Stand development shifts nitrogen cycling strategies and supply-demand balance in larch plantations

Abstract

Nitrogen (N) often constrains forest productivity, yet changes in N-use strategies and ecosystem N balance during stand development remain poorly understood in cold-temperate plantations. In this study, N stocks and key fluxes were quantified across vegetation, litter, and soil in larch (Larix principis-rupprechtii) plantations representing four stand ages (11, 23, 37, and 47 years) in northern China. Nutrient strategy indices were calculated to characterize N acquisition and internal cycling, and piecewise structural equation modeling (pSEM) was used as a descriptive path-analysis framework to summarize multivariate associations. Tree-layer biomass and N stocks increased across the chronosequence: tree-layer biomass peaked at 256.77 t·hm⁻² at 37, while tree-layer N stocks reached their maximum of 0.94 t·hm⁻² at 47 years. Soil dominated ecosystem N storage (> 87%) across all stand ages. Soil available N stocks consistently exceeded annual plant N uptake, with the largest surplus approximately 0.01 t·hm⁻² in the 37-year-old stand, suggesting that N supply was generally sufficient to meet plant demand during the study period. Nutrient-use indices indicated a developmental shift from relatively greater reliance on N acquisition in younger stands toward stronger internal N recycling in older stands, as reflected by increased litter N return and higher N resorption efficiency. Nitrogen use efficiency reached its highest observed value in the 37-year-old stand. In the pSEM framework, N acquisition was more closely associated with plant nutrient status, whereas N cycling was more strongly associated with stand age and litter nutrient status. These findings suggest that stand development plays an important role in regulating N-use strategies in larch plantations.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09454-1.

Introduction

Nitrogen (N) is a fundamental element regulating plant growth and ecosystem primary productivity. As a key component of proteins, nucleic acids, and chlorophyll, N directly supports essential physiological processes such as photosynthesis and respiration [1–3]. However, the availability of bioavailable N in soils often fails to match plant demand, making N one of the most pervasive constraints on ecosystem productivity. Numerous studies have shown that N limitation is widespread across forest ecosystems globally, with particularly strong effects in boreal and temperate forests where limited N supply can markedly restrict biomass accumulation and carbon sequestration [2, 4]. During stand development, growth rates, biomass allocation, and soil nutrient status are continuously altered, and stage-dependent variations in the intensity and ecological consequences of N limitation are therefore expected [5].

At the ecosystem scale, N limitation is not determined solely by the magnitude of soil N pool, but instead depends on the balance between soil N supply and plant N demand (i.e., the N supply-demand balance) [6]. This balance has been regarded as a core indicator for diagnosing N limitation in forest ecosystems [7]. When supply and demand are mismatched, tree growth may be reduced, litter decomposition may be constrained, and soil acidification may be intensified. Because most soil N occurs in organic forms that are not directly available to plants, total soil N storage alone cannot adequately reflect actual N availability. In contrast, available N pools (e.g., alkali-hydrolysable N) have been considered to better approximate the effective N supply available to plants [8]. Consistent with this view, pronounced adjustments in soil N supply capacity and its coordination with plant demand have been reported across stand developmental stages [9].

From a process-based perspective, the ecosystem N supply-demand balance is jointly regulated by coupled fluxes, including plant N uptake, internal retention, litter return to soil, and N resorption prior to senescence [10–13]. Plant N demand is influenced not only by growth rate, but also by N use efficiency (NUE) and the capacity for internal N recycling [14–16]. When soil available N becomes limiting or stands approach maturity, higher N resorption efficiency is often induced, internal N recycling is strengthened, and biomass allocation is adjusted, thereby reducing reliance on external N inputs [17, 18]. Consequently, stand development is typically accompanied by a shift from a resource-acquisitive strategy toward a resource-conservative strategy [19]. In parallel, N cycling is often transitioned from a relatively open system to a more closed system that increasingly depends on litter-microbe-mediated internal recycling to maintain nutrient balance [20].

N cycling is inherently a complex, multi-component system involving vegetation, litter, and soil, which are tightly coupled through plant uptake, internal retention, litter return, soil N transformation, and N resorption [21]. During stand development, N distribution and cycling among ecosystem components may be reorganized through changes in vegetation biomass accumulation, litter inputs, and soil nutrient turnover, rather than through changes in total N storage alone [22]. Therefore, a multivariate framework is useful for summarizing how stand development is associated with plant-litter-soil N cycling. In observational chronosequences, however, path models should be interpreted as evidence for hypothesized associations rather than as definitive proof of causality. Accordingly, we used piecewise structural equation modeling (pSEM) as a descriptive path-analysis tool to examine whether the observed variation in N acquisition and N cycling was consistent with an a priori plant-litter-soil framework [23].

Larch (Larix principis-rupprechtii), a dominant afforestation species in the cold-temperate regions of northern China [24], is characterized by rapid growth and strong environmental adaptability, and is widely recognized for its contributions to timber production and ecosystem functioning [25–27]. However, productivity decline and site degradation have been frequently reported in near-mature and mature larch plantations. These declines have been closely linked to shifts in nutrient availability, particularly changes in N supply-demand relationships [5]. To date, research has largely emphasized soil C: N ratios or individual N pool characteristics, whereas integrated quantification of ecosystem N supply-demand balance and its regulatory mechanisms across stand development remains limited [13, 20]. This gap has constrained a comprehensive understanding of N cycling dynamics in these plantations.

