management strategies.

2. Materials and Methods

2.1. Climatic Data

The annual meteorological data (NCEP reanalysis datasets) were acquired from the National Oceanic and Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) to serve as a baseline for regional climate change assessments. Specifically, the temperature data were extracted from the NCEP reanalysis datasets (retrieved from https://psl.noaa.gov/ on 13 January 2022), featuring a spatial resolution of 2.5° × 2.5°. These datasets comprise global monthly mean surface temperature records spanning from 1948 to 2021, generated through the integration of upper-atmospheric observations with land surface process modeling. This methodological approach enables the more robust detection of large-scale climate patterns associated with greenhouse gas forcing and atmospheric circulation dynamics [16]. The NCEP reanalysis products have been extensively validated and utilized in numerous domestic and international climate studies. Notably, both our previous research and Xu et al. have demonstrated strong consistency between NCEP-derived data and ground-based measurements when analyzing short-term climate fluctuations and interannual variability, confirming their high reliability for climate diagnostics [7,16].

2.2. Stand Volume Estimation of Quercus spp.

Quercus spp. data were obtained from permanent sample plots in the 7th (2004–2008), 8th (2009–2013), and 9th (2014–2018) National Forest Inventories (NFIs). A complete list of Quercus spp. considered in this study is provided in Table S6. This study utilizes officially published provincial-level forestry statistics, where the original plot-level measurements had already been compiled by provincial forestry authorities into mean stand volume values for Quercus spp. within their respective jurisdictions. Consequently, the unit of analysis and the number of cases are defined at the provincial administrative level, which aligns with the official reporting system of China’s NFI and is suited for national-scale analysis. China’s NFI employs a systematic permanent sample plot method at the provincial level, with surveys repeated quinquennially to maintain data continuity and timeliness. Plot sizes ranged between 0.06 and 0.08 hectares among provinces, requiring the standardization of all stand volume measurements to per hectare units for analytical consistency. The survey comprises two primary components: (1) the measurements of plot-level covering land classification, geographic coordinates, site conditions (elevation, slope gradient, aspect, and soil characteristics), and stand attributes (tree composition, age structure, mean diameter at breast height (DBH), average height, etc.); followed by (2) the recordings of individual tree-level covering species identification, DBH (≥5 cm threshold), spatial positioning, etc. [17]. According to the ‘Technical Regulations for Continuous Forest Inventory’ (GB/T 38590-2020), stands of varying ages with Quercus spp. as the dominant tree group were selected for further analysis. The dataset comprised 63,701 permanent plots from the 7th, 8th, and 9th NFIs, including 2145 plots with Quercus spp. stands. Stand volume was calculated based on individual tree data from each plot. The formulas and parameters for stand volume estimation were derived from ‘Tree biomass models and related parameters to carbon accounting for major tree species’ (GB/T 43648-2024). The per hectare stand volume of Quercus spp. was subsequently aggregated across thirty provinces, five age classes, and two stand origins (natural and artificial forests), while incorporating repeated measurements from multiple survey years. This data integration process resulted in a comprehensive dataset of 745 cases for the final analysis. Due to data limitations, this study excludes Hong Kong, Macao, and Taiwan province.

2.3. Classification of Elevation and Age Classes

Considering the provincial-level structure of the NFI data and aligning with this study’s focus on broad-scale trends—while minimizing the influence of microtopography—we classified provinces into three elevation tiers based on major geographical features and the elevations of provincial capitals/core regions, in accordance with China’s three-terrain-step distribution: (1) High-elevation provinces: Over 50% of the area exceeds 3000 m, or core zones are located in high-elevation regions (e.g., Tibet and Qinghai, representing the Qinghai–Tibet Plateau). (2) Mid-elevation provinces: Most areas range between 500 and 3000 m, with core zones on plateaus or mountainous regions (e.g., Yunnan–Guizhou Plateau, Loess Plateau, Inner Mongolia Plateau, and other mountainous provinces). (3) Low-elevation provinces: Over 50% of the area lies below 500 m, with core zones on plains, coastal areas, or hills (e.g., eastern plains, coastal regions, and hilly areas) (Table 1).
The age-class classification systems differ between natural and artificial forests due to fundamental distinctions in stand development characteristics, management objectives, and ecological functions. According to the ‘Technical Regulations for Continuous Forest Inventory’ (GB/T 38590-2020) in China, the age-class thresholds are defined as follows: (1) Natural forests: age class 1: ≤40 years, age class 2: 41–60 years, age class 3: 61–80 years, age class 4: 81–120 years, and age class 5: ≥120 years. (2) Artificial forests: age class 1: ≤20 years, age class 2: 21–40 years, age class 3: 41–50 years, age class 4: 51–70 years, and age class 5: ≥71 years.

2.4. Data Analysis

A comprehensive analysis of the stand volume dynamics of Quercus spp. under climate change was conducted using statistical models in Python 3.11, following initial data organization in Excel 2010. The analyses, which examined volume variations across stand and site conditions, included one-way ANOVA (with Duncan’s test) and multi-factor ANOVA for main and interaction effects. Linear mixed effects models (LMMs) were further utilized to incorporate hierarchical data structures [18], while hurdle models were employed to address zero inflation through the separate modeling of presence–absence and positive volume values, thus enabling the testing of predictor effects on both components [19].

