2023
DOI: 10.1016/j.ecoinf.2023.102312
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A workflow to create trait databases from collections of textual taxonomic descriptions

David Coleman,
Rachael V. Gallagher,
Daniel Falster
et al.
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Cited by 6 publications
(18 citation statements)
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“…To construct a dataset with a single harmonised trait value for all of Australia's taxa, three additional steps beyond Coleman et al's (2023) efforts were required: 1) manually interpreting ambiguous trait values; 2) merging disparate trait values offered by different floras; and 3) manually gap-filling trait data for all remaining taxa from the literature. The processes for completing each step are outlined below.…”
Section: Methods and Resultsmentioning
confidence: 99%
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“…To construct a dataset with a single harmonised trait value for all of Australia's taxa, three additional steps beyond Coleman et al's (2023) efforts were required: 1) manually interpreting ambiguous trait values; 2) merging disparate trait values offered by different floras; and 3) manually gap-filling trait data for all remaining taxa from the literature. The processes for completing each step are outlined below.…”
Section: Methods and Resultsmentioning
confidence: 99%
“…Ultimately, despite a strong interest in plant traits and morphological and functional strategies, the patchy coverage of tabular trait data across national (or global) floras has limited the scope and uptake of trait-based approaches, especially at large spatial scales. directly to ecological function, such as plant growth form and woodiness (Coleman et al 2023). In addition, nearly all taxon descriptions include the taxon's life history.…”
Section: Introductionmentioning
confidence: 99%
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“…This approach should integrate traditional taxonomic methods, contemporary molecular approaches and advanced automated monitoring and identification tools (Slade & Ong, 2023). Despite current limitations in taxonomic, geographic and life history knowledge, the ongoing development of methods like metabarcoding (e.g., Theodorou et al, 2020) and automated insect sensors (e.g., Möglich et al, 2023), coupled with comprehensive trait assembly (e.g., Coleman et al, 2023;Shirey et al, 2021), presents promising avenues for expanding and scaling future research.…”
Section: Conclusion and Next Stepsmentioning
confidence: 99%