Next Article in Journal
Medicago Pasture Soil C:N:P Stoichiometry Mediated by N Fertilization in Northern China
Previous Article in Journal
Population Structure and Genetic Diversity of Agave Germplasms in China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 3
10.3390/agronomy15030723
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Leaf Traits, Biomass Accumulation and Allocation of Gentiana lawrencei Burkill Along an 800 m Elevation Gradient in Alpine Grasslands

by
Yuan Yang
1,
Longxin Zhang
1,2,
Zuoyi Wang
3,
Linlin Shuai
3,
Xiaoying Zhang
1,
Yufang Huang
1,
Ying Wang
1,
Zhen Ma
2,4 and
Chunhui Zhang
1,3,4,*
1
State Key Laboratory of Plateau Ecology and Agriculture, College of Eco-Environmental Engineering, Qinghai University, Xining 810016, China
2
CAS Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
3
Qinghai Provincial Key Laboratory of Adaptive Management on Alpine Grassland, Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining 810016, China
4
Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(3), 723; https://doi.org/10.3390/agronomy15030723
Submission received: 25 January 2025 / Revised: 10 March 2025 / Accepted: 14 March 2025 / Published: 17 March 2025
(This article belongs to the Topic Plant Physiological and Ecological Responses to Environmental Stress)
Browse Figures
Versions Notes

Abstract

:
Elevation, as a comprehensive ecological variable, is considered one of the decisive factors in the distribution pattern of plants in a region. We explored changes in functional traits and biomass accumulation and allocation of Gentiana lawrenceni along an elevational gradient and their relationships. We found that leaf size and specific leaf area (SLA) of this species showed a trend of first increasing and then decreasing with elevation, while leaf thickness and leaf dry matter content (LDMC) showed a trend of first decreasing and then increasing. As elevation increases, the aboveground biomass, belowground biomass and total biomass all decline, and above- and belowground biomass allocation is initially reduced and then rise. Leaf size and LDMC positively affected biomass accumulation, while four leaf traits did not affect biomass allocation. In sum, this study found that there is a threshold at ~3600 m above sea level that causes changes in functional traits and biomass allocation strategies of this species to adapt to harsher high-elevation environments. Gentiana lawrenceni can maintain its biomass accumulation and fitness by adjusting leaf size and LDMC. This study has enhanced our understanding of the changes in functional traits, biomass accumulation and allocation strategies of alpine plants along an elevation gradient.

1. Introduction

Plant functional traits, the morphological and physiological characteristics shaped through long-term evolution, are pivotal mediators of plant-environment interactions and ecosystem functioning [1,2]. These traits not only determine growth and survival but also reflect adaptive strategies that optimize resource allocation under environmental constraints [3,4]. Plant functional traits serve as proxies for adaptive strategies that enable species to cope with environmental pressures. For instance, Grime’s CSR theory [5] categorizes plants into competitive (C), stress-tolerant (S), and ruderal (R) strategies, while the leaf economics spectrum [6] describes a continuum from acquisitive (high specific leaf area, SLA; rapid growth; rapid resource capture) to conservative (high leaf dry matter content, LDMC; slow growth; resource retention) resource-use strategies. These frameworks highlight how trait combinations—such as leaf thickness, SLA, and LDMC—reflect trade-offs in survival, growth, and reproduction under varying environmental conditions. In alpine ecosystems, shifts in these traits along elevation gradients are critical indicators of adaptive responses to temperature, radiation, and nutrient limitations [7].
Global climate change refers to the long-term increase in Earth’s average surface temperature (currently ~1.1 °C above pre-industrial levels) driven primarily by anthropogenic greenhouse gas emissions, which disproportionately affects high-altitude and polar regions [8]. Elevation gradients provide a natural laboratory to study climate change impacts, as temperature declines with altitude at a rate of ~5–6 °C per 1000 m elevation gain [7]. This vertical thermal gradient mimics the latitudinal or temporal changes in temperature projected under global warming scenarios [9,10,11]. For example, descending 300–500 m in elevation approximates the temperature increase equivalent to a 1–2 °C warming at a fixed latitude [9]. By studying plant responses across elevations, we can infer how species may adapt to future climate warming or cooling by observing analogous trait shifts along this thermal gradient. How plant functional traits vary with elevation has aroused widespread interest [7]. A previous study found that as elevation increases, leaf thickness and LDMC of Quercus aquifolioides increased linearly, while SLA decreased linearly on the southeastern Qinghai-Tibet Plateau [12]. The pine trees (Pinus spp.) in the Rocky Mountains of North America experienced a reduction in temperature and growth season length with increasing elevation, which limited leaf development and leaded to a gradual reduction in needle length and SLA [7]. These changes of leaf with elevation reflect the plant’s self-protection mechanism towards high elevation, reducing leaf water evaporation and improving leaf cold resistance to cope with the harsh environment of nutrient depletion, low temperature, and high radiation in alpine environments [13].
Biomass accumulation and allocation further reveal adaptive trade-offs. Biomass accumulation plays a crucial role in the survival of organisms and the functioning of ecosystems [14,15]. Specifically, it not only affects the energy storage, growth and development, reproduction, and stress resistance of organisms, but also has profound impacts on the productivity and material cycling of ecosystems [14]. Therefore, studying and managing biomass accumulation is of critical importance for ecological conservation and sustainable development [16]. Changes of biomass with elevation is a complex ecological phenomenon that is influenced by various environmental factors, including temperature, light, soil properties, etc. [7]. As elevation increases, temperature usually gradually decreases, which affects the photosynthesis and respiration of plants, thereby affecting biomass accumulation [17]. The growing season in high-elevation areas is usually shorter, which limits the growth time of plants and thus affects the accumulation of biomass [7,18]. The light intensity in high-elevation areas is usually higher because the atmosphere is thinner and ultraviolet radiation is stronger [7]. Strong light can promote photosynthesis, but excessive light can also cause damage to plant leaves [7]. The soil in high-elevation areas has usually low fertility which can limit plant growth and biomass accumulation, and at the same time, soil moisture conditions can also affect biomass [19].
The allocation of aboveground and underground biomass refers to the way in which plants allocate the organic matter they produce and accumulate between their roots and crowns [20,21]. Biomass allocation is an important sensitive indicator of the functional response of plants to environmental factors such as light, water, and nutrients [7]. This allocation pattern is naturally influenced by elevation. Changes of biomass allocation with elevation involves how plants allocate their resources at different elevations to adapt to environmental conditions. As elevation increases, the temperature usually gradually decreases. And under low temperature conditions in high elevations, the rates of photosynthesis and respiration of plants decrease. As a strategy to adapt to the low temperature environment, plants may allocate more biomass to their roots to enhance their ability to absorb and store nutrients [22].
Plant functional traits (e.g., leaf size, leaf thickness, SLA, and LDMC) may also affect biomass accumulation and allocation [6]. Plants with high SLA are generally suitable for rapid growth, leading to rapid accumulation of biomass, while plants with low SLA are suitable for long-term survival in resource limited environments [6,23]. Plants with high LDMC may be more persistent in stress conditions, as they tend to allocate biomass to parts that can improve survival rates, such as roots and water storage tissues [21]. In resource rich environments, large leaved plants tend to allocate more biomass to the aboveground parts to support rapid growth, while in resource limited environments, plants may reduce leaf size and thickness to reduce water evaporation and improve water use efficiency, simultaneously allocating more biomass to the roots [24].
The Qinghai-Tibet Plateau, a global hotspot for climate sensitivity, offers an ideal system to study elevation-driven adaptations [25,26,27]. Here we focus on Gentiana lawrencei Burkill (Gentianaceae; common name, Lawrence’s gentian; for taxonomic, morphological and distributional information, see refs. [28,29,30]), a common perennial herb species endemic to China [11,31], for three key reasons: (1) G. lawrencei has a wide elevation distribution span [31], which can potentially exhibit marked large plasticity in leaf traits and reflect changes of adaptive strategies; (2) G. lawrencei thrives under extreme abiotic stressors (e.g., low temperatures, high UV radiation) [11], making it a sensitive indicator of alpine plant resilience; (3) despite its ecological and medicinal importance [32], no study has systematically linked its trait variation to biomass allocation mechanisms across elevations. By analyzing G. lawrencei’s functional traits and biomass dynamics along an 800 m gradient (3200 m, 3500 m, 3750 m, 3900 m, 4000 m above sea level (a.s.l.)), we address: (1) How do leaf traits and biomass allocation shift with elevation, and what adaptive strategies do these shifts represent? (2) Does a critical threshold (e.g., 3600 m a.s.l.) exist where trait-mediated strategies transition, and what are its implications for climate resilience? This study will enhance our understanding of the changes in functional traits, biomass accumulation and allocation strategies of alpine plants with elevation, offering insights for conserving vulnerable alpine ecosystems under global change [11].

