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Article

Patterns of Intra-Order Variation in Shoot Traits Are Order-Specific Along the Branch Basal Height Gradient of Larix principis-rupprechtii

by
Yang Yu
1,
Huayong Zhang
1,2,*,
Zhongyu Wang
1 and
Zhao Liu
2
1
Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China
2
Theoretical Ecology and Engineering Ecology Research Group, School of Life Sciences, Shandong University, Qingdao 250100, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(6), 1016; https://doi.org/10.3390/f16061016
Submission received: 10 April 2025 / Revised: 17 May 2025 / Accepted: 3 June 2025 / Published: 17 June 2025
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Intra-order trait variation is a key driver of aboveground shoot performance at different branch basal heights. Although the basic light exposure and nutrient supply to shoots vary with branch basal height, most studies have focused on inter-order variation in shoot traits. However, how and to what extent shoot traits change with branch basal height, as well as whether a general intra-order pattern exists among different shoot orders, remain largely unclear. We compared intra-order variation in shoot diameter, length, specific stem length (SSL), and stem tissue density (STD) across four branching orders of Larix principis-rupprechtii along a vertical height gradient of 5.5–6.0 m. We tested (a) the degree of intra-order versus intra-order variation in shoot traits along the gradient and (b) whether intra-order trait patterns and their relationship with branch basal height were consistent across the four branching orders. Specifically, we hypothesized that within a branching order, shoot traits would undergo adjustments: shoots at higher positions would focus on growth (by increasing diameter and length), whereas shoots at lower positions would enhance resource acquisition (by increasing SSL) and protection (by increasing STD). Branching order explained most of the overall variation in shoot traits, including shoot diameter and length, but accounted for only a small portion of the variation in SSL and STD. Branch basal height explained only a small fraction of intra-order shoot trait variation, which was larger within than between basal heights. Moreover, the relationships between traits and branch basal height rarely aligned with our hypotheses and varied considerably across different shoot orders. Along the complex branch basal height gradient, where multiple traits change simultaneously, shoots of different shoot orders exhibit distinct patterns of variation, leading to specific intra-order trait variation. The lack of support for our hypothesis may result from the multifaceted interactions between light availability, spatial constraints, nutrient heterogeneity, and dynamic branch-order interactions. Our findings suggest that to better understand the impact of environmental variation on shoot performance, future research should integrate a more comprehensive analysis of shoot responses to change and measure a broader range of shoot traits and environmental variables.

