Stand Development Stages Reshape Climate-Structure Interactions in Boreal Forest Productivity: A Case Study of China's Cold-Temperate Conifers

Stand Development Stages Reshape Climate-Structure Interactions in Boreal Forest Productivity: A Case Study of China's Cold-Temperate Conifers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stand Development Stages Reshape Climate-Structure Interactions in Boreal Forest Productivity: A Case Study of China's Cold-Temperate Conifers Zirui Wang, Yuanshuo Hao, Lihu Dong, Zheng Miao, Xingji Jin, Xuehan Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6728635/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Da Xing'an Mountains region is the only area of cold–temperate coniferous forests in China and functions as an essential ecological barrier. It has a crucial purpose in forest ecosystems and carbon sequestration processes. Stand age is influenced by interactions among population dynamics, mechanisms of disturbance, and forest management approaches, significantly influencing the global carbon cycle. Growth data indicate that forest development is correlated with variations in productivity. Nonetheless, the variability in production throughout several phases of stand development remains largely unexamined, and the influence of contributing elements in this process is still ambiguous. Utilizing the 2005–2010 National Forest Continuous Inventory (NFCI) data from the eastern Da Xing'an Mountains, we examined the influence of stand characteristics, structural diversity, and environmental variables on forest productivity throughout various developmental stages, from young to overaged forests. The findings indicate that (1) forest productivity is collectively limited by stand characteristics, structural diversity, and environmental factors, with stand factors exerting the greatest influence, especially through direct effects. (2) As tree growth stages advance, the impacts of structural variety (ranging from 8.68 to 16.44) and soil (ranging from 8.80 to 10.30) on forest productivity intensify. (3) Altered tree growth stages decrease the influence of climate (from 30.40 to 17.67) and terrain (from 14.55 to 6.28) on forest productivity. By thoroughly integrating the determinants of forest production, our study provides essential system–level insights that establish a theoretical basis for forecasting alterations in forest productivity amid global change. These findings enhance the formulation of more efficacious forest management methods to address the difficulties posed by climate change and biodiversity decline. Forest productivity Environmental factors Stand age Natural forests Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Forest productivity, i.e., the production potential of plant communities in forest ecosystems under natural environmental conditions (Mamo and Sterba, 2006 ), constitutes a fundamental aspect of forest ecosystem research (Toda et al., 2023 ; Xingzhao et al., 2017 ). Forest productivity data serve as the basis for examining nutrient cycling and energy flow within forest ecosystems, elucidating both the fundamental characteristics of community structure (Viet et al., 2023 )and the specific expression of the relationship between forests and their environment (Peng et al., 2017 ). Many studies have evaluated forest productivity (Shuai et al., 2019 ; Zhang et al., 2024a ). While this static perspective of forest productivity offers essential information on the present conditions of forest carbon storage, it does not adequately clarify the dynamic temporal changes within forest ecosystems and their responses to environmental variations. Forest productivity is influenced by three processes: the growth of mature trees, the recruitment of new individuals, and carbon loss resulting from mortality (Yuan et al., 2019 ). For a long time, species diversity(Barrufol et al., 2013 ), structural diversity (Wang et al., 2021b ), climate (Jing et al., 2022 ), and stand characteristics (Lin et al., 2021 ) have been considered the primary determinants of forest productivity. However, in forests, the composition of tree species frequently changes as the forest ages, which is a process that might take several decades. During this period, the influence of environmental factors on ecosystem functioning may be altered (Zhao et al., 2024b ). Understanding the variation in productivity between phases of forest development, from the initial phase of secondary succession to old growth, is essential for restoring ecosystem functionality in damaged environments. The prevalent view is that environmental elements in forests, including climate and site conditions, along with stand structure, significantly influence forest productivity (Du et al., 2022 ; Zhang et al., 2024a ). Climate elements are regarded as the principal environmental determinants influencing forest productivity at a regional scale (Sun et al., 2021 ), with temperature and precipitation serving as the fundamental drivers of spatiotemporal patterns of forest productivity (Xi and Yuan, 2022 ). However, at smaller spatial scales, factors such as stand structure, terrain, and soil significantly influence the spatial variability of forest production (Jian et al., 2022 ). Furthermore, researchers have reported that in temperate larch forests, the beneficial impact of rising temperatures on the wood supply decreases as the forest matures. Forest production progressively increases, stabilizes, and subsequently decreases as the forest develops (Kira and Shidei, 1967 ). In northern and mountainous areas, elevated temperatures increase arboreal development and prolong the growing season of vegetation. Under climate change conditions, forests are expected to exhibit accelerated growth, reach maturity sooner, and experience earlier mortality, resulting in varied responses to climate change among forests of different ages (Collalti et al., 2019 ). Conversely, studies have determined that stand structure is positively connected with forest productivity (Wang et al., 2021a ), suggesting that within a stand, trees enhance forest productivity through resource allocation, facilitation, and biological feedback. Nonetheless, the significance of stand structure in forecasting productivity may evolve over time, as tree interactions intensify with growth during forest succession (Wang et al., 2022 ). Consequently, it is imperative to account for variations at various ages when forecasting the impacts of environmental conditions and stand structure on forest productivity. Moreover, stand variables, including age and density, are crucial determinants of forest productivity. Stand density indicates the area used by trees and their internal horizontal structure, which affects the forms of tree development, stand production, and ecological stability (Liu et al., 2022 ). Increased stand densities enhance forest carbon storage and timber output by increasing canopy density, thereby increasing the amount of light captured. Stand age is a significant determinant of biomass and productivity (Liu et al., 2018 ). Stand age can increase biomass and productivity through increases in tree size (Becknell and Powers, 2014 ) and variations in size (Zhang and Chen, 2015 ). Throughout the forest restoration process, tree size, stand age, stand density, and species composition substantially change, whereas forest productivity markedly increases. Nonetheless, the primary biological processes influencing stand production remain a debated subject. In recent years, machine learning technology has demonstrated superiority over traditional statistical methods by overcoming the limitations of big data analysis (Christin et al., 2018 ; Özçelik et al., 2013 ) and is regarded as a potent tool that is extensively utilized in forestry (Jevšenak and Skudnik, 2021 ; Lan et al., 2023 ; Wu et al., 2019 ). The random forest (RF) algorithm has been widely employed to assess variable significance and facilitate variable selection because of its ability to effectively mitigate overfitting and variation (Lin et al., 2004 ). Numerous studies have employed linear statistical approaches to examine the correlations between forest productivity and numerous variables, frequently overlooking the direct or indirect effects of these factors. Furthermore, identifying the ideal range of environmental variables using these methods requires substantial sample sizes and may not adequately elucidate the real mechanisms driving variations in forest productivity due to stand and environmental factors (Zhang et al., 2024a ). Partial least squares structural equation modeling (PLS–SEM) is extensively employed to analyze the direct, indirect, and cumulative impacts of specific variables on other variables. This approach accommodates nonnormal distributions, nonlinear associations, and scenarios with numerous variables (Hair et al., 2021 ). Integrating the RF model with PLS–SEM to investigate the physiological principles of productivity may enhance the understanding of forest dynamics and serve as a reference in the context of impending climate change. The Da Xing'an Mountains, situated in Northeast China, form a delicate mountainous region with abundant resources (He et al., 2021 ). This abundant-resource region maintains an unspoiled boreal forest ecology, being China's sole boreal coniferous forest area and one of the few extant biological gene pools (Zhao et al., 2024a ). The forests of the Da Xing'an Mountains are essential for ecological security in northeastern China and the broader North China region, and their function in the carbon cycle is significant (Jiang et al., 2002 ). This location has garnered considerable interest from forest managers in recent research (Wang et al., 2008 ). Nonetheless, prolonged human logging activities have significantly compromised stand structure, functionality, and stability, hence substantially impeding the sustainable growth of the forest ecosystem in the Da Xing'an Mountains (Wu et al., 2022 ). Consequently, statistically assessing the impacts of stand structure and environmental variables on forest production and providing scientifically valid restoration strategies are imperative. This assessment is crucial for reinstating biological functions, increasing forest quality, and increasing the capacities of carbon sequestration and sinks in the Da Xing'an Mountains. In this research, 782 natural forest plots were evaluated, and RF and PLS–SEM were utilized to ascertain the direct and indirect influences of stand characteristics, structural diversity, and environmental variables on forest production. Our dataset included forests at various developmental stages, from the initial phases of secondary succession to mature old-growth forests. We categorized these plots into five forest developmental stages on the basis of stand age to investigate the variations in production as the forests matured. Consequently, we propose ways to increase forest productivity in the Da Xing'an Mountains to improve forest quality. The primary aims of this study are as follows: (1) to elucidate the impacts of stand characteristics, structural diversity, and environmental variables on forest productivity, and (2) to examine the comparative impacts and evolutionary trends of stand characteristics, structural diversity, and environmental variables on forest production during forest development. Materials and methods Study area and sample plot data The research area is located in the key state-owned forest region of the Da Xing'an Mountains in Heilongjiang Province. Geographically, the area is located in northwestern Heilongjiang Province, northeastern Inner Mongolia Autonomous Region, and on the northeastern slope of Da Xing'an ridge (Fig. 1 ). It spans 121°10’53”-127°01’21”E, 50°07’02”-53°33’42” Nand encompasses a total area of 8.02 × 10 6 hectares. The area has a distinct cold–temperate continental monsoon climate, with an annual average temperature of -2°C. Annual precipitation values range from 430–460 mm, and precipitation is concentrated from July–September. The