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Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time‐honored methods, we can predict theoretical leaves that are possible, enabling us to understand the genetics, development, and environmental responses of plants in new ways. SummaryGrapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genusVitispredicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution.We measure the shapes of 139 grapevine leaves using 1672 pseudo‐landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo‐landmarks that results in a high‐resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10Vitisspp.We visualize a high‐resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers,Vitis vinifera,Vitisspp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models.By predicting a high‐resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genusVitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high‐resolution space, or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers.
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How many trees to see the forest? Assessing the effects of morphospace coverage and sample size in performance surface analysis
Abstract Linking morphology and function is critical to understanding the evolution of organismal shape. Performance landscapes, or performance surfaces, associate empirical functional performance data with a morphospace to assess how shape variation relates to functional variation. Performance surfaces for multiple functions also can be combined to understand the functional trade‐offs that affect the morphology of a particular structure across species. However, morphological performance surfaces usually require empirical determination of performance for a number of theoretical shapes that are evenly distributed throughout the morphospace. This process is time‐consuming, and is problematic for structures that are difficult to precisely manipulate.We sought to (a) understand the degree and pattern of sampling required to produce a reliable and nuanced performance surface and (b) investigate the possibility of building a surface using only naturally occurring morphologies. To do this, we subsampled a pre‐existing set of turtle shell performance surfaces in four different ways: first, uniform subsampling of theoretical morphologies across the surface; second, random subsampling of theoretical morphologies across the surface; third, a combination uniform/random subsampling method called close‐pairs sampling and fourth, subsampling only points on the surface known to correspond to a naturally occurring turtle shell morphology. Each subset was interpolated with ordinary Kriging to produce a new performance surface for comparison to the original.We found that using a fraction of the theoretical morphologies examined in the original study (half as many or fewer) was sufficient to produce a performance surface bearing close resemblance to the original (Pearson correlation ≥0.90); close‐pairs sampling dramatically increased the power of small sample sizes. We also discovered that only sampling points on the surface corresponding to naturally occurring morphologies produced an accurate surface, but results were better when individual specimens, rather than species averages, were used.Our findings demonstrate the viability of using performance surfaces to understand the evolution of complex morphologies for which theoretical shape modelling is difficult or computationally burdensome. Both lower levels of carefully configured sampling throughout the theoretical morphospace, and development of performance surfaces using only data from naturally occurring morphologies, are acceptable alternatives to the dense theoretical shape sampling employed in previous studies.
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- Award ID(s):
- 1811627
- PAR ID:
- 10449674
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 1411-1424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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