1. Introduction

As the vertical height of woody plants increases, environmental factors such as wind speed, humidity, carbon dioxide concentration, and light intensity exhibit gradient changes. Even small-scale vertical changes in tree structure can lead to significant variations in the availability of nutrients and light, with light conditions having the most pronounced impact. Typically, ambient light increases with tree height [1,2,3,4]. Previous findings suggested that when studying the mechanisms of canopy development, it is essential to consider both light conditions and the structural position of branches [5,6]. Therefore, the vertical height gradient in woody plants is considered a highly valuable framework for studying plant responses to environmental changes.
As the most dynamic terminal branches in the aboveground branching system of woody plants, shoots serve as the fundamental structural units of trees, performing multiple functions such as mechanical support, transportation, storage, and reproduction. Costes’s research found that the structural position of branches significantly influences shoot growth and branching patterns: shoot length varies with branching order and the vertical position of shoots within the canopy [7]. Gaaliche also discovered that the growth and reproductive patterns of aboveground shoots are closely linked to branching order—the lower-order shoots would exhibit greater vegetative growth but less flowering, whereas higher-order shoots have limited growth but more prolific flowering [8]. These findings suggest that when studying the development of tree canopy structure, it is essential to consider not only individual branches but also their structural positions. Terminal branches should not be treated as functionally equivalent modular units; instead, they should be classified according to branching order.
Functional trait variation is the result of the interaction between vegetation and the environment during evolution [9]. It serves as a crucial window for understanding plant responses to environmental gradients and is a core issue in contemporary ecological research [10]. Plant trait variation can be divided into interspecific variation and intraspecific variation [11]. While many ecological theoretical frameworks are based on estimates and comparisons of interspecific mean values, intraspecific trait variation (ITV) is equally important [12]. ITV refers to trait differences among individuals or organs within the same species and represents a mechanism through which plants respond to local spatial resource heterogeneity [12,13,14]. It is also closely related to environmental gradients within a species’ distribution range. For species with broad geographic distributions [15,16,17], greater ITV indicates stronger adaptive capacity to environmental changes [9,14,17]. Therefore, determining the extent and patterns of ITV is essential for understanding key physiological and ecological processes across different environments. However, most studies on ITV have focused on root and leaf traits [13,18,19], while research on trait variation in the aboveground branching systems of woody plants remains limited. This study aims to explore whether a general pattern of intraspecific trait variation exists among branches of different orders along a branch basal height gradient.
Shoot traits may play a crucial role in determining how individual plants and species respond to environmental gradients [20,21]. Shoot length is considered essential for resource acquisition, particularly in aboveground competition [22]. Shoot dry mass reflects the photosynthetic investment required to generate shoot length. Specific stem length (SSL) is regarded as the aboveground counterpart to specific root length and is a core trait in the economics of aboveground plant function. It indicates the potential for leaf development (and thus photosynthesis) relative to biomass investment [23]. Stem tissue density (STD) is a key functional trait in woody plant species, as it is linked to important ecological characteristics such as mechanical stability, hydraulic conductivity, and life-history strategies [4,24,25,26,27]. It has been proposed as an integrator of wood economics and a key axis of plant functional strategies [28,29].
Along the complex vertical height gradient of trees, changes in shoot traits may either follow a consistent pattern or exhibit spatial separation, with different influences occurring at different height levels. For instance, cold, dry conditions with low carbon dioxide concentrations and abundant light are typically found in the upper canopy, whereas warm, humid conditions with high carbon dioxide concentrations and limited light characterize the lower canopy. As a result, in the lower, light- and nutrient-limited regions of the tree, shoot SSL may be relatively high, while in the higher, colder, and low-CO2 regions, shoot SSL may also be elevated, potentially leading to a U-shaped pattern of SSL variation along the height gradient. Nonlinear patterns along elevation gradients have been observed in various traits, such as root turnover rate [30], nutrient concentration [31], and arbuscular mycorrhizal colonization [32]. Previous studies on leaf traits along elevation gradients and root traits along climate and precipitation gradients suggest that inconsistent trait variation patterns indicate that different traits may respond to environmental factors in distinct ways [33,34]. Similarly, it is necessary to examine whether the type and intensity of trait variation within branching orders along the branch base height gradient also depend on specific functional traits within each order.
As previous studies have shown, root diameter and tissue density negatively affect specific root length (SRL) [35]. Mathematically, we hypothesize that specific stem length (SSL), stem tissue density (STD), and shoot diameter are not entirely independent of each other. Moreover, if different branching orders produce shoots with various combinations of SSL, diameter, and tissue density, this suggests that multiple shoot trait strategies can coexist, allowing plants to adjust their resource acquisition strategies. This is similar to the concept of the root economics spectrum [36,37,38]. Furthermore, in low-resource environments, plants are likely to adopt both acquisitive and conservative strategies simultaneously. That is, they may enhance resource uptake by increasing SSL, shoot length, or biomass allocation while also adopting conservative strategies, such as increasing STD and extending shoot lifespan, to regulate resource absorption and protection.
This research seeks to enhance our knowledge of within-order variation in shoot traits. We examined whether there were general trends in intra-order shoot trait variation along a 5.5–6.0 m basal height gradient in Larix principis-rupprechtii (Figure 1). To this end, we measured and calculated shoot diameter, length, specific stem length (SSL), and stem tissue density (STD) of four different branching orders. We tackle three major problems: (a) How much does intra-order trait variation explain the total (i.e., within- and between-order) variation in shoot traits along the height gradient?; (b) Are patterns of intra-order shoot trait variation along a basal height gradient uniform across shoot orders?; and (c) Does shoot trait covariation between shoot diameter, SSL, and STD account for the expected intra-order shoot trait relationships with basal height? Question 1 focuses on the extent of intra-order variation, and we assumed (Hypothesis 1) that intra-order shoot trait variation would be substantial given ambient variation along the basal height gradient but still less than inter-order shoot trait variation. Question 2 concentrates on the nature of trait patterns along the basal height gradient, and we hypothesized (Hypothesis 2) that branch order would show different relationships between shoot traits and basal height because of different ecological strategies for acquiring and maintaining aboveground resources. Concerning our third question, we hypothesized (Hypothesis 3) that with increasing basal height (a), shoot diameter and length will increase to gain light and apical dominance more efficiently, and (b) SSL will decrease as plants allocate more resources to the upper canopy, and STD will also decrease to have a higher conduit fraction, which could result in a higher rate of transpiration, photosynthesis, and biomass growth.