primary soil types in the area include brown coniferous forest soil, dark brown soil, gray–black soil, meadow soil, and marsh soil. The climax community of the forest ecosystem in the Da Xing'an Mountains comprises a bright coniferous forest typical of a cold–temperate zone. The dominant tree species include Larix gmelinii , Betula platyphylla , Populus davidiana , Quercus mongolica , and Pinus sylvestris var. mongolica , among others. The data for this study were sourced from the seventh and eighth national forest resource inventories (NFCIs) of the Da Xing'an Mountains. Utilizing systematic sampling and a kilometer grid approach, 782 sample plots were set up over a span of five years. In the Da Xing'an Mountains, the fixed sample areas were organized in a grid measuring 8 km by 8 km, each spanning 0.06 hectares. Measurements were taken for various tree species, breast height diameters (≥ 5 cm), and the mean age of the stands in every fixed plot. The mean carbon reserves in this region's natural forests stood at 33.35 t·hm − 2 in 2010, which was a 4.38 t·hm − 2 increase from 2005. On average, stand productivity was 1.55 t·hm − 2 ·a − 1 , accompanied by growth, recruitment, and mortality rates of 0.88 t·hm − 2 ·a − 1 , 0.23 t·hm − 2 ·a − 1 , and 0.43 t·hm − 2 ·a − 1 , respectively. Generally, as forests aged, there was a notable reduction in both growth rates and entry rates, alongside an increase in mortality rates. The mortality percentages for forests categorized as young, middle-aged, near-mature, mature, and overaged were 6.73%, 24.66%, 19.28%, 23.77%, and 30.49% of the overall mortality, respectively (Table 1 ). Table 1 Statistics of the basic characteristics of the sample plots. Age group Initial carbon stock /(t·hm − 2 ) Ending carbon stock /(t·hm − 2 ) Recruitment/(t·hm − 2 ·a − 1 ) Growth/(t·hm − 2 ·a − 1 ) Mortality/(t·hm − 2 ·a − 1 ) Forest productivity /(t·hm − 2 ·a − 1 ) Young stand 11.23 ± 13.48a 17.38 ± 14.44a 0.57 ± 0.43b 1.23 ± 0.79d 0.15 ± 0.39a 1.94 ± 1.00c Middle-aged stand 35.02 ± 20.44b 39.77 ± 20.27b 0.16 ± 0.18a 0.94 ± 1.04c 0.55 ± 0.77ab 1.66 ± 0.68b Near-mature stand 39.67 ± 18.39bc 43.33 ± 18.49bc 0.11 ± 0.14a 0.79 ± 0.78bc 0.43 ± 0.67b 1.33 ± 0.60a Mature stand 43.75 ± 20.64c 46.96 ± 20.69c 0.13 ± 0.17a 0.65 ± 0.87b 0.53 ± 0.69ab 1.30 ± 0.66a Overmature stand 50.85 ± 23.50d 52.64 ± 22.96d 0.13 ± 0.20a 0.36 ± 0.94a 0.68 ± 0.87c 1.17 ± 0.69a Total 33.35 ± 22.85 37.73 ± 22.28 0.23 ± 0.31 0.88 ± 0.92 0.43 ± 0.69 1.55 ± 0.80 Note: Data with the same small letter in each panel indicate no significant difference at the P = 0.05 level (Duncan test). Calculating carbon sequestration in the forest stands of permanent sample plots The biomass of each tree was obtained by substituting the tree measurement data into a biomass model. Subsequently, the biomass was multiplied by the ratio of the carbon content of each species to ascertain the carbon storage capacity of every tree. The term 'growth' denotes the variation in the carbon sequestration of preserved trees across two survey intervals; 'recruitment' indicates the rise in carbon sequestration due to trees having a diameter at breast height (DBH) < 5 cm in the initial survey, increasing to 5 cm in the subsequent survey; 'mortality' denotes the reduction in carbon storage resulting from tree death during the survey. This research defines forest productivity as the aggregate of growth, mortality, and recruitment. Stand variables The variables in our study consisted of stand age, stand density, and DBH. The stand age was ascertained by computing the arithmetic mean of the ages of the typical dominant trees via tree ring analysis. The DBH for each plot was computed for all trees within the plot. The stand density (N⋅ha − 1 ) was calculated by dividing the total tree count in the plot by the plot area. Structural diversity indicators We employed the diversity index of the tree species composition to delineate structural diversity. This index was utilized to evaluate data, including the quantity of tree species, their relative abundance, and the fraction of species biomass. It also accurately depicts the uniformity and extent of species intermingling within a stand. Environmental data Climate data were obtained from the ClimateAP (v2.30) application, which generates climate variables on the basis of latitude, longitude, and elevation of the sample plots. We obtained soil data from the Harmonized World Soil Database (HWSD) of the Food and Agriculture Organization ( http://www.fao.org/faostat/en/#data ) (Milovac et al., 2018 ), which offers extensive soil property and nutrient information at a resolution of 1000 m. Using kriging interpolation, the data were resampled to a 30 m resolution and extractable by the geographic coordinates of a sample plot. Topographic data were acquired during the creation of sample plots, and a GPS was used to identify and document topographic information accurately. In total, there were 16 climate factors, 10 soil variables, and 3 topographic variables, as shown in Table 2 . Table 2 Description of the environmental data used in this study Factors Variables Units Description Climate AHM Annual heat: moisture index CMD mm Hargreaves climatic moisture deficit DD_0 Days Degree-days below 0°C TD °C Temperature difference between MWMT and MCMT, or continentality DD18 days Degree-days above 18°C DD5 days Degree-days above 5°C EMT °C Extreme minimum temperature over a 30- year period EXT Extreme maximum temperature over a 30- year period EREF Mm Hargreaves reference evaporation MAP mm Mean annual precipitation MAT °C Mean annual temperature MCMT °C Mean coldest month temperature MWMT °C Mean warmest month temperature NFFD Days Number of frost-free days PAS Mm Precipitation as snow between August in previous year and July in current year RH % Relative humidity Soil DEPTH cm Reference soil depth REF_BULK_DENSITY kg/dm 3 Soil reference bulk density OC weight % Soil organic carbon PH_H 2 O -log(H + ) Soil pH (H 2 O) CEC_SOIL cmol/kg Soil CEC (soil) BS % Soil base saturation TEB cmol/kg Soil TEB CACO 3 weight % Soil calcium carbonate ESP % Soil sodicity (ESP) ECE dS/m Soil salinity (ECE) Statistical analyses Pearson correlation analysis was used to initially assess the significant association of prospective predictors associated with forest productivity (at the 0.05 level) and the threshold for the correlation coefficient between predictor variables and forest productivity. The predictors were utilised for subsequent analysis only if the absolute values of their correlation coefficients above 0.2 (threshold of 1). Second, the absolute value of the correlation coefficient among the significant candidate variables themselves was guaranteed to be < 0.4 (threshold 2). A predictor was excluded if the absolute value of the correlation coefficient between itself and all other predictors was greater than 0.4, and the variance inflation factor (VIF) was used in stepwise regression analysis to evaluate the multicollinearity. All the VIF values < 10 indicate that collinearity between variables has no significant impact on our results (Graham, 2003 ). The VIF and Pearson methods were implemented in SPSS software (version 23.0). The RF algorithm chooses the variables on the basis of the importance scores of the input variables(Breiman 2001). In the RF algorithm, the importance value was calculated by permuting on out-of-bag (OOB) data: (1) the prediction error (the mean sum of the squares of residuals, MSE) on the OOB portion of the data was recorded for each tree, (2) the same was done after permuting each predictor variable, and (3) the difference between the two was then averaged over all trees as importance scores(Grömping, 2009 ). The importance scores of all the predictors were normalized to a percentage. The RF method was implemented in the randomForests package in the R platform (version 4.3.2). Before statistical analysis was conducted, the classical theory of ecology was used to divide the life cycle of a forest into five developmental stages: young forest, middle-aged forest, near-aged forest, mature forest, and overaged forest. During various stages, changes in growth rates, biomass buildup and photosynthetic efficiency result in different degrees of forest production. Consequently, tree species and age were categorized into separate classifications for analysis. Consequently, we categorized the trees on the basis of various tree types and stand ages. Larix gmelinii , Pinus sylvestris , and Picea asperata were categorized on the basis of the following age ranges: ≤ 40, 41–60, 61–80, 81–120 and > 120 a. Populus davidiana and Betula platyphylla were categorized into the following age groups: ≤ 30, 31–50, 51–60, 61–80 and > 80 a. The age classification for Quercus mongolica and Betula davurica was as follows: ≤ 40, 41–60, 61–80, 81–120 and > 120 a. PLS–SEM was employed to investigate the direct, indirect, and interactive links among different variables affecting the response ratio (RR) of agricultural yields. The net effect of one variable on another was determined by integrating all direct and indirect pathways connecting the two variables. The route coefficients and coefficients of determination (R²) were computed using the R package "plspm". All data analysis was conducted using R version 4.0.2. Results Significant variables affecting stand biomass via multiple feature selection methods In the examination of stand productivity, considerable discrepancies were observed in the number of variables chosen by the three feature selection approaches, and the selected variables varied among these methods. For example, relative humidity (RH) values were identified as significant using correlation analysis and redundancy analysis but excluded by alternative methods. Moreover, many feature selection strategies exhibited limited consistency in terms of the chosen variables. Age, Dg and density in stand structural diversity were consistently significant variables across all three techniques. Ultimately, five critical biodiversity factors were identified by the intersection approach, as follows: (1) climate: MAT and MAP; (2) stand: age, Dg, and density; (3) soil: depth; (4) structural diversity: ISCD; and (5) topography: altitude (Fig. 2 ). Results of partial least squares path modeling The variables identified by the VIF, Pearson, and RF methods were incorporated into the PLS–SEM with forest productivity (growth, recruitment, and mortality). The PLS–SEM performed well, with a strong explanatory ability for the causal paths. Specifically, the AVE, alpha, CR, and Rho_A values indicated that the model's fit was within an acceptable range. The goodness-of-fit (GOF) values revealed that the model's overall quality was quite high (Table 3 ). The analysis indicated that stand factors, structural diversity, and environmental variables contributed 68.2%, 39.2%, and 35.2%, respectively, to the variability in growth, recruitment, and mortality. Furthermore, growth, recruitment, and mortality jointly accounted for 93.8% of the variation in stand productivity, underscoring their pivotal role in influencing forest production dynamics(Fig. 3 ). Table 3 Model performance of the PLS–SEM. Types GOF AVE Cronbach’ alpha CR Rho_A Topography / 1.00 1.00 1.00 1.00 Climate / 0.78 0.64 0.80 0.73 Soil / 1.00 1.00 1.00 1.00 Structural diversity / 1.00 1.00 1.00 1.00 Forest / 0.99 0.99 1.00 0.99 Values 0.54 / / / / The growth of a stand was directly influenced by factors such as stand (b = 0.66), climate (b = 0.28), topography (b = 0.05), structural diversity (b = 0.02), and soil (b = 0.08; Fig. 3 a). The increase in stand growth was particularly evident due to stand features, as optimal tree density fosters stand efficacy, facilitates canopy shading, and improves water-use efficiency. Nonetheless, overall, topography constrained stand growth, as elevated altitudes typically result in lower temperatures, which is a critical determinant of plant growth, hence substantially impeding vegetation survival and development. The advantageous influence of climatic