Habitat factors and phylogeny jointly drive leaf stoichiometry in dry-hot valley region of Jinsha River, Yunnan, China

doi:10.17521/cjpe.2025.0179

Abstract

Abstract:

Aims The objective of this study was to determine whether leaf stoichiometry is predominantly shaped by habitat filtering or phylogenetic conservatism in the extreme dry-hot valley of the Jinsha River. We further evaluated the applicability of the biogeochemical niche and stoichiometric plasticity hypotheses at a regional scale.

Methods We collected 240 leaf samples from 13 shrub and herbaceous species at 5 sampling sites along the Jinsha River. Leaf carbon (C), nitrogen (N), and phosphorus (P) stoichiometric traits were measured. Corresponding soil physicochemical properties were also analyzed. Bayesian phylogenetic linear mixed models were used to quantify the relative contributions of habitat factors and phylogeny to variation in leaf stoichiometry, and to explore how major habitat gradients regulate plant nutrient use strategies.

Important findings Habitat factors collectively explained 1.9% to 13.7% of the variability in leaf elements and their stoichiometric ratios. Elevation was the primary driver of leaf C and P concentrations and of C:P and N:P, whereas soil pH was the main regulator of leaf N concentration and C:N. Importantly, significant phylogenetic signals were observed in leaf N and P concentrations, as well as in C:N and C:P, indicating the conservatism of these traits. In addition to phylogenetic constraints, we found that leaf C concentration was primarily associated with plant life form and N:P showed greater intraspecific plasticity. In conclusion, regional leaf stoichiometric patterns were governed mainly by phylogenetic conservatism and refined by phenotypic plasticity. These findings enhance our understanding of how plant diversity is maintained under habitat stress and broaden knowledge of plant nutrient-use strategies at regional scales.

Key words: leaf stoichiometry, phylogeny, phenotypic plasticity, habitat filtering, dry-hot river valley

FENG Zhe, XU Ge-Xi, LIU Shun, CHEN Jian, LI Fei-Fan, GONG Shan-Shan, JIA Lei, SUN Zhen, YU Mei-Ni, SHI Zuo-Min, ZHOU Qing-Hong, JIANG Dong-Mei. Habitat factors and phylogeny jointly drive leaf stoichiometry in dry-hot valley region of Jinsha River, Yunnan, China[J]. Chin J Plant Ecol, 2026, 50(2): 352-361.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2025.0179

https://www.plant-ecology.com/EN/Y2026/V50/I2/352

Figures/Tables 6

Fig. 1 Study sites in dry-hot valley of Jinsha River. HP, Huaping; LQ, Luquan; YM, Yuanmou; YR, Yongren; YS, Yongsheng.

Table 1 Basic information on the study sites in dry-hot valley of Jinsha River

采样地点 Study site	纬度 Latitude (° N)	经度 Longitude (° E)	海拔 Altitude (m)	年平均气温 Mean annual temperature (℃)	年降水量 Mean annual precipitation (mm)	胸高断面积 Basal area (cm²·m^-2)	草本盖度 Herb cover (%)
永胜 Yongsheng	26.20	100.62	1 297	18.3	825.0	5.85	90
华坪 Huaping	26.37	101.23	1 307	20.5	857.8	20.42	88
永仁 Yongren	26.40	101.46	1 496	18.5	925.6	34.51	93
元谋 Yuanmou	25.72	101.78	1 217	20.2	827.9	2.70	92
禄劝 Luquan	26.30	102.64	1 023	21.3	879.6	3.42	95

Fig. 2 Recursive Feature Elimination based on random forest and 5-fold cross-validation was used to select habitat variables associated with plant leaf stoichiometry. The final selection of variables is indicated by the light bars. Dark bars indicate additional, next most important predictors, but not used for subsequent analyses. C, carbon; N, nitrogen; P, phosphorus. AP, soil effective phosphorus content; ELE, elevation above sea level; MAP, mean annual precipitation; MAT, mean annual air temperature; NH+ 4-N, soil ammonium nitrogen content; NO- 3-N, soil nitrate nitrogen content; pH, soil pH; SBD, soil bulk density; STC, soil total carbon content; STN, soil total nitrogen content; STP, soil total phosphorus content; TK, soil total potassium content; WC, soil water content; MSE, mean squared error.

Fig. 3 Relationships between leaf stoichiometric and the selected variables were analyzed using linear mixed effect models. Effect magnitudes are expressed as normalized fixed effect coefficients. See Fig. 2 for variable abbreviations. C, carbon; N, nitrogen; P, phosphorus. ELE, elevation above sea level; MAP, mean annual precipitation; MAT, mean annual air temperature; pH, soil pH; STN, soil total nitrogen content; STP, soil total phosphorus content; TK, soil total potassium content; WC, soil water content; LMM, linear mixed-effect model.

