Important role of organ age in variation and coordination of stoichiometry in Pinus tabuliformis leaves, twigs and roots

doi:10.17521/cjpe.2025.0097

Abstract

Abstract:

Aims As a dominant and keystone species in temperate coniferous forests of China, Pinus tabuliformis exhibits strong environmental adaptability. Investigating the stoichiometric characteristics of leaves, twigs and roots in both current-year (newly developed) and perennial (≥1 year old, mature) organs, as well as their relationships, is essential for understanding intraspecific variation and plant resource allocation strategies.

Methods This study was conducted in natural P. tabuliformis forests across nine regions in China. A total of 81 individual trees were sampled to measure the carbon (C), nitrogen (N) and phosphorus (P) content in their six organs (current-year leaves, perennial leaves, current-year twigs, perennial twigs, absorptive roots and transport roots).

Important findings (1) Current-year organs exhibited higher N and P contents but lower C:N, C:P, and N:P ratios compared to perennial organs. Stoichiometric traits showed considerable intraspecific variation across organs of different ages, with organ age identified as the primary factor influencing these differences. (2) Standardized major axis analysis revealed that perennial leaves had higher P content per unit mass than current-year leaves, whereas current-year twigs and absorptive roots showed higher C, N and P contents than their perennial counterparts (perennial twigs and transport roots). (3) Additionally, absorptive roots had significantly higher C, N and P contents per unit mass than both current-year leaves and twigs. (4) Redundancy analysis indicated that the main environmental drivers of stoichiometric traits in current-year and perennial leaves were mean annual temperature and soil P content, respectively. The most significant environmental factors affecting the stoichiometric traits of current-year and perennial twigs were annual precipitation and soil P content, respectively; while the main environmental factors influencing absorptive and transport roots were soil moisture and soil N content, respectively. This study reveals the resource allocation strategies and environmental adaptability of leaves, twigs, and roots across different age classes in P. tabuliformis. The findings demonstrate that the studies on the stoichiometry of evergreen species should fully consider age-related differences among organs, which is essential for elucidating their nutrient allocation strategies and environmental adaption mechanisms.

Key words: intraspecific variation, Pinus tabuliformis, leaf, twig, root, age, stoichiometric characteristic

YU Jiang-Shan, XU Hao, GUO Yong-Zhong, HOU Ji-Hua. Important role of organ age in variation and coordination of stoichiometry in Pinus tabuliformis leaves, twigs and roots[J]. Chin J Plant Ecol, 2026, 50(2): 306-317.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2025.0097

https://www.plant-ecology.com/EN/Y2026/V50/I2/306

Figures/Tables 5

Fig. 1 Violin plots showing the distribution of carbon (C), nitrogen (N), and phosphorus (P) stoichiometry in leaves, twigs and roots of P. tabuliformis across nine natural forests. Different lowercase letters indicate significant differences among organs (p < 0.05).

Fig. 2 Stoichiometric characteristics of the carbon (C), nitrogen (N) and phosphorus (P) in current-year and perennial organs of P. tabuliformis: Relative frequency (A-C) and variance partitioning using nested linear mixed models for leaves, branches, and roots (D-F). A and D represent leaves, B and E represent twigs, C and F represent roots. The vertical lines in A-C indicate the mean values for each age group, with blue and yellow font denoting the coefficient of variation for current-year and perennial organs, respectively. In D-F, the variance partitioning is divided into four nested levels: site, plot, individual, and organ age. C, carbon content; N, nitrogen content; P, phosphorus content.

Fig. 3 Standardized major axis regression analysis of carbon, nitrogen, phosphorus content between current-year and perennial organs in P. tabuliformis. Regression lines are shown only when the relationship is significant (p < 0.05). The allometric slopes with 95% confidence intervals are indicated in the lower corner of each panel. Different lowercase letters represent significant difference among organs (p < 0.05); ns, no common slope.