circumstances on stand production was significant, particularly as regions with elevated temperatures and more precipitation typically exhibit enhanced forest growth (Fig. 3 a). The PLS‒SEM analysis for recruitment indicated that stand factors exerted the most substantial positive direct influence on recruitment (b = -0.40). Structural variety and soil were positively correlated with recruitment (b = 0.02, b = 0.21), but climate had a negative impact on recruitment (b=-0.17; Fig. 3 b). Although topography did not exert a substantial direct influence on recruitment, both factors indirectly affected recruitment because of their negative correlation with climate and positive association with stands (Fig. 3 b). The PLS–SEM analysis of mortality revealed the indirect impacts of topography (b = 0.10), climate (b = 0.28), structural variety (b = -0.19), and soil (b = 0.19) on mortality productivity via their interactions with stands (b = 0.43; Fig. 3 c). In the statistical examination of the three separate carbon pools, growth and death constituted the primary sources of variation in stand productivity, with recruitment following thereafter (Fig. 3 c). Factors controlling forest productivity across different stand ages In summary, topography, structural diversity, soil, climate, and stand factors exerted direct effects on the processes of stand growth, mortality, and recruitment, and these factors also indirectly shaped stand productivity. Path coefficient analysis revealed a decreasing trend with forest age for climate variables such as MAP (from 0.9973 to 0.8539) and MAT (from 0.1328 to -0.2142; Appendix S3). Notably, among the stand factors, density remained the most significant factor across all stages, except for the overaged forest. An analysis of the contributions of various factors to stand productivity revealed that with increasing forest age, the impacts of topography (from 14.55% to 6.28%) and climate (from 30.40% to 17.67%) on stand productivity gradually decreased. In stark contrast, the influence of structural diversity (from 8.68% to 16.44%) and soil (from 8.80% to 10.30%) on stand productivity increased with forest age (Table 4). As anticipated, stand factors consistently demonstrated the most significant influence across all growth stages Table 4 Contribution of relationships (%) Age group Topography Structural diversity Soil Climate Stand Mortality Growth Recruitment Young forest 14.55 8.68 8.80 30.40 37.58 33.82 34.65 31.53 Middle-aged forest 11.86 16.06 24.85 21.28 25.95 45.60 42.32 12.08 Near-aged forest 9.13 17.69 14.75 27.53 30.90 45.06 44.99 9.95 Mature forest 10.58 13.52 20.79 19.61 35.50 41.54 46.25 12.21 Overaged forest 6.28 16.44 10.30 17.67 49.32 50.95 30.64 18.41 Discussion Impact of stand factors and structural diversity on forest productivity We employed PLS–SEM to analyze the multivariate links driving stand factors and structural diversity. The PLS–SEM effectively elucidated the relationships utilizing the variables chosen through the intersection strategy. The stand variables (age, dg, and density) significantly contributed to forest productivity. Researchers have demonstrated that stand density directly influences the environment in which trees develop, including light, heat, temperature, humidity, and soil nutrients (Cui et al., 2022 ). At low stand densities, tree interactions are minimal or weak, rendering niche complementarity effects insignificant. As stand density escalates, interactions intensify, with trees occupying greater space and utilizing more resources (Forrester et al., 2013 ; Morin, 2015 ). Initially, the productivity of forests increases as trees grow and the stand matures over time. However, as forests evolve from middle-aged to fully mature, there is an increase in the need for nutrients and water to facilitate the outward growth of trees, simultaneously increasing the rates of transpiration. The outcomes of our study reveal a beneficial association between forest productivity and increased structural diversity. Previous studies have validated the influence of structural diversity on forest output, which has been successfully included in predictive models for forest productivity (LaRue et al., 2023 ). Importantly, an increase in structural diversity amplifies interactions, such as competition among trees. A varied assortment of structures generates openings in the canopy, enhancing light penetration and promoting a vibrant natural habitat. Layered canopy structures, along with diverse layering of trees, frequently occupy greater spatial areas and exploit a broader range of resources, which is a concept that is elucidated by the principle of niche complementarity (Morin, 2015 ). The complex design of a stand contributes to minimizing temperature fluctuations, enhancing soil moisture retention, facilitating litter decomposition, and promoting nutrient recycling, thereby improving resource utilization efficiency and increasing both productivity and biomass growth (Crockatt and Bebber, 2015 ; Schwarz et al., 2014 ). Although cold–temperate forests lack the characteristic stratification found in subtropical forests, their structural diversity is essential for influencing ecosystem services. Impact of environmental factors on forest productivity Observations reveal that climate variables, specifically MAT and MAP, exert the greatest influence on forest production among environmental factors, with increasing temperatures and precipitation increasing productivity. Temperature fluctuations contribute to clarifying the spatial pattern of forest productivity (Liu et al., 2014 ; Ni et al., 2022 ). Moreover, the efficacy of forests is constrained by water availability, as precipitation governs water distribution, subsequently affecting tree habitats. The findings of our study indicate that geographical factors, particularly elevation, significantly affect forest productivity. This was due mainly to topographic factors affecting the spatial distributions of solar radiation and precipitation, together with soil moisture, nutrients, and depth, resulting in intricate impacts on forest productivity within different ecosystems (Wu et al., 2021 ). Nevertheless, elements such as slope and gradient have been identified as critical drivers influencing forest production in some studies (He et al., 2023 ; Jafarian et al., 2023 ). However, within the parameters of our research area, the impact of these components is less pronounced than that of height. This may be attributed to the predominant tree species ( Larix gmelinii and Betula platyphylla ) exhibiting markedly low sensitivity to slope orientation and gradient. Larix gmelinii , recognized for its strong cold resistance and moderate water requirements, can thrive on both steep and shaded slopes. Betula platyphylla necessitates increased moisture; nonetheless, its tolerance to shadow and cold enables it to flourish over diverse slopes and gradients, mitigating the effects of fluctuations in light and temperature. Beneficial impact of forest age on forest productivity via structural diversity and soil factors The effects of soil and especially structural diversity on forest productivity increased with stand age, as predicted. These greater effects may be partly due to increasing tree–tree interactions and complementary effects (Wang et al., 2022 ), coupled with increased build-up of soil organic matter and heightened microbial activity in the soil, which can intensify the impact on forest productivity. With respect to forest age, the greater positive effects of structural diversity with stand age illustrate that processes such as resource partitioning, facilitation or trophic interactions may result in greater benefits for tree growth in plots with high stand ages than in plots with low stand ages (Hatami et al., 2020 ; Zhang et al., 2020 ). This finding is supported by those of previous studies showing that as a forest advances through its developmental phases, its structure becomes increasingly intricate, and the linkages between resource distribution among trees and biodiversity become more evident. Structural variety enhances resource utilization efficiency and stabilizes ecosystem functioning by affecting tree distribution, density, canopy architecture, and interorganism interactions (Aakala et al., 2013 ; Buechling et al., 2017 ; Jiang et al., 2018 ). In addition, as the trees in the forest undergo maturation, organic matter from litterfall and root systems progressively accumulates, leading to an increase in the soil organic matter content. This accumulation not only augments soil fertility but also improves the physical structure of the soil (Post and Kwon, 2000 ), including the formation and stabilization of soil aggregates, thereby augmenting soil aeration and water retention capacity. Soil structure directly influences root development and nutrient assimilation. A well-developed soil structure facilitates the efficient cycling of water and nutrients, which is imperative for the optimal growth and vitality of trees (Jandl et al., 2007 ). Role of forest age in mitigating the impact of climate and topographical variations on forest productivity The results of our study reinforce the mitigating effect of forest age in mitigating climate and topographic shifts, showing that mature forests are less susceptible to climate and topographical changes than their younger counterparts are (Vangi et al., 2024 ). Research has indicated that mature forests are more sensitive to climate change than younger forests are (Zhang et al., 2024b ). This disagreement may arise from variations in the chosen study regions, as the Da Xing'an Mountains are situated in a cold–temperate humid to semihumid zone. The growth dynamics of several species may be affected by habitat factors. Trees in arid environments are more vulnerable to climate change than those in humid environments are, irrespective of their presence in mature or young forests (Xue et al., 2025 ).. The first piece of evidence proves that there is indeed a dampening effect of forest heterogeneity within the ecosystem on the climate sensitivity of forests, which can be related to the fact that multiage forests benefit from increasing structural complexity (de Wergifosse et al., 2022 ; Jandl et al., 2019 ). Compared with young forests, overaged forests exhibit greater diversity in the age structure, characterized by a combination of young, middle-aged, and mature trees. The creation of vertical stratification enhances ecosystem complexity and stability. This stratification provides gradients in biomass (deadwood and living aboveground biomass) and different ways to allocate carbon (Maréchaux et al., 2021 ). This stratification has also enabled the development of unique carbon distribution tactics and distinguished the ecological roles of plants and animals, thus increasing biodiversity and bolstering the ecosystem's ability to withstand environmental disruptions (Lafond et al., 2014 ; Pardos et al., 2021 ). From a functional perspective, a forest of diverse ages exhibits a variety of physiological and life cycle characteristics among its trees. This variety translates into a greater capacity to absorb and store carbon, thereby mitigating the effects of climate change, even in a mosaic of even-aged patches, as we simulated in this study. Young trees grow rapidly and absorb significant amounts of CO2 from the atmosphere, contributing to carbon capture, and are more efficient in converting photosynthates in biomass (Campioli et al., 2015 ; Collalti et al., 2014 ; Va et al., 2007). In contrast, older trees accumulate more biomass and serve as long-term carbon sinks and regenerative shelters. Concurrently, the increased diversity between age groups offsets the beneficial and detrimental effects linked to each age group. Consequently, in fluctuating weather scenarios, the existence of various age groups in forest communities is vital for preserving their functional variety since this diversity offers advantages in terms of resilience and the ability to adapt to climatic shifts (Ehbrecht