2. Materials and Methods

2.1. Study Area

The experiment was conducted at the Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem in China (37°37′ N, 101°12′ E). The Station is located in the northeast of the Qinghai-Tibet Plateau. The region has a typical plateau continental climate, with long and cold winters and short and cool summers [33]. In September 2023, every 100–300 m along a 3200–4000 m elevational gradient on the south slope of the Qilian Mountains, we selected the G. lawrencei population that met the sampling requirements (including at least 30 healthy, fully developed flowering individuals). We then randomly selected 12 apparently healthy, fully developed flowering individuals from the population of G. lawrencei at each elevation (3200, 3500, 3750, 3900 and 4000 m a.s.l.; see Figure S1) within an approximate 20 m2 plot. These plots were established under homogenized conditions to minimize microenvironmental variability. A long-term field study reported in the same site [9] that annual average soil temperatures at a depth of 5 cm were 3.9, 2.5, 2.0, and 0.4 °C, respectively, and annual average soil moisture at a depth of 20 cm was 11.8, 11.3, 12.7, and 10.2%, respectively, at elevations of 3200, 3400, 3600, and 3800 m a.s.l.

2.2. Sampling and Data Measurement

We randomly selected 12 apparently healthy and fully developed flowering individuals from the population of G. lawrenceni at each elevation (3200 m, 3500 m, 3750 m, 3900 m, and 4000 m a.s.l.; Table S1; see photographs of the habitat of G. lawrencei in Figure S2). We divided each individual into three parts (leaves, stems and roots), scanned fresh leaves using Canon scanner (LIDE 300), and weighted fresh leaves. We simultaneously measured the thickness of fresh leaves using a thickness gauge (Aipu Metering Instrument Co. Ltd., Quzhou, China). The three parts were dried at 60 °C for 48 h, and then weighed. Based on these data, we obtained data on aboveground biomass (g), underground biomass (g), total biomass (aboveground biomass + underground biomass) (g), biomass allocation (aboveground biomass/underground biomass), leaf size (single leaf area) (mm2), leaf thickness (mm), SLA (leaf area/leaf dry weight) (m2/kg), and LDMC (leaf dry weight/leaf fresh weight) (g/g). All leaf functional traits were measured following the standard protocols developed by Pérez et al. [34].