3. Results

3.1. Analysis of Temperature Change

The variation tendency in annual average nationwide temperatures of China had significant differences from 1948 to 2020, shown in Figure 1 and Table S1. The overall temperature showed an upward trend, with a regression slope of 0.13 °C per decade in Figure 1. The minimum temperature recorded was −3.13 °C in January 1969, while the maximum reached 22.65 °C in July 1950. Moreover, the seasonal fluctuations vary across spring (March to May), summer (June to August), autumn (September to November), and winter (January, February, and December) (Figure 2). The most significant volatility occurs in spring, autumn, and winter, with standard deviations of 5.93 (autumn) > 5.22 (spring) > 1.27 (winter), respectively, while summer exhibits the most stable conditions, with a standard deviation of 0.80 (Table S2).

3.2. Comparative Analysis of Quercus spp. Stand Volume Using Multi-Factor ANOVA, Linear Mixed Effects, and Hurdle Models

Variations in Quercus spp. stand volume across China, as influenced by temporal, elevational, and origin factors, are illustrated in Figure 3 as well as Table 2, Table 3, Table 4 and Table S3. The following effects identified by multi-factor ANOVA: Temporally, Quercus spp. stand volume per hectare in China showed no significant differences (p > 0.05) across the three survey periods. In contrast to temporal effects, elevation and origin significantly influenced nationwide Quercus spp. stand volume per hectare (p < 0.05). The stand volume of Quercus spp. varied significantly by elevation (p < 0.05), with medium-elevation (70.2 m3/ha) exceeding low-elevation stands (49.8 m3/ha) and high-elevation forests (38.3 m3/ha) (Figure 3B). Natural Quercus forests exhibited significantly higher stand volume (84.3 m3/ha) compared to plantations (17.5 m3/ha; p < 0.01) (Figure 3C).
However, the results from both the linear mixed effects and hurdle models indicated that Quercus spp. stand volume per hectare in China varied significantly (p < 0.05) across the three survey periods. The stand volume of Quercus spp. decreased from 53.7 m3/ha (2004–2008) to 51.5 m3/ha (2009–2013). Following China’s phased ban on commercial logging in natural forests (initiated in 2015, fully implemented in 2017), stand volume increased to 61.2 m3/ha during 2014–2018 (Figure 3A). Elevation showed no significant effect on the stand volume of Quercus spp.
Collectively, all three modeling approaches demonstrated that stand origin exerted a significant influence on the stand volume of Quercus spp. Furthermore, significant elevation × origin interaction effects (p < 0.05) were detected, with stand volume per hectare increasing with elevation in both natural and artificial forests (Figure 4). However, no artificial Quercus forests have been established in high-elevation regions.

3.3. Interactive Effects of Age, Origin, and Elevation on Quercus spp. Stand Volume Variation

The changes in Quercus spp. stand volume per hectare across age classes under different origins (natural and artificial forests) and elevations (high-, mid-, and low-elevation), as well as the interactive effects among origin, elevation, and age class, are presented in Figure 5 and Figure 6. Overall, stand volume trajectories across age classes exhibited significant variation by elevation and origin (Table S5). Among different elevations, both high- and mid-elevation regions exhibited an initial increase, followed by a decrease and then a sustained rise in stand volume per hectare during the first to fifth age classes. In contrast, Quercus spp. at low-elevation regions showed a parabolic trend, with the lowest stand volume in the first and fifth age classes and the highest in the third age class (Figure 5A). Regarding origin, natural forests displayed a sharp increase in stand volume from the first to second age class, followed by a gradual upward trend, whereas artificial forests peaked at the second age class and then declined sharply (Figure 5B).
The three-factor interaction plot demonstrates that high-elevation natural forests follow a consistent ‘increase-decrease-increase’ trend (Figure 6). In low-elevation regions, both natural and artificial forests exhibited parabolic trends, although natural forests demonstrated significantly higher stand volume (p < 0.05). Notably, Quercus spp. in mid-elevation regions deviated from the general pattern: natural forests exhibited a steady increase across age classes, whereas artificial forests peaked slightly in the second age class and maintained low levels thereafter.

