Plant and Soil
Supporting Information for
Altitudinal patterns of leaf stociometry and nutrient resorption in Quercus variabilis inthe Baotianman Mountains, China
Baoming Du·Huawei Ji ·Chuan Pen ·Xiaojing Liu ·Chunjiang Liu
B. Du· H.Ji ·C. Liu ()
School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, Shanghai 200240, China
e-mail:
B. Du·C. Liu
Shanghai Urban Forest Ecosystem Research Station, State Forestry Bureau, Shanghai 200000, China
C. Peng
School of Life Sciences, East China Normal University, Shanghai 200241, China
X. Liu
Baotianman Natural Reserve Administration, Neixiang 474350, China
C. Liu
Key Laboratory of Urban Agriculture (South), Ministry of Agriculture, Shanghai 200240, China
Contents of this file
Table S1 to S3
Figures S1 to S9
Table S1 Climate, plant, and soil characteristics at different altitudes in Baotianman Mountains
Sites / Altitude (m) / MAT(oC) / MAP (mm) / Age (year) / LMA(kg m-2) / RWC (%) / Soil pH
Xuyaogou / 546 / 13.62 / 627.3 / 40 / 0.11±0.00a / 12.5±0.3c / 4.92
Dashiyao / 856 / 12.07 / 677.7 / 40 / 0.15±0.05a / 71.5±1.0b / 4.86
Laoshanmen / 1105 / 10.83 / 718.2 / 90 / 0.11±0.00a / 73.0±1.8b / 4.87
Manzizhang / 1323 / 9.74 / 753.6 / 90 / 0.14±0.03a / 79.5±0.5a / 4.70
Note: Mean annual temperature (MAT) and mean annual precipitation (MAP) were obtained from Baotianman Natural Reserve Administration (BNRA) and Neixiang Meteorological Bureau. Tree age was from BNRA, and calculated according to DBH (diameter at breast height)-age equation of Q. variabilis(Jiang et al. 2010). LMA indicates leaf mass per area. RWC indicates leaf relative water content.Lowercase indicate significant differences of LMA and RWC at four altitudes (p < 0.05).
Table S2 Pearson correlation coefficient of green leaf element concentrations in Q. variabilis.
N / P / S / K / Na / Ca / Mg / Al / Fe / Mn / Zn / Cu / Ba / N:PC / -0.37 / -0.16 / 0.20 / -0.02 / 0.59* / 0.34 / 0.28 / 0.66** / 0.73** / 0.17 / 0.17 / -0.26 / 0.33 / -0.09
N / 0.47 / 0.58* / 0.25 / -0.72** / -0.42 / -0.07 / -0.09 / -0.25 / -0.87*** / -0.14 / 0.39 / -0.52* / 0.19
P / 0.48 / 0.57* / -0.59* / -0.33 / -0.25 / 0.06 / -0.01 / -0.36 / 0.52* / 0.55* / 0.38 / -0.78***
S / 0.67** / -0.41 / -0.24 / -0.16 / 0.26 / 0.25 / -0.48 / 0.29 / 0.41 / -0.11 / -0.12
K / -0.44 / -0.28 / -0.46 / -0.16 / -0.12 / -0.10 / 0.55* / 0.48 / 0.00 / -0.46
Na / 0.50* / 0.57* / 0.42 / 0.51* / 0.47 / 0.07 / -0.42 / 0.37 / 0.15
Ca / 0.22 / 0.25 / 0.38 / 0.49 / -0.06 / -0.40 / 0.54* / 0.06
Mg / 0.13 / 0.12 / -0.11 / -0.27 / -0.46 / 0.14 / 0.23
Al / 0.92*** / -0.03 / 0.37 / 0.12 / -0.02 / -0.13
Fe / 0.09 / 0.39 / -0.07 / 0.16 / -0.16
Mn / 0.18 / -0.25 / 0.63** / -0.22
Zn / 0.58* / 0.01 / -0.67**
Cu / -0.56* / -0.33
Ba / 0.05
p < 0.05 “*”, p < 0.01 “**”, p < 0.001 “***”.
Table S3 Pearson correlation coefficient of nutrient resorption efficiencies in Q. variabilis.
N / P / S / K / Na / Ca / Mg / Al / Fe / Mn / Zn / Cu / BaC / -0.57* / -0.02 / -0.18 / 0.63** / -0.01 / -0.25 / 0.03 / 0.10 / 0.17 / -0.12 / 0.26 / 0.31 / -0.16
N / -0.20 / 0.56* / -0.35 / 0.05 / -0.21 / -0.16 / -0.04 / -0.16 / -0.36 / -0.06 / 0.10 / -0.19
P / 0.37 / 0.40 / 0.06 / -0.04 / 0.02 / -0.03 / -0.01 / 0.60* / 0.25 / -0.32 / 0.55*
S / 0.24 / 0.27 / -0.33 / -0.19 / 0.33 / 0.36 / -0.19 / 0.57* / -0.01 / 0.08
K / 0.45 / -0.20 / 0.07 / -0.21 / -0.05 / -0.09 / 0.40 / 0.44 / 0.16
Na / 0.20 / 0.26 / -0.13 / -0.01 / -0.31 / -0.11 / 0.35 / 0.47
Ca / 0.17 / 0.06 / 0.05 / 0.34 / -0.22 / 0.20 / 0.54*
Mg / -0.27 / -0.23 / -0.08 / -0.20 / 0.08 / 0.24
Al / 0.96*** / -0.02 / 0.51* / -0.21 / -0.11
Fe / -0.07 / 0.61* / -0.19 / -0.07
Mn / -0.00 / -0.29 / 0.49
Zn / 0.03 / -0.14
Cu / -0.03
p < 0.05 “*”, p < 0.01 “**”, p < 0.001 “***”.
Figure S1 Relationships between leaf ( both green and senesced)element concentrations and altitude.
Figure S2Relationships between leaf (both green and senesced) element concentrations and MAT (mean annual temperature).
Figure S3Relationships betweenleaf (both green and senesced)element concentrations and MAP (mean annual precipitation).
Figure S4 Relationships between leaf(both green and senesced)elementconcentrationsand corresponding soil element concentrations.
Figure S5 Linear relationships between green leaf concentrationsand senesced leaf concentrations.
Figure S6 Linear relationships between element resorptions and altitude.
Figure S7 Linear relationships between element resorptions and MAT (mean annual temperature).
Figure S8 Linear relationships betweent element resorptions and MAP (mean annual precipitation).
Figure S9 Grouping of elements based on their biological function. Green stars, green leaf, principal component analysis (PCA), Zhang et al. (2011); cyan stars, green leaf, PCA , Garten (1978); blue stars, green leaf, PCA , Ågren and Weih (2012); red stars, nutrient resorption efficiency, euclidian distances, this paper.