Fig. 4 Redundancy analysis (RDA) of stoichiometry in leaves, twigs and roots of P. tabuliformis with environmental factors. A, Current-year leaves. B, Current-year twigs. C, absorptive roots. D, Perennial leaves. E, Perennial twigs. F, transport roots. Current_C, current-year organ carbon content; Current_N, current-year organ nitrogen content; Current_P, current-year organ phosphorus content; Current_C:N, carbon-to-nitrogen ratio of current-year organ; Current_C:P, carbon-to-phosphorus ratio of current-year organ; Current_N:P, nitrogen-to-phosphorus ratio of current-year organ; Perennial_C, perennial organ carbon content; Perennial_N, perennial organ nitrogen content; Perennial_P, perennial organ phosphorus content; Perennial_C:N, carbon-nitrogen ratio of perennial organ; Perennial_C:P, carbon-phosphorus ratio of perennial organ; Perennial_N:P, nitrogen-phosphorus ratio of perennial organ. MAP, mean annual precipitation; MAT, mean annual air temperature; SBD, soil bulk density; SMC, soil moisture content; SNC, soil total nitrogen content; SPC, soil total phosphorus content.

Table 1 Proportion of variance explained by environmental factors for the stoichiometry of leaf, twig and root of Pinus tabuliformis

年龄组 Age group	叶 Leaf			枝 Twig			根 Root
年龄组 Age group	环境因子 Environmental factor	解释率 Contribution rate (%)	p	环境因子 Environmental factor	解释率 Contribution rate (%)	p	环境因子 Environmental factor	解释率 Contribution rate (%)	p
当年生 Current-year	SBD	0.25	0.91	SBD	4.52	0.17	SBD	6.94	0.07
	SNC	4.24	0.18	SNC	4.80	0.13	SNC	2.15	0.40
	SPC	2.66	0.34	SPC	14.60	0.002	SPC	2.15	0.43
	SMC	3.78	0.24	SMC	5.81	0.09	SMC	32.59	<0.01
	MAT	10.50	<0.05	MAT	17.20	<0.01	MAT	7.72	0.04
	MAP	1.90	0.47	MAP	18.67	<0.01	MAP	0.61	0.79
多年生 Perennial	SBD	5.23	0.11	SBD	0.02	0.99	SBD	16.46	<0.01
	SNC	34.03	<0.01	SNC	13.17	<0.01	SNC	34.20	<0.01
	SPC	47.41	<0.01	SPC	37.12	<0.01	SPC	24.41	<0.01
	SMC	4.07	0.20	SMC	10.72	<0.01	SMC	28.47	<0.01
	MAT	5.01	0.14	MAT	16.48	<0.01	MAT	32.99	<0.01
	MAP	11.53	<0.01	MAP	12.03	<0.01	MAP	4.52	0.16