4. Discussion

Human activities are driving global warming, causing rapid and widespread transformations in Earth’s climate system. Under current emission scenarios, the global mean temperature is projected to rise by 1.4–5.8 °C by 2100 [20]. Both the IPCC Fifth Assessment Report and long-term observational data confirm that recent climate change has exhibited pronounced seasonal variability, characterized by divergent warming rates across seasons [21,22]. As visually summarized in Figure 2 and quantified in Table S2, analyses of climate reanalysis data reveal that China’s annual mean temperature is undergoing a gradual rise, accompanied by significant seasonal fluctuations—most notably, spring, autumn, and winter exhibit significantly greater temperature variability than summer. This pronounced inter-seasonal disparity suggests that the impacts of climate change on forest ecosystems are not uniform throughout the year. The high volatility in the shoulder seasons (spring and autumn) could be linked to greater instability in atmospheric circulation patterns, which may disproportionately affect key phenological events such as budburst and leaf senescence in Quercus species. Such a pronounced seasonal variability is highly likely to drive changes in the distribution and growth of biological populations, particularly in northern regions with distinct seasonal patterns [23]. Temperature acts as a critical environmental regulator of tree phenology and growth. This principle is evidenced by Wang et al., who demonstrated that accumulated degree days (ADDs) strongly influence Populus budbreak phenology and revealed complex interactions between leaf dynamics and biomass accumulation [24]. Should similar thermal dependencies govern Quercus species, the phenological disruptions and increased climate extremes projected by Parmesan et al.—including early spring warming, late frosts, and summer droughts [25]—will inevitably modify Quercus spp. dynamics. These impending changes necessitate a systematic investigation of elevation-dependent variations in Quercus spp. across different age classes and origins under climate change.
The current mainstream research focuses primarily on biomass in forest ecosystems [26,27], while research on stand volume remains primarily focused on timber utilization or multi-source data-driven estimation and biomass inversion [28,29]. Admittedly, biomass provides more comprehensive coverage of root, stem, and foliage systems, yet its data acquisition poses greater challenges. In contrast, stand volume measurement is relatively straightforward, with extensive existing modeling studies supporting data acquisition from multiple sources still effectively capturing trend-related patterns. While ANOVA indicated statistically significant differences in Quercus stand volume along elevational gradients (p < 0.05), neither the linear mixed effects models nor the hurdle models in the nationwide analysis detected statistically significant differences. This uncertainty diverges from the findings of Long et al.’s study on natural Quercus forests in Hunan Province, which identified elevation as the dominant factor and demonstrated a significant positive correlation with stand volume [30]. The discrepancy may stem from the dilution of region-specific indicator weights in nationwide-scale analyses. Nevertheless, the overall trend revealed higher stand volume at mid-elevations, with lower values at both low and high elevations. This pattern aligns with several regional studies: Sun et al. showed that rising temperatures lead to aggregated distribution patterns in Quercus species, including range contraction and upward/poleward shifts in suitable habitats [31]; Joshi et al., in their study of Uttarakhand’s Binsar Wildlife Sanctuary, also found positive correlations between elevation and multiple structural variables—including biomass and carbon stocks—particularly in mid- and upper-elevation zones dominated by Banj oak and mixed species [32]. Together, these findings may support a distribution trend of Quercus species characterized by mid-elevation aggregation and marginal contraction.
In addition, plantations show an establishment gap at higher elevations, where large-scale Quercus spp. have not been successfully developed. The zero-part analysis of hurdle models confirmed this pattern quantitatively, showing a significantly higher probability of zero stand volume at high elevation (p < 0.001; Table S4). This limitation likely stems from ecological constraints in high-elevation environments—including low temperatures, intense precipitation, and lack of mature tree canopy protection—all of which impede natural sapling development [33]. The standard deviation in stand volume per hectare over three years across elevational gradients reveals distinct patterns: Quercus spp. at high elevations show relatively clustered values (~5), whereas mid- and low-elevation populations display greater dispersion (~30). This suggests that although high-elevation oak stands are less likely to form dense forests, they exhibit greater ecosystem stability when established. Concurrently, multiple studies confirm a migration trend of Quercus spp. toward higher elevations and latitudes under projected warming, with potential range expansion often coinciding with a contraction or decline in native habitats. For instance, Gao’s study on Q. variabilis population dynamics across geographical gradients revealed that southern and eastern populations may decline under climate change, with distributional shifts toward mid-elevation zones [34]. The projections of Chen et al. also indicate that the suitable habitat range of Quercus oxyphylla will persistently expand during the 2050s and 2070s across various SSP scenarios, suggesting improved climatic suitability for its cultivated populations in the future [35]. These phenomena highlight the critical need to enhance research on cultivated Quercus spp. in high-elevation zones, aiming to buffer ecological vulnerabilities under climate change.
In contrast to these spatial patterns, our study reveals significant temporal dynamics. The stand volume per hectare of Quercus spp. showed highly significant temporal changes, characterized by a moderate increase from 2004 to 2013 followed by a marked rise from 2013 to 2018. This pattern likely results from the combined effects of climate change and forest policy interventions. The initial gradual increase may be associated with the still-moderate intensity of early climate change impacts, coupled with the fact that China’s comprehensive natural forest logging ban was only fully implemented in 2015. It is also plausible that the observed trend was driven by a complex interplay of multiple factors. This perspective aligns with research by Vigren et al. [13], which highlights the interactive effects of stand type, age, weather conditions, and stand density on shaping forest dynamics.
Climate change impacts all dimensions of environmental variables, necessitating analysis of interactive effects between diverse environmental factors on Quercus spp. stand volume dynamics. Rubio-Cuadrado et al. systematically analyzed the mechanisms underlying climate-growth sensitivity in natural and artificial Pinus sylvestris forests, incorporating age, size, and origin as key explanatory factors [36]. The results demonstrated highly complex relationships among the variables (age, diameter at breast height, and stand origin), tree growth sensitivity to climate, and climatic impacts on growth, with significant interactive effects between these factors. Our research on Quercus spp. stand volume also reveals significant interactive effects of age class and stand origin (Figure 5B and Table S5). Specifically, the stand volume of natural Quercus spp. follows a logistic growth pattern—initially increasing with age class before stabilizing—exhibiting excellent agreement with theoretical growth models, which is primarily associated with the impact of China’s natural forest logging ban protection policy, effectively preserving natural Quercus spp. [37,38]. In contrast, Quercus plantation stand volume exhibits rapid initial growth followed by a stepwise decline across age classes—a pattern directly shaped by China’s regulatory harvesting cycles, which set a 51-year rotation age for timber plantations under the national forestry standard (LY/T 2908-2017), in stark contrast to the protected, long-term growth of natural forests. Vangi et al. demonstrated that climate change effects on forest cohorts are generally less significant than age effects, particularly regarding standing biomass quantity [39]. These findings are consistent with our results on Quercus spp. stand volume, showing non-significant interactive effects of time, elevation, and origin, but significant three-way interactions between age class, elevation, and origin. Interestingly, although natural oak forests exhibit an overall logistic growth pattern in stand volume across age classes, significant elevational variations in this trend are observed. Stand volume exhibits a distinct parabolic trend across age classes in low-elevation regions, whereas a continuous increasing pattern characterizes mid-elevation regions. However, in high-elevation areas, stand volume undergoes a notable decline during the 41–80 year age period before resuming growth. This phenomenon may be associated with the abrupt environmental shifts at high elevations—as noted earlier—where young trees undergo natural selection. The observed volume reduction during 41–80 years likely reflects this selective bottleneck, where only optimally adapted individuals survive. These selected specimens ultimately develop into climax communities exhibiting lower climate sensitivity and greater ecological stability than those at mid-elevations [33]. Gao demonstrated in a Q. variabilis study that population regeneration potential peaked in the central distribution area and declined toward the edges, with southern and eastern low-elevation populations particularly vulnerable to degradation from intensified interspecific competition under projected climate warming [34].
Current research on stand biomass/volume variations along elevational and age gradients under climate change remains relatively limited. Findings reveal distinct regional and species-specific characteristics, with most studies showing enhanced biomass accumulation at higher elevations [40,41,42,43,44], while limited studies exhibit more favorable outcomes at lower elevations [45]. Forest age is a key determinant of carbon storage capacity and biomass accumulation in forest ecosystems, with its importance becoming particularly prominent amid future climate uncertainties. The influence of forest age is generally manifested through higher biomass in mature forests, whereas middle-aged forests tend to exhibit greater growth rates [39]. However, there is an established and undeniable consensus that stand biomass/volume dynamics are driven by the interactive effects of multiple factors, including climate, tree species characteristics, and topographic conditions. Current research predominantly indicates that mid-elevation zones and middle-aged forests constitute the optimal combination for biomass accumulation, as they achieve a balance between hydrothermal resources and stand structural stability [46,47,48,49]. Our analysis of stand volume variations in Quercus spp. under the interactive effects of forest age, elevation, and origin aligns with these findings (Figure 6).
In summary, this study primarily examines stand volume as the key parameter using nationwide aggregated plot data (albeit lacking soil/topographic variables), yet the large-scale trends effectively capture essential patterns. These results substantially advance our understanding of Chinese Quercus spp. growth distribution and inform subsequent forest management strategies with valuable insights. It should be noted that this study, based on 15 years of forest inventory data (2004–2018), did not establish statistical correlations with longer climate series due to scale mismatch and limited temporal span. The province-level elevation classification, while consistent with the data scale, may not fully capture topographic heterogeneity. Furthermore, as our models lack key covariates like policy variables and management intensity, the observed associations should not be interpreted causally. Although significant changes in Quercus stand volume were observed, these dynamics likely result from combined effects of climate variability, succession, and management practices. Thus, the patterns identified should be viewed as preliminary macro-scale signals within a specific time window, providing a foundation for future research, while deterministic causal relationships require long-term verification.

