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基于叶长、叶宽和叶形的叶片大小估计。

Leaf size estimation based on leaf length, width and shape.

机构信息

Department of Biological Sciences, Macquarie University, NSW 2109, Australia.

Department of Biodiversity, Macroecology and Biogeography, University of Goettingen, Goettingen, Germany.

出版信息

Ann Bot. 2021 Sep 3;128(4):395-406. doi: 10.1093/aob/mcab078.

Abstract

BACKGROUND AND AIMS

Leaf size has considerable ecological relevance, making it desirable to obtain leaf size estimations for as many species worldwide as possible. Current global databases, such as TRY, contain leaf size data for ~30 000 species, which is only ~8% of known species worldwide. Yet, taxonomic descriptions exist for the large majority of the remainder. Here we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait.

METHODS

Using a global dataset of all major leaf shapes measured on 3125 leaves from 780 taxa, we quantified scaling functions that estimate leaf size as a product of leaf length, width and a leaf shape-specific correction factor. We validated our method by comparing leaf size estimates with those obtained from image recognition software and compared our approach with the widely used correction factor of 2/3.

KEY RESULTS

Correction factors ranged from 0.39 for highly dissected, lobed leaves to 0.79 for oblate leaves. Leaf size estimation using leaf shape-specific correction factors was more accurate and precise than estimates obtained from the correction factor of 2/3.

CONCLUSION

Our method presents a tractable solution to accurately estimate leaf size when only information on leaf length, width and shape is available or when labour and time constraints prevent usage of image recognition software. We see promise in applying our method to data from species descriptions (including from fossils), databases, field work and on herbarium vouchers, especially when non-destructive in situ measurements are needed.

摘要

背景与目的

叶片大小具有重要的生态学意义,因此尽可能多地获取全球范围内物种的叶片大小估计值是很有必要的。目前,全球数据库(如 TRY)中包含了约 30000 个物种的叶片大小数据,这仅占全球已知物种的 8%左右。然而,对于其余大部分物种,都有其分类学描述。在这里,我们提出了一种简单的方法,利用物种描述中的叶片长度、宽度和形状信息,稳健地估计叶片面积,从而弥补这一重要植物功能性状的巨大知识空白。

方法

我们使用了一个全球数据集,其中包含了 780 个分类群中 3125 片叶子的所有主要叶片形状的测量值,我们量化了估计叶片大小的比例函数,将其作为叶片长度、宽度和叶片形状特定校正因子的乘积。我们通过将叶片大小估计值与图像识别软件获得的结果进行比较来验证我们的方法,并将我们的方法与广泛使用的 2/3 校正因子进行了比较。

主要结果

校正因子的范围从高度分裂、有裂片的叶片的 0.39 到扁平面的叶片的 0.79。使用叶片形状特定校正因子进行叶片大小估计比使用 2/3 校正因子获得的估计值更准确和精确。

结论

当只有叶片长度、宽度和形状的信息可用时,或者当劳动力和时间限制妨碍使用图像识别软件时,我们的方法提供了一种可行的解决方案,可以准确估计叶片大小。我们认为,将我们的方法应用于物种描述(包括化石)、数据库、野外工作和标本上的数据具有很大的潜力,尤其是在需要非破坏性原位测量时。

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