2.3. Statistical Analysis

To assess the influences of the elevational gradient on leaf traits (leaf size, leaf thickness, SLA, or LDMC), We performed one-way ANOVAs using the aov function in the base R [35]. Tukey-HSD tests were conducted to compare differences between populations using the HSD.test function the R package agricolae [36]. The coefficient of variation (CV) (i.e., the inter-individual variation in leaf functional traits) was calculated as the ratio of the standard deviation to the mean of the leaf functional traits of each population along the elevational gradient. Linear models were conducted to study the relationships of CV for leaf traits with elevation changes using the ggtrendline function in the R package ggtrendline [37].
We fitted linear or nonlinear models to study the relationships of leaf traits (leaf size, leaf thickness, SLA, or LDMC) or biomass accumulation (aboveground biomass, underground biomass, and total biomass) and allocation with elevation changes using the ggtrendline function in the R package ggtrendline [37].
We conducted linear mixed-effect models to explore the effects of leaf traits (leaf size, leaf thickness, SLA, and LDMC) on biomass accumulation and allocation, with elevation as a random variable, using the lme function in the R package nlme [38]. We also detected multicollinearity with variance-inflation factors (VIFs) for the linear mixed-effect models using the “vif” function in the R package car [39].
All statistical testing was carried out using R version 4.3.2 [35].

3. Results

The elevation gradient significantly influenced leaf size (F value = 45.890, p < 0.0001), leaf thickness (F value = 2.871, p = 0.0313), SLA (F value = 6.732, p < 0.0002) and LDMC (F value = 6.082, p < 0.0004), respectively. Leaf size and SLA reached their maxima at 3500 m, whereas leaf thickness and LDMC exhibited minima at 3500 m and 3750 m, respectively (Table 1). The CV for leaf size, thickness, and LDMC peaked at 3500 m but showed no clear elevational trend (Table 1; Figure S3). Notably, the CV of SLA decreased linearly with increasing elevation, contrasting with the fluctuating variability observed in other traits (Table 1; Figure S3). Detailed results of post hoc Tukey HSD tests and inter-individual trait variability (quantified as CV) are summarized in Table 1.
Leaf size, leaf thickness, SLA, and LDMC of G. lawrenceni exhibited a non-linear relationship with increasing elevation (Figure 1). Specifically, leaf size and SLA showed a trend of first increasing and then decreasing with elevation (Figure 1a,c), while leaf thickness and LDMC showed a trend of first decreasing and then increasing with elevation (Figure 1b,d). The turning point of these changes was probably around 3600 m a.s.l.
There were significantly negative linear correlations of elevation with aboveground biomass, underground biomass, and total biomass (Figure 2a–c). Biomass allocation exhibited a non-linear relationship with increasing elevation (Figure 2d), specifically showing a trend of first decreasing and then increasing with altitude, reaching its minimum value around 3600 m a.s.l.
Leaf size significantly and positively affected biomass accumulation (i.e., aboveground biomass, belowground biomass, total biomass) (Figure 3a–c). LDMC significantly and positively affected aboveground biomass and total biomass (Figure 3a,c). Other leaf traits did not significantly affect biomass accumulation (Figure 3a–c).
Leaf size, leaf thickness, SLA, and LDMC did not significantly affect biomass allocation (Figure 3d).
VIFs of leaf size, leaf thickness, SLA, and LDMC were 1.24, 1.78, 2.03, and 1.34, respectively (i.e., all are less than 5).

4. Discussion

We found that leaf traits and biomass allocation of G. lawrenceni did not exhibit a simple linear relationship with altitude, but rather a U-shaped or n-shaped curve relationship. In addition, we found that biomass accumulation of G. lawrenceni decreased linearly with elevation, and leaf size and LDMC were important factors affecting biomass accumulation.

4.1. Influences of Elevation on Leaf Functional Traits

As the largest nutrient organ for photosynthesis and contact with the external environment in plants, leaves have great plasticity and sensitivity in the face of complex and changing external environments, and can reflect changes in plant ecological strategies [40]. Our research found that leaf thickness and LDMC of G. lawrenceni showed a trend of first decreasing and then increasing with elevation, while the leaf size and SLA showed a trend of increasing first and then decreasing with altitude. As elevation increases (3200–3600 m), leaves became larger, thinner, and had less dry matter, and plants then chose strategies increasing leaf photosynthetic efficiency (i.e., acquisitive strategy, competitive strategy) to reduce the negative impact of low temperatures on biomass accumulation and increase species fitness. When elevation reaches a threshold of around 3600 m a.s.l., the leaves began to shrink, thicken, and have more dry matter, and plants then shifted to a conservative strategy (or a stress-tolerant strategy), where structural reinforcement and resource retention outweigh growth efficiency—a response to intensified low-temperature stress and high ultraviolet radiation, reducing population mortality. Our results are consistent with a previous study in the Tianshan Mountains of China, which found that with increasing elevation, LDMC and leaf mass per unit area (1/SLA) of Picea schrenkiana var. tianschanica showed a trend of initially decreasing and then increasing [41].
Our findings reveal that the CV of SLA decreased linearly with increasing elevation, contrasting with the fluctuating variability observed in other traits. These patterns suggest divergent adaptive mechanisms among traits under high-elevation environmental pressures. The linear decline in SLA CV likely reflects intensified environmental filtering (e.g., low temperatures, high UV radiation, shortened growing seasons) at higher elevations [7], driving phenotypic convergence to enhance survival efficiency. Such convergence may stem from strong natural selection favoring reduced trait variability under extreme conditions [42]