5. Conclusions

This study investigates the interactive effects of elevation, forest age, and origin on Quercus spp. stand volume dynamics across China under climate change. Through nationwide analysis of stand volume variations across elevational gradients, age classes, and forest origins, we provide new insights into how Quercus species respond to climatic changes through growth and life-history strategies. Analysis of NCEP reanalysis data reveals a consistent warming trend across China from 1948 to 2021, with significant seasonal heterogeneity in temperature variability. Spring, autumn, and winter exhibited substantially stronger temperature fluctuations (p < 0.05) compared to summer months. Our results demonstrate that Quercus spp. stand volume shows non-significant variation along elevational gradients but displays marked differences across temporal and origin gradients. Natural forests maintained significantly higher stand volume than plantations (p < 0.05). Forest age emerged as a critical factor influencing stand volume, particularly under climate uncertainty scenarios. Plantation oak forests followed a parabolic volume trend characterized by initial rapid growth followed by decline, reflecting management interventions and economic considerations. In contrast, natural oak forests generally exhibited a classical logistic growth pattern. When examining elevational effects on natural forests, distinct developmental trajectories emerged: high-elevation stands showed a “decline-recovery” pattern during 41–80 years, mid-elevation stands maintained steady increases, and low-elevation stands displayed parabolic patterns peaking at 61–80 years. These findings provide dual insights: they reveal the growth resilience of high-elevation Quercus populations while supporting the hypothesis of mid-elevation concentration and low-elevation decline under climate change. This improved understanding facilitates optimized management strategies to mitigate climate impacts on Quercus species. Specifically, tailored silvicultural approaches and adaptive management are essential for enhancing the quality and productivity of diverse Quercus populations across elevational gradients and age classes. Additionally, these results underscore the increasing importance of oak afforestation in high-elevation zones under progressive climate warming scenarios.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16121769/s1, Table S1: Statistical description of temperature data from NCEP reanalysis datasets; Table S2: Seasonal Temperature Variability in China (1948–2021); Table S3: Differential statistical analysis of Quercus spp. stand volume under tri-factor effects based on multi-factor ANOVA (temporal × elevational × origin); Table S4: Statistical analysis based on hurdle models_zero part (reference group: low-elevation plantations); Table S5: Multivariate analysis of stand volume variation in Quercus populations under interactive effects of time, elevation, age class, and origin; Table S6: List of major Quercus spp. in China’s National Forest Inventory; Table S7: Linear mixed effects model results for Quercus stand volume; Table S8: Variance components of linear mixed effects model for Quercus stand volume; Table S9: Model fit statistics for linear mixed effects analysis; Table S10: Model comparison summary; Table S11: Model performance metrics.

