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dc.contributor.authorAsner, Gregory P.-
dc.contributor.authorDraper, Frederick C.H.-
dc.contributor.authorBaker, Timothy R.-
dc.contributor.authorBaraloto, Christopher-
dc.contributor.authorChave, Jérôme-
dc.contributor.authorCosta, Flávia Regina Capellotto-
dc.contributor.authorMartin, Roberta E.-
dc.contributor.authorPennington, R. Toby-
dc.contributor.authorVicentini, Alberto-
dc.date.accessioned2020-09-17T20:57:02Z-
dc.date.available2020-09-17T20:57:02Z-
dc.date.issued2020-
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/36394-
dc.description.abstractTropical 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. © 2020 Elsevier Ltden
dc.language.isoenpt_BR
dc.subjectartificial intelligenceen
dc.subjectDnaen
dc.subjectplant biodiversityen
dc.subjectSpectroscopyen
dc.subjectTechnologyen
dc.subjecttropical botanyen
dc.titleQuantifying Tropical Plant Diversity Requires an Integrated Technological Approachen
dc.typeArtigopt_BR
dc.typeArtigopt_BR
dc.identifier.doi10.1016/j.tree.2020.08.003-
dc.publisher.journalTrends in Ecology and Evolutionpt_BR
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