Against this background, larch plantations with four stand ages (11, 23, 37, and 47 years) were investigated. Biomass and N stocks in vegetation, litter, and soil were quantified; ecosystem-scale N fluxes (uptake, retention, litter return, and resorption) were estimated; and indices describing N acquisition and cycling strategies were calculated. In addition, pSEM was applied as a descriptive path-analysis framework to summarize the direct associations among stand age, plant nutrient status, litter nutrient status, soil nutrient status, N acquisition, and N cycling. Specifically, this study aimed to: (1) characterize variations in N cycling fluxes and N nutritional strategies across stand ages; (2) evaluate ecosystem-scale N supply-demand status during stand development; and (3) identify key associations underlying the stand age-driven transition from external N acquisition to internally regulated N cycling.

Materials and methods

Study area and plot establishment

The study area is located in the Saihanba Forest Center, Hebei Province, China (42°02′−42°36′ N, 116°51′−117°39′ E) [26], at elevations of 750–1998 m. The region is typified by a semi-arid to semi-humid, cold-temperate continental monsoon climate. The mean annual temperature is -1.3 °C, with extreme temperatures ranging from − 43.2 to 33.4 °C. The mean annual precipitation is approximately 460 mm, of which 65.8% occurs during the growing season (June−August). The average frost-free period is approximately 64 days per year [27]. Saihanba is located on the southern edge of the Inner Mongolia Plateau. Its elevation gradient creates marked vertical climatic differentiation: lower elevations (750–1500 m) show transitional temperate characteristics, whereas higher elevations (1500–1940 m), where the study plots were located, conform more closely to cold-temperate climatic conditions.

Extensive overgrazing during the early twentieth century resulted in severe degradation of natural forests in this region. In response, large-scale afforestation programs were launched in 1962 using larch and other cold- and drought-tolerant species (e.g., spruce). Larch plantations were initially established at a density of approximately 5,000 trees ha⁻¹ [28]. Since 1983, forest management has shifted from afforestation to stand tending and structural regulation [29]. To date, five large-scale thinning interventions have been implemented, which have reduced the stand density of mature forests to about 600 − 900 trees·ha⁻¹. Currently, larch plantations cover approximately 38,000 ha in the study region, with a total standing volume of 6.59 million m³.

Vegetation in the study area comprises coniferous and broadleaved forests, shrublands, and meadow ecosystems. The main plantation species include larch, Pinus sylvestris var. mongolica, and Betula platyphylla. The shrub layer is dominated by Rhododendron mucronulatum, Spiraea salicifolia, and Rosa davurica, while the herb layer is characterized by Trollius chinensis, Taraxacum mongolicum and Picris hieracioides.

In June 2023, larch plantations representing four stand-age stages (11, 23, 37, and 47 years) were selected. For each stand age, three independent larch stands located in separate forest compartments were selected under similar topographic conditions, soil types, and management histories. One 30 m × 30 m plot was established within each independent stand. Thus, a total of 12 plots were investigated, representing four stand ages × three independent stands per age. The plot, corresponding to an independent stand, was used as the statistical unit for stand-age comparisons. Adjacent plots were separated by > 100 m [16, 30] (Fig. 1). A complete tree inventory was conducted in each plot (Table 1).

Fig. 1.

Distribution of the sample plots in larch plantations with four stand ages. Each point represents one independent stand-level plot. For each stand age, three independent larch stands located in separate forest compartments were selected, and one 30 m × 30 m plot was established in each stand

Table 1.

Stand characteristics of larch plantations across the four stand ages

Age (year)	Elevation (m)	Number of plots	Aspect	Slope (°)	DBH (cm)	Average height (m)	Stand density (tree·ha− 1)
11	1606 − 1616	3	North	0 − 1	7.31 ± 2.46	7.29 ± 1.15	2006 ± 59
23	1624 − 1635	3	North	1 − 5	15.22 ± 2.60	14.75 ± 1.28	1511 ± 169
37	1639 − 1652	3	North	4 − 8	23.22 ± 4.76	20.56 ± 3.06	903 ± 73
47	1664 − 1672	3	North	0 − 3	24.06 ± 4.11	21.63 ± 3.48	737 ± 23

Values are shown as mean ± standard deviation based on three independent stand-level replicates for each stand age

All plant materials used in this study were obtained from larch plantations located in Saihanba Mechanical Forest Farm, Hebei Province, China. The stands were established using locally sourced seeds, with local seed collection, seedling cultivation, and outplanting, and were naturally regenerated through seed dispersal. All tree, shrub, and herb species recorded in the study plots are native to the region and were not associated with any introduced or invasive species.

Sample collection was conducted with permission from the Saihanba Mechanical Forest Farm administration. Field sampling complied with relevant institutional, national, and international guidelines, as well as local legislation. The studied species is not listed as a protected species under Chinese regulations. Species identification was carried out by a qualified forestry expert from Saihanba Mechanical Forest Farm based on morphological characteristics and standard taxonomic references. Representative samples were documented photographically during field sampling. No voucher specimens were deposited, as the study focused on ecological processes in managed plantation stands rather than taxonomic verification.