2. Materials and Methods

2.1. Study Species

As a dominant tree species in cold-temperate coniferous forests, Larix principis-rupprechtii is mainly distributed in northern China [39]. The Larix principis-rupprechtii is a tall tree with dark gray–brown bark that is irregularly longitudinally cracked and sheds in small pieces. The branches are flat and have irregular fine teeth. The bracts are dark purple, nearly ribbon-shaped and oval, 0.8 to 1.2 cm long, wide at the base, slightly narrower in the middle, and rounded at the tip. The central rib extends into a tail-like pointed tip, with only the tip of the bract at the base of the cone exposed. The seeds are obliquely inverted oval, gray–white with irregular brown spots, 3 to 4 mm long, and about 2 mm in diameter. The upper part of the seed wing is triangular, with the middle about 4 mm wide, and the seed and wing together are 1 to 1.2 cm long. The cotyledons are 5 to 7 in number, needle-shaped, about 1 cm long, with no stomatal lines on the underside. The flowering period is from April to May, and the cones mature in October (Figure 1). Due to its high timber value and strong cold resistance, it is commonly used for afforestation in northern China. Therefore, studying the growth and development mechanisms of shoots in Larix principis-rupprechtii plantations is of great significance for improving forest structure, enhancing forest productivity, and maximizing multiple ecological and social benefits.
The research subject of the plantation is the succession of stand structure and long-term changes in ecosystem services, focusing on 30-year-old mature or overmature larch forests. In open areas with ample, unobstructed light, the trees selected should be free from obvious pests and diseases, mechanical damage, or severe malnutrition. At the same time, trees with similar diameters at breast height and height should be chosen as much as possible to minimize the impact of growth differences.

2.2. Study Area

The study was carried out in a plantation forest dominated by Larix principis-rupprechtii at the 2022 Winter Olympic core zone (40°57′ to 40°59′ N, 115°26′ to 115°27′ E), approx. 24 km northeast of the Chongli District, Zhangjiakou City, Hebei Province, North China. The soil types in the study area include mountain brown soil, meadow brown soil, low mountain cinnamon soil, sandy black soil, and herbaceous brown soil. Brown soils are mainly distributed on the shady slopes of mountainous forestlands, with relatively high organic matter and nutrient content, and a pH value of 6.5–7.0. The forest floor soil in mountainous areas generally has a moderately thick layer and contains gravel and stones. Cinnamon soils are mainly found in relatively flat, rocky low mountains, located in the lower part of the brown soil zone. These soils have thin layers, lower organic matter content, and a pH value of 6.5–7.5.
The study area falls under the East Asian continental monsoon climate. Winters are cold with low precipitation, frequent cold air activity, and strong winds. Summers are warm, with rapid temperature increases and frequent heavy rainfall events. The average winter temperature is −12 °C, the average summer temperature is 18.4 °C, and the annual average temperature is 7.5 °C. The extreme maximum temperature reaches 42 °C, while the extreme minimum temperature drops to −34.7 °C. The maximum wind speed is 20 m/s. The area experiences early snowfall, thick snow accumulation, and a long snow retention period, with total winter snow accumulation reaching about 1 m. The average frost-free period is over 150 days. The multi-year average precipitation is around 426 mm, with highly uneven temporal distribution. Most rainfall occurs from June to September, accounting for about 80% of the annual total, and is often accompanied by localized rainstorms and hail. Interannual precipitation variation is significant, with alternating years of excessive and insufficient rainfall (Figure 2).

