Morphological leaf traits are frequently used to quantify, understand and predict plant and vegetation functional diversity and ecology, including environmental and climate change responses. Although morphological leaf traits are easy to measure, their coverage for characterising variation within species and across temporal scales is limited. At the same time, there are about 3100 herbaria worldwide, containing approximately 390 million plant specimens dating from the 16th to 21st century, which can potentially be used to extract morphological leaf traits. Globally, plant specimens are rapidly being digitised and images are made openly available via various biodiversity data platforms, such as iDigBio and GBIF. Based on a pilot study to identify the availability and appropriateness of herbarium specimen images for comprehensive trait data extraction, we developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species. After scrutinising the metadata of digitised herbarium specimen images available from iDigBio and GBIF (21.9 million and 31.6 million images for Tracheophyta; accessed date December 2020), we identified approximately 10 million images potentially appropriate for our study. From the 10 million images, we selected seven species (Salix bebbiana Sarg., Alnus incana (L.) Moench, Viola canina L., Salix glauca L., Chenopodium album L., Impatiens capensis Meerb. and Solanum dulcamara L.) , which have a simple leaf shape, are well represented in space and time and have high availability of specimens per species. We downloaded 17,383 images. Out of these, we discarded 5779 images due to quality issues. We used the remaining 11,604 images to measure the area, length, width and perimeter on 32,009 individual leaf blades using the semi-automated tool TraitEx. The resulting dataset contains 128,036 trait records. We demonstrate its comparability to trait data measured in natural environments following standard protocols by comparing trait values from the TRY database. We conclude that the herbarium specimens provide valuable information on leaf sizes. The dataset created in our study, by extracting leaf traits from the digitised herbarium specimen images of seven selected species, is a promising opportunity to improve ecological knowledge about the adaptation of size-related leaf traits to environmental changes in space and time.
Plant traits are vital to quantify, understand and predict plant and vegetation ecology, including responses to environmental and climate change. Leaf traits are among the best sampled, with more than 200,000 records for individual traits. Nevertheless, their coverage is still strongly limited, especially with respect to characterizing variation within species and across longer time scales. However, to date, more than 3000 herbaria worldwide have collected 390 million plant specimens, dating from the 16th century. At present, the herbarium specimens are rapidly digitized and the images are made openly available to facilitate research and biodiversity conservation. In this study, we determined the potential of the digitized herbarium specimens images to: overcome limitations of data availability for quantitative leaf traits such as the area, length, width along with petiole length and use the trait values to understand the intraspecific variability across spatio-temporal scales. overcome limitations of data availability for quantitative leaf traits such as the area, length, width along with petiole length and use the trait values to understand the intraspecific variability across spatio-temporal scales. For the study, initially, specimen metadata was analysed from various online resources such as the Global Plants Database, Natural History Museum Paris, iDigBio and Global Biodiversity Information Facility (GBIF). Based on the completeness of the metadata, image availability, and the ease of measuring the leaf traits, we selected Salix bebbiana, Alnus incana, Viola canina, Salix glauca, Impatiens capensis, Chenopodium album, and Solanum dulcamara for the study. The semi-automated tool TraitEx (Gaikwad et al. 2019) was used to measure quantitative leaf traits such as the leaf area, perimeter, width, length and petiole length. Finally, excluding duplicates, we downloaded 17383 digital herbarium specimen images from iDigBio and GBIF, which included specimens from the 17th century to the present. However, about 5000 had insufficient information or quality issues, including not-yet-identified duplicates, or no intact leaves. For each selected image we measured four leaf traits - area, length, width and perimeter of the leaf blade - on up to 5 leaves. In sum, we collected about 120,000 trait records from 32009 leaves. Comparison of measured leaf traits to data from the TRY Plant Trait database (Kattge et al. 2019) revealed that we could improve the database for studying intraspecific trait variability by several orders of magnitude (from less than 10-100 records per species to >1000). The variation of trait records within the seven species shows reasonable patterns, which improves trust in the data quality. The extracted trait measurements were used to analyse the intraspecific variability for the species across different spatio-temporal resolutions. Machine learning method (random forest) was used to perform the analysis and the results revealed the imprint of spatial and temporal climate variation, including long term trends and climate change as well as seasonality effects, on leaf area. Through this study, we demonstrate the high benefits of digitizing herbarium specimens and reusing it for research studies to improve ecological knowledge and predictability of size-related leaf traits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.