Sample collection and laboratory analysis

In each sampling plot, three shrub quadrats (3 m × 3 m) were randomly established along the plot diagonal. In each shrub quadrat, one 1 m × 1 m herb plot was further randomly selected. To quantify tree growth, 10 healthy trees were randomly selected in each plot, and dendrometer bands were installed at breast height (1.3 m) to monitor radial growth from July 2023 to July 2024. Measurements were conducted monthly during the growing season (late April to early September) and at three-month intervals during the non-growing season. In addition, three healthy trees with mean DBH and height were selected per plot as sample trees. Stem, branch, leaf, and coarse-root samples were collected separately from each sample tree, with approximately 200 g obtained for each tissue type. For each sample tree, branch, leaf, and coarse-root samples were collected from at least four orientations. Branches and leaves were further sampled from at least three canopy layers within each orientation to improve representativeness. Samples of the same tissue type from each plot were pooled and homogenized before nutrient analysis. Fine roots were sampled from the soil surrounding each sample tree in at least four orientations using a soil auger with a diameter of 10 cm. Plant tissue samples were collected in July 2023 and July 2024, corresponding to the peak growing season, to compare annual nutrient status and growth while minimizing potential seasonal confounding.

Litterfall was collected at three evenly distributed points within each plot. At each point, a litter trap (0.75 m × 0.75 m; 0.5625 m²) was installed using nylon mesh supported by a PVC frame and suspended 0.75 m above the ground. Litterfall was collected periodically in October and December 2023 and May 2024. In addition, a 20 cm × 20 cm forest floor quadrat was established adjacent to each trap to collect accumulated surface litter. All litter samples were sorted, oven-dried, and analyzed for N concentration.

Soil was sampled adjacent to each litter collection point. After removing surface litter, soil cores were obtained at 0 − 10, 10 − 20, and 20–30 cm using a cutting-ring corer (100 cm³) to determine bulk density. Additional soil samples (approximately 500 g) from the same depth intervals were collected using a 10 cm diameter auger for physicochemical analyses. Organic-layer samples (0–1 cm) were also collected to determine organic matter and topsoil N enrichment.

For each plot, soil samples from the same depth were composited, air-dried, sieved, and cleared of roots and stones before the determination of total N and alkali-hydrolysable N. Soil sampling was conducted once in July 2023, corresponding to the peak growing season when plant N demand and microbial activity are relatively high. This sampling time was selected to evaluate peak-season soil available N status; therefore, the results represent peak-season rather than full annual soil N dynamics.

All plant tissue, litter, and soil samples were processed using a consistent protocol. Plant tissue and litter samples were oven-dried at 65 °C for 48 h to constant weight and then ground into a fine homogeneous powder using a laboratory mill. Total N concentrations in plant tissue, litter, and soil samples were determined using an elemental analyzer (Vario EL III, Elementar, Germany). Soil alkali-hydrolysable N was determined using the alkaline hydrolysis diffusion method. All analyses were conducted in triplicate, with certified reference materials and blanks included for quality control, and the same analytical procedures were applied across all stand ages and ecosystem components.

Estimation of nitrogen stocks

Biomass of different tree organs (stem, branches, leaves, and coarse roots) was estimated using species-specific allometric equations [31] (Formula 1, Table 2). N stocks (t·ha⁻¹) in tree, shrub, and herbaceous components, as well as in different litter fractions, were estimated by multiplying biomass by the corresponding N concentration (g·kg⁻¹). Total N stock for each ecosystem component was then obtained by summing the corresponding N pools in each plot. Calculations of total N and alkali-hydrolysable N stocks in different soil layers are provided in Formula 2 (Table 2).

Table 2.

Equations used to estimate ecosystem N stocks, N fluxes, nitrogen use efficiency, and nitrogen strategy indices

No.	Formula	Variable Explanation
Formula 1		w: Biomass (kg);  D: Denote diameter at breast height (cm);  H: Tree height (m); For stem biomass estimation, a = 0.0334 and b = 0.9171; For branch biomass, a = 0.0028 and b = 0.9417; For leaf biomass, a = 0.0050 and b = 0.7309; For coarse root biomass, a = 0.0226 and b = 0.8520.
Formula 2		: Stock of total nitrogen (t·ha− 1) or alkali-hydrolyzable nitrogen (t·ha− 1) in a specific soil layer;  BD: Denotes soil bulk density (g·cm− 3) of the corresponding layer; : Concentration of total nitrogen (g·kg− 1) or alkali-hydrolyzable nitrogen (mg·kg−¹) in that soil layer;  Z: Thickness of the soil layer (cm);  10: A conversion coefficient used for unit standardization.
Formula 3		: Amount of nitrogen resorption from litter collected at the i-th sampling (t·ha− 1); : Nitrogen concentration in the litterfall needles collected at that sampling (g·kg− 1); : Nitrogen concentration in mature needles (g·kg− 1);  MLCF: Correction factor for mass loss during leaf senescence; in this study, the average correction coefficient of 0.745 for conifer functional groups proposed by Vergutz et al. was adopted;Litterfall needle biomass collected at the i-th sampling (t·ha−¹).
Formula 4		ANRA:Calculated as the sum of the nitrogen resorption() from needles across all sampling events within one year (t·ha− 1).
Formula 5		NUE: Nitrogen use efficiency;: Annual increment in tree biomass (t·ha− 1); : Annual increment in tree N stock (t·ha− 1).
Formula 6		NCC: Nitrogen cycling coefficient; : Annual nitrogen return (t·ha− 1); : Annual nitrogen uptake (t·ha− 1).
Formula 7		NUpE: Nitrogen uptake efficiency; : Total soil nitrogen stock in the 0–30 cm soil layer (t·ha− 1).
Formula 8		SSNE: Nitrogen enrichment in topsoil; : Nitrogen stock in the 0–1 cm topsoil (t·ha− 1); : Nitrogen stock in the 0–10 cm soil layer (t·ha− 1).
Formula 9		SANP: Proportion available nitrogen in soil; : Available soil nitrogen stock in the 0–30 cm profile (t·ha− 1).
Formula 10		FFNA: Accumulation of nitrogen in forest floor litter; : Nitrogen stock of areal accumulated litter (t·ha− 1).
Formula 11		LTR: Turnover rate of forest floor litter;  ALB: Annual litterfall biomass (t·ha− 1);  FLB: Forest floor litter biomass (t·ha− 1).
Formula 12		INC: Internal nitrogen cycling; : Nitrogen concentration in mature green needles during the growing season (g·kg− 1); : Nitrogen concentration in freshly fallen needle litter (g·kg− 1).