4.2. Impacts of Elevation on Biomass Accumulation and Allocation

We found that as elevation increases, biomass accumulation of G. lawrenceni decreases. This is mainly due to the decrease in temperature at high elevations, which slows down the rate of plant cell division and proliferation, thereby affecting the accumulation of plant biomass [7,43]. Plants growing in high-elevation areas face severe survival pressures, with limited dry matter production capacity and resources obtained from the environment, and additionally, the presence of low temperature stress restricts plant metabolism and growth and development [44].
The mechanisms governing above- and underground biomass allocation are primarily explained by two competing hypotheses: isometric growth and optimal partitioning [45,46]. The isometric growth hypothesis posits that above- and underground biomass accumulate at a constant rate, and their allocation does not shift with changes in environmental conditions [45]. In contrast, the optimal partitioning hypothesis asserts that plants dynamically allocate biomass to organs facing the strongest environmental constraints (e.g., roots under nutrient scarcity or shoots under light competition) to maximize fitness [45]. Our results supported the optimal allocation hypothesis, demonstrating that G. lawrencei dynamically shifts biomass allocation strategies across elevations to balance survival and reproduction under environmental constraints. Specially, from 3200 to 3600 m, plants prioritized belowground biomass allocation—likely to enhance nutrient uptake and storage under declining temperatures and reduced soil nutrient mineralization—while reaching a critical threshold at ~3600 m where allocation equilibrium minimized trade-offs. Above this elevation, plants abruptly shifted to aboveground investment. This may be due to the compressed growing seasons exacerbated by extreme low-temperature stress at upper elevations, where plants need to complete growth and reproduction within a limited time [11]. Therefore, plants may allocate more biomass to their reproductive organs (such as flowers and fruits) to ensure successful reproduction [11,47,48], resulting in an increase in aboveground biomass allocation. In sum, this dual strategy—root conservation under moderate stress versus reproductive ‘bet-hedging’ under extreme stress—reveals a sophisticated adaptive plasticity in G. lawrenceni, challenging the isometric growth hypothesis. This study underscores how elevation-driven environmental filters shape trait-mediated resource trade-offs in alpine plants.

4.3. Relationship Between Leaf Functional Traits and Biomass Accumulation and Allocation

When the habitat conditions of plants change, they will promptly reconfigure, compensate, and balance their own resources to minimize the negative impact of environmental changes on plants [49,50]. Changes in plant functional traits can well reflect this ecological feedback mechanism [51]. The relationship between plant functional traits and the accumulation and allocation of plant biomass is an important basis for evaluating global change response and adaptation mechanisms of plants [52].
We found that the factors affecting biomass accumulation are leaf size and LDMC, rather than leaf thickness and SLA. The larger the leaves, the higher the LDMC, the higher the photosynthetic rate, and the higher the biomass accumulation. The dominant roles of leaf size and LDMC in biomass accumulation highlight trait-mediated trade-offs between carbon gain and stress resilience. Larger leaves at mid-elevations maximize light interception, aligning with acquisitive strategies to exploit favorable conditions [4]. It is commonly acknowledged that with the rise in elevation, low temperatures restrict the rates of soil nitrogen mineralization, thereby leading to poor soil nutrient conditions [7]. Conservative strategies at high elevations likely reflect integrated responses to both low-temperature stress and nutrient limitation. As a result, the high LDMC observed at upper elevations not only contributes to improved water retention but also likely enhances nutrient-use efficiency within the oligotrophic alpine soil environment [6]. A previous study found that larger leaf size significantly improved the photosynthetic efficiency of maize, thereby increasing biomass accumulation [53]. Higher LDMC significantly improved the photosynthetic efficiency of rice, thereby increasing biomass accumulation [54]. Thicker leaves can reduce water evaporation and improve drought tolerance, but the effect of leaf thickness on biomass accumulation depends on environmental conditions [55]. Leaves with low SLA values typically have higher LDMC and longer lifespan, allowing for more efficient utilization of resources in resource limited environments. However, leaves with high SLA values, although capable of rapid growth, may perform poorly in resource limited environments [56].
We also found that none of the four leaf traits in this study affected the allocation of biomass. Compared to other studies, our results are different. For example, larger leaf size helps to improve photosynthetic efficiency, thereby increasing biomass allocation to aboveground parts (especially stems and leaves) and reducing the allocation to roots [57]. A higher dry matter content in leaves helps to improve photosynthetic efficiency, thereby increasing biomass allocation to aboveground parts (especially stems and leaves) and reducing allocation to roots [58]. Thicker leaves promote the distribution of more carbohydrates to the roots and reduce the allocation to the aboveground parts [48]. Leaves with low SLA values can more effectively utilize resources in resource limited environments, thereby promoting the allocation of more carbohydrates to aboveground parts [59]. However, most of these results came from specific environmental conditions (such as fully irrigated conditions, high light conditions) [56,57,58], and this may also be a reason why our results were different.

4.4. Limitations of This Study

Similar to this research, most other studies investigating the effects of elevation on plant traits and reproductive strategies (e.g., refs. [9,60,61,62,63]) have primarily focused on elevation itself without statistically accounting for the environmental changes caused by elevation. All our plots were located on south-facing slopes with alpine meadow vegetation and alpine meadow soils [64] to minimize microhabitat heterogeneity. Long-term monitoring data from Li et al. [9] confirmed that temperature declines linearly with elevation (−1.5 °C per 400 m) in our study site, while other factors (e.g., soil moisture, soil type) exhibit minimal variation across the elevational gradient. Therefore, our study can reflect changes in plant strategies under temperature variations. Certainly, future studies should incorporate additional comprehensive factors beyond elevation (e.g., plant neighbors of G. lawrencei, changes in soil microbial communities) to clearly elucidate the adaptive changes and mechanisms of alpine plants.