2.3. Sample Collection

In early August 2019, coinciding with the peak of the plant growing season, conditions were suitable for conducting research on the functional traits of terminal shoots in the aboveground branching system of Larix principis-rupprechtii. Within the experimental forest, a 20 × 20 m plot was established, and we randomly selected three larch saplings with a height of 5.5–6.0 m at the sampling site. Starting from the ground, the stem of each sampled tree was divided into sections of 50 cm or 100 cm. Branching systems were selected from each vertical section, except for the 0–50 cm section, which had no distinct branching system. A total of 29 branching systems were selected (10, 10, and 9 from each tree, respectively). To analyze the relationship between bud growth patterns and branching order, we determined the sequence number of each terminal shoot from the branching structure diagram (centrifugal ranking). In this study, a two-dimensional diagram was drawn to illustrate the sampled branching structure of each tree (Figure 3). The main trunk was designated as the 0th-order axis; branches emerging directly from the trunk were classified as 1st-order axes; branches growing on 1st-order axes were classified as 2nd-order axes, and so on [7]. Here, “axis” refers to each linear sequence of shoots within the same branching order. The term “branching system” refers to the set of lateral axes on a main axis, meaning that a branching system consists of a set of axes formed by a 1st-order axis and all its lateral 2nd-, 3rd-, and 4th-order axes. Importantly, terminal branches were defined as the unbranched terminal segments of each branch, corresponding to 1st-order branches in a centripetal ranking system, similar to the Strahler system [40].

2.4. Shoot Trait Measurements

We collected more than twenty intact shoots for morphological measurements. And the number of shoots at each branching order within each height gradient was recorded in Table 1. Here, we primarily measured the most distal shoots, referred to as first-order shoots [6]. The diameter of the first-order shoot was measured using a vernier caliper. For shoot length, a vernier caliper was used for relatively short segments, while a measuring tape was used for relatively long segments. All shoot segments were dried at 65 °C for 48 h and then weighed (±0.01 g). Assuming that the shoots were cylindrical, stem tissue density (STD) was calculated as the ratio of stem dry weight to volume, and the specific stem length (SSL) was calculated as shoot length divided by dry weight [41,42,43,44,45].

2.5. Data Analysis

We partitioned the variation in shoot traits among different biological hierarchical (nested) levels of individual shoots to determine the extent to which branching order and basal height explain the overall variation in shoot traits. Specifically, we quantified the percentage of trait variation explained by differences between branching orders, intra-order basal height differences (i.e., intra-order trait variation along the height gradient), and within-height trait variation (i.e., intra-order variation at the same height).
A linear mixed model was used to partition the variance of shoot traits, with a given trait as the response variable and only random effects included: “height” (i.e., same-order shoots at different heights) nested within “order.” The remaining variance was attributed to trait differences among shoots of the same order growing at the same basal height. Additionally, linear regression models, with traits as the dependent variable and basal height as the independent variable, were used to examine the relationship between shoot traits and basal height (research question 3) and to test our trait order hypothesis (Hypothesis 2). A linear or quadratic polynomial model was applied to each trait and branching order based on the Akaike Information Criterion (AIC). We also tested within-order relationships among diameter, SSL, and STD, as these traits are not entirely independent. Among branching orders, within orders, and between each trait pair, we applied linear, quadratic polynomial, and exponential models, selecting the best-fitting model based on AIC before testing these trait relationships. All statistical analyses were conducted using R v.4.1.3 (R Core Team, 2022).

3. Results

3.1. Proportion of Intra-Order Variation to Total Shoot Trait Variation

The variation in diameter, length, SSL, and STD of all shoots is mainly due to shoot order characteristics (20%–70%; Figure 4). Overall (i.e., across all shoots), intra-order trait variation contributes 30%–80% of this variation, with the majority (27%–62% of total variation) explained by differences within the same order at the same basal height of the shoot (i.e., ITVwithin in Figure 4), while only a small portion (3%–19%) is attributed to differences within the same order along the basal height gradient of the shoot (i.e., ITVbetween in Figure 4). Basal height (61%) and intra-order trait differences (20%) contribute relatively equally to the variation in stem tissue density, while intra-order trait differences along the basal height gradient explain 19% of its variation.