Calculation of nitrogen fluxes, nitrogen use efficiency, and nitrogen cycling indices

At the ecosystem scale, N fluxes were quantified as annual N uptake, annual N retention, annual N return, and annual N resorption amount (ANRA) [30]. Annual N uptake was defined as the total amount of N absorbed from the soil and assimilated into plant organs within one year [32]. Following ecosystem nutrient-budget approaches, annual N uptake was estimated as the sum of annual N return and annual N resorption amount [33]. Annual N retention was defined as the annual increase in N stocks of tree organs (t·ha⁻¹) [34]. For belowground N return, annual fine-root turnover was assumed; thus, fine-root N stocks were used to represent belowground N inputs. Annual N return was calculated as the sum of N input from aboveground litterfall and fine-root turnover. During July 2023–July 2024, litterfall was collected throughout the year, and fine roots in the 0–20 cm soil layer were sampled in July 2023. N stocks of litter fractions and fine roots were determined and summed to estimate annual N return.

N resorption represents an important internal nutrient-conservation strategy whereby N is withdrawn from senescing leaves and reallocated to perennial tissues (e.g., branches, stems, and roots) before abscission [35]. In this study, ANRA was calculated from the difference in N concentration between mature and senesced needles, corrected for mass loss during senescence using the mass-loss correction factor, and scaled by litterfall biomass [30, 36] (Formulas 3 − 4, Table 2). This approach was used to quantify the absolute N flux recovered through resorption for ecosystem N budget analysis. Given the comparable site conditions and management histories across the larch plantation chronosequence, stand-age-related differences in N resorption were interpreted as temporal changes. However, this assumption may not be directly applicable to heterogeneous natural forests.

NUE characterizes the organic biomass yield per unit of absorbed N [37]. In addition, the N cycling coefficient (NCC) was calculated to represent the proportion of plant-acquired N that is returned to the soil via litterfall and internally recycled, rather than being sustained only by continued external N inputs (Formulas 5 − 6, Table 2).

Calculation of nitrogen nutritional strategy indices

To characterize plant N nutritional strategies, indicators were selected to represent N acquisition and N cycling, following Lang et al. [38]. N uptake efficiency (NUpE), N enrichment in topsoil, and the proportion of available nitrogen in soil were used to quantify N acquisition strategies, accumulation of N in forest floor litter, turnover rate of forest floor litter (%), and the internal N cycling ratio were used to quantify N cycling strategies [30] (Formulas 7 − 12, Table 2).

Statistical analysis

Statistical analyses were conducted using R 4.3.3 [39]. Before statistical analyses, the data were tested for normality and homogeneity of variances. One-way ANOVA was used to test differences among stand ages, followed by Tukey’s HSD test for multiple comparisons when significant differences were detected. The study examined whether biomass, nutrient concentration, nutrient storage, nutrient flux, and nutrient strategy indices differ among stand age classes. The significance level was set at p < 0.05.

PCA was performed separately for plant nutrients, litter nutrients, soil nutrients, N uptake, and N cycling variables. Scores of the first principal component (PC1) were extracted and used as composite indicators of plant nutrient status (Plant_N), litter nutrient status (Litter_N), soil nutrient status (Soil_N), N acquisition status (N_acquisition), and N cycling status (N_cycling) in subsequent analyses (Table S1).

A piecewise structural equation modeling (pSEM) approach was applied as a descriptive path-analysis framework to evaluate whether the hypothesized plant-litter-soil associations were consistent with the observed data [40]. Before model construction, all variables were standardized. Correlation analysis and redundancy screening were conducted to reduce collinearity and avoid model overparameterization. In the pSEM framework, stand age was specified as an exogenous variable, whereas plant nutrient status, litter nutrient status, soil nutrient status, N acquisition status, and N cycling status were specified as endogenous variables. Model fit was evaluated using Fisher’s C statistic, where p > 0.05 indicates that the hypothesized structure was not rejected by the data. The model with the lowest AIC was selected as the best-supported model. Standardized path coefficients and coefficients of determination (R²) were used to quantify the strength of direct associations.