References 57

[1]	Ågren GI (2004). The C:N:P stoichiometry of autotrophs—Theory and observations. Ecology Letters, 7, 185-191. DOI URL
[2]	Bao SD (2000). Soil Agrochemical Analysis. China Agriculture Press, Beijing.
	[鲍士旦 (2000). 土壤农化分析. 中国农业出版社, 北京.]
[3]	Chabot BF, Hicks DJ (1982). The ecology of leaf life spans. Annual Review of Ecology and Systematics, 13, 229-259. DOI URL
[4]	Chavana-Bryant C, Malhi Y, Wu J, Asner GP, Anastasiou A, Enquist BJ, Cosio Caravasi EG, Doughty CE, Saleska SR, Martin RE, Gerard FF (2017). Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytologist, 214, 1049-1063. DOI PMID
[5]	Ding XX, Tang LY, Zhang BN, Ye LF, Xie JB, Wang ZY (2025). Adaptive strategies of twig functional traits in Robinia pseudoacacia along a precipitation gradient and their driving mechanisms. Journal of Zhejiang A&F University, 42, 503-512.
	[丁新新, 汤璐瑶, 张博纳, 叶琳峰, 谢江波, 王忠媛 (2025). 刺槐小枝功能性状沿降水梯度的适应策略及其驱动机制. 浙江农林大学学报, 42, 503-512.]
[6]	Elser JJ, Fagan WF, Denno RF, Dobberfuhl DR, Folarin A, Huberty A, Interlandi S, Kilham SS, McCauley E, Schulz KL, Siemann EH, Sterner RW (2000). Nutritional constraints in terrestrial and freshwater food webs. Nature, 408, 578-580. DOI
[7]	Fajardo A, Piper FI (2011). Intraspecific trait variation and covariation in a widespread tree species (Nothofagus pumilio) in southern Chile. New Phytologist, 189, 259-271. DOI PMID
[8]	Fortunel C, Ruelle J, Beauchêne J, Fine PVA, Baraloto C (2014). Wood specific gravity and anatomy of branches and roots in 113 Amazonian rainforest tree species across environmental gradients. New Phytologist, 202, 79-94. DOI PMID
[9]	Freschet GT, Pagès L, Iversen CM, Comas LH, Rewald B, Roumet C, Klimešová J, Zadworny M, Poorter H, Postma JA, Adams TS, Bagniewska-Zadworna A, Bengough AG, Blancaflor EB, Brunner I, et al. (2021). A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements. New Phytologist, 232, 973-1122.
[10]	Hodge A (2004). The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytologist, 162, 9-24. DOI URL
[11]	Huang ZG, Yu WW, Luo HH, Li YF, Dai WS, Hu YY, Wu JS (2015). Photosynthetic characteristics and their relationships with leaf nitrogen content and nitrogen allocation in leaves at different leaf age. Scientia Silvae Sinicae, 51(2), 44-51.
	[黄增冠, 喻卫武, 罗宏海, 李昱飞, 戴文圣, 胡渊渊, 吴家胜 (2015). 香榧不同叶龄叶片光合能力与氮含量及其分配关系的比较. 林业科学, 51(2), 44-51.]
[12]	Kong DL, Wang JJ, Wu HF, Valverde-Barrantes OJ, Wang RL, Zeng H, Kardol P, Zhang HY, Feng YL (2019). Nonlinearity of root trait relationships and the root economics spectrum. Nature Communications, 10, 2203. DOI: 10.1038/s41467-019-10245-6.
[13]	Kumordzi BB, Aubin I, Cardou F, Shipley B, Violle C, Johnstone J, Anand M, Arsenault A, Bell FW, Bergeron Y, Boulangeat I, Brousseau M, de Grandpré L, Delagrange S, Fenton NJ, et al. (2019). Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants. Functional Ecology, 33, 1771-1784. DOI
[14]	Kuusk V, Niinemets Ü, Valladares F (2018a). A major trade-off between structural and photosynthetic investments operative across plant and needle ages in three Mediterranean pines. Tree Physiology, 38, 543-557. DOI URL
[15]	Kuusk V, Niinemets Ü, Valladares F (2018b). Structural controls on photosynthetic capacity through juvenile-to-adult transition and needle ageing in Mediterranean pines. Functional Ecology, 32, 1479-1491. DOI URL
[16]	Li A, Guo DL, Wang ZQ, Liu HY (2010). Nitrogen and phosphorus allocation in leaves, twigs, and fine roots across 49 temperate, subtropical and tropical tree species: a hierarchical pattern. Functional Ecology, 24, 224-232. DOI URL
[17]	Li JL, Le XG, Chen XP, Reich PB, Niklas KJ, Li XQ, Wu PP, Zhou YJ, Zhong QL, Hu DD, Cheng DL (2024). Divergent intra- and interspecific root order variability identifies a two-dimensional root economics spectrum. Plant and Soil, 499, 473-490. DOI
[18]	Liao HL (2018). Effects of Drought to Absorptive Root Traits and Growth of 4 Tree Species in Subtropical China. Master degree dissertation, Nanchang Institute of Technology, Nanchang.
	[廖海玲 (2018). 干旱对亚热带4个树种苗木吸收根属性及生长的影响. 硕士学位论文, 南昌工程学院, 南昌.]