Author Contributions

C.-R.L.: writing—original draft, formal analysis, visualization, methodology, project administration, and conceptualization. J.-X.O.: writing—review and editing, supervision, project administration, conceptualization, and resources. Y.-H.L.: writing—review and editing, investigation. H.-B.J.: writing—review and editing, visualization. Y.-Y.W.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Dataset available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Meng, L.; Zhou, Y.; Gu, L.; Richardson, A.D.; Peñuelas, J.; Fu, Y.; Wang, Y.; Asrar, G.R.; De Boeck, H.J.; Mao, J.; et al. Photoperiod Decelerates the Advance of Spring Phenology of Six Deciduous Tree Species under Climate Warming. Glob. Change Biol. 2021, 27, 2914–2927. [Google Scholar] [CrossRef]
  2. Xie, Y.; Thammavong, H.T.; Park, D.S. The Ecological Implications of Intra- and Inter-Species Variation in Phenological Sensitivity. New Phytol. 2022, 236, 760–773. [Google Scholar] [CrossRef]
  3. Arco Molina, J.G.; Altman, J.; Rai, S.; Korznikov, K.; Pejcha, V.; Dvorsky, M.; Doležal, J. Climate Drivers of Pinus Ponderosa Tree Development on Volcanic Tephra Deposits in the Southwestern USA: Insights from Radial Increment and Wood Density Variations. Dendrochronologia 2024, 88, 126242. [Google Scholar] [CrossRef]
  4. Wang, H.; Duan, A.; Liu, X.; Zhu, A.; Zhang, J. Combining Tree-Ring Growth and Carbon Isotope Data Enhances the Understanding of Climate Sensitivity and Physiological Responses for Chinese Fir in a Common Garden. Agric. For. Meteorol. 2024, 358, 110246. [Google Scholar] [CrossRef]
  5. Passos, I.; Vila-Viçosa, C.; Gonçalves, J.; Ribeiro, M.M.; Figueiredo, A. Tracking Submediterranean Ecotone Shifts under Climate Change Scenarios Using Marcescent Oaks as Indicators. Sci. Rep. 2025, 15, 39201. [Google Scholar] [CrossRef] [PubMed]
  6. Zhao, Y.-H.; Jiang, L.-M.; Qiu, L.; Fu, Q.-L.; Tan, S.-L. Climate-Driven “Nowhere to Go” for Alpine Plants: Impact of Climate Change on the Geographic Distribution of Dipsacoideae Species in China. Glob. Ecol. Conserv. 2025, 63, e03892. [Google Scholar] [CrossRef]
  7. Liu, B.; Liao, C.; Chang, Y. Changing Dynamic of Tree Species Composition and Diversity: A Case Study of Secondary Forests in Northern China in Response to Climate Change. Forests 2024, 15, 322. [Google Scholar] [CrossRef]
  8. Root, T.L.; Price, J.T.; Hall, K.R.; Schneider, S.H.; Rosenzweig, C.; Pounds, J.A. Fingerprints of Global Warming on Wild Animals and Plants. Nature 2003, 421, 57–60. [Google Scholar] [CrossRef]
  9. Fu, Y.; Liu, C.; He, H.S.; Wang, S.; Wang, L.; Xie, Z. Assessing the Impact of Climate Warming on Tree Species Composition and Distribution in the Forest Region of Northeast China. Front. Plant Sci. 2024, 15, 1430025. [Google Scholar] [CrossRef]
  10. Subedi, S.C.; Drake, S.; Adhikari, B.; Coggeshall, M.V. Climate-Change Habitat Shifts for the Vulnerable Endemic Oak Species(Quercus arkansana Sarg.). J. For. Res. 2024, 35, 117–127. [Google Scholar] [CrossRef]
  11. Sun, S.; Zhang, Y.; Huang, D.; Wang, H.; Cao, Q.; Fan, P.; Yang, N.; Zheng, P.; Wang, R. The Effect of Climate Change on the Richness Distribution Pattern of Oaks (Quercus L.) in China. Sci. Total Environ. 2020, 744, 140786. [Google Scholar] [CrossRef]
  12. Mirhashemi, H.; Heydari, M.; Ahmadi, K.; Karami, O.; Kavgaci, A.; Matsui, T.; Heung, B. Species Distribution Models of Brant’s Oak (Quercus Brantii Lindl.): The Impact of Spatial Database on Predicting the Impacts of Climate Change. Ecol. Eng. 2023, 194, 107038. [Google Scholar] [CrossRef]
  13. Vigren, C.; Vospernik, S.; Morin, X.; Toïgo, M.; Bielak, K.; Bravo, F.; Heym, M.; Löf, M.; Pach, M.; Ponette, Q.; et al. Divergent Regional Volume Growth Responses of Scots Pine and Oak Stands to Climate Change in Europe. Sci. Total Environ. 2025, 969, 178858. [Google Scholar] [CrossRef]
  14. Xu, X.; Wang, Z.; Rahbek, C.; Lessard, J.-P.; Fang, J. Evolutionary History Influences the Effects of Water–Energy Dynamics on Oak Diversity in Asia. J. Biogeogr. 2013, 40, 2146–2155. [Google Scholar] [CrossRef]
  15. Zhang, M.-G.; Zhou, Z.-K.; Chen, W.-Y.; Cannon, C.H.; Raes, N.; Slik, J.W.F. Major Declines of Woody Plant Species Ranges under Climate Change in Yunnan, China. Divers. Distrib. 2014, 20, 405–415. [Google Scholar] [CrossRef]
  16. Xu, Y.; Ding, Y.H.; Zhao, Z.C. Confidence Analysis of NCEP/NCAR 50-Year Global Reanalyzed Data in Climate Change Research in China. J. Appl. Meteor. Sci. 2001, 12, 337–347. [Google Scholar]
  17. Qin, Y.; He, X.; Guo, H.; Zhou, C.; Zeng, W.; Chen, X.; Lei, X. National Growth Models for Stand Basal Area, Volume, and Biomass in Chinese Larch Plantations: Integrating Stand Structure and Species Effects. Trees For. People 2025, 21, 100943. [Google Scholar] [CrossRef]