Results

Nitrogen concentrations of ecosystem components across stand ages

N concentrations varied among ecosystem components and among the four studied stand ages (Table 3). Across all stands, leaves consistently had the highest N concentration, with little variation among stand ages. By contrast, N concentrations in stems, branches, fine roots, and coarse roots were significantly higher in the 11- and 47-year-old stands than in the 23- and 37-year-old stands. Understory vegetation and surface litter showed only minor variation in N concentration across stand ages, with clearer differences observed only for the herbaceous layer among certain age classes. Soil N concentrations showed limited variation among stand ages, and an apparent difference occurred mainly in the 10–20 cm layer, where the 11-year-old stand had a lower N concentration than the other stands.

Table 3.

Total N concentration (g·kg⁻¹) in different components of larch plantations across stand ages

Organs	Stand age (year)
	11	23	37	47
Tree
Tree stem	3.59 ± 0.22a	1.61 ± 0.18b	1.58 ± 0.15b	3.04 ± 0.21a
Tree branch	2.99 ± 0.04ab	1.18 ± 0.25c	1.97 ± 0.17bc	3.55 ± 0.38a
Tree leaf	21.10 ± 0.57a	23.47 ± 1.00a	21.58 ± 0.98a	24.35 ± 1.23a
Tree coarse root	5.85 ± 0.74a	3.81 ± 0.37ab	3.09 ± 0.42b	5.10 ± 0.08ab
Tree fine root	10.85 ± 0.42ab	8.37 ± 0.08c	9.18 ± 0.35bc	11.64 ± 0.60a
Shrub-herbs
Shrub	10.99 ± 0.31a	12.73 ± 1.16a	11.25 ± 0.85a	13.93 ± 0.44a
Herbs	12.91 ± 0.72b	17.85 ± 1.14a	16.30 ± 0.19ab	19.44 ± 0.80a
Forest floor litter	11.92 ± 0.73a	11.01 ± 0.56a	11.85 ± 0.34a	11.23 ± 1.33a
Soil
0–1 cm organic horizon	5.29 ± 0.03a	5.02 ± 0.33a	6.97 ± 0.25a	5.50 ± 0.81a
0–10 cm mineral	2.84 ± 0.31a	3.45 ± 0.61a	3.81 ± 0.17a	3.40 ± 0.15a
10–20 cm mineral	1.86 ± 0.11b	3.06 ± 0.34a	3.08 ± 0.12a	2.47 ± 0.03ab
20–30 cm mineral	2.41 ± 0.39a	2.10 ± 0.06a	3.64 ± 0.47a	3.03 ± 0.22a

Values are shown as mean ± standard deviation based on three independent stand-level replicates for each stand age. Different lowercase letters indicate significant differences among stand ages within the same component or soil layer at p < 0.05

Biomass, nitrogen stocks and nitrogen allocation across stand ages

Age-related differences were observed in the biomass and N stocks of vegetation components across the four stand ages (Fig. 2). Both biomass and N stocks in the tree layer increased continuously with stand development. Specifically, the proportion of tree-layer biomass in the total vegetation biomass pool increased from 58% in the 11-year-old stands to 92% in the 47-year-old stands, while the proportion of tree-layer N stocks increased from 1.51% at 11 years to 9.54% at 47 years. Tree-layer biomass peaked at 256.77 t·hm⁻² in the 37-year-old stands, whereas tree-layer N stocks reached a maximum of 0.94 t·hm⁻² in the 47-year-old stands. These results indicated that the tree layer became the dominant vegetation N pool during stand development (Table S1).

Fig. 2.

Biomass (t·ha−¹) (a) and N stocks (t·ha−¹) (b) of vegetation components in larch plantations across stand ages. Different lowercase letters indicate significant differences among stand ages within the same vegetation component. (p < 0.05)

Biomass and N stocks differed among organs in the tree, shrub, and herb layers (Fig. 3). In the tree layer, biomass and N stocks of all organs increased with stand age. Stems consistently accounted for the largest proportion of both biomass and N storage, whereas leaves contributed relatively little throughout stand development (Fig. 3a, d). In the shrub layer, organ biomass and N stocks decreased sharply from the 11-year-old to the 23-year-old stands, followed by only a slight recovery in older stands (Fig. 3b, e). In the herb layer, organ biomass and N stocks generally decreased with increasing stand age (Fig. 3c, f).

Fig. 3.

Biomass (t·ha−¹) (a, b, c) and N stocks (t·ha−¹) (d, e, f) of different organs in the tree, shrub, and herb layers of larch plantations across stand ages. Symbols indicate observed values. The coefficient of determination (R²) for each regression is shown within the corresponding panel. A: stand age; O: organ; A × O: interaction. * p < 0.05, ** p < 0.01, *** p < 0.001; ns, not significant

Nitrogen fluxes, nitrogen use efficiency, nitrogen cycling coefficient and soil nitrogen supply status

Annual N uptake and annual N retention tended to increase with stand age (Fig. 4). Annual N return was lowest in the 11-year-old stand and highest in the 47-year-old stand. ANRA showed a similar increasing tendency from 11 to 37 years and remained relatively stable thereafter. NUE increased from the 11-year to the 23-year stands and reached its maximum in the 37-year stand (Table S3). A similar pattern was observed for NCC, which increased from 11 to 37 years and decreased slightly at 47 years. These flux patterns indicate increasing reliance on internal N return and resorption during stand development.

Fig. 4.

