Data from: Complex data produce better characters

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.

本研究开展两项实验，旨在探索复杂数据在性状描述与杂交鉴定中的应用。
为验证复杂数据能否助力获得更优质的性状，研究人员将两类植物器官绘图素材分发给八组植物系统学家，并要求其在两项独立实验中，将素材划分为性状状态（character states，即聚类簇）。
第一类绘图仅包含子叶（cotyledons）；第二类绘图为三联绘图组合，分别包含子叶、幼苗叶与花序苞片（inflorescence bract），此类三联组合用于模拟通过实地观测植株所能采集到的复杂数据类型。
每项实验中，每组系统学家均可生成四类性状（聚类簇分组）。对数据的可视化与统计分析结果显示，使用复杂绘图素材的组别，能够划分出尺度更小、定义更精准的性状状态；基于复杂绘图生成的性状状态，在不同组系统学家间的一致性更高，且与独立开展的系统发育评估结果契合度更佳。
为探究复杂数据在实际应用场景中的效用，四名观察者针对250份Dubautia ciliolata × arborea的杂交植株开展两项评估：其一基于植株整体形态（格式塔，Gestalt）进行评分，其二对同一批植株的多项形态特征开展测量。研究人员通过主成分分析（principal components analysis）对测量数据构建综合评分模型，随后计算第一主成分得分与格式塔评分之间的相关系数。结果显示二者存在显著相关性，表明基于整体相似度的评估方法，在判定植物杂交类群归属时，可与传统形态学分析手段同样有效。