Fig. 4 Results of the Bayesian phylogenetic mixture model (A) and variance partitioning of species-level variation (B). In panel A, “Environments” represents the fixed effects (a subset of habitat factors selected through recursive feature elimination). C, carbon; N, nitrogen; P, phosphorus.

Table 2 Phylogenetic signals of plant leaf stoichiometric traits

	Blomberg’s K	p	Pagel’s λ	p
C	0.155	0.135	0.507	0.297
N	0.479^**	0.003	0.915^*	0.017
P	0.385^*	0.016	0.736^*	0.014
C:N	0.583^**	0.004	0.951^*	0.011
C:P	0.354^*	0.025	0.746^*	0.021
N:P	0.290	0.526	0.868	0.190

References 48

[1]	Ågren GI, Weih M (2012). Plant stoichiometry at different scales: element concentration patterns reflect environment more than genotype. New Phytologist, 194, 944-952. DOI PMID
[2]	Anderegg LDL, Berner LT, Badgley G, Sethi ML, Law BE, HilleRisLambers J (2018). Within-species patterns challenge our understanding of the leaf economics spectrum. Ecology Letters, 21, 734-744. DOI PMID
[3]	Bai KD, Lv SH, Ning SJ, Zeng DJ, Guo YL, Wang B (2019). Leaf nutrient concentrations associated with phylogeny, leaf habit and soil chemistry in tropical karst seasonal rainforest tree species. Plant and Soil, 434, 305-326. DOI
[4]	Bates D, Mächler M, Bolker B, Walker S (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1-48.
[5]	Bürkner PC (2017). Brms: an R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80, 1-28.
[6]	Cai Q, Ding JX, Zhang ZL, Hu J, Wang QT, Yin MZ, Liu Q, Yin HJ (2019). Distribution patterns and driving factors of leaf C, N and P stoichiometry of coniferous species on the eastern Qinghai-Xizang Plateau, China. Chinese Journal of Plant Ecology, 43, 1048-1060. DOI
	[蔡琴, 丁俊祥, 张子良, 胡君, 汪其同, 尹明珍, 刘庆, 尹华军 (2019). 青藏高原东缘主要针叶树种叶片碳氮磷化学计量分布格局及其驱动因素. 植物生态学报, 43, 1048-1060.] DOI
[7]	Chen J, Chen M, Liu S, Xing HS, Li FF, Xu GX, Shi ZM (2024a). Elevational patterns and drivers of leaf carbon, nitrogen, and phosphorus stoichiometry in an Alpine-gorge region on the eastern Qinghai-Tibetan Plateau. Journal of Soil Science and Plant Nutrition, 24, 3953-3968. DOI
[8]	Chen Y, Li YQ, Wang LL, Duan YL, Cao WJ, Wang XY, Li YL (2024b). Heterogeneity of leaf stoichiometry of different life forms along environmental transects in typical ecologically fragile areas of China. Science of the Total Environment, 910, 168495. DOI: 10.1016/j.scitotenv.2023.168495.
[9]	Elser JJ, Sterner RW, Gorokhova E, Fagan WF, Markow TA, Cotner JB, Harrison JF, Hobbie SE, Odell GM, Weider LW (2000). Biological stoichiometry from genes to ecosystems. Ecology Letters, 3, 540-550. DOI URL
[10]	Finger RA, Turetsky MR, Kielland K, Ruess RW, Mack MC, Euskirchen ES (2016). Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland. Journal of Ecology, 104, 1542-1554. DOI URL
[11]	Finzi AC, Austin AT, Cleland EE, Frey SD, Houlton BZ, Wallenstein MD (2011). Responses and feedbacks of coupled biogeochemical cycles to climate change: examples from terrestrial ecosystems. Frontiers in Ecology and the Environment, 9, 61-67. DOI URL
[12]	Flexas J, Ribas-Carbó M, Bota J, Galmés J, Henkle M, Martínez-Cañellas S, Medrano H (2006). Decreased Rubisco activity during water stress is not induced by decreased relative water content but related to conditions of low stomatal conductance and chloroplast CO₂ concentration. New Phytologist, 172, 73-82. DOI PMID
[13]	Güsewell S, Koerselman W (2002). Variation in nitrogen and phosphorus concentrations of wetland plants. Perspectives in Plant Ecology, Evolution and Systematics, 5, 37-61. DOI URL
[14]	He JS, Fang JY, Wang ZH, Guo DL, Flynn DFB, Geng Z (2006). Stoichiometry and large-scale patterns of leaf carbon and nitrogen in the grassland biomes of China. Oecologia, 149, 115-122. DOI URL