Changes in annual N uptake (a), annual N retention (b), annual N return (c), and annual N resorption amount (d) across stand ages in larch plantations (all values are expressed in t·ha−¹). Different lowercase letters indicate significant differences among stand ages (p < 0.05)

Soil available N stocks exhibited a significant unimodal pattern across the stand-age gradient, whereas annual N uptake showed a comparable but statistically non-significant pattern (p > 0.05; Fig. 5). Across all stand ages, soil available N stocks were consistently higher than corresponding annual N uptake. The surplus of soil available N over annual N uptake was lowest at 11 years and peaked at 37 years (approximately 0.01 t·hm⁻²). As stand age further increased to 47 years, the surplus gradually declined. This comparison suggests no evidence of acute N shortage in the studied stands, although seasonal dynamics and atmospheric N inputs were not directly quantified.

Fig. 5.

Relationship between soil alkali-hydrolysable N stocks and annual N uptake across stand ages in larch plantations. *** p < 0.001

Nitrogen nutrition strategy indicators

Among the N acquisition indices, NUpE and the proportion of available nitrogen in soil increased along the stand-age gradient (Fig. 6a, c). By contrast, N enrichment in topsoil tended to decrease with stand age (Fig. 6b). For N cycling indices, accumulation of N in forest floor litter, turnover rate of forest floor litter, and the internal N cycling ratio all increased with stand age (Fig. 6d − f). These patterns indicate a shift from greater reliance on soil N acquisition toward stronger litter-mediated internal cycling during stand development.

Fig. 6.

Relationships between stand age and N nutritional strategy indices in larch plantations. Solid lines represent fitted regressions, and shaded areas indicate 95% confidence intervals. N uptake efficiency (a). N enrichment in topsoil (b). Proportion available N in soil (c). Accumulation of N in forest floor (d). Turnover time of forest floor litter (e). Internal N cycling (f)

Multivariate controls of nitrogen cycling

The pSEM was used as a descriptive path-analysis framework to summarize the associations among stand age, plant nutrient status, litter nutrient status, soil nutrient status, N acquisition, and N cycling across the four stand ages (Fig. 7). The model structure was not rejected by the data (Fisher’s C = 8.04, df = 8, p = 0.43; Tables S4-S5), indicating that the hypothesized structure was broadly consistent with the observed covariance patterns. Stand age showed a supported direct association with plant nutrient status and N cycling status. Plant nutrient status was associated with N acquisition, whereas litter nutrient status was associated with N cycling. Overall, the pSEM results suggest that N acquisition was mainly associated with plant nutrient status, while N cycling was more closely related to stand age and litter nutrient status. These relationships should be interpreted as statistically supported associations within the proposed conceptual framework rather than as definitive evidence of causal pathways.

Fig. 7.

Piecewise structural equation model (pSEM) summarizing associations among stand age, plant nutrient status, litter nutrient status, soil nutrient status, N acquisition, and N cycling in larch plantations. Solid arrows indicate statistically supported direct pathways (p < 0.05), whereas dashed arrows indicate hypothesized but non-significant pathways retained for transparency and not used for mechanistic interpretation. Numbers adjacent to arrows are standardized path coefficients. Gray and blue arrows indicate positive and negative associations, respectively. Model fit was assessed using Fisher's C statistic, tests of directed separation df, and AIC. Numbers adjacent to arrows indicate standardized path coefficients

Discussion

Stand development reshapes nitrogen allocation across ecosystem components

Stand development was associated with a systematic reorganization of N allocation among ecosystem components in the larch plantations. N stock in ecosystem plant components increased steadily across stand development and peaked at 47 years, which is consistent with reported stand-age patterns in N balance for larch plantations [41]. Meanwhile, the proportional contribution of the tree layer to total plant biomass and N stocks increased continuously with stand age. This pattern indicates that the tree layer progressively became the dominant and relatively stable long-term N pool during stand development. Similar shifts have been observed in temperate and boreal coniferous forests, where forest maturation is accompanied by nutrient transfer from rapidly cycling pools to structural and storage compartments [42].

At the organ scale, stage-dependent differentiation in N accumulation was evident. Leaf tissues consistently maintained the highest N concentrations, reflecting their central role in photosynthesis and N metabolism, particularly during early development when carbon acquisition and growth are rapidly initiated [20]. Although N accumulation increased with stand age in most organs, its trajectory was not fully synchronized with structural growth, suggesting stage-dependent adjustments in resource allocation. During early development, N was preferentially allocated to functional organs to sustain rapid growth, resulting in relative N dilution in woody tissues [43]. As stands approached mid and late development, growth rates declined after structural construction had largely been completed, and greater N allocation to woody tissues and long-term storage pools was indicated [44]. This pattern is consistent with a transition from a resource-acquisitive phase to a more conservative phase in which internal recycling is strengthened. Comparable shifts have been reported in other temperate coniferous forests [45] and may be facilitated by increased litter inputs, stabilization of soil N supply, and improved N resorption and redistribution in plants [46].

Although the shrub-herb layer contributed only a small fraction of total ecosystem biomass and N stocks, its marked decline after the young stage likely reflected progressive light limitation associated with canopy closure [47]. Understory vegetation often exhibits relatively high N concentrations and rapid turnover, and may therefore function as a catalytic component of ecosystem N cycling despite its small biomass [48]. In particular, during early to middle development, understory turnover may contribute disproportionately to nutrient fluxes and productivity, and its ecological role should not be inferred solely from pool magnitude [49].