et al., 2021 ; Kauppi et al., 2022 ; Zampieri et al., 2021 ). Furthermore, the age of forests plays a role in lessening the impact of topographical alterations. Forest age distribution plays a role in shaping how topography impacts forest ecosystems (Durán Zuazo and Rodríguez Pleguezuelo, 2008 ; Selkimäki et al., 2012 ). For example, forests of various ages, owing to their intricate structures, might show greater resilience to topographical changes such as soil erosion and landslides. Such varied structures aid in stabilizing the soil, diminishing erosion, and increasing the forest's ability to retain water. Limitations Factors affecting forest productivity were examined in the brief research period from 2005 to 2010. Short-term data may neglect the effects of extreme climate events, which frequently transpire over extended periods and can profoundly affect patterns of forest development. This constraint may result in an undervaluation of the influence of climate variability on forest productivity. Future research should incorporate extended datasets or integrate evaluations of extreme climate events to assess their potential impacts on forest ecosystems more accurately. Conclusions Using NFCI data from 2005 to 2010, PLS–SEM was employed to examine the correlations among stand variables, structural diversity, and environmental variables in assessing the productivity of natural forests. Our research indicates that stand variables are the principal determinants of forest productivity, with direct influences being notably substantial. Furthermore, the age of forests enhances the impact of structural variety and soil on forest productivity while alleviating the effects of climate and topography. In light of impending climate change, forest management practices must be customized to various stages of forest development. In young forests during the early successional stage, the application of moderate and selective thinning techniques can diminish competition among trees, foster the establishment of dominant species, and preserve a degree of species diversity to improve structural complexity. In middle-aged and near-aged forests in the mid-successional and mature phases, implementing moderate thinning and selective logging can foster a multilayered vertical structure, optimize light conditions, promote understory regeneration, and increase ecosystem stability. In mature and overaged forests in the late successional stage, excessive intervention should be curtailed while preserving a proportion of standing dead trees and fallen logs to increase soil nutrient cycling and fertility. Furthermore, artificial regeneration can be effectively executed by introducing seedlings of varying ages to improve the age structure variety within a stand. The implementation of these measures enables forests to significantly contribute to climate change mitigation, improve carbon sequestration, and preserve biodiversity. Our research findings establish a foundational theoretical framework for the development of sustainable forest management practices in the Da Xing'an Mountains of Northeast China. Declarations Fundings This research was financially supported by the National Key R&D Program of China (No. 2022YFD2201001), the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China (No. U21A20244), and the Heilongjiang Touyan Innovation Team Program (Technology Development Team for High-efficient Silviculture of Forest Resources). 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. Availability of data and material The data and material analysed during the current study are during the current study are available from the corresponding author on reasonable request. Code availability The code availability generated during the current study are during the current study are available from the corresponding author on reasonable request. 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Global Ecology and Conservation. https:// doi: 10.1016/j.gecco.2024.e02935 Additional Declarations No competing interests reported. Supplementary Files SupplmentaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6728635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466967695,"identity":"2e39eadf-f70c-4f6b-b26c-9af1802f8084","order_by":0,"name":"Zirui Wang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zirui","middleName":"","lastName":"Wang","suffix":""},{"id":466967696,"identity":"f29e9075-a977-4a61-be96-c9571992bb52","order_by":1,"name":"Yuanshuo Hao","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yuanshuo","middleName":"","lastName":"Hao","suffix":""},{"id":466967697,"identity":"2420522b-a5ea-48d1-b73f-b7ba9f05787e","order_by":2,"name":"Lihu Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYeBgYgYuAH4g9AzNhASAsbTItkAwPjDOK0MEC1GBwgWgs77zGJNxV37Dbfbj7YzMNgI7vhAPOzB/gdxpcmOefMs+Rtd44lArWkGW84wGZuQMAvZtK8bYeTzW7kmD/mYTicuOEAD5sEUVqMZ+R/BNryn3gtdgYSOYxALQeI0mJsOefM4QSJG2mGjXMMko1nHmYzw6uFn/+M4Y03FYft+WckP2x4U2En23e8+RleLTCQ2ACmQEHFTIx6ILAnUt0oGAWjYBSMRAAA5WdDL2Cq+AMAAAAASUVORK5CYII=","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Lihu","middleName":"","lastName":"Dong","suffix":""},{"id":466967698,"identity":"04f28991-6ad3-4dff-b958-14996e439ae7","order_by":3,"name":"Zheng Miao","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Miao","suffix":""},{"id":466967699,"identity":"9f9d367e-0ae9-4cc5-943f-ced5a6196d65","order_by":4,"name":"Xingji Jin","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xingji","middleName":"","lastName":"Jin","suffix":""},{"id":466967700,"identity":"152a51e1-8394-4460-be60-b2c1af520b8c","order_by":5,"name":"Xuehan Zhao","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xuehan","middleName":"","lastName":"Zhao","suffix":""},{"id":466967701,"identity":"da1a10e8-ebf3-4811-8e78-b66e9b7f9fe3","order_by":6,"name":"Shoumin Cheng","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Shoumin","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-05-23 02:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6728635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6728635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84339118,"identity":"9fe51137-cca0-4cd2-adc9-1c8178b05f10","added_by":"auto","created_at":"2025-06-10 18:12:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212145,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical locations of the study area in the eastern Da Xing'an Mountains, Northeast China.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/d2c38cfce7144aafc62d2915.png"},{"id":84339117,"identity":"2156e228-5d4f-4a90-9270-13554a84137a","added_by":"auto","created_at":"2025-06-10 18:12:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100862,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the variable importance ranking obtained with the final random forest models\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/316e2e92c565fb78508b83ef.png"},{"id":84339121,"identity":"b85453b6-dbf7-40f9-9f37-8cca750212bc","added_by":"auto","created_at":"2025-06-10 18:12:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":434483,"visible":true,"origin":"","legend":"\u003cp\u003ePLS–SEM revealed the effects of stand factors, structural diversity, and environmental variables on forest productivity. The climate variables included MAT and MAP; the stand attributes included DBH, stand density, and stand age; the topographic factor was elevation; the soil property was soil depth; and the structural diversity was represented by the diversity index of the tree species composition. The solid and dotted lines indicate that the path is significant (P\u0026lt;0.05) and insignificant (P\u0026gt;0.05), respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/8a943dad039b304c0835bc14.png"},{"id":84341514,"identity":"f584bf44-02ea-4ff3-8ccd-12c29ed0bb66","added_by":"auto","created_at":"2025-06-10 18:36:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289983,"visible":true,"origin":"","legend":"\u003cp\u003eBeta coefficients and the relative contributions of stand factors, structural diversity, and environmental variables to forest productivity. The filled bars indicate direct effects, and the striped bars indicate indirect effects of stand factors, structural diversity, and environmental variables on growth, recruitment, and mortality. The pie charts show the relative importance of each predictor for growth, recruitment, and mortality.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/19cca7414f5ed36889b3a1ef.png"},{"id":84341515,"identity":"9a09e9c8-92de-470f-b15e-e03ace993e8e","added_by":"auto","created_at":"2025-06-10 18:36:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":296327,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the direct, indirect, and total effects of multiple predictors on recruitment, mortality, and growth\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/a9233713bfb21bd3c666fb1a.png"},{"id":85641265,"identity":"b50e28b7-1742-4f61-8a73-f07e42b96d03","added_by":"auto","created_at":"2025-06-30 07:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2323223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/ae688d97-a3e1-4cbf-a7fa-293f529d61c2.pdf"},{"id":84339125,"identity":"34eeea75-1585-4fe0-ac1e-5c81f8fa8f89","added_by":"auto","created_at":"2025-06-10 18:12:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":429386,"visible":true,"origin":"","legend":"","description":"","filename":"SupplmentaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6728635/v1/c7fb8b9ff8efbe14309784fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stand Development Stages Reshape Climate-Structure Interactions in Boreal Forest Productivity: A Case Study of China's Cold-Temperate Conifers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eForest productivity, i.e., the production potential of plant communities in forest ecosystems under natural environmental conditions (Mamo and Sterba, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), constitutes a fundamental aspect of forest ecosystem research (Toda et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xingzhao et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Forest productivity data serve as the basis for examining nutrient cycling and energy flow within forest ecosystems, elucidating both the fundamental characteristics of community structure (Viet et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)and the specific expression of the relationship between forests and their environment (Peng et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Many studies have evaluated forest productivity (Shuai et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). While this static perspective of forest productivity offers essential information on the present conditions of forest carbon storage, it does not adequately clarify the dynamic temporal changes within forest ecosystems and their responses to environmental variations. Forest productivity is influenced by three processes: the growth of mature trees, the recruitment of new individuals, and carbon loss resulting from mortality (Yuan et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For a long time, species diversity(Barrufol et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), structural diversity (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), climate (Jing et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and stand characteristics (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have been considered the primary determinants of forest productivity. However, in forests, the composition of tree species frequently changes as the forest ages, which is a process that might take several decades. During this period, the influence of environmental factors on ecosystem functioning may be altered (Zhao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Understanding the variation in productivity between phases of forest development, from the initial phase of secondary succession to old growth, is essential for restoring ecosystem functionality in damaged environments.