Roumet Catherine, Picon-Cochard Catherine, Dawson Lorna A, Joffre Richard, Mayes Robert, Blanchard Alain, Brewer Mark J
Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, UMR 5175, 1919 route de Mende, 34293 Montpellier Cedex 5, France.
New Phytol. 2006;170(3):631-8. doi: 10.1111/j.1469-8137.2006.01698.x.
Understanding of plant interactions is greatly limited by our ability to identify and quantify roots belonging to different species. We proposed and compared two methods for estimating the root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial root mixtures composed of two or three herbaceous species were prepared. The proportion of root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and actual root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.
我们识别和量化不同物种根系的能力极大地限制了对植物相互作用的理解。我们提出并比较了两种估算人工混合物中各物种根系生物量比例的方法:近红外反射光谱法(NIRS)和植物蜡标记法。制备了两组由两种或三种草本物种组成的人工根系混合物。混合物中各物种根系物质的比例通过NIRS光谱数据(i)以及正构烷烃(ii)、正构醇(iii)和正构烷烃与正构醇组合(iv)的浓度模式进行估算。对于每个数据集,使用多元统计模型建立校准方程。对于所有考虑的物种,根系混合物的植物组成都得到了很好的预测。不同方法的准确性略有差异:烷烃<醇类=烷烃+醇类<NIRS。对于烷烃+醇类预测,预测值与实际根系比例之间的相关系数在0.89至0.99之间,对于NIRS预测,相关系数在0.97至0.99之间。这两种方法为理解分配模式和竞争相互作用提供了有前景的潜力。