  18. McKenzie, Z. A Reconstruction and Comparison of Grand Bahama Pine Forest Age during the Pre-Major Hurricane Era Using Ridge Regression and Nested Linear Mixed-Effects Model. Trees For. People 2024, 18, 100723. [Google Scholar] [CrossRef]
  19. Li, Y.; Kang, X.; Zhang, Q.; Guo, W. Modelling Tree Mortality across Diameter Classes Using Mixed-Effects Zero-Inflated Models. J. For. Res. 2020, 31, 131–140. [Google Scholar] [CrossRef]
  20. Symonds, M.E. Faculty Opinions Recommendation of IPCC, 2021: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  21. Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P.; et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Pachauri, R., Meyer, L., Eds.; IPCC Fifth Assessment Report; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2014. [Google Scholar]
  22. Wang, Y.; Wang, Z.; Wang, J.; Li, W. Transient and Lagged Response of Seasonal Vegetation Changes to Climate in China during the Past Two Decades. Geogr. Geo-Inf. Sci. 2020, 36, 33–40+76. [Google Scholar]
  23. Gao, J.; Yang, B.; Mura, C.; Boucher, Y.; Rossi, S. Increased Inter-Annual Variability in Budburst Dates towards the Northern Range Edge of Black Spruce. Agric. For. Meteorol. 2023, 333, 109410. [Google Scholar] [CrossRef]
  24. Wang, J.; Rousseau, R.J.; Himes, A.; Siegert, C.; Ouyang, Y.; Renninger, H.J. Migrating Populus with Climate Change: Phenology, Coppice Management, Cold Spell Susceptibility, Leaf Dynamics, and Biomass Production. For. Ecosyst. 2025, 14, 100343. [Google Scholar] [CrossRef]
  25. Parmesan, C.; Morecroft, M.D.; Trisurat, Y. Terrestrial and Freshwater Ecosystems and Their Services. In Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022. [Google Scholar]
  26. Enderle, L.; Gribbe, S.; Coners, H.; Leuschner, C.; Hertel, D. Impact of a 2 °C Warmer Climate on the Fine Root System of European Beech, Sessile Oak, Scots Pine, and Douglas Fir in Central European Lowland Forests. Ecosystems 2025, 28, 49. [Google Scholar] [CrossRef]
  27. Skiadaresis, G.; Leban, J.-M.; Schnabel, F.; Schwarz, J.; Guillemot, J.; Potvin, C.; Bauhus, J. Interacting and Dynamic Effects of Species and Structural Diversity Promote Annual Woody Biomass Production in a Tropical Tree Diversity Experiment. For. Ecol. Manag. 2025, 593, 122844. [Google Scholar] [CrossRef]
  28. Chen, L.; Ren, C.; Zhang, B.; Wang, Z.; Liu, M.; Man, W.; Liu, J. Improved Estimation of Forest Stand Volume by the Integration of GEDI LiDAR Data and Multi-Sensor Imagery in the Changbai Mountains Mixed Forests Ecoregion (CMMFE), Northeast China. Int. J. Appl. Earth Obs. Geoinf. 2021, 100, 102326. [Google Scholar] [CrossRef]
  29. Liu, N.; Van den Bulcke, J.; Van Acker, J.; Liu, F.; Gao, C.; Yu, J.; Su, X.; Liu, C.; Huang, Q. Enhancing Large-Diameter Timber Production: Evaluating Poplars by Genotype and Spacing. Ind. Crop. Prod. 2025, 223, 120148. [Google Scholar] [CrossRef]
  30. Long, S.L. Study on Self-Thinning Law and Growth Model of Oak Natural Forest in Hunan Province. Ph.D. Thesis, Central South University of Forestry & Technology, Changsha, China, 2021. [Google Scholar]
  31. Sun, S.X. The Effect of Climate Change on the Potential Distribution Pattern of Oaks (Quercus L.) and Its Richness in China. Ph.D. Thesis, Shandong University, Jinan, China, 2021. [Google Scholar]
  32. Joshi, V.C.; Sundriyal, R.C.; Chandra, N.; Arya, D. Unlocking Nature’s Hidden Treasure: Unveiling Forest Status, Biomass and Carbon Wealth in the Binsar Wildlife Sanctuary, Uttarakhand for Climate Change Mitigation. Environ. Chall. 2024, 14, 100825. [Google Scholar] [CrossRef]
  33. Tian, Y.; Lu, J.; Wang, X.; Yang, L. Response of Population Structure of Quercus aquifolioides to Altitude Gradient in Sejila Mountain. J. Sichuan For. Sci. Technol. 2023, 44, 74–81. [Google Scholar]
  34. Gao, W.Q. Population Dynamics and Environmental Interpretation of Quercus variabilis in Different Geographical Gradients. Ph.D. Thesis, Chinese Academy of Forestry, Beijing, China, 2017. [Google Scholar]
  35. Chen, S.; You, C.; Zhang, Z.; Xu, Z. Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios. Forests 2024, 15, 1033. [Google Scholar] [CrossRef]
  36. Rubio-Cuadrado, Á.; Montes, F.; Alberdi, I.; Cañellas, I.; Aulló-Maestro, I.; Sánchez-Salguero, R.; Hevia, A.; Pérez-Cruzado, C.; Molina-Valero, J.A.; Camarero, J.J. Analyses from Stand to Tree Level Allow Disentangling the Effects of Age, Size, Origin and Competition on Tree Growth Sensitivity to Climate in Natural and Afforested Scots Pine Forests. Agric. For. Meteorol. 2024, 355, 110148. [Google Scholar] [CrossRef]
  37. Lin, Z.; Yong, H.; Tianxiang, L.; Qiang, D.; Kunmei, D. Age Effects on Stand Biomass Allocations to Different Components: A Case Study in Forests of Cunninghamia Lanceolata and Pinus Massoniana. J. Grad. Sch. Chin. Acad. Sci. 2005, 22, 170–178. [Google Scholar]