[15]	He NP, Liu CC, Piao SL, Sack L, Xu L, Luo YQ, He JS, Han XG, Zhou GS, Zhou XH, Lin Y, Yu Q, Liu SR, Sun W, Niu SL, et al. (2019). Ecosystem traits linking functional traits to macroecology. Trends in Ecology & Evolution, 34, 200-210. DOI URL
[16]	Hetherington AM, Woodward FI (2003). The role of stomata in sensing and driving environmental change. Nature, 424, 901-908. DOI
[17]	Jin Y, Qian H (2022). V. PhyloMaker2: an updated and enlarged R package that can generate very large phylogenies for vascular plants. Plant Diversity, 44, 335-339. DOI
[18]	Kerkhoff AJ, Enquist BJ, Elser JJ, Fagan WF (2005). Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecology and Biogeography, 14, 585-598. DOI URL
[19]	Kerkhoff AJ, Fagan WF, Elser JJ, Enquist BJ (2006). Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. The American Naturalist, 168, E103-E122.
[20]	Kuhn M (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1-26.
[21]	Li ZL, Zeng ZQ, Tian DS, Wang JS, Wang BX, Chen HYH, Quan Q, Chen WN, Yang JL, Meng C, Wang Y, Niu SL (2020). Global variations and controlling factors of soil nitrogen turnover rate. Earth-Science Reviews, 207, 103250. DOI: 10.1016/j.earscirev.2020.103250.
[22]	Lin YT, Lai Y, Tang SB, Qin ZF, Liu JF, Kang FF, Kuang YW (2022). Climatic and edaphic variables determine leaf C, N, P stoichiometry of deciduous Quercus species. Plant and Soil, 474, 383-394. DOI
[23]	Liu S, Xu GX, Chen J, Chen M, Shi ZM, Zhou QH, Jiang DM (2024). Soil microbial community structure and influencing factors in dry-hot valley region of Jinsha River, Yunnan Province. Acta Ecologica Sinica, 44, 7172-7182.
	[刘顺, 许格希, 陈健, 陈淼, 史作民, 周庆宏, 蒋冬梅 (2024). 云南金沙江干热河谷土壤微生物群落结构特征及影响因素. 生态学报, 44, 7172-7182.]
[24]	Lu JN, Zhao XY, Wang SK, Feng S, Ning ZY, Wang RX, Chen XP, Zhao HS, Chen M (2023). Untangling the influence of abiotic and biotic factors on leaf C, N, and P stoichiometry along a desert-grassland transition zone in northern China. Science of the Total Environment, 884, 163902. DOI: 10.1016/j.scitotenv.2023.163902.
[25]	Peñuelas J, Fernández-Martínez M, Ciais P, Jou D, Piao S, Obersteiner M, Vicca S, Janssens IA, Sardans J (2019). The bioelements, the elementome, and the biogeochemical niche. Ecology, 100, e02652. DOI: 10.1002/ecy.2652.
[26]	Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, Bret-Harte MS, Cornwell WK, Craine JM, Gurvich DE, Urcelay C, Veneklaas EJ, Reich PB, Poorter L, Wright IJ, et al. (2016). Corrigendum to, New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 64, 715-716. DOI URL
[27]	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
[28]	Revell LJ (2012). Phytools: an R package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution, 3, 217-223. DOI URL
[29]	Sardans J, Janssens IA, Alonso R, Veresoglou SD, Rillig MC, Sanders TG, Carnicer J, Filella I, Farré-Armengol G, Peñuelas J (2015). Foliar elemental composition of European forest tree species associated with evolutionary traits and present environmental and competitive conditions. Global Ecology and Biogeography, 24, 240-255. DOI URL
[30]	Sardans J, Vallicrosa H, Zuccarini P, Farré-Armengol G, Fernández-Martínez M, Peguero G, Gargallo-Garriga A, Ciais P, Janssens IA, Obersteiner M, Richter A, Peñuelas J (2021). Empirical support for the biogeochemical niche hypothesis in forest trees. Nature Ecology & Evolution, 5, 184-194.
[31]	Saura-Mas S, Lloret F (2007). Leaf and shoot water content and leaf dry matter content of Mediterranean woody species with different post-fire regenerative strategies. Annals of Botany, 99, 545-554. DOI PMID
[32]	Spicer ME, Radhamoni HVN, Duguid MC, Queenborough SA, Comita LS (2022). Herbaceous plant diversity in forest ecosystems: patterns, mechanisms, and threats. Plant Ecology, 223, 117-129. DOI
