2020
DOI: 10.1007/s10682-020-10072-y
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Using full-length metabarcoding and DNA barcoding to infer community assembly for speciose taxonomic groups: a case study

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Cited by 4 publications
(1 citation statement)
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“…Numerous phylogeny-based ecological studies have been published in the recent decades since Cam Webb's work on community phylogenetics [20], in which the degree of relatedness among species in ecological communities was calculated using branch lengths in a phylogenetic tree. Historically, these studies have focused on DNA barcode trees, which are constructed using either single-or multiple-gene sequence data, and several studies have indicated that barcode trees may have promising performance for inferring community phylogenetic structures [21][22][23]. Nonetheless, there is a growing awareness that barcode trees are still problematic, compared with more robust phylogeny trees (e.g., placement trees, which place the barcodes on published phylogenies), whether they are robust or not, when considering the assembly mechanisms of communities [24], especially in species-rich geographical regions [25].…”
Section: Introductionmentioning
confidence: 99%
“…Numerous phylogeny-based ecological studies have been published in the recent decades since Cam Webb's work on community phylogenetics [20], in which the degree of relatedness among species in ecological communities was calculated using branch lengths in a phylogenetic tree. Historically, these studies have focused on DNA barcode trees, which are constructed using either single-or multiple-gene sequence data, and several studies have indicated that barcode trees may have promising performance for inferring community phylogenetic structures [21][22][23]. Nonetheless, there is a growing awareness that barcode trees are still problematic, compared with more robust phylogeny trees (e.g., placement trees, which place the barcodes on published phylogenies), whether they are robust or not, when considering the assembly mechanisms of communities [24], especially in species-rich geographical regions [25].…”
Section: Introductionmentioning
confidence: 99%