Chitwood Daniel H, Mullins Joey
Department of Horticulture, Michigan State University, East Lansing, Michigan 48823, USA.
Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan 48823, USA.
Quant Plant Biol. 2022 Oct 7;3:e22. doi: 10.1017/qpb.2022.13. eCollection 2022.
Using conventional statistical approaches there exist powerful methods to classify shapes. Embedded in morphospaces is information that allows us to visualise theoretical leaves. These unmeasured leaves are never considered nor how the negative morphospace can inform us about the forces responsible for shaping leaf morphology. Here, we model leaf shape using an allometric indicator of leaf size, the ratio of vein to blade areas. The borders of the observable morphospace are restricted by constraints and define an orthogonal grid of developmental and evolutionary effects which can predict the shapes of possible grapevine leaves. Leaves in the genus are found to fully occupy morphospace available to them. From this morphospace, we predict the developmental and evolutionary shapes of grapevine leaves that are not only possible, but exist, and argue that rather than explaining leaf shape in terms of discrete nodes or species, that a continuous model is more appropriate.
使用传统统计方法,存在强大的形状分类方法。形态空间中蕴含的信息使我们能够可视化理论上的叶片。这些未测量的叶片从未被考虑过,也未考虑负形态空间如何能让我们了解塑造叶片形态的力量。在这里,我们使用叶片大小的异速生长指标(叶脉与叶片面积之比)对叶片形状进行建模。可观测形态空间的边界受到约束限制,并定义了一个发育和进化效应的正交网格,该网格可以预测可能的葡萄叶片形状。发现该属中的叶片完全占据了它们可用的形态空间。从这个形态空间中,我们预测了葡萄叶片不仅可能存在而且实际存在的发育和进化形状,并认为用连续模型而非离散节点或物种来解释叶片形状更为合适。