[19]	Liu C, Jin GZ, Liu ZL (2021). Importance of organ age in driving intraspecific trait variation and coordination for three evergreen coniferous species. Ecological Indicators, 121, 107099. DOI: 10.1016/j.ecolind.2020.107099.
[20]	Liu ZL, Hikosaka K, Li FR, Jin GZ (2020). Variations in leaf economics spectrum traits for an evergreen coniferous species: tree size dominates over environment factors. Functional Ecology, 34, 458-467. DOI URL
[21]	McCormack ML, Adams TS, Smithwick EAH, Eissenstat DM (2012). Predicting fine root lifespan from plant functional traits in temperate trees. New Phytologist, 195, 823-831. DOI PMID
[22]	McCormack ML, Dickie IA, Eissenstat DM, Fahey TJ, Fernandez CW, Guo DL, Helmisaari HS, Hobbie EA, Iversen CM, Jackson RB, Leppälammi-Kujansuu J, Norby RJ, Phillips RP, Pregitzer KS, Pritchard SG, et al. (2015). Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. New Phytologist, 207, 505-518. DOI PMID
[23]	Mucha J, Zadworny M, Helmisaari HS, Nihlgård B, Repo T, Żytkowiak M, Małek S, Reich PB, Oleksyn J (2020). Fine root classification matters: nutrient levels in different functional categories, orders and diameters of roots in boreal Pinus sylvestris across a latitudinal gradient. Plant and Soil, 447, 507-520. DOI
[24]	Nambiar EKS, Fife DN (1987). Growth and nutrient retranslocation in needles of radiata pine in relation to nitrogen supply. Annals of Botany, 60, 147-156. DOI URL
[25]	Niinemets Ü (2016). Leaf age dependent changes in within-canopy variation in leaf functional traits: a meta-analysis. Journal of Plant Research, 129, 313-338. DOI PMID
[26]	Olofinboba MO, Kozlowski TT (1973). Accumulation and utilization of carbohydrate reserves in shoot growth of Pinusresinosa. Canadian Journal of Forest Research, 3, 346-353. DOI URL
[27]	Ono K, Nishi Y, Watanabe A, Terashima I (2001). Possible mechanisms of adaptive leaf senescence. Plant Biology, 3, 234-243. DOI URL
[28]	Paradis E, Schliep K (2019). Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35, 526-528. DOI PMID
[29]	Pedrotti A, Pauletto EA, Crestana S, Holanda FSR, Cruvinel PE, Vaz CMP (2005). Evaluation of bulk density of Albaqualf soil under different tillage systems using the volumetric ring and computerized tomography methods. Soil and Tillage Research, 80, 115-123. DOI URL
[30]	Peñuelas J, Sardans J (2009). Elementary factors. Nature, 460, 803-804. DOI
[31]	Pregitzer KS, DeForest JL, Burton AJ, Allen MF, Ruess RW, Hendrick RL (2002). Fine root architecture of nine North American trees. Ecological Monographs, 72, 293-309. DOI URL
[32]	Qian Y, Sun HG, Dong RX, Jiang JM (2018). Research progress of carbohydrates allocation in conifers. Scientia Silvae Sinicae, 54(1), 141-153.
	[钱杨, 孙洪刚, 董汝湘, 姜景民 (2018). 针叶树碳水化合物分配研究进展. 林业科学, 54(1), 141-153.]
[33]	Reich PB, Oleksyn J (2004). Global patterns of plant leaf N and P in relation to temperature and latitude. Proceedings of the National Academy of Sciences of the United States of America, 101, 11001-11006. DOI PMID
[34]	Reich PB, Rich RL, Lu X, Wang YP, Oleksyn J (2014). Biogeographic variation in evergreen conifer needle longevity and impacts on boreal forest carbon cycle projections. Proceedings of the National Academy of Sciences of the United States of America, 111, 13703-13708. DOI PMID
[35]	Robakowski P, Bielinis E (2017). Needle age dependence of photosynthesis along a light gradient within an Abies alba crown. Acta Physiologiae Plantarum, 39, 83. DOI: 10.1007/s11738-017-2376-y.
[36]	Rüther E, Hertel D, Leuschner C (2024). Intraspecific variation in fine root morphology of European beech: a root order-based analysis of phenotypic root morphospace. Oecologia, 205, 121-133. DOI PMID
[37]	Sides CB, Enquist BJ, Ebersole JJ, Smith MN, Henderson AN, Sloat LL (2014). Revisiting Darwin’s hypothesis: Does greater intraspecific variability increase species’ ecological breadth. American Journal of Botany, 101, 56-62. DOI URL
[38]	Siefert A, Violle C, Chalmandrier L, Albert CH, Taudiere A, Fajardo A, Aarssen LW, Baraloto C, Carlucci MB, Cianciaruso MV, de L Dantas V, de Bello F, Duarte LDS, Fonseca CR, Freschet GT, et al. (2015). A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecology Letters, 18, 1406-1419. DOI PMID