3.2. Intra-Order Relationships Between Shoot Traits and Basal Height

Diameter varied significantly with branch basal height within four shoot orders (Figure 5; Table 2), and these variations were linear and positive within the fourth order, U-shaped within the second and third shoot orders, and bell-shaped within the first shoot order (Figure 5; Table 2). Basal height explained 31% of the variation in diameter within the first shoot order and explained 5%–10% of the variation in diameter across the remaining three shoot orders.
Three shoot orders showed a significant relationship between length and branch basal height (Figure 5; Table 2). The second order had a U-shaped pattern, the third order with a bell-shaped pattern, and a linear and positive pattern within the fourth order with increasing basal height (Figure 5; Table 2). Branch basal height explained 4%–11% of the variation in SL, depending on the shoot orders.
SSL was significantly related to branch basal height within four shoot orders (Figure 5; Table 2). The first shoot order showed a U-shaped correlation between SSL and basal height, and the three other shoot orders showed a bell-shaped pattern with increasing basal height. Branch basal height explained 47% of the variation within the first shoot order (Figure 5; Table 2). Branch basal height explained 1%–6% of the intra-order variation in SSL depending on the other three shoot orders.
Variation in STD was significantly related to branch basal height within three shoot orders (Figure 5; Table 2): within the third and fourth shoot orders, STD declined, and in the rest of the second shoot order, bell-shaped patterns emerged with increasing basal height (Figure 5; Table 2). Branch basal height explained 4%–12% of the within-order variation in STD, depending on shoot order.

3.3. Bivariate Relationship Across Shoot Orders

When all order shoots were combined, SSL and shoot diameter, as well as STD and shoot diameter, were highly significant and followed a U-shaped relationship (Figure 6). The relationship between SSL and STD was significant and best characterized by a bell-shaped pattern (Figure 6).

4. Discussion

4.1. Small Intra-Order Variance in Shoot Traits by Branch Basal Height

Despite considerable intra-order variation in shoot traits across the four branching orders in our study, this variation is primarily due to differences between orders at a given branch base height and not from variation along the 5.5–6.0 m branch base height gradient. This result suggests that most of the variation in shoot traits is accounted for at small scales (i.e., positions with similar light and nutrient conditions within the gradient) instead of at large scales (i.e., the approximately 6.0 m height gradient). Similarly, Defrenne et al. [46] discovered that their narrowest sampling scale—the single root branch and the soil plot from which the root system was collected—explained 100% of the variation in Douglas-fir root traits across a 600 km biogeographical gradient in Canada. The substantial heterogeneity in light and nutrient availability at fine spatial scales may cause shoots to respond to highly localized resource availability, for example, by generating greater SSL under light and nutrient stress. This ability to respond strongly to fine-scale environmental changes may be particularly advantageous for plants competing with others under resource pressure. These results highlight the need for a more detailed characterization of the microenvironment of the sampled shoots, such as measuring resource availability, wind speed, humidity, CO2 concentration, and relative photon flux density (PFD), all of which influence intra-order variation in shoot traits. The substantial intra-order variation in shoot traits at a given branch basal height further suggests that caution is required when evaluating mean trait data at the branch level, taking into account small-scale environmental conditions.
Consistent with our first hypothesis, branching order characteristics were the optimal predictors of shoot trait variation, explaining 20% to 70% of the total variation in shoot traits. The coefficient of variation (CV) for diameter and length between branching orders was generally higher than within branching orders, which contrasted with the patterns observed for SSL and STD. Therefore, compared to intra-order shoot trait variation, shoot trait variation at the individual level may be more driven by branching order succession. However, since branching orders were not predominant in vegetation cover at all base heights in our study, we were unable to test this potential outcome. Although light and nutrient availability vary significantly along the branch base height gradient, height explained only a small portion (3% to 19%) of the overall variation in shoot traits. Meanwhile, since regression models for individual branching orders (Figure 5) allowed for testing nonlinear models, whereas the ANOVA conducted on all shoots across all branching orders (Figure 4) assumed only linear relationships, height was able to explain 5% to 24% of the intra-order variation in shoot traits. Depending on the branching order and traits of interest, it is necessary to consider the nonlinear changes in trait relationships and the complex base height gradient to determine the extent to which individual branching orders respond to environmental changes.