\u003c/p\u003e \u003cp\u003eThe prevalent view is that environmental elements in forests, including climate and site conditions, along with stand structure, significantly influence forest productivity (Du et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Climate elements are regarded as the principal environmental determinants influencing forest productivity at a regional scale (Sun et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with temperature and precipitation serving as the fundamental drivers of spatiotemporal patterns of forest productivity (Xi and Yuan, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, at smaller spatial scales, factors such as stand structure, terrain, and soil significantly influence the spatial variability of forest production (Jian et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, researchers have reported that in temperate larch forests, the beneficial impact of rising temperatures on the wood supply decreases as the forest matures. Forest production progressively increases, stabilizes, and subsequently decreases as the forest develops (Kira and Shidei, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). In northern and mountainous areas, elevated temperatures increase arboreal development and prolong the growing season of vegetation. Under climate change conditions, forests are expected to exhibit accelerated growth, reach maturity sooner, and experience earlier mortality, resulting in varied responses to climate change among forests of different ages (Collalti et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, studies have determined that stand structure is positively connected with forest productivity (Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), suggesting that within a stand, trees enhance forest productivity through resource allocation, facilitation, and biological feedback. Nonetheless, the significance of stand structure in forecasting productivity may evolve over time, as tree interactions intensify with growth during forest succession (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, it is imperative to account for variations at various ages when forecasting the impacts of environmental conditions and stand structure on forest productivity.\u003c/p\u003e \u003cp\u003eMoreover, stand variables, including age and density, are crucial determinants of forest productivity. Stand density indicates the area used by trees and their internal horizontal structure, which affects the forms of tree development, stand production, and ecological stability (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Increased stand densities enhance forest carbon storage and timber output by increasing canopy density, thereby increasing the amount of light captured. Stand age is a significant determinant of biomass and productivity (Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Stand age can increase biomass and productivity through increases in tree size (Becknell and Powers, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and variations in size (Zhang and Chen, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Throughout the forest restoration process, tree size, stand age, stand density, and species composition substantially change, whereas forest productivity markedly increases. Nonetheless, the primary biological processes influencing stand production remain a debated subject.\u003c/p\u003e \u003cp\u003eIn recent years, machine learning technology has demonstrated superiority over traditional statistical methods by overcoming the limitations of big data analysis (Christin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u0026Ouml;z\u0026ccedil;elik et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and is regarded as a potent tool that is extensively utilized in forestry (Jevšenak and Skudnik, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The random forest (RF) algorithm has been widely employed to assess variable significance and facilitate variable selection because of its ability to effectively mitigate overfitting and variation (Lin et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Numerous studies have employed linear statistical approaches to examine the correlations between forest productivity and numerous variables, frequently overlooking the direct or indirect effects of these factors. Furthermore, identifying the ideal range of environmental variables using these methods requires substantial sample sizes and may not adequately elucidate the real mechanisms driving variations in forest productivity due to stand and environmental factors (Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Partial least squares structural equation modeling (PLS\u0026ndash;SEM) is extensively employed to analyze the direct, indirect, and cumulative impacts of specific variables on other variables. This approach accommodates nonnormal distributions, nonlinear associations, and scenarios with numerous variables (Hair et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Integrating the RF model with PLS\u0026ndash;SEM to investigate the physiological principles of productivity may enhance the understanding of forest dynamics and serve as a reference in the context of impending climate change.\u003c/p\u003e \u003cp\u003eThe Da Xing'an Mountains, situated in Northeast China, form a delicate mountainous region with abundant resources (He et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This abundant-resource region maintains an unspoiled boreal forest ecology, being China's sole boreal coniferous forest area and one of the few extant biological gene pools (Zhao et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). The forests of the Da Xing'an Mountains are essential for ecological security in northeastern China and the broader North China region, and their function in the carbon cycle is significant (Jiang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This location has garnered considerable interest from forest managers in recent research (Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Nonetheless, prolonged human logging activities have significantly compromised stand structure, functionality, and stability, hence substantially impeding the sustainable growth of the forest ecosystem in the Da Xing'an Mountains (Wu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, statistically assessing the impacts of stand structure and environmental variables on forest production and providing scientifically valid restoration strategies are imperative. This assessment is crucial for reinstating biological functions, increasing forest quality, and increasing the capacities of carbon sequestration and sinks in the Da Xing'an Mountains.\u003c/p\u003e \u003cp\u003eIn this research, 782 natural forest plots were evaluated, and RF and PLS\u0026ndash;SEM were utilized to ascertain the direct and indirect influences of stand characteristics, structural diversity, and environmental variables on forest production. Our dataset included forests at various developmental stages, from the initial phases of secondary succession to mature old-growth forests. We categorized these plots into five forest developmental stages on the basis of stand age to investigate the variations in production as the forests matured. Consequently, we propose ways to increase forest productivity in the Da Xing'an Mountains to improve forest quality. The primary aims of this study are as follows: (1) to elucidate the impacts of stand characteristics, structural diversity, and environmental variables on forest productivity, and (2) to examine the comparative impacts and evolutionary trends of stand characteristics, structural diversity, and environmental variables on forest production during forest development.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and sample plot data\u003c/h2\u003e \u003cp\u003eThe research area is located in the key state-owned forest region of the Da Xing'an Mountains in Heilongjiang Province. Geographically, the area is located in northwestern Heilongjiang Province, northeastern Inner Mongolia Autonomous Region, and on the northeastern slope of Da Xing'an ridge (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It spans 121\u0026deg;10\u0026rsquo;53\u0026rdquo;-127\u0026deg;01\u0026rsquo;21\u0026rdquo;E, 50\u0026deg;07\u0026rsquo;02\u0026rdquo;-53\u0026deg;33\u0026rsquo;42\u0026rdquo; Nand encompasses a total area of 8.02 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e hectares. The area has a distinct cold\u0026ndash;temperate continental monsoon climate, with an annual average temperature of -2\u0026deg;C. Annual precipitation values range from 430\u0026ndash;460 mm, and precipitation is concentrated from July\u0026ndash;September. The primary soil types in the area include brown coniferous forest soil, dark brown soil, gray\u0026ndash;black soil, meadow soil, and marsh soil. The climax community of the forest ecosystem in the Da Xing'an Mountains comprises a bright coniferous forest typical of a cold\u0026ndash;temperate zone. The dominant tree species include \u003cem\u003eLarix gmelinii\u003c/em\u003e, \u003cem\u003eBetula platyphylla\u003c/em\u003e, \u003cem\u003ePopulus davidiana\u003c/em\u003e, \u003cem\u003eQuercus mongolica\u003c/em\u003e, and \u003cem\u003ePinus sylvestris var. mongolica\u003c/em\u003e, among others.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data for this study were sourced from the seventh and eighth national forest resource inventories (NFCIs) of the Da Xing'an Mountains. Utilizing systematic sampling and a kilometer grid approach, 782 sample plots were set up over a span of five years. In the Da Xing'an Mountains, the fixed sample areas were organized in a grid measuring 8 km by 8 km, each spanning 0.06 hectares. Measurements were taken for various tree species, breast height diameters (\u0026ge;\u0026thinsp;5 cm), and the mean age of the stands in every fixed plot. The mean carbon reserves in this region's natural forests stood at 33.35 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in 2010, which was a 4.38 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e increase from 2005. On average, stand productivity was 1.55 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, accompanied by growth, recruitment, and mortality rates of 0.88 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 0.23 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 0.43 t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Generally, as forests aged, there was a notable reduction in both growth rates and entry rates, alongside an increase in mortality rates. The mortality percentages for forests categorized as young, middle-aged, near-mature, mature, and overaged were 6.73%, 24.66%, 19.28%, 23.77%, and 30.49% of the overall mortality, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of the basic characteristics of the sample plots.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial carbon stock\u003c/p\u003e \u003cp\u003e/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnding carbon stock\u003c/p\u003e \u003cp\u003e/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecruitment/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrowth/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMortality/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eForest productivity\u003c/p\u003e \u003cp\u003e/(t\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e )\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.48a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.38\u0026thinsp;\u0026plusmn;\u0026thinsp;14.44a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle-aged stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.02\u0026thinsp;\u0026plusmn;\u0026thinsp;20.44b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.77\u0026thinsp;\u0026plusmn;\u0026thinsp;20.27b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNear-mature stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.67\u0026thinsp;\u0026plusmn;\u0026thinsp;18.39bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.33\u0026thinsp;\u0026plusmn;\u0026thinsp;18.49bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.75\u0026thinsp;\u0026plusmn;\u0026thinsp;20.64c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.96\u0026thinsp;\u0026plusmn;\u0026thinsp;20.69c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvermature stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.85\u0026thinsp;\u0026plusmn;\u0026thinsp;23.50d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.64\u0026thinsp;\u0026plusmn;\u0026thinsp;22.96d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.35\u0026thinsp;\u0026plusmn;\u0026thinsp;22.