  38. Chayaporn, P.; Sasaki, N.; Venkatappa, M.; Abe, I. Assessment of the Overall Carbon Storage in a Teak Plantation in Kanchanaburi Province, Thailand—Implications for Carbon-Based Incentives. Clean. Environ. Syst. 2021, 2, 100023. [Google Scholar] [CrossRef]
  39. Vangi, E.; Dalmonech, D.; Morichetti, M.; Grieco, E.; Giannetti, F.; D’Amico, G.; Nakhavali, M.; Chirici, G.; Collalti, A. Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics. Forests 2024, 15, 1120. [Google Scholar] [CrossRef]
  40. Luo, T.; Shi, P.; Luo, J.; Ouyang, H. Distribution Patterns of Aboveground Biomass in Tibetan Alpine Vegetation Transects. Acta Phytoecol. Sin. 2002, 26, 668–676. [Google Scholar]
  41. Jiang, S.; Liang, Y.; Chang, R.; Huang, C.; Ma, T.; Liu, B.; Wu, M.; Zhang, P. Responses of Chuanxi Spruce Forest Biomass to Climate Change on the Tibetan Plateau. Chin. J. Ecol. 2020, 39, 3903–3912. [Google Scholar]
  42. Wang, Y.; Liu, Y.; Chen, P.; Song, J.; Fu, B. Interannual Precipitation Variability Dominates the Growth of Alpine Grassland Above-Ground Biomass at High Elevations on the Tibetan Plateau. Sci. Total Environ. 2024, 931, 172745. [Google Scholar] [CrossRef]
  43. Davis, E.L.; Brown, R.; Daniels, L.; Kavanagh, T.; Gedalof, Z. Regional Variability in the Response of Alpine Treelines to Climate Change. Clim. Chang. 2020, 162, 1365–1384. [Google Scholar] [CrossRef]
  44. Du, H.; Li, M.-H.; Rixen, C.; Zong, S.; Stambaugh, M.; Huang, L.; He, H.S.; Wu, Z. Sensitivity of Recruitment and Growth of Alpine Treeline Birch to Elevated Temperature. Agric. For. Meteorol. 2021, 304–305, 108403. [Google Scholar] [CrossRef]
  45. Yang, L.; Li, W. The Underground Root Biomass and C Storage in Different Forest Ecosystems of Changbai Mountains in China. J. Nat. Resour. 2003, 18, 204–209. [Google Scholar]
  46. Wei, W.; You, W.; Zhang, H.; Yan, T.; Huo, C.; Zhao, G.; Xing, Z. Biomass Carbon Pool of Larch Forests in Liaoning Province. J. Northeast For. Univ. 2011, 39, 26–29. [Google Scholar]
  47. Takeda, S.; Takahashi, K. Elevational Variation in Abundance of Coarse Woody Debris in Subalpine Forests, Central Japan. For. Ecol. Manag. 2020, 473, 118295. [Google Scholar] [CrossRef]
  48. Erfu, D.; Xiaofan, W.; Jianjia, Z.; Xiaoli, W. Modeling the Long-Term Impacts of Harvest and Artificial Regeneration on Forest Area and Aboveground Biomass in Red Soil Hilly Region: A Case Study in Moshao Forest Farm of Huitong County. J. Nat. Resour. 2020, 35, 2995–3006. [Google Scholar] [CrossRef]
  49. Pooja; Prakash, P.; Kumar, P.; Bishist, R.; Kumar, P.; Shilpa; Singh, R.; Bhatia, A.K. Impact of Altitudinal Gradients on Composition, Biomass and Soil Nutrients of Cedrus Deodara Forests in the Northwestern Himalaya. Trees For. People 2025, 21, 100937. [Google Scholar] [CrossRef]
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Figure 1. The interannual variation trend in temperature in China from 1948 to 2021. Three asterisks (***) indicate that the difference in temperature change is highly significant.
Figure 1. The interannual variation trend in temperature in China from 1948 to 2021. Three asterisks (***) indicate that the difference in temperature change is highly significant.
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Figure 2. The seasonal temperature fluctuations in China from 1948 to 2021.
Figure 2. The seasonal temperature fluctuations in China from 1948 to 2021.
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Figure 3. Differences in the stand volume of Quercus spp. in China across temporal, elevational, and origin factors. L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. (A–C) represent the changes in volume under the influence of the main effect factors (Time, Elevation, and Origin), respectively. The values of (B,C) represent the mean (±SE) calculated from three NFI cycles.
Figure 3. Differences in the stand volume of Quercus spp. in China across temporal, elevational, and origin factors. L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. (A–C) represent the changes in volume under the influence of the main effect factors (Time, Elevation, and Origin), respectively. The values of (B,C) represent the mean (±SE) calculated from three NFI cycles.
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Figure 4. Interactive effects of elevation × origin on the dynamics of stand volume in Quercus spp. L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles.
Figure 4. Interactive effects of elevation × origin on the dynamics of stand volume in Quercus spp. L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles.
