Data from: Near infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species

1. The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. 2. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. 3. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740–1800, 2100–2300 and 2410–2490 nm spectral regions. 4. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth–survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality.

1. 非结构性碳水化合物（non-structural carbohydrates, NSCs）向储备库的分配，是与树木生长和存活相关的重要生理机制。然而，植物组织中NSCs的检测流程成本高昂且耗时耗力。近红外光谱法（near-infrared spectroscopy, NIRS）是一种高通量技术，基于化学分析的经验校准，能够快速且低成本地推断大量样本的有机成分浓度。
2. 本研究的主要目标有二：其一，开发一套整合了树种、组织类型等多种变异来源的通用NSC浓度校准模型；其二，识别与NSC分子相关的特征光谱区域。本研究共分析了来自热带和温带生物群区的73个树种的不同树体器官（根、茎、枝条、叶片）的180份样本。为识别关键波长，本研究采用偏最小二乘回归（partial least squares regression, PLSR）与变量选择程序（竞争性自适应重加权采样，competitive adaptive reweighted sampling, CARS），评估了NSC浓度与NIRS光谱之间的统计关联。
3. 最终获得了针对总NSC的简洁精准校准模型（外部验证的决定系数r²为0.91，均方根误差RMSE为1.34%），其次是淀粉（r²=0.85，RMSE=1.20%）与可溶性糖（r²=0.82，RMSE=1.10%）。这些模型的关键波长具有一致性，主要分布于1740–1800 nm、2100–2300 nm以及2410–2490 nm的光谱区间内。
4. 本研究证明，通用校准模型能够快速且经济高效地推断不同树种与组织类型的NSC浓度。因此，利用NIRS开展植物NSC检测，可作为功能生物多样性研究的工具，尤其适用于研究生长-存活权衡关系及其对环境变化（包括生长限制与树木死亡）的响应机制。