4.2. Idiosyncratic Shoot Trait Patterns Along a Complex Branch Basal Height Gradient

As is expected (Hypothesis 2), we found little evidence for a general pattern of shoots traits varying with branch base height within the four branching orders. Instead, we observed linear (both positive and negative), non-linear (bell-shaped and U-shaped), or no significant relationships between shoot traits and branch base height. We further hypothesized that shoot diameter and length would increase with base height, as this would facilitate rapid upward growth to capture light, while SSL and STD would show the opposite pattern. However, shoot diameter increased in only one branching order, length, and STD increased in two orders, and SSL did not increase in any order. These different trait patterns may be attributed to different branching orders, just as previous studies have also observed specific patterns of trait variation in leaves and roots across different geographical gradients [33,34,47,48], suggesting that order-specific trait patterns may be a pervasive phenomenon. As the degree and pattern of within-order trait variation may be influenced by hierarchical investment, apical dominance, and spatial availability at the branch level [49], these factors should be considered in future studies.
The intricacy of the basal height gradient may, to some extent, explain why trait variation patterns exhibit rank-specific characteristics. Plants are expected to possess a set of optimized traits to enhance the absorption of the most limiting resources [41,43,44] and generally exhibit adaptive responses to environmental stresses [42]. Along basal height and other natural gradients, the most limiting factors and major stresses vary [50], and different plant ranks may perceive these conditions differently [51,52], leading to differences in trait responses between ranks [41]. For example, compared to herbaceous species, light poses a greater functional constraint on trees, as evidenced by the presence of treelines in high-altitude regions worldwide [53]. Below the treelines, however, light may be a significant limiting factor for herbaceous species, but less so for trees. Although we were unable to test multivariate regression models, the multivariate nature of the altitude gradient is likely one of the reasons for the nonlinear variation in bud traits with increasing altitude. Therefore, differences in resource demands among ranks, variations in resource limitations along the gradient, and the presence of alternative strategies for responding to these changes may partially explain the rank-specific (linear vs. nonlinear) relationships of shoot traits along the basal height gradient.

4.3. Trait Coordination

The covariation of shoot traits, predicted by mathematical functions among SSL, shoot diameter, and STD, does not clearly explain the observed relationships between within-order shoot traits and basal height variation. The negative trait interrelationships suggest that trait patterns along the basal height gradient are opposite; however, we only observed this pattern within the first order (shoot tissue density–diameter: fourth order) or the third order (SSL–diameter: first, second, and third orders; SSL–STD: none; Figure 6; Table 3). Different environmental factors may influence the constituent traits (i.e., STD and shoot diameter) in different ways, leading to varying effects on their composite trait (i.e., SSL). This could explain why changes in SSL along the basal height gradient are generally not well coordinated with changes in its constituent traits. Uncovering these mechanisms requires more detailed studies on shoots, such as examining their anatomical characteristics (e.g., cortex and stem thickness), which underlie variations in shoot diameter, and SSL but are influenced by different environmental variables. Our findings on within-order shoot trait variation are at least partially consistent with previous observations across orders—namely, that multidimensional trait space allows for multiple synonymous strategies in response to environmental drivers rather than adhering strictly to a single resource acquisition or conservation strategy.

5. Conclusions

This study illustrates the extent to which patterns of intra-order shoot trait variation exhibit order specificity along a complex basal height gradient. It further suggests that, in similar environments, trait adjustments at different orders may confer equivalent adaptive benefits (e.g., through investment in resource acquisition and conservation or by adopting different resource acquisition strategies) to overcome specific environmental constraints. Finally, we emphasize that intra-order shoot trait variation may be more dependent on small-scale heterogeneity rather than large-scale environmental variation. In summary, our study highlights that future research on complex basal height gradients may require a more integrative approach to investigating and understanding shoot responses, targeting a broader set of relevant traits and environmental descriptors, and considering nonlinear relationships to enhance our understanding of the mechanisms underlying how shoots adapt to multiple coexisting environmental variations.

Author Contributions

H.Z. designed experiments, determined the article framework and research methods, and wrote the paper. Y.Y. completed experiment sampling, performed data analysis, and wrote the paper. Z.W. and Z.L. contributed to the research and writing of the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Major Science and Technology Program for Water Pollution Control and Treatment (No. 2017ZX07101-002) and the Discipline Construction Program of Huayong Zhang, at the School of Life Sciences, Shandong University (61200082363001).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author or the first author upon reasonable request.

Acknowledgments

The authors would like to acknowledge with great gratitude the support of the National Major Science and Technology Program for Water Pollution Control and Treatment and the Discipline Construction Program of Huayong Zhang, Shandong University, School of Life Sciences.

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.