Beyond vegetation, distinct yet complementary roles were indicated for litter and soil in shaping ecosystem N dynamics. Surface litter, characterized by relatively high N concentration and rapid accumulation, formed an important transient N pool during the middle stage (23 years), likely reflecting a temporary imbalance between litter inputs and decomposition. From an ecosystem development perspective, a shift from rapid nutrient accumulation to more efficient recycling is widely regarded as a hallmark of forest maturation, and higher NUE has frequently been observed in mature stands [50]. Throughout stand development, soil remained the overwhelmingly dominant N pool (> 87% of total ecosystem N), underscoring the foundational role of soil organic matter in sustaining long-term productivity and nutrient stability in plantations [51].

Nitrogen supply-demand balance and age-dependent nitrogen use efficiency in larch plantations

Across the stand-age gradient, soil available N stocks consistently exceeded annual plant N uptake, and the supply-demand surplus was greatest at the mid-aged stage (37 years). These patterns suggest that there was no evidence of acute N shortage based on the comparison between soil available-N stock and annual plant uptake. However, this result should not be interpreted as definitive evidence for the complete absence of N limitation, because seasonal soil N dynamics, atmospheric N deposition, and microbial N immobilization were not directly quantified in this study. Although plant N demand increased with stand age, the large soil organic N pool (> 87% of total ecosystem N), together with sustained litter inputs, internal soil N transformations, and potential atmospheric N inputs, may have contributed to maintaining a relatively stable N supply and reducing persistent supply-demand imbalance [50]. Further studies incorporating repeated seasonal measurements and direct N deposition monitoring are needed to verify these supply–demand dynamics.

Against this background, NUE and NCC both exhibited unimodal patterns, with maxima at 37 years, indicating that the mid-aged stage represented the most efficient phase of N utilization. At this stage, enhanced N resorption and internal cycling may have increased N reuse, thereby reducing reliance on continued soil N acquisition and strengthening the coupling between growth demand and nutrient recycling [52, 53]. The co-occurrence of high N-use efficiency and relatively sufficient available N supply may partly explain the apparent nutrient balance observed in the 37-year-old stands. In the near-mature stage (47 years), NUE was moderately reduced, potentially because growth was slowed, maintenance respiration was increased, and competition for nutrients between plants and soil microorganisms was intensified [54]. Similar age-related declines in NUE have been reported for coniferous forests (e.g., Scots pine and Norway spruce) [55], consistent with physiological aging and diminishing marginal returns of nutrient use in mature trees [56].

The NCC was increased from the young (11 years) to mid-aged (37 years) stands, indicating that an increasingly large fraction of acquired N was returned to soil via litter inputs, thereby strengthening ecosystem N retention and internal recycling. A slight decline from 37 to 47 years was observed, potentially reflecting stagnation in litter production or reduced litter quality. However, NCC remained relatively high, indicating that cycling efficiency was largely maintained in the mature stand [53]. Overall, the larch plantations showed an N-use pattern characterized by relatively high available-N supply and efficient internal cycling. Therefore, our results are best interpreted as showing no evidence of acute N shortage based on the available-N stock versus annual plant uptake comparison, rather than as definitive proof that the ecosystem was free from N limitation under all temporal or environmental conditions [57] .

Shifts in nitrogen nutrition strategies along stand development

Stage-dependent changes in N fluxes and strategy indices indicated a clear shift in N utilization during stand development. NUE, ANRA, and NCC all peaked at or near the mid-aged stage [53], suggesting that N was used most efficiently when growth demand, soil available N supply, and internal recycling were likely most closely coordinated [31, 58].

At the acquisition level, NUpE showed a positive but weak trend with stand age, implying that intrinsic plant N acquisition capacity was not substantially altered over development. Because NUpE is jointly regulated by root morphology, mycorrhizal associations, and physiological uptake capacity [54, 59], the limited increase suggests that stronger acquisition was not the main adjustment pathway. Consistently, the proportion of available nitrogen in soil increased with stand age, indicating enhanced N availability during forest development [60]. This increase was likely promoted by greater litter inputs and accelerated mineralization, which would have stabilized external N supply and reduced reliance on improved uptake efficiency. The slight decline in the N enrichment in topsoil may have been driven by deeper rooting and sustained nutrient uptake [61, 62].

In contrast, N cycling strategies were adjusted more strongly than acquisition traits. With increasing stand age, both forest floor N accumulation and litter turnover rate increased significantly, indicating an intensification of litter-mediated recycling. In mid-aged and mature stands, faster litter decomposition may promote more rapid N release and re-entry into plant uptake pathways, thereby tightening internal cycling [50]. Such litter-driven enhancement of N cycling is considered a key indicator of ecosystem maturation and reflects improved self-maintenance and nutrient retention capacity [63]. Overall, the studied larch plantations exhibited a progressive shift from acquisition-oriented to recycling-oriented N strategies, highlighting an age-related reorganization of N cycling pathways.

Direct associations related to nitrogen cycling identified by piecewise structural equation modeling

The pSEM provided a descriptive synthesis of the multivariate associations among stand age, plant nutrient status, litter nutrient status, soil nutrient status, N acquisition, and N cycling. Because several pathways in the model were weak or non-significant, we restricted our interpretation to statistically supported direct relationships and avoided assigning mechanistic meaning to unsupported paths. The supported pathways indicated that stand age was directly associated with plant nutrient status and N cycling status, suggesting that stand development was accompanied by coordinated changes in vegetation N accumulation and internal cycling processes. Plant nutrient status was directly associated with N acquisition, implying that variation in N uptake-related processes was more closely linked to plant nutrient demand than to stand age alone [64]. In addition, litter nutrient status was directly associated with N cycling, which is consistent with the observed increase in litter-mediated N return and internal cycling indices during stand development [65–67].