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Figure 5. Stand volume dynamics in Quercus spp. across age classes under varying elevation and origin conditions. AC: age class; L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles. Data are combined in (A) to show time × elevation effects, and separated in (B) to show origin × age class effects.
Figure 5. Stand volume dynamics in Quercus spp. across age classes under varying elevation and origin conditions. AC: age class; L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles. Data are combined in (A) to show time × elevation effects, and separated in (B) to show origin × age class effects.
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Figure 6. Interactive effects of age class, elevation, and origin on stand volume in Quercus spp. AC: age class; L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles.
Figure 6. Interactive effects of age class, elevation, and origin on stand volume in Quercus spp. AC: age class; L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest. The values represent the mean (±SE) calculated from three NFI cycles.
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Table 1. Classification of provincial-level administrative regions into three elevation tiers based on the three-terrain-step distribution of China.
Table 1. Classification of provincial-level administrative regions into three elevation tiers based on the three-terrain-step distribution of China.
ElevationCriteriaRepresentative Regions and Examples
High-elevation- Over 50% of the area exceeds 3000 m
- Core zones are located in high-elevation regions		Qinghai–Tibet Plateau (e.g., Tibet, Qinghai)
Mid-elevation- Most areas range between 500 and 3000 m
- Core zones are on plateaus or in mountainous regions		Yunnan–Guizhou Plateau, Loess Plateau, Inner Mongolia Plateau, and other mountainous provinces
Low-elevation- Over 50% of the area lies below 500 m
- Core zones are on plains, in coastal areas, or hills		Eastern plains, coastal regions, and hilly areas (e.g., eastern provinces)
Table 2. Statistical description of stand volume in Quercus spp. along temporal, elevational, and origin gradients based on multi-factor ANOVA.
Table 2. Statistical description of stand volume in Quercus spp. along temporal, elevational, and origin gradients based on multi-factor ANOVA.
TimeOriginElevationCountMeanStdMinMax
2004–2008AFH100000
M2516.4329.37099.67
L5019.8936.50171.58
NFH1069.8183.710212.08
M5095.4288.090463.75
L7565.8264.070294.23
2009–2013AFH100000
M3010.1524.820105.67
L7517.3932.410150
NFH1078.1891.130236.76
M50109.96134.860918.12
L8564.8268.540302.65
2014–2018AFH100000
M4010.3925.10117.09
L7031.0446.750237.08
NFH1082.0599.550290.59
M50116.16140.780964
L8582.572.180316.69
Note: L: low elevation, M: mid-elevation, and H: high elevation; NF: natural forest, AF: artificial forest.
Table 3. Statistical analysis based on linear mixed effects (reference group: low-elevation plantations).
Table 3. Statistical analysis based on linear mixed effects (reference group: low-elevation plantations).
Model ParametersCoefficientStd. Errorz-Valuep-Value
Group Var 8.450.00
Intercept11.026.781.630.10
Elevation [T.H]−6.4515.85−0.410.68
Elevation [T.M]15.598.851.760.08
Origin [T.NF]68.378.178.370.00
Time1.390.334.270.00
Table 4. Statistical analysis based on hurdle model count part (reference group: low-elevation plantations).
Table 4. Statistical analysis based on hurdle model count part (reference group: low-elevation plantations).
Model ParametersCoefficientStd. Errorz-Valuep-Value
Group Var 6.760.00
Intercept50.0211.184.470.00
Elevation [T.H]25.6032.740.780.43
Elevation [T.M]11.7012.570.930.35
Origin [T.NF]52.8113.034.050.00
Time2.100.405.280.00
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Liao, C.-R.; Ouyang, J.-X.; Li, Y.-H.; Jiang, H.-B.; Wang, Y.-Y. Dynamic Interactions of Stand Characteristics and Site on Quercus spp. Volume in China Under Climate Change. Forests 2025, 16, 1769. https://doi.org/10.3390/f16121769

AMA Style

Liao C-R, Ouyang J-X, Li Y-H, Jiang H-B, Wang Y-Y. Dynamic Interactions of Stand Characteristics and Site on Quercus spp. Volume in China Under Climate Change. Forests. 2025; 16(12):1769. https://doi.org/10.3390/f16121769

Chicago/Turabian Style

Liao, Cheng-Rui, Jun-Xiang Ouyang, Yu-Hao Li, Hong-Bo Jiang, and Yin-Yin Wang. 2025. "Dynamic Interactions of Stand Characteristics and Site on Quercus spp. Volume in China Under Climate Change" Forests 16, no. 12: 1769. https://doi.org/10.3390/f16121769

APA Style

Liao, C.-R., Ouyang, J.-X., Li, Y.-H., Jiang, H.-B., & Wang, Y.-Y. (2025). Dynamic Interactions of Stand Characteristics and Site on Quercus spp. Volume in China Under Climate Change. Forests, 16(12), 1769. https://doi.org/10.3390/f16121769

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