5. Conclusions

This study found that there is an elevational threshold (~3600 m a.s.l.) that causes changes in the functional traits and biomass allocation strategies of G. lawrenceni to adapt to more harsh high-altitude environments. In addition, G. lawrenceni can maintain its own biomass accumulation by adjusting leaf size and LDMC, potentially affecting its adaptability to environmental changes caused by elevation. This study has enhanced our understanding of the changes in functional traits and biomass accumulation and allocation strategies of alpine plants with elevation. Furthermore, this work underscores the value of integrating trait-based ecology with adaptive theory to forecast responses of alpine plants in warming mountain ecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15030723/s1, Figure S1: Digital Elevation Map (DEM) of the Qinghai-Tibet Plateau and the study site; Figure S2: Photographs of the habitat of Gentiana lawrencei; Figure S3: Relationships of elevation with the coefficient of variation (CV) for leaf size, leaf thickness, SLA, and LDMC; Table S1: The longitude and latitude at each elevation; Table S2: Data of this study.

Author Contributions

Conceptualization, Z.M. and C.Z.; Data curation, Z.W., L.S. and X.Z.; Formal analysis, Y.Y. and L.Z.; Funding acquisition, C.Z.; Investigation, Y.Y., L.Z., Z.W., X.Z., Y.H. and Y.W.; Methodology, Z.M.; Project administration, Z.M.; Resources, Y.H.; Software, L.S.; Supervision, C.Z.; Visualization, Y.W.; Writing—original draft, Y.Y., Z.M. and C.Z.; Writing—review & editing, L.Z., Z.W., L.S., X.Z., Y.H. and Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Project of Qinghai Science & Technology Department (2024-SF-102).

Data Availability Statement

The data that support our paper can be found in Supplementary Materials.

Acknowledgments

We thank Fei Ren, Haitao Miao, and Mengjiao Chen for their help with field work.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
a.s.l.Above sea level
CVCoefficient of Variation
LDMCLeaf dry matter content
SLASpecific leaf area

