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Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach

Title: Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach
Authors: FC Draper; TR Baker; C Baraloto; J Chave; F Costa; RE Martin; RT Pennington; A Vicentini; GP Asner
Publication Year: 2020
Collection: Erasmus University Rotterdam (EUR): Figshare
Subject Terms: tropical botany; plant biodiversity; technology; spectroscopy; DNA; artificial intelligence
Description: 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.
Document Type: article in journal/newspaper
Language: unknown
Relation: 10871/124999; https://figshare.com/articles/journal_contribution/Quantifying_Tropical_Plant_Diversity_Requires_an_Integrated_Technological_Approach/29775191
Availability: https://figshare.com/articles/journal_contribution/Quantifying_Tropical_Plant_Diversity_Requires_an_Integrated_Technological_Approach/29775191
Rights: CC BY-NC-ND
Accession Number: edsbas.56456317
Database: BASE