Draper, FC, Baker, TR orcid.org/0000-0002-3251-1679, Baraloto, C et al. (6 more authors) (2020) Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach. Trends in Ecology & Evolution, 35 (12). pp. 1100-1109. ISSN 0169-5347
Abstract
Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources – including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Trends in Ecology & Evolution. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | tropical botany; plant biodiversity; technology; spectroscopy; DNA; artificial intelligence |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Ecology & Global Change (Leeds) |
Funding Information: | Funder Grant number EU - European Union 794973 |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Nov 2020 11:48 |
Last Modified: | 07 Sep 2021 00:38 |
Status: | Published |
Publisher: | Elsevier (Cell Press) |
Identification Number: | 10.1016/j.tree.2020.08.003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168439 |
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