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Forests 16 01016 g001
Figure 1. Photograph of the subject—Larix principis-rupprechtii.
Figure 1. Photograph of the subject—Larix principis-rupprechtii.
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Figure 2. Location of the study area and sample site (red diamond-shaped markers). Different colors represent different altitudes.
Figure 2. Location of the study area and sample site (red diamond-shaped markers). Different colors represent different altitudes.
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Figure 3. Diagrams of the branching structure of Larix principis-rupprechtii and branch ordering in this study. The circles represent the whorls of the main stem.
Figure 3. Diagrams of the branching structure of Larix principis-rupprechtii and branch ordering in this study. The circles represent the whorls of the main stem.
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Figure 4. The percentage of total trait variance explained by different branching orders and intra-order trait variation between basal heights (ITVbetween) and within basal heights (ITVwithin).
Figure 4. The percentage of total trait variance explained by different branching orders and intra-order trait variation between basal heights (ITVbetween) and within basal heights (ITVwithin).
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Figure 5. Measured variation in shoot traits across the basal height gradient within shoot orders. Each point corresponds to a trait value for a single shoot. Regression lines denote significant relationships between shoot traits and basal height within a branching order. Statistics of the models are presented in Table 2. Colors indicate different shoot orders. The dashed line indicates non-significance.
Figure 5. Measured variation in shoot traits across the basal height gradient within shoot orders. Each point corresponds to a trait value for a single shoot. Regression lines denote significant relationships between shoot traits and basal height within a branching order. Statistics of the models are presented in Table 2. Colors indicate different shoot orders. The dashed line indicates non-significance.
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Figure 6. Shoot trait relationships across (black lines) and within branching orders (colored lines). Data points represent one shoot of a given branching order at a given basal height. Continuous and dashed lines represent significant (p < 0.05) and non-significant (p > 0.05) effects, respectively, by fitting ordinary least squares (OLS) regressions.
Figure 6. Shoot trait relationships across (black lines) and within branching orders (colored lines). Data points represent one shoot of a given branching order at a given basal height. Continuous and dashed lines represent significant (p < 0.05) and non-significant (p > 0.05) effects, respectively, by fitting ordinary least squares (OLS) regressions.
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Table 1. Characteristics of shoots across the different branch basal heights and branching orders (Mean ± SE).
Table 1. Characteristics of shoots across the different branch basal heights and branching orders (Mean ± SE).
PositionnDiameterLengthSSLSTD
HeightOrderMeanSEMeanSEMeanSEMeanSE
0.5 m2371.580.0911.561.020.630.061.220.11
3401.440.077.240.820.840.071.060.09
4121.250.127.851.211.050.161.070.12
1.0 m2531.950.0714.491.010.620.050.720.06
3921.320.058.570.551.060.051.020.06
4141.160.136.850.821.210.181.190.15
1.5 m21141.730.0514.930.680.620.030.910.04
31421.270.0311.190.450.860.031.190.04
4321.010.068.240.751.250.081.270.10
2.0 m2751.800.0613.230.780.760.050.680.03
31221.500.0311.670.571.020.040.680.02
4251.420.0910.211.081.080.070.710.05
3.0 m21371.460.0414.620.650.820.030.980.03
31461.270.0312.970.541.070.040.990.03
4281.430.1012.041.110.910.050.910.08
4.0 m1292.350.0732.350.880.570.020.430.02
21361.790.0415.120.650.730.020.670.03
3831.500.0512.930.640.930.040.780.04
4.5 m1102.690.1034.782.880.430.020.420.03
2561.830.0415.670.710.780.030.550.02
3121.890.1015.401.020.820.050.470.03
5.0 m1123.020.1228.882.200.360.030.420.03
2212.260.1021.790.930.580.050.510.04
5.5 m1182.950.1333.540.900.390.020.410.02
