Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna, Austria.
Front Plant Sci. 2013 Aug 1;4:292. doi: 10.3389/fpls.2013.00292. eCollection 2013.
Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for "plant functional type" identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential.
植物根系在生态学和农学中具有关键作用。尽管根系研究迅速增加，但仍然没有一种分类方法可以区分不同根系策略的独特特征。我们的假设是，生态学生物功能型识别的多元方法可以应用于根系的分类。所提出的分类方法基于数据定义的统计程序，而无需对分类器进行先验决策。该研究表明，基于主成分的根系类型为多特征分类器提供了有效且有意义的方法。分类方法用模拟根系结构和形态野外数据进行了说明。模拟根系结构表明，具有空间分布参数的形态属性可以捕捉到根系多样性中最具特色的特征。虽然发育类型（主根与侧根系统）是一个强大但粗糙的分类器，但拓扑特征在不同群体之间提供了最详细的区分。野外数据支持常见形态特征对分类的充分性。从实测数据中出现的根系类型主要由直径/重量和密度主导类型区分。不同群体内根系的相似性是系统发育关系以及环境和人为选择压力的共同结果。我们得出结论，数据定义的分类适合整合不同根系测量方法和不同尺度获得的知识。由于广泛使用通用测量协议，目前根系形态是分类的最有前途的基础。为了捕捉根系多样性的细节，在结构测量技术方面需要做出努力。
