Kirchoff B K, Richter S J, Remington D L, Wisniewski E
Department of Biology, P. O. Box 26170, University of North Carolina, Greensboro, North Carolina 27402, USA.
Syst Biol. 2004 Feb;53(1):1-17. doi: 10.1080/10635150490424376.
Two studies were conducted to explore the use of complex data in character description and hybrid identification. In order to determine if complex data allow the production of better characters, eight groups of plant systematists were given two classes of drawings of plant parts, and asked to divide them into character states (clusters) in two separate experiments. The first class of drawings consisted only of cotyledons. The second class consisted of triplets of drawings: a cotyledon, seedling leaf, and inflorescence bract. The triplets were used to simulate complex data such as might be garnered by looking at a plant. Each experiment resulted in four characters (groups of clusters), one for each group of systematists. Visual and statistical analysis of the data showed that the systematists were able to produce smaller, more precisely defined character states using the more complex drawings. The character states created with the complex drawings also were more consistent across systematists, and agreed more closely with an independent assessment of phylogeny. To investigate the utility of complex data in an applied task, four observers rated 250 hybrids of Dubautia ciliolata X arborea based on the overall form (Gestalt) of the plants, and took measurements of a number of features of the same plants. A composite score of the measurements was created using principal components analysis. The correlation between the scores on the first principal component and the Gestalt ratings was computed. The Gestalt ratings and PC scores were significantly correlated, demonstrating that assessments of overall similarity can be as useful as more conventional approaches in determining the hybrid status of plants.
进行了两项研究，以探索复杂数据在性状描述和杂种鉴定中的应用。为了确定复杂数据是否能产生更好的性状，八组植物系统学家被给予两类植物部分的绘图，并要求他们在两个独立的实验中将其划分为性状状态（聚类）。第一类绘图仅由子叶组成。第二类由三联绘图组成：一片子叶、幼苗叶和花序苞片。这些三联图用于模拟通过观察植物可能获得的复杂数据。每个实验产生了四个性状（聚类组），每组系统学家各一个。对数据的视觉和统计分析表明，系统学家能够使用更复杂的绘图产生更小、定义更精确的性状状态。用复杂绘图创建的性状状态在系统学家之间也更一致，并且与系统发育的独立评估更紧密地一致。为了研究复杂数据在一项应用任务中的效用，四名观察者根据植物的整体形态（格式塔）对250个杜氏纤毛杜若×乔木杂种进行了评分，并对同一植物的一些特征进行了测量。使用主成分分析创建了测量的综合得分。计算了第一主成分得分与格式塔评分之间的相关性。格式塔评分与主成分得分显著相关，表明在确定植物的杂种状态时，对整体相似性的评估与更传统的方法一样有用。