2842.140.0518.060.780.630.030.520.02
3211.810.0814.541.450.820.070.550.02
Table 2. Linear model statistics on intra-order shoot trait relationships with basal height of 4 shoot orders. ‘Shape’ indicates whether the optimal model (according to the Akaike Information Criterion, AIC) describes a linear (‘L’) or quadratic (‘Q’) relationship between shoot traits and basal height as illustrated in Figure 5. Relationships were tested with linear (second-degree polynomial) regression models for each shoot order separately. F, F-statistic; p, probability value. Bold p-values indicate a significant relationship between a shoot trait and height (p < 0.05).
Table 2. Linear model statistics on intra-order shoot trait relationships with basal height of 4 shoot orders. ‘Shape’ indicates whether the optimal model (according to the Akaike Information Criterion, AIC) describes a linear (‘L’) or quadratic (‘Q’) relationship between shoot traits and basal height as illustrated in Figure 5. Relationships were tested with linear (second-degree polynomial) regression models for each shoot order separately. F, F-statistic; p, probability value. Bold p-values indicate a significant relationship between a shoot trait and height (p < 0.05).
TraitsOrderModelShapeFpR2AIC
Diameter1Q∩15.9<0.0010.3183.8
2Q∪42.4<0.0010.101014.7
3Q∪18.4<0.0010.05752.3
4L/6.70.0110.05145.4
Length1L\0.00.9990.00449.5
2Q∪15.3<0.0010.044829.0
3Q∩31.1<0.0010.084204.3
4L/14.3<0.0010.11669.1
SSL1Q∪30.8<0.0010.47−123.3
2Q∩15.6<0.0010.04410.0
3Q∩4.50.0120.01772.5
4Q∩4.50.0130.06131.9
STD1L\0.90.3430.00−124.5
2Q∩49.9<0.0010.12627.2
3L\43.3<0.0010.06826.0
4L\6.00.0160.04158.4
Table 3. Linear model statistics on bivariate trait relationships. ‘Shape’ indicates whether the optimal model (according to the Akaike Information Criterion, AIC) describes a linear (‘L’) or quadratic (‘Q’) relationship as illustrated in Figure 6. Relationships were tested with linear (second-degree polynomial) regression models for each pairwise shoot trait. Degrees of freedom were 1 for linear and 2 for quadratic models; F, F-statistic; p, probability value. Bold p-values indicate a non-significant relationship (p > 0.05).
Table 3. Linear model statistics on bivariate trait relationships. ‘Shape’ indicates whether the optimal model (according to the Akaike Information Criterion, AIC) describes a linear (‘L’) or quadratic (‘Q’) relationship as illustrated in Figure 6. Relationships were tested with linear (second-degree polynomial) regression models for each pairwise shoot trait. Degrees of freedom were 1 for linear and 2 for quadratic models; F, F-statistic; p, probability value. Bold p-values indicate a non-significant relationship (p > 0.05).
Y-AxisX-AxisOrderModelShapeFpR2AIC
SSLDiameter1Q∪59.4<0.0010.63−148.8
2Q∪325.7<0.0010.48−23.4
3Q∪291.0<0.0010.47363.1
4Q∪57.5<0.0010.5160.3
AllQ∪928.5<0.0010.54445.2
STDDiameter1L\44.6<0.0010.39−158.8
2Q∪248.5<0.0010.41342.7
3Q∪216.4<0.0010.40536.2
4Q∪59.7<0.0010.5283.7
AllQ∪639.7<0.0010.45974.6
SSLSTD1L/0.300.5890.00−80.1
2Q∩2.40.0940.00436.0
3Q∩3.70.0250.01774.0
4L/0.90.3410.00137.8
AllQ∩19.0<0.0010.021630.3
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Yu, Y.; Zhang, H.; Wang, Z.; Liu, Z. Patterns of Intra-Order Variation in Shoot Traits Are Order-Specific Along the Branch Basal Height Gradient of Larix principis-rupprechtii. Forests 2025, 16, 1016. https://doi.org/10.3390/f16061016

AMA Style

Yu Y, Zhang H, Wang Z, Liu Z. Patterns of Intra-Order Variation in Shoot Traits Are Order-Specific Along the Branch Basal Height Gradient of Larix principis-rupprechtii. Forests. 2025; 16(6):1016. https://doi.org/10.3390/f16061016

Chicago/Turabian Style

Yu, Yang, Huayong Zhang, Zhongyu Wang, and Zhao Liu. 2025. "Patterns of Intra-Order Variation in Shoot Traits Are Order-Specific Along the Branch Basal Height Gradient of Larix principis-rupprechtii" Forests 16, no. 6: 1016. https://doi.org/10.3390/f16061016

APA Style

Yu, Y., Zhang, H., Wang, Z., & Liu, Z. (2025). Patterns of Intra-Order Variation in Shoot Traits Are Order-Specific Along the Branch Basal Height Gradient of Larix principis-rupprechtii. Forests, 16(6), 1016. https://doi.org/10.3390/f16061016

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