Importantly, the pSEM results should not be interpreted as definitive evidence for complete indirect causal pathways linking stand age to N cycling through plant, litter, or soil nutrient status. Rather, they indicate that age-related changes in N cycling were statistically consistent with a limited set of direct associations involving stand development, plant nutrient status, and litter-mediated cycling. Although the study included independent stand-level replicates within each age class, the chronosequence design remains observational. Therefore, these relationships should be regarded as hypothesis-generating patterns that require further confirmation through long-term monitoring, replicated chronosequence studies across broader site conditions, or experimental manipulation of litter inputs and soil N availability.

Our pSEM analysis focused on internal ecosystem pathways associated with stand age and plant-litter-soil interactions, but did not include atmospheric N deposition as an external N input. The relatively stable N supply-demand balance observed here may therefore reflect both enhanced internal recycling and potential external N inputs from atmospheric deposition. Future studies should quantify the relative contributions of internal recycling and atmospheric N deposition to better evaluate the mechanisms maintaining N availability during stand development.

Limitations and future directions

Several limitations should be acknowledged. First, atmospheric nitrogen deposition was not directly measured in this study, although it represents an important external input to forest N budgets. Northern China is recognized as a region with relatively high atmospheric N deposition [68]. Therefore, regional N deposition should be considered when interpreting the relatively high soil available N stocks observed in this study. Although direct deposition measurements were not conducted, the consistently higher soil available N stocks compared with annual plant N uptake across all stand ages may be partially attributable to atmospheric N deposition, in addition to internal cycling processes. This external input may have contributed to maintaining relatively high soil N availability and reducing the likelihood of acute N shortage during stand development.

Second, soil available N was measured only once during the peak growing season. This sampling strategy provided a snapshot of soil N availability during a period of relatively high plant N demand and microbial activity, but did not capture seasonal variation in N availability, mineralization, immobilization, or plant uptake. As a result, the comparison between soil available N stocks and annual plant N uptake should be interpreted as a peak-season assessment rather than a complete annual evaluation of ecosystem N balance.

Third, the pSEM should be interpreted cautiously. The model was used as a descriptive path-analysis framework to summarize associations among stand age, plant nutrient status, litter nutrient status, soil nutrient status, N acquisition, and N cycling. Although the model structure was not rejected by the data, as indicated by Fisher’s C, this result does not confirm causality or generalizability. Because the study was observational rather than experimental, the pSEM results should be interpreted as statistically supported associations within the proposed conceptual framework, rather than as definitive causal pathways [69].

Future studies should incorporate direct measurements of atmospheric N deposition, repeated seasonal assessments of soil N dynamics, microbial N transformation processes, and replicated chronosequence or long-term monitoring approaches across broader environmental gradients. Such efforts would improve understanding of the relative contributions of external N inputs and internal N cycling to ecosystem N budgets and help clarify the mechanisms regulating N availability during stand development in larch plantations.

Conclusions

Across an 11–47-year chronosequence of larch plantations, stand development was associated with substantial increases in vegetation biomass and N stocks, while soil remained the dominant ecosystem N pool (> 87%). Soil alkali-hydrolysable N stocks consistently exceeded annual plant N uptake, indicating no evidence of acute N shortage based on the comparison between available-N stocks and annual uptake. However, this result should not be interpreted as definitive evidence for the complete absence of N limitation under all temporal or environmental conditions. Indices describing N-use characteristics indicated a shift from relatively greater reliance on N acquisition in younger stands toward stronger internal N recycling in older stands, while nitrogen use efficiency reached its highest observed value in the 37-year-old stand. Variation in N acquisition was more strongly associated with plant nutrient status, whereas variation in N cycling was more closely associated with stand age and litter nutrient status. Given the chronosequence design and an observational modeling framework, these findings should be interpreted as age-related patterns and statistical associations rather than confirmed developmental trajectories or causal mechanisms.

Abbreviations

ANRA

Annual nitrogen resorption amount

NUE

Nitrogen use efficiency

NCC

Nitrogen cycling coefficient

NUpE

Nitrogen uptake efficiency

SANP

Proportion available nitrogen in soil

FFNA

Accumulation of nitrogen in forest floor

LTR

Turnover rate of forest floor litter

INC

Internal nitrogen cycling

Authors’ contributions

X.T.C., original draft, visualization, methodology, investigation, and data curation; Z.X.G., review and editing, conceptualization, and visualization; Q.L., performed investigation and data curation; Y.P., investigation and conceptualization; S.P.P., N.R., and P.N.P., consulting, supervision, reviewed and edited final draft, Z.D.Z., review and editing, project administration, funding acquisition, and validation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the State Key Research and Development Program (2023YFD2200803), and Major Science and Technology Support Program of Hebei Province (252L6802D, 252L6801D).

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable. This study did not involve human participants or animals.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Research involving plants

All experimental and field studies involving plants in this work complied with relevant institutional, national, and international guidelines and legislation in China.