[33]	Stalenberg E, Hutchinson MF, Foley WJ (2018). Using historical normals to improve modern monthly climate normal surfaces for Madagascar. International Journal of Climatology, 38, 5746-5765. DOI URL
[34]	Sun Y, Liao JH, Zou XM, Xu X, Yang JY, Chen HYH, Ruan HH (2020). Coherent responses of terrestrial C, N stoichiometry to drought across plants, soil, and microorganisms in forests and grasslands. Agricultural and Forest Meteorology, 292, 108104. DOI: 10.1016/j.agrformet.2020.108104.
[35]	Tian D, Yan ZB, Fang JY (2021). Review on characteristics and main hypotheses of plant ecological stoichiometry. Chinese Journal of Plant Ecology, 45, 682-713. DOI
	[田地, 严正兵, 方精云 (2021). 植物生态化学计量特征及其主要假说. 植物生态学报, 45, 682-713.] DOI
[36]	Tian D, Yan ZB, Niklas KJ, Han WX, Kattge J, Reich PB, Luo YK, Chen YH, Tang ZY, Hu HF, Wright IJ, Schmid B, Fang JY (2018). Global leaf nitrogen and phosphorus stoichiometry and their scaling exponent. National Science Review, 5, 728-739. DOI URL
[37]	Tian D, Yan ZB, Schmid B, Kattge J, Fang JY, Stocker BD (2024). Environmental versus phylogenetic controls on leaf nitrogen and phosphorous concentrations in vascular plants. Nature Communications, 15, 5346. DOI: 10.1038/s41467-024-49665-4.
[38]	Townsend AR, Cleveland CC, Asner GP, Bustamante MMC (2007). Controls over foliar N:P ratios in tropical rain forests. Ecology, 88, 107-118. DOI PMID
[39]	Wang C, Yang ZH, He MZ, Hu YK, Lei YB, Hou YH, Ji SN, Wang YH, Yu LF, Peñuelas J, Sardans J, Li XN (2025). Coordinated variations in leaf and root biogeochemical niches. New Phytologist, 246, 947-960. DOI URL
[40]	Wang DC, Wang FC, Huang Y, Duan XW, Liu JY, Hu BX, Sun ZC, Chen JH (2018a). Examining the effects of hydropower station construction on the surface temperature of the Jinsha River Dry-Hot Valley at different seasons. Remote Sensing, 10, 600. DOI: 10.3390/rs10040600.
[41]	Wang S, Wang XB, Han XG, Deng Y (2018b). Higher precipitation strengthens the microbial interactions in semi-arid grassland soils. Global Ecology and Biogeography, 27, 570-580. DOI URL
[42]	Wang XG, Lü XT, Dijkstra FA, Zhang HY, Wang XB, Wuyunna, Wang ZW, Feng J, Han XG (2019). Changes of plant N:P stoichiometry across a 3000-km aridity transect in grasslands of northern China. Plant and Soil, 443, 107-119. DOI
[43]	Wright IJ, Reich PB, Westoby M (2003). Least-cost input mixtures of water and nitrogen for photosynthesis. The American Naturalist, 161, 98-111. DOI URL
[44]	Xing H, Wu RH, Yu HB, Shi WJ, Li XJ, Zhang QF (2024). Stoichiometric characteristics and influencing factors of carbon, nitrogen and phosphorus in leaves of steppe plants in Xilingol. Acta Agrestia Sinica, 32, 3774-3787.
	[邢浩, 乌日汗, 于红博, 史文杰, 李雪杰, 张巧凤 (2024). 锡林郭勒草原植物叶片碳、氮、磷化学计量特征及其影响因子. 草地学报, 32, 3774-3787.] DOI
[45]	Xu XQ, Li WJ, Zhou XW, Lü SH, Bai KD (2024). Leaf ecological stoichiometry in understory plants with different life forms in a subtropical evergreen broad-leaved forest. Journal of Tropical and Subtropical Botany, 32, 725-736.
	[许雪晴, 李文君, 周雪雯, 吕仕洪, 白坤栋 (2024). 亚热带常绿阔叶林林下不同生活型植物叶片生态化学计量特征. 热带亚热带植物学报, 32, 725-736.]
[46]	Zhang JH, Zhao N, Liu CC, Yang H, Li ML, Yu GR, Wilcox K, Yu Q, He NP (2018). C:N:P stoichiometry in China’s forests: from organs to ecosystems. Functional Ecology, 32, 50-60. DOI URL
[47]	Zhang ZZ, Ming QZ, Zhang HC, Li HY, Duan LZ (2013). Progress and issues on geographical environment evolution in dry-hot valley of Jinsha River. Geographical Science Research, 2, 1-7. DOI URL
[48]	Zhou Y, Yi YJ, Liu HX, Tang CH, Zhu YL, Zhang SH (2022). Effect of geomorphologic features and climate change on vegetation distribution in the arid hot valleys of Jinsha River, Southwest China. Journal of Mountain Science, 19, 2874-2885. DOI