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.73\u0026thinsp;\u0026plusmn;\u0026thinsp;22.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Data with the same small letter in each panel indicate no significant difference at the P\u0026thinsp;=\u0026thinsp;0.05 level (Duncan test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCalculating carbon sequestration in the forest stands of permanent sample plots\u003c/h3\u003e\n\u003cp\u003eThe biomass of each tree was obtained by substituting the tree measurement data into a biomass model. Subsequently, the biomass was multiplied by the ratio of the carbon content of each species to ascertain the carbon storage capacity of every tree. The term 'growth' denotes the variation in the carbon sequestration of preserved trees across two survey intervals; 'recruitment' indicates the rise in carbon sequestration due to trees having a diameter at breast height (DBH)\u0026thinsp;\u0026lt;\u0026thinsp;5 cm in the initial survey, increasing to 5 cm in the subsequent survey; 'mortality' denotes the reduction in carbon storage resulting from tree death during the survey. This research defines forest productivity as the aggregate of growth, mortality, and recruitment.\u003c/p\u003e\n\u003ch3\u003eStand variables\u003c/h3\u003e\n\u003cp\u003eThe variables in our study consisted of stand age, stand density, and DBH. The stand age was ascertained by computing the arithmetic mean of the ages of the typical dominant trees via tree ring analysis. The DBH for each plot was computed for all trees within the plot. The stand density (N\u0026sdot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was calculated by dividing the total tree count in the plot by the plot area.\u003c/p\u003e\n\u003ch3\u003eStructural diversity indicators\u003c/h3\u003e\n\u003cp\u003eWe employed the diversity index of the tree species composition to delineate structural diversity. This index was utilized to evaluate data, including the quantity of tree species, their relative abundance, and the fraction of species biomass. It also accurately depicts the uniformity and extent of species intermingling within a stand.\u003c/p\u003e\n\u003ch3\u003eEnvironmental data\u003c/h3\u003e\n\u003cp\u003eClimate data were obtained from the ClimateAP (v2.30) application, which generates climate variables on the basis of latitude, longitude, and elevation of the sample plots. We obtained soil data from the Harmonized World Soil Database (HWSD) of the Food and Agriculture Organization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/faostat/en/#data\u003c/span\u003e\u003cspan address=\"http://www.fao.org/faostat/en/#data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Milovac et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which offers extensive soil property and nutrient information at a resolution of 1000 m. Using kriging interpolation, the data were resampled to a 30 m resolution and extractable by the geographic coordinates of a sample plot. Topographic data were acquired during the creation of sample plots, and a GPS was used to identify and document topographic information accurately. In total, there were 16 climate factors, 10 soil variables, and 3 topographic variables, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the environmental data used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual heat: moisture index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHargreaves climatic moisture deficit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDD_0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree-days below 0\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemperature difference between MWMT and MCMT, or continentality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDD18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree-days above 18\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree-days above 5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtreme minimum temperature over a 30-\u003c/p\u003e \u003cp\u003eyear period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtreme maximum temperature over a 30-\u003c/p\u003e \u003cp\u003eyear period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHargreaves reference evaporation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean annual precipitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean annual temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMCMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean coldest month temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMWMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean warmest month temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNFFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of frost-free days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrecipitation as snow between August in\u003c/p\u003e \u003cp\u003eprevious year and July in current year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEPTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference soil depth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREF_BULK_DENSITY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg/dm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil reference bulk density\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweight %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil organic carbon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH_H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-log(H\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil pH (H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEC_SOIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecmol/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil CEC (soil)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil base saturation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTEB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecmol/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil TEB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweight %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil calcium carbonate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil sodicity (ESP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edS/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil salinity (ECE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was used to initially assess the significant association of prospective predictors associated with forest productivity (at the 0.05 level) and the threshold for the correlation coefficient between predictor variables and forest productivity. The predictors were utilised for subsequent analysis only if the absolute values of their correlation coefficients above 0.2 (threshold of 1). Second, the absolute value of the correlation coefficient among the significant candidate variables themselves was guaranteed to be \u0026lt;\u0026thinsp;0.4 (threshold 2). A predictor was excluded if the absolute value of the correlation coefficient between itself and all other predictors was greater than 0.4, and the variance inflation factor (VIF) was used in stepwise regression analysis to evaluate the multicollinearity. All the VIF values\u0026thinsp;\u0026lt;\u0026thinsp;10 indicate that collinearity between variables has no significant impact on our results (Graham, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The VIF and Pearson methods were implemented in SPSS software (version 23.0).\u003c/p\u003e \u003cp\u003eThe RF algorithm chooses the variables on the basis of the importance scores of the input variables(Breiman 2001). In the RF algorithm, the importance value was calculated by permuting on out-of-bag (OOB) data: (1) the prediction error (the mean sum of the squares of residuals, MSE) on the OOB portion of the data was recorded for each tree, (2) the same was done after permuting each predictor variable, and (3) the difference between the two was then averaged over all trees as importance scores(Gr\u0026ouml;mping, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The importance scores of all the predictors were normalized to a percentage. The RF method was implemented in the randomForests package in the R platform (version 4.3.2).\u003c/p\u003e \u003cp\u003eBefore statistical analysis was conducted, the classical theory of ecology was used to divide the life cycle of a forest into five developmental stages: young forest, middle-aged forest, near-aged forest, mature forest, and overaged forest. During various stages, changes in growth rates, biomass buildup and photosynthetic efficiency result in different degrees of forest production. Consequently, tree species and age were categorized into separate classifications for analysis. Consequently, we categorized the trees on the basis of various tree types and stand ages. \u003cem\u003eLarix gmelinii\u003c/em\u003e, \u003cem\u003ePinus sylvestris\u003c/em\u003e, and \u003cem\u003ePicea asperata\u003c/em\u003e were categorized on the basis of the following age ranges: \u0026le; 40, 41\u0026ndash;60, 61\u0026ndash;80, 81\u0026ndash;120 and \u0026gt;\u0026thinsp;120 a. \u003cem\u003ePopulus davidiana\u003c/em\u003e and \u003cem\u003eBetula platyphylla\u003c/em\u003e were categorized into the following age groups: \u0026le; 30, 31\u0026ndash;50, 51\u0026ndash;60, 61\u0026ndash;80 and \u0026gt;\u0026thinsp;80 a. The age classification for \u003cem\u003eQuercus mongolica\u003c/em\u003e and \u003cem\u003eBetula davurica\u003c/em\u003e was as follows: \u0026le; 40, 41\u0026ndash;60, 61\u0026ndash;80, 81\u0026ndash;120 and \u0026gt;\u0026thinsp;120 a. PLS\u0026ndash;SEM was employed to investigate the direct, indirect, and interactive links among different variables affecting the response ratio (RR) of agricultural yields. The net effect of one variable on another was determined by integrating all direct and indirect pathways connecting the two variables. The route coefficients and coefficients of determination (R\u0026sup2;) were computed using the R package \"plspm\". All data analysis was conducted using R version 4.0.