[39]	Sterner RW, Elser JJ (2002). Ecological Stoichiometry: the Biology of Elements from Molecules to Biosphere. Princenton University Press, Princenton.
[40]	Tang DT, Peng GQ, Zhang S (2019). Age-related variations of needles and twigs in nutrient, nonstructural carbon and isotope composition along altitudinal gradients. Journal of Mountain Science, 16, 1546-1558. DOI
[41]	Tian J, Yang XQ, Yuan WP, Lin SR, Han LS, Zheng Y, Xia XS, Liu LY, Wang M, Zheng W, Fan L, Yan K, Chen XZ (2024). A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests. Global Change Biology, 30, e17454. DOI: 10.1111/gcb.17454.
[42]	Umaña MN, Swenson NG (2019). Does trait variation within broadly distributed species mirror patterns across species? A case study in Puerto Rico. Ecology, 100, e02745. DOI: 10.1002/ecy.2745.
[43]	Valladares F, Sánchez-Gómez D, Zavala M (2006). Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications. Journal of Ecology, 94, 1103-1116. DOI URL
[44]	Wang SQ, Li Y, Ju WM, Chen B, Chen JH, Croft H, Mickler RA, Yang FT (2020). Estimation of leaf photosynthetic capacity from leaf chlorophyll content and leaf age in a subtropical evergreen coniferous plantation. Journal of Geophysical Research: Biogeosciences, 125, e2019JG005020. DOI: 10.1029/2019JG005020.
[45]	Wang SY, Wang WN, Wang SZ, Yang LX, Gu JC (2021). Intraspecific variations of anatomical, morphological and chemical traits in leaves and absorptive roots along climate and soil gradients: a case study with Ginkgo biloba and Eucommia ulmoides. Plant and Soil, 469, 73-88.
[46]	Wang YJ, Jin GZ, Liu ZL (2024). Effects of tree size and organ age on variations in carbon, nitrogen, and phosphorus stoichiometry in Pinus koraiensis. Journal of Forestry Research, 35, 52. DOI: 10.1007/s11676-024-01705-x.
[47]	Wang YN, Gao GQ, Wang N, Wang ZQ, Gu JC (2019). Effects of morphology and stand structure on root biomass and length differed between absorptive and transport roots in temperate trees. Plant and Soil, 442, 355-367.
[48]	Warton DI, Wright IJ, Falster DS, Westoby M (2006). Bivariate line-fitting methods for allometry. Biological Reviews, 81, 259-291. DOI PMID
[49]	Weemstra M, Mommer L, Visser EJW, van Ruijven J, Kuyper TW, Mohren GMJ, Sterck FJ (2016). Towards a multidimensional root trait framework: a tree root review. New Phytologist, 211, 1159-1169. DOI PMID
[50]	Weemstra M, Roumet C, Cruz-Maldonado N, Anthelme F, Stokes A, Freschet GT (2022). Environmental variation drives the decoupling of leaf and root traits within species along an elevation gradient. Annals of Botany, 130, 419-430. DOI PMID
[51]	Westerband AC, Funk JL, Barton KE (2021). Intraspecific trait variation in plants: a renewed focus on its role in ecological processes. Annals of Botany, 127, 397-410. DOI PMID
[52]	Yang Y, Russo SE (2024). Trade-offs in rooting strategy dimensions along an edaphic gradient in a grassland ecosystem. Functional Ecology, 38, 792-807. DOI URL
[53]	Yao FY, Chen YH, Yan ZB, Li P, Han WX, Fang JY (2015). Biogeographic patterns of structural traits and C:N:P stoichiometry of tree twigs in China’s forests. PLoS ONE, 10, e0116391. DOI: 10.1371/journal.pone.0116391.
[54]	Zadworny M, McCormack ML, Żytkowiak R, Karolewski P, Mucha J, Oleksyn J (2017). Patterns of structural and defense investments in fine roots of Scots pine (Pinus sylvestris L.) across a strong temperature and latitudinal gradient in Europe. Global Change Biology, 23, 1218-1231. DOI PMID
[55]	Zhang JH, He NP, Liu CC, Xu L, Yu Q, Yu GR (2018). Allocation strategies for nitrogen and phosphorus in forest plants. Oikos, 127, 1506-1514. DOI URL
[56]	Zhang K, Hou JH, He NP (2017). Leaf functional trait distribution and controlling factors of Pinus tabuliformis. Acta Ecologica Sinica, 37, 736-749.
	[张凯, 侯继华, 何念鹏 (2017). 油松叶功能性状分布特征及其控制因素. 生态学报, 37, 736-749.]
[57]	Zhao X, Liu ZL, Jin GZ (2024). Effects of nitrogen addition and leaf age on needle traits and the relationships among traits in Pinus koraiensis. Environmental and Experimental Botany, 223, 105795. DOI: 10.1016/j.envexpbot.2024.105795.