References

  1. Reich, P.B.; Wright, I.J.; Cavender-Bares, J.; Craine, J.M.; Oleksyn, J.; Westoby, M.; Walters, M.B. The evolution of plant functional variation: Traits, spectra, and strategies. Int. J. Plant Sci. 2003, 164, S143–S164. [Google Scholar] [CrossRef]
  2. Malik, A.; Kumar, K.; Kumari, G.; Kumar, N.; Singh, H. Application of Functional Traits in Modelling Productivity and Resilience Under Climate Change. In Plant Functional Traits for Improving Productivity; Springer Nature: Singapore, 2024; pp. 77–96. [Google Scholar]
  3. Cornelissen, J.H.; Lavorel, S.; Garnier, E.; Díaz, S.; Buchmann, N.; Gurvich, D.E.; Reich, P.B.; Ter Steege, H.; Morgan, H.D.; Van Der Heijden, M.G.A.; et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 2003, 51, 335–380. [Google Scholar] [CrossRef]
  4. Walker, T.W.; Alexander, J.M.; Allard, P.M.; Baines, O.; Baldy, V.; Bardgett, R.D.; Capdevila, P.; Coley, P.D.; David, B.; Defossez, E.; et al. Functional Traits 2.0: The power of the metabolome for ecology. J. Ecol. 2022, 110, 4–20. [Google Scholar] [CrossRef]
  5. Grime, J.P. Plant Strategies and Vegetation Processes; Wiley: Chichester, UK, 1979. [Google Scholar]
  6. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.; Diemer, M.; et al. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef] [PubMed]
  7. Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems, 3rd ed.; Springer: Cham, Switzerland, 2021. [Google Scholar]
  8. IPCC. Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2023. [Google Scholar]
  9. Li, X.; Jiang, L.; Meng, F.; Wang, S.; Niu, H.; Iler, A.M.; Duan, J.; Zhang, Z.; Luo, C.; Cui, S.; et al. Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows. Nat. Commun. 2016, 7, 12489. [Google Scholar] [CrossRef] [PubMed]
  10. Körner, C. Concepts in alpine plant ecology. Plants 2023, 12, 2666. [Google Scholar] [CrossRef]
  11. Wang, M.; Wang, Z.; Yang, Y.; Zhao, X.; Zhou, H.; Zhou, S.; Yin, X.; Ren, Y.; Dong, H.; Zhang, L.; et al. Influences of elevational gradient on flower size and number of Gentiana lawrencei var. farreri. Ecol. Evol. 2024, 14, e11393. [Google Scholar] [CrossRef]
  12. Wang, C.; Lu, J.; Zhou, C. Altitude distribution of leaf functional traits of Quercus aquifolioides in southeastern Tibet. J. For. Environ. 2021, 41, 366–372. [Google Scholar]
  13. Zhou, A.; Ge, B.; Chen, S.; Kang, D.; Wu, J.; Zheng, Y.; Ma, H. Leaf ecological stoichiometry and anatomical structural adaptation mechanisms of Quercus sect. Heterobalanus in southeastern Qinghai–Tibet Plateau. BMC Plant Biol. 2024, 24, 325. [Google Scholar]
  14. Chapin, F.S., III; Matson, P.A.; Mooney, H.A. Principles of Terrestrial Ecosystem Ecology, 2nd ed.; Springer: Cham, Switzerland, 2002. [Google Scholar]
  15. Tariq, A.; Graciano, C.; Sardans, J.; Zeng, F.; Hughes, A.C.; Ahmed, Z.; Ullah, A.; Ali, S.; Gao, Y.; Peñuelas, J. Plant root mechanisms and their effects on carbon and nutrient accumulation in desert ecosystems under changes in land use and climate. New Phytol. 2024, 242, 916–934. [Google Scholar] [CrossRef]
  16. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl. Acad. Sci. USA 1997, 94, 13730–13734. [Google Scholar] [CrossRef] [PubMed]
  17. He, J.D.; Xue, J.Y.; Gao, J.; Wang, J.N.; Wu, Y. Adaptations of the floral characteristics and biomass allocation patterns of Gentiana hexaphylla to the altitudinal gradient of the eastern Qinghai-Tibet Plateau. J. Mt. Sci. 2017, 14, 1563–1576. [Google Scholar] [CrossRef]
  18. Callaway, R.M.; Brooker, R.W.; Choler, P.; Kikvidze, Z.; Lortie, C.J.; Michalet, R.; Paolini, L.; Pugnaire, F.I.; Newingham, B.; Aschehoug, E.T.; et al. Positive interactions among alpine plants increase with stress. Nature 2002, 417, 844–848. [Google Scholar] [CrossRef]
  19. Fernández-Marín, B.; Sáenz-Ceniceros, A.; Solanki, T.; Robson, T.M.; García-Plazaola, J.I. Alpine forbs rely on different photoprotective strategies during spring snowmelt. Physiol. Plant. 2021, 172, 1506–1517. [Google Scholar] [CrossRef] [PubMed]
  20. Silvertown, J.; Charlesworth, D. Introduction to Plant Population Biology; John Wiley & Sons: Hoboken, NJ, USA, 2009. [Google Scholar]
  21. Poorter, H.; Niklas, K.J.; Reich, P.B.; Oleksyn, J.; Poot, P.; Mommer, L. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 2012, 193, 30–50. [Google Scholar] [CrossRef]
  22. Walther, G.R. Plants in a warmer world. Perspect. Plant Ecol. 2003, 6, 169–185. [Google Scholar] [CrossRef]
  23. Sack, L.; Scoffoni, C. Leaf venation: Structure, function, development, evolution, ecology and applications in the past, present and future. New Phytol. 2013, 198, 983–1000. [Google Scholar] [CrossRef]
  24. Niinemets, Ü. Global-scale climatic controls of leaf dry mass per area, density, and thickness in trees and shrubs. Ecology 2001, 82, 453–469. [Google Scholar] [CrossRef]
  25. Zhang, C.; Willis, C.G.; Klein, J.A.; Ma, Z.; Li, J.; Zhou, H.; Zhao, X. Recovery of plant species diversity during long-term experimental warming of a species-rich alpine meadow community on the Qinghai-Tibet plateau. Biol. Conserv. 2017, 213, 218–224. [Google Scholar] [CrossRef]
  26. Zhang, C.; Li, J.; Willis, C.G.; Ma, Z. Among-population variation in seed mass for 190 Tibetan plant species: Phylogenetic pattern and ecological correlates. Glob. Ecol. Conserv. 2020, 23, e01163. [Google Scholar] [CrossRef]
  27. Wu, L.; Ren, Y.; Wan, J.-Z.; Wang, M.; Wang, Z.; Fu, F.; Sun, J.; Fu, Y.; Ma, Z.; Zhang, C. Effects of Precipitation Change and Nitrogen and Phosphorus Additions on Traits and Abundance of Potentilla anserine in an Alpine Meadow. Atmosphere 2022, 13, 1820. [Google Scholar] [CrossRef]
  28. Royal Botanic Gardens, Kew. Gentiana lawrencei. Plants of the World Online. Available online: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:368407-1 (accessed on 15 February 2025).
  29. World Flora Online. Gentiana lawrencei. WFO Plant List. Available online: https://wfoplantlist.org/taxon/wfo-0000697834-2024-12?page=1 (accessed on 15 February 2025).
  30. Wu, Z.Y.; Raven, P.H. (Eds.) Gentiana lawrencei. In Flora of China (Vol. 16); Science Press: Beijing, China; Missouri Botanical Garden Press: St. Louis, MI, USA, 2013; Available online: http://www.efloras.org/florataxon.aspx?flora_id=2&taxon_id=210000604 (accessed on 15 February 2025).
  31. Ho, T.N.; Liu, S.W. A Worldwide Monograph of Gentiana; Science Press: Beijing, China, 2001. [Google Scholar]
  32. Yang, Y.C.; Ho, T.N.; Lu, S.L.; Huan, R.F.; Wang, Z.X.; Wang, W.Y.; Liu, S.W.; Wu, G.Z.; Zhou, L.H.; Pan, J.T.; et al. Tibetan Medicine Record; Qinghai People’s Publishing House: Xining, China, 1991. [Google Scholar]
  33. Ma, Z.; Willis, C.G.; Zhang, C.; Zhou, H.; Zhao, X.; Dong, S.; Yao, B.; Huang, X.; Zhao, F.; Yin, G.; et al. Direct and indirect effect of seed size on seedling survival along an experimental light availability gradient. Agric. Ecosyst. Environ. 2019, 281, 64–71. [Google Scholar] [CrossRef]
  34. Pérez, H.N.; Díaz, S.; Garnier, E.; Lavore, S.; Poorter, H.; Jaureguiberry, P.; Bret-Harte, M.S.; Cornwell, W.K.; Craine, J.M.; Gurvich, D.E.; et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 2013, 61, 167–234. [Google Scholar] [CrossRef]
  35. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2024. [Google Scholar]
  36. de Mendiburu, F.; de Mendiburu, M.F. Package ‘agricolae’. R Package Version 2019, 1, 1143–1149. [Google Scholar]
  37. Mei, W.; Yu, G.; Greenwell, B. ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’, R Package Version 1.0.3. 2022. Available online: https://CRAN.R-project.org/package=ggtrendline (accessed on 5 December 2024).
  38. Pinheiro, J.; Bates, D.; DebRoy, S.; Sarkar, D.; Heisterkamp, S.; Van Willigen, B.; Maintainer, R. Package ‘nlme’. Linear and nonlinear mixed effects models. R Package Version 2017, 3, 1–131. [Google Scholar]
  39. Fox, J.; Weisberg, S. An R Companion to Applied Regression, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2019; Available online: https://www.john-fox.ca/Companion/ (accessed on 5 December 2024).
  40. Fitter, A.H.; Hay, R.K.M. Environmental Physiology of Plants, 3rd ed.; Academic Press: San Diego, CA, USA, 2002. [Google Scholar]
  41. Zhang, H.W.; Ma, J.Y.; Sun, W.; Chen, F.H. Altitudinal variation in functional traits of Picea schrenkiana var. tianschanica and their relationship to soil factors in Tianshan Mountains, Northwest China. Acta Ecol. Sin. 2010, 30, 5747–5758. [Google Scholar]