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSignificant variables affecting stand biomass via multiple feature selection methods\u003c/h2\u003e \u003cp\u003eIn the examination of stand productivity, considerable discrepancies were observed in the number of variables chosen by the three feature selection approaches, and the selected variables varied among these methods. For example, relative humidity (RH) values were identified as significant using correlation analysis and redundancy analysis but excluded by alternative methods. Moreover, many feature selection strategies exhibited limited consistency in terms of the chosen variables. Age, Dg and density in stand structural diversity were consistently significant variables across all three techniques. Ultimately, five critical biodiversity factors were identified by the intersection approach, as follows: (1) climate: MAT and MAP; (2) stand: age, Dg, and density; (3) soil: depth; (4) structural diversity: ISCD; and (5) topography: altitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults of partial least squares path modeling\u003c/h2\u003e \u003cp\u003eThe variables identified by the VIF, Pearson, and RF methods were incorporated into the PLS\u0026ndash;SEM with forest productivity (growth, recruitment, and mortality). The PLS\u0026ndash;SEM performed well, with a strong explanatory ability for the causal paths. Specifically, the AVE, alpha, CR, and Rho_A values indicated that the model's fit was within an acceptable range. The goodness-of-fit (GOF) values revealed that the model's overall quality was quite high (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis indicated that stand factors, structural diversity, and environmental variables contributed 68.2%, 39.2%, and 35.2%, respectively, to the variability in growth, recruitment, and mortality. Furthermore, growth, recruitment, and mortality jointly accounted for 93.8% of the variation in stand productivity, underscoring their pivotal role in influencing forest production dynamics(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel performance of the PLS\u0026ndash;SEM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo; alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRho_A\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe growth of a stand was directly influenced by factors such as stand (b\u0026thinsp;=\u0026thinsp;0.66), climate (b\u0026thinsp;=\u0026thinsp;0.28), topography (b\u0026thinsp;=\u0026thinsp;0.05), structural diversity (b\u0026thinsp;=\u0026thinsp;0.02), and soil (b\u0026thinsp;=\u0026thinsp;0.08; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The increase in stand growth was particularly evident due to stand features, as optimal tree density fosters stand efficacy, facilitates canopy shading, and improves water-use efficiency. Nonetheless, overall, topography constrained stand growth, as elevated altitudes typically result in lower temperatures, which is a critical determinant of plant growth, hence substantially impeding vegetation survival and development. The advantageous influence of climatic circumstances on stand production was significant, particularly as regions with elevated temperatures and more precipitation typically exhibit enhanced forest growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eThe PLS‒SEM analysis for recruitment indicated that stand factors exerted the most substantial positive direct influence on recruitment (b = -0.40). Structural variety and soil were positively correlated with recruitment (b\u0026thinsp;=\u0026thinsp;0.02, b\u0026thinsp;=\u0026thinsp;0.21), but climate had a negative impact on recruitment (b=-0.17; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Although topography did not exert a substantial direct influence on recruitment, both factors indirectly affected recruitment because of their negative correlation with climate and positive association with stands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe PLS\u0026ndash;SEM analysis of mortality revealed the indirect impacts of topography (b\u0026thinsp;=\u0026thinsp;0.10), climate (b\u0026thinsp;=\u0026thinsp;0.28), structural variety (b = -0.19), and soil (b\u0026thinsp;=\u0026thinsp;0.19) on mortality productivity via their interactions with stands (b\u0026thinsp;=\u0026thinsp;0.43; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In the statistical examination of the three separate carbon pools, growth and death constituted the primary sources of variation in stand productivity, with recruitment following thereafter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors controlling forest productivity across different stand ages\u003c/h2\u003e \u003cp\u003eIn summary, topography, structural diversity, soil, climate, and stand factors exerted direct effects on the processes of stand growth, mortality, and recruitment, and these factors also indirectly shaped stand productivity. Path coefficient analysis revealed a decreasing trend with forest age for climate variables such as MAP (from 0.9973 to 0.8539) and MAT (from 0.1328 to -0.2142; Appendix S3). Notably, among the stand factors, density remained the most significant factor across all stages, except for the overaged forest. An analysis of the contributions of various factors to stand productivity revealed that with increasing forest age, the impacts of topography (from 14.55% to 6.28%) and climate (from 30.40% to 17.67%) on stand productivity gradually decreased. In stark contrast, the influence of structural diversity (from 8.68% to 16.44%) and soil (from 8.80% to 10.30%) on stand productivity increased with forest age (Table 4). As anticipated, stand factors consistently demonstrated the most significant influence across all growth stages\u003c/p\u003e\u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution of relationships (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026nbsp;group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructural diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRecruitment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u0026nbsp;forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle-aged\u0026nbsp;forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNear-aged\u0026nbsp;forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature\u0026nbsp;forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOveraged\u0026nbsp;forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of stand factors and structural diversity on forest productivity\u003c/h2\u003e \u003cp\u003eWe employed PLS\u0026ndash;SEM to analyze the multivariate links driving stand factors and structural diversity. The PLS\u0026ndash;SEM effectively elucidated the relationships utilizing the variables chosen through the intersection strategy. The stand variables (age, dg, and density) significantly contributed to forest productivity. Researchers have demonstrated that stand density directly influences the environment in which trees develop, including light, heat, temperature, humidity, and soil nutrients (Cui et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At low stand densities, tree interactions are minimal or weak, rendering niche complementarity effects insignificant. As stand density escalates, interactions intensify, with trees occupying greater space and utilizing more resources (Forrester et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Morin, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Initially, the productivity of forests increases as trees grow and the stand matures over time. However, as forests evolve from middle-aged to fully mature, there is an increase in the need for nutrients and water to facilitate the outward growth of trees, simultaneously increasing the rates of transpiration.\u003c/p\u003e \u003cp\u003eThe outcomes of our study reveal a beneficial association between forest productivity and increased structural diversity. Previous studies have validated the influence of structural diversity on forest output, which has been successfully included in predictive models for forest productivity (LaRue et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, an increase in structural diversity amplifies interactions, such as competition among trees. A varied assortment of structures generates openings in the canopy, enhancing light penetration and promoting a vibrant natural habitat. Layered canopy structures, along with diverse layering of trees, frequently occupy greater spatial areas and exploit a broader range of resources, which is a concept that is elucidated by the principle of niche complementarity (Morin, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The complex design of a stand contributes to minimizing temperature fluctuations, enhancing soil moisture retention, facilitating litter decomposition, and promoting nutrient recycling, thereby improving resource utilization efficiency and increasing both productivity and biomass growth (Crockatt and Bebber, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schwarz et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although cold\u0026ndash;temperate forests lack the characteristic stratification found in subtropical forests, their structural diversity is essential for influencing ecosystem services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImpact of environmental factors on forest productivity\u003c/h2\u003e \u003cp\u003eObservations reveal that climate variables, specifically MAT and MAP, exert the greatest influence on forest production among environmental factors, with increasing temperatures and precipitation increasing productivity. Temperature fluctuations contribute to clarifying the spatial pattern of forest productivity (Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ni et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the efficacy of forests is constrained by water availability, as precipitation governs water distribution, subsequently affecting tree habitats. The findings of our study indicate that geographical factors, particularly elevation, significantly affect forest productivity. This was due mainly to topographic factors affecting the spatial distributions of solar radiation and precipitation, together with soil moisture, nutrients, and depth, resulting in intricate impacts on forest productivity within different ecosystems (Wu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, elements such as slope and gradient have been identified as critical drivers influencing forest production in some studies (He et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jafarian et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, within the parameters of our research area, the impact of these components is less pronounced than that of height. This may be attributed to the predominant tree species (\u003cem\u003eLarix gmelinii\u003c/em\u003e and \u003cem\u003eBetula platyphylla\u003c/em\u003e) exhibiting markedly low sensitivity to slope orientation and gradient. \u003cem\u003eLarix gmelinii\u003c/em\u003e, recognized for its strong cold resistance and moderate water requirements, can thrive on both steep and shaded slopes. \u003cem\u003eBetula platyphylla\u003c/em\u003e necessitates increased moisture; nonetheless, its tolerance to shadow and cold enables it to flourish over diverse slopes and gradients, mitigating the effects of fluctuations in light and temperature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBeneficial impact of forest age on forest productivity via structural diversity and soil factors\u003c/h2\u003e \u003cp\u003eThe effects of soil and especially structural diversity on forest productivity increased with stand age, as predicted. These greater effects may be partly due to increasing tree\u0026ndash;tree interactions and complementary effects (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), coupled with increased build-up of soil organic matter and heightened microbial activity in the soil, which can intensify the impact on forest productivity. With respect to forest age, the greater positive effects of structural diversity with stand age illustrate that processes such as resource partitioning, facilitation or trophic interactions may result in greater benefits for tree growth in plots with high stand ages than in plots with low stand ages (Hatami et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This finding is supported by those of previous studies showing that as a forest advances through its developmental phases, its structure becomes increasingly intricate, and the linkages between resource distribution among trees and biodiversity become more evident. Structural variety enhances resource utilization efficiency and stabilizes ecosystem functioning by affecting tree distribution, density, canopy architecture, and interorganism interactions (Aakala et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Buechling et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, as the trees in the forest undergo maturation, organic matter from litterfall and root systems progressively accumulates, leading to an increase in the soil organic matter content. This accumulation not only augments soil fertility but also improves the physical structure of the soil (Post and Kwon, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), including the formation and stabilization of soil aggregates, thereby augmenting soil aeration