Julian Schrader1,2, Peijian Shi3, Dana L Royer4, Daniel J Peppe5, Rachael V Gallagher1, Yirong Li3, Rong Wang3, Ian J Wright1. 1. Department of Biological Sciences, Macquarie University, NSW 2109, Australia. 2. Department of Biodiversity, Macroecology and Biogeography, University of Goettingen, Goettingen, Germany. 3. Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, P.R. China. 4. Department of Earth and Environmental Sciences, Wesleyan University, Middletown, CT 06459, USA. 5. Terrestrial Paleoclimatology Research Group, Department of Geosciences, Baylor University, Waco, TX 76706, USA.
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.
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.
Authors: Zoë Migicovsky; Joel F Swift; Zachary Helget; Laura L Klein; Anh Ly; Matthew Maimaitiyiming; Karoline Woodhouse; Anne Fennell; Misha Kwasniewski; Allison J Miller; Peter Cousins; Daniel H Chitwood Journal: Am J Bot Date: 2022-07-19 Impact factor: 3.325