Specific leaf area estimation model building based on leaf dry matter content of Cunninghamia lanceolata

doi:10.17521/cjpe.2017.0132

Abstract

Abstract:

Aims With progresses of leaf functional traits study, there is an increasing demand to explore the life history strategy and trade-offs in plants, as well as estimate stand productivity, by employing easy and simple leaf parameters. For instance, the interconversion between leaf dry matter content (LDMC) and specific leaf area (SLA) just fit the bill. Cunninghamia lanceolata serves as one of the most important afforestation evergreen needle species in subtropical zone. Building the SLA estimation model based on LDMC could provide a new approach to estimate SLA, and establish a connection path between mechanism explanation and productivity evaluation. Moreover, it could also build a bridge between individual level and large-scale, as well as between actuarial and estimation.

Methods Leaf samples were collected from two sampling sites located in C. lanceolata growing region: Huitong County of Hunan Province and Xinyang City of Henan Province. The samples covered fundamentally different niches (aspect, slope position, and canopy depth), and different life history (stand age and leaf age). SLA and LDMC were determined along leaf age gradients, and their value distributions in linkage to different factors were discussed. A general model based on LDMC of C. lanceolata was built to estimate SLA, and the impact of leaf age on the model was explored.

Important findings The SLA of C. lanceolata was (103.15 ± 69.54) cm ²·g ^-1, while LDMC was 0.39 ± 0.11. The LDMC and SLA of C. lanceolata can be estimated by nonlinear model (R ² = 0.718β4, p < 0.001), which meets the estimation requirements. One-year-old leaves showed the best fitting model (R ² = 0.889, p < 0.001), while old leaves (more than 2-year-old) showed the worst (R ² = 0.100β1, p < 0.001). Old leaves with a lower SLA (52.28-75.74 cm ²·g ^-1) might imply the relative independence among the variation of LDMC. The model based on LDMC to evaluate SLA is credible and effective. The effects on LDMC and SLA along leaf age gradients indicate leaf sensitivity, life history strategies and trade-offs.

http://jtp.cnki.net/bilingual/detail/html/ZWSB201802008

Key words: Cunninghamia lanceolata, specific leaf area, leaf dry matter content, model estimation, leaf functional traits

PENG Xi, YAN Wen-De, WANG Feng-Qi, WANG Guang-Jun, YU Fang-Yong, ZHAO Mei-Fang. Specific leaf area estimation model building based on leaf dry matter content of Cunninghamia lanceolata[J]. Chin J Plant Ecol, 2018, 42(2): 209-219.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2017.0132

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Figures/Tables 4

Fig. 1 Summary characteristics and degree of discretization of leaf dry matter content (LDMC) (A) and specific leaf area (SLA) (B) of Cunninghamia lanceolata along different factors gradients (n = 374).

Fig. 2 Nonlinear fitting model of specific leaf area (SLA) based on leaf dry matter content (LDMC) (A) and SLA scatter plot the regression residuals of LDMC (B) of Cunninghamia lanceolata. Model selection y = a(1 + x)b; N, sampling size; R2, adjusted R-squared; p, significant difference degree.

Fig. 3 Nonlinear fitting model of specific leaf area (SLA) based on leaf dry matter content (LDMC) of Cunninghamia lanceolata along different leaf age gradients. Model selection y = a(1 + x)b; N, sampling size; R2, adjusted R-squared; p, significant difference degree.

Fig. 4 Specific leaf area (SLA) scatter plot the regression residuals of leaf dry matter content (LDMC) of Cunninghamia lanceolata along different leaf age gradients.

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