and water retention capacity. Soil structure directly influences root development and nutrient assimilation. A well-developed soil structure facilitates the efficient cycling of water and nutrients, which is imperative for the optimal growth and vitality of trees (Jandl et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRole of forest age in mitigating the impact of climate and topographical variations on forest productivity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of our study reinforce the mitigating effect of forest age in mitigating climate and topographic shifts, showing that mature forests are less susceptible to climate and topographical changes than their younger counterparts are (Vangi et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research has indicated that mature forests are more sensitive to climate change than younger forests are (Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). This disagreement may arise from variations in the chosen study regions, as the Da Xing'an Mountains are situated in a cold\u0026ndash;temperate humid to semihumid zone. The growth dynamics of several species may be affected by habitat factors. Trees in arid environments are more vulnerable to climate change than those in humid environments are, irrespective of their presence in mature or young forests (Xue et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).. The first piece of evidence proves that there is indeed a dampening effect of forest heterogeneity within the ecosystem on the climate sensitivity of forests, which can be related to the fact that multiage forests benefit from increasing structural complexity (de Wergifosse et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jandl et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared with young forests, overaged forests exhibit greater diversity in the age structure, characterized by a combination of young, middle-aged, and mature trees. The creation of vertical stratification enhances ecosystem complexity and stability. This stratification provides gradients in biomass (deadwood and living aboveground biomass) and different ways to allocate carbon (Mar\u0026eacute;chaux et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This stratification has also enabled the development of unique carbon distribution tactics and distinguished the ecological roles of plants and animals, thus increasing biodiversity and bolstering the ecosystem's ability to withstand environmental disruptions (Lafond et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pardos et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From a functional perspective, a forest of diverse ages exhibits a variety of physiological and life cycle characteristics among its trees. This variety translates into a greater capacity to absorb and store carbon, thereby mitigating the effects of climate change, even in a mosaic of even-aged patches, as we simulated in this study. Young trees grow rapidly and absorb significant amounts of CO2 from the atmosphere, contributing to carbon capture, and are more efficient in converting photosynthates in biomass (Campioli et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Collalti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Va et al., 2007). In contrast, older trees accumulate more biomass and serve as long-term carbon sinks and regenerative shelters. Concurrently, the increased diversity between age groups offsets the beneficial and detrimental effects linked to each age group. Consequently, in fluctuating weather scenarios, the existence of various age groups in forest communities is vital for preserving their functional variety since this diversity offers advantages in terms of resilience and the ability to adapt to climatic shifts (Ehbrecht et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kauppi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zampieri et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the age of forests plays a role in lessening the impact of topographical alterations. Forest age distribution plays a role in shaping how topography impacts forest ecosystems (Dur\u0026aacute;n Zuazo and Rodr\u0026iacute;guez Pleguezuelo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Selkim\u0026auml;ki et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, forests of various ages, owing to their intricate structures, might show greater resilience to topographical changes such as soil erosion and landslides. Such varied structures aid in stabilizing the soil, diminishing erosion, and increasing the forest's ability to retain water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eFactors affecting forest productivity were examined in the brief research period from 2005 to 2010. Short-term data may neglect the effects of extreme climate events, which frequently transpire over extended periods and can profoundly affect patterns of forest development. This constraint may result in an undervaluation of the influence of climate variability on forest productivity. Future research should incorporate extended datasets or integrate evaluations of extreme climate events to assess their potential impacts on forest ecosystems more accurately.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUsing NFCI data from 2005 to 2010, PLS\u0026ndash;SEM was employed to examine the correlations among stand variables, structural diversity, and environmental variables in assessing the productivity of natural forests. Our research indicates that stand variables are the principal determinants of forest productivity, with direct influences being notably substantial. Furthermore, the age of forests enhances the impact of structural variety and soil on forest productivity while alleviating the effects of climate and topography. In light of impending climate change, forest management practices must be customized to various stages of forest development. In young forests during the early successional stage, the application of moderate and selective thinning techniques can diminish competition among trees, foster the establishment of dominant species, and preserve a degree of species diversity to improve structural complexity. In middle-aged and near-aged forests in the mid-successional and mature phases, implementing moderate thinning and selective logging can foster a multilayered vertical structure, optimize light conditions, promote understory regeneration, and increase ecosystem stability. In mature and overaged forests in the late successional stage, excessive intervention should be curtailed while preserving a proportion of standing dead trees and fallen logs to increase soil nutrient cycling and fertility. Furthermore, artificial regeneration can be effectively executed by introducing seedlings of varying ages to improve the age structure variety within a stand. The implementation of these measures enables forests to significantly contribute to climate change mitigation, improve carbon sequestration, and preserve biodiversity. Our research findings establish a foundational theoretical framework for the development of sustainable forest management practices in the Da Xing'an Mountains of Northeast China.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by the National Key R\u0026amp;D Program of China (No. 2022YFD2201001), the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China (No. U21A20244), and the Heilongjiang Touyan Innovation Team Program (Technology Development Team for High-efficient Silviculture of Forest Resources).\u003c/p\u003e\n\u003ch3\u003eConflicts of interest\u003c/h3\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and material\u003c/h3\u003e\n\u003cp\u003eThe data and material analysed during the current study are during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eCode availability\u003c/h3\u003e\n\u003cp\u003eThe code availability generated during the current study are during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eAuthors' contributions\u003c/h3\u003e\n\u003cp\u003eZirui Wang: Methodology, Validation, Writing – original draft. Xuehan Zhao and Shoumin Cheng Writing – review. Zheng Miao and Xingji Jin: Writing – review \u0026amp; editing. Yuanshuo Hao and Lihu Dong: Conceptualization, Supervision, Writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAakala, T., Fraver, S., D\u0026rsquo;Amato, A.W., Palik, B.J., 2013. Influence of competition and age on tree growth in structurally complex old-growth forests in northern Minnesota, USA. Forest Ecology and Management 308, 128-135. https://\u003cu\u003edoi: 10.1016/j.foreco.2013.07.057\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eBarrufol, M., Schmid, B., Bruelheide, H., Chi, X., Hector, A., Ma, K., Michalski, S.G., Tang, Z., Niklaus, P.A., 2013. Biodiversity Promotes Tree Growth during Succession in Subtropical Forest. PLoS ONE 8. https://\u003cu\u003edoi: 10.1371/journal.pone.0081246\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eBecknell, J.M., Powers, J.S., 2014. 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Global Ecology and Conservation. https://\u003cu\u003edoi: 10.1016/j.gecco.2024.e02935\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forest productivity, Environmental factors, Stand age, Natural forests","lastPublishedDoi":"10.21203/rs.3.rs-6728635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6728635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Da Xing'an Mountains region is the only area of cold\u0026ndash;temperate coniferous forests in China and functions as an essential ecological barrier. It has a crucial purpose in forest ecosystems and carbon sequestration processes. Stand age is influenced by interactions among population dynamics, mechanisms of disturbance, and forest management approaches, significantly influencing the global carbon cycle. Growth data indicate that forest development is correlated with variations in productivity. Nonetheless, the variability in production throughout several phases of stand development remains largely unexamined, and the influence of contributing elements in this process is still ambiguous. Utilizing the 2005\u0026ndash;2010 National Forest Continuous Inventory (NFCI) data from the eastern Da Xing'an Mountains, we examined the influence of stand characteristics, structural diversity, and environmental variables on forest productivity throughout various developmental stages, from young to overaged forests. The findings indicate that (1) forest productivity is collectively limited by stand characteristics, structural diversity, and environmental factors, with stand factors exerting the greatest influence, especially through direct effects. (2) As tree growth stages advance, the impacts of structural variety (ranging from 8.68 to 16.44) and soil (ranging from 8.80 to 10.30) on forest productivity intensify. (3) Altered tree growth stages decrease the influence of climate (from 30.40 to 17.67) and terrain (from 14.55 to 6.28) on forest productivity. By thoroughly integrating the determinants of forest production, our study provides essential system\u0026ndash;level insights that establish a theoretical basis for forecasting alterations in forest productivity amid global change. These findings enhance the formulation of more efficacious forest management methods to address the difficulties posed by climate change and biodiversity decline.\u003c/p\u003e","manuscriptTitle":"Stand Development Stages Reshape Climate-Structure Interactions in Boreal Forest Productivity: A Case Study of China's Cold-Temperate Conifers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:12:52","doi":"10.21203/rs.3.rs-6728635/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca55d9ae-e43a-484c-9d3e-a03893d5ec2a","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T07:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 18:12:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6728635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6728635","identity":"rs-6728635","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}