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Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries

BACKGROUND: 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 variati...

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Autores principales: Kommineni, Vamsi Krishna, Tautenhahn, Susanne, Baddam, Pramod, Gaikwad, Jitendra, Wieczorek, Barbara, Triki, Abdelaziz, Kattge, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pensoft Publishers 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292298/
https://www.ncbi.nlm.nih.gov/pubmed/34316273
http://dx.doi.org/10.3897/BDJ.9.e69806
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author Kommineni, Vamsi Krishna
Tautenhahn, Susanne
Baddam, Pramod
Gaikwad, Jitendra
Wieczorek, Barbara
Triki, Abdelaziz
Kattge, Jens
author_facet Kommineni, Vamsi Krishna
Tautenhahn, Susanne
Baddam, Pramod
Gaikwad, Jitendra
Wieczorek, Barbara
Triki, Abdelaziz
Kattge, Jens
author_sort Kommineni, Vamsi Krishna
collection PubMed
description BACKGROUND: 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. NEW INFORMATION: 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.
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spelling pubmed-82922982021-07-26 Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries Kommineni, Vamsi Krishna Tautenhahn, Susanne Baddam, Pramod Gaikwad, Jitendra Wieczorek, Barbara Triki, Abdelaziz Kattge, Jens Biodivers Data J Data Paper (Biosciences) BACKGROUND: 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. NEW INFORMATION: 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. Pensoft Publishers 2021-07-13 /pmc/articles/PMC8292298/ /pubmed/34316273 http://dx.doi.org/10.3897/BDJ.9.e69806 Text en Vamsi Krishna Kommineni, Susanne Tautenhahn, Pramod Baddam, Jitendra Gaikwad, Barbara Wieczorek, Abdelaziz Triki, Jens Kattge https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Data Paper (Biosciences)
Kommineni, Vamsi Krishna
Tautenhahn, Susanne
Baddam, Pramod
Gaikwad, Jitendra
Wieczorek, Barbara
Triki, Abdelaziz
Kattge, Jens
Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title_full Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title_fullStr Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title_full_unstemmed Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title_short Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
title_sort comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
topic Data Paper (Biosciences)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292298/
https://www.ncbi.nlm.nih.gov/pubmed/34316273
http://dx.doi.org/10.3897/BDJ.9.e69806
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