  42. Losos, J.B. Convergence, adaptation, and constraint. Evolution 2011, 65, 1827–1840. [Google Scholar] [CrossRef]
  43. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol. Monogr. 1992, 62.3, 365–392. [Google Scholar] [CrossRef]
  44. Zhang, X.; Kuang, T.; Dong, W.; Qian, Z.; Zhang, H.; Landis, J.B.; Feng, T.; Li, L.; Sun, Y.; Huang, J.; et al. Genomic convergence underlying high-altitude adaptation in alpine plants. J. Integr. Plant Biol. 2023, 65, 1620–1635. [Google Scholar] [CrossRef]
  45. McConnaughay, K.D.M.; Coleman, J.S. Biomass allocation in plants: Ontogeny or optimality? A test along three resource gradients. Ecology 1999, 80, 2581–2593. [Google Scholar] [CrossRef]
  46. Enquist, B.J.; Niklas, K.J. Global allocation rules for patterns of biomass partitioning in seed plants. Science 2002, 295, 1517–1520. [Google Scholar] [CrossRef]
  47. Billings, W.D. Arctic and alpine vegetation: Plant adaptations to cold summer climates. In Arctic and Alpine Environments; Ives, J.D., Barry, R.G., Eds.; Methuen: London, UK, 1974; pp. 403–443. [Google Scholar]
  48. Spehn, E.; Körner, C.; Mountain Biodiversity—A Global Heritage. Mountain Forum Bulletin. 2009. Available online: https://lib2.icimod.org/record/14111 (accessed on 1 December 2024).
  49. Bloom, A.J.; Chapin, F.S.; Mooney, H.A. Resource limitation in plants-an economic analogy. Annu. Rev. Ecol. Syst. 1985, 16, 363–392. [Google Scholar] [CrossRef]
  50. Shipley, B.; Meziane, D. The balanced growth hypothesis and the allometry of leaf and root biomass allocation. Funct. Ecol. 2002, 16, 326–331. [Google Scholar] [CrossRef]
  51. Gratani, L. Plant phenotypic plasticity in response to environmental factors. Adv. Bot. 2014, 2014, 208747. [Google Scholar] [CrossRef]
  52. Chapin, F.S., III. Effects of plant traits on ecosystem and regional processes: A conceptual framework for predicting the consequences of global change. Ann. Bot. 2003, 91, 455–463. [Google Scholar] [CrossRef]
  53. Evans, J.R. Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 1989, 78, 9–19. [Google Scholar] [CrossRef] [PubMed]
  54. Zhu, X.G.; Long, S.P.; Ort, D.R. What is the maximum efficiency with which photosynthesis can convert solar energy into biomass? Curr. Opin. Biotech. 2008, 19, 153–159. [Google Scholar] [CrossRef]
  55. Morison, J.I.L.; Morecroft, M.D. Plant Growth and Climate Change; John Wiley & Sons: Hoboken, NJ, USA, 2006. [Google Scholar]
  56. Reich, P.B. The world-wide ‘fast–slow’ plant economics spectrum: A traits manifesto. J. Ecol. 2014, 102, 275–301. [Google Scholar] [CrossRef]
  57. Nezhadahmadi, A.; Prodhan, Z.H.; Faruq, G. Drought tolerance in wheat. Sci. World J. 2013, 2013, 610721. [Google Scholar] [CrossRef]
  58. Sinclair, T.R.; Muchow, R.C. Radiation use efficiency. Adv. Agron. 1999, 65, 215–265. [Google Scholar]
  59. Freschet, G.T.; Kichenin, E.; Wardle, D.A. Explaining within-community variation in plant biomass allocation: A balance between organ biomass and morphology above vs below ground? J. Veg. Sci. 2015, 26, 431–440. [Google Scholar] [CrossRef]
  60. Guo, H.; Weiner, J.; Mazer, S.J.; Zhao, Z.; Du, G.; Li, B. Reproductive allometry in Pedicularis species changes with elevation. J. Ecol. 2012, 100, 452–458. [Google Scholar] [CrossRef]
  61. Qi, W.; Bu, H.; Cornelissen, J.H.; Zhang, C.; Guo, S.; Wang, J.; Zhou, X.; Li, W.; Du, G. Untangling interacting mechanisms of seed mass variation with elevation: Insights from the comparison of inter-specific and intra-specific studies on eastern Tibetan angiosperm species. Plant Ecol. 2015, 216, 283–292. [Google Scholar] [CrossRef]
  62. Kiełtyk, P. Patterns of floral allocation along an elevation gradient: Variation in Senecio subalpinus growing in the Tatra Mountains. Alp. Bot. 2021, 131, 117–124. [Google Scholar] [CrossRef]
  63. Rixen, C.; Wipf, S.; Rumpf, S.B.; Giejsztowt, J.; Millen, J.; Morgan, J.W.; Nicotra, A.B.; Venn, S.; Zong, S.; Dickinson, K.J.; et al. Intraspecific trait variation in alpine plants relates to their elevational distribution. J. Ecol. 2022, 110, 860–875. [Google Scholar] [CrossRef]
  64. Institute of Soil Science. The Soil Atlas of China; Cartographic Publishing House: Beijing, China, 1986. [Google Scholar]
Agronomy 15 00723 g001
Figure 1. Relationships of elevation with leaf size (a), leaf thickness (b), SLA (c), and LDMC (d).
Figure 1. Relationships of elevation with leaf size (a), leaf thickness (b), SLA (c), and LDMC (d).
Agronomy 15 00723 g001
Agronomy 15 00723 g002
Figure 2. Relationships of elevation with aboveground biomass (a), belowground biomass (b), total biomass (c), and above- and belowground biomass allocation (d).
Figure 2. Relationships of elevation with aboveground biomass (a), belowground biomass (b), total biomass (c), and above- and belowground biomass allocation (d).
Agronomy 15 00723 g002
Agronomy 15 00723 g003
Figure 3. Effect sizes of four leaf traits on aboveground biomass (a), belowground biomass (b), total biomass (c), and above- and belowground biomass allocation (d). Effect sizes are slopes of the relationships of four leaf traits with biomass accumulation and allocation in the linear mixed-effect models. Points and error bars represent the estimated effect sizes and their 95% CIs. Effect sizes are determined as significant if the 95% CI do not include zero (i.e., p < 0.05), and significant ones are indicated by solid dots and error bars. And non-significant ones are indicated by empty dots and error bars.
Figure 3. Effect sizes of four leaf traits on aboveground biomass (a), belowground biomass (b), total biomass (c), and above- and belowground biomass allocation (d). Effect sizes are slopes of the relationships of four leaf traits with biomass accumulation and allocation in the linear mixed-effect models. Points and error bars represent the estimated effect sizes and their 95% CIs. Effect sizes are determined as significant if the 95% CI do not include zero (i.e., p < 0.05), and significant ones are indicated by solid dots and error bars. And non-significant ones are indicated by empty dots and error bars.
Agronomy 15 00723 g003
Table 1. Mean, standard deviation and the coefficient of variation of leaf functional traits of Gentiana lawrencei along the elevational gradient. The coefficient of variation (i.e., the inter-individual variation in leaf functional traits) was calculated as the ratio of the standard deviation to the mean of the leaf functional traits of each population along the elevational gradient. Tukey-HSD tests were conducted to compare differences between populations. Different lowercase letters indicate statistically significant differences between populations.
Table 1. Mean, standard deviation and the coefficient of variation of leaf functional traits of Gentiana lawrencei along the elevational gradient. The coefficient of variation (i.e., the inter-individual variation in leaf functional traits) was calculated as the ratio of the standard deviation to the mean of the leaf functional traits of each population along the elevational gradient. Tukey-HSD tests were conducted to compare differences between populations. Different lowercase letters indicate statistically significant differences between populations.
Elevation3200 m3500 m3750 m3900 m4000 m
Leaf size
(mm2)		Mean14.670 b28.365 a12.945 bc9.887 c12.858 bc
Standard Deviation3.6935.0323.6141.1653.132
Coefficient of Variation0.2520.1770.2790.1180.244
Leaf thickness
(mm)		Mean0.465 ab0.422 b0.503 ab0.516 a0.496 ab
Standard Deviation0.0660.1040.0620.0520.073
Coefficient of Variation0.1420.2460.1240.1010.147
SLA
(m2/kg)		Mean16.147 b22.538 a18.961 ab15.498 b18.519 ab
Standard Deviation4.5305.9032.2541.4380.801
Coefficient of Variation0.2810.2620.1190.0930.043
LDMC
(mg/mg)		Mean0.238 a0.212 ab0.192 b0.226 a0.210 ab
Standard Deviation0.0230.0300.0160.0140.029
Coefficient of Variation0.0980.1430.0840.0620.137
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, Y.; Zhang, L.; Wang, Z.; Shuai, L.; Zhang, X.; Huang, Y.; Wang, Y.; Ma, Z.; Zhang, C. Leaf Traits, Biomass Accumulation and Allocation of Gentiana lawrencei Burkill Along an 800 m Elevation Gradient in Alpine Grasslands. Agronomy 2025, 15, 723. https://doi.org/10.3390/agronomy15030723

AMA Style

Yang Y, Zhang L, Wang Z, Shuai L, Zhang X, Huang Y, Wang Y, Ma Z, Zhang C. Leaf Traits, Biomass Accumulation and Allocation of Gentiana lawrencei Burkill Along an 800 m Elevation Gradient in Alpine Grasslands. Agronomy. 2025; 15(3):723. https://doi.org/10.3390/agronomy15030723

Chicago/Turabian Style

Yang, Yuan, Longxin Zhang, Zuoyi Wang, Linlin Shuai, Xiaoying Zhang, Yufang Huang, Ying Wang, Zhen Ma, and Chunhui Zhang. 2025. "Leaf Traits, Biomass Accumulation and Allocation of Gentiana lawrencei Burkill Along an 800 m Elevation Gradient in Alpine Grasslands" Agronomy 15, no. 3: 723. https://doi.org/10.3390/agronomy15030723

APA Style

Yang, Y., Zhang, L., Wang, Z., Shuai, L., Zhang, X., Huang, Y., Wang, Y., Ma, Z., & Zhang, C. (2025). Leaf Traits, Biomass Accumulation and Allocation of Gentiana lawrencei Burkill Along an 800 m Elevation Gradient in Alpine Grasslands. Agronomy, 15(3), 723. https://doi.org/10.3390/agronomy15030723

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop