Literature DB >> 30673361

Biodiversity assessments in the 21st century: the potential of insect traps to complement environmental samples for estimating eukaryotic and prokaryotic diversity using high-throughput DNA metabarcoding 1.

Camila D Ritter1, Sibylle Häggqvist2, Dave Karlsson3, Ilari E Sääksjärvi4, A Muthama Muasya5, R Henrik Nilsson6,7, Alexandre Antonelli6,7,8.   

Abstract

The rapid loss of biodiversity, coupled with difficulties in species identification, call for innovative approaches to assess biodiversity. Insects make up a substantial proportion of extant diversity and play fundamental roles in any given ecosystem. To complement morphological species identification, new techniques such as metabarcoding make it possible to quantify insect diversity and insect-ecosystem interactions through DNA sequencing. Here we examine the potential of bulk insect samples (i.e., containing many non-sorted specimens) to assess prokaryote and eukaryote biodiversity and to complement the taxonomic coverage of soil samples. We sampled 25 sites on three continents and in various ecosystems, collecting insects with SLAM traps (Brazil) and Malaise traps (South Africa and Sweden). We then compared our diversity estimates with the results obtained with biodiversity data from soil samples from the same localities. We found a largely different taxonomic composition between the soil and insect samples, testifying to the potential of bulk insect samples to complement soil samples. Finally, we found that non-destructive DNA extraction protocols, which preserve insect specimens for morphological studies, constitute a promising choice for cost-effective biodiversity assessments. We propose that the sampling and sequencing of insect samples should become a standard complement for biodiversity studies based on environmental DNA.

Entities:  

Keywords:  16S rDNA; 18S rDNA; ADN environnemental; ADNr 16S; ADNr 18S; COI ADNmt; COI mtDNA; environmental DNA; metabarcoding; métacodage à barres; non-destructive DNA extraction; protocole d’extraction d’ADN non-destructif

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Year:  2019        PMID: 30673361     DOI: 10.1139/gen-2018-0096

Source DB:  PubMed          Journal:  Genome        ISSN: 0831-2796            Impact factor:   2.166


  7 in total

1.  Toward global integration of biodiversity big data: a harmonized metabarcode data generation module for terrestrial arthropods.

Authors:  Paula Arribas; Carmelo Andújar; Kristine Bohmann; Jeremy R deWaard; Evan P Economo; Vasco Elbrecht; Stefan Geisen; Marta Goberna; Henrik Krehenwinkel; Vojtech Novotny; Lucie Zinger; Thomas J Creedy; Emmanouil Meramveliotakis; Víctor Noguerales; Isaac Overcast; Hélène Morlon; Anna Papadopoulou; Alfried P Vogler; Brent C Emerson
Journal:  Gigascience       Date:  2022-07-19       Impact factor: 7.658

2.  Metabarcoding Malaise traps and soil eDNA reveals seasonal and local arthropod diversity shifts.

Authors:  Ameli Kirse; Sarah J Bourlat; Kathrin Langen; Vera G Fonseca
Journal:  Sci Rep       Date:  2021-05-18       Impact factor: 4.996

Review 3.  Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance.

Authors:  Alexander M Piper; Jana Batovska; Noel O I Cogan; John Weiss; John Paul Cunningham; Brendan C Rodoni; Mark J Blacket
Journal:  Gigascience       Date:  2019-08-01       Impact factor: 6.524

4.  A DNA barcode survey of insect biodiversity in Pakistan.

Authors:  Muhammad Ashfaq; Arif M Khan; Akhtar Rasool; Saleem Akhtar; Naila Nazir; Nazeer Ahmed; Farkhanda Manzoor; Jayme Sones; Kate Perez; Ghulam Sarwar; Azhar A Khan; Muhammad Akhter; Shafqat Saeed; Riffat Sultana; Hafiz Muhammad Tahir; Muhammad A Rafi; Romana Iftikhar; Muhammad Tayyib Naseem; Mariyam Masood; Muhammad Tufail; Santosh Kumar; Sabila Afzal; Jaclyn McKeown; Ahmed Ali Samejo; Imran Khaliq; Michelle L D'Souza; Shahid Mansoor; Paul D N Hebert
Journal:  PeerJ       Date:  2022-04-25       Impact factor: 3.061

5.  Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

Authors:  Tobias Andermann; Alexandre Antonelli; Russell L Barrett; Daniele Silvestro
Journal:  Front Plant Sci       Date:  2022-04-19       Impact factor: 6.627

6.  A Non-Destructive High-Speed Procedure to Obtain DNA Barcodes from Soft-Bodied Insect Samples with a Focus on the Dipteran Section of Schizophora.

Authors:  Frederik Stein; Stefan Wagner; Nadine Bräsicke; Oliver Gailing; Carina C M Moura; Monika Götz
Journal:  Insects       Date:  2022-07-27       Impact factor: 3.139

7.  BIN overlap confirms transcontinental distribution of pest aphids (Hemiptera: Aphididae).

Authors:  Muhammad Tayyib Naseem; Muhammad Ashfaq; Arif Muhammad Khan; Akhtar Rasool; Muhammad Asif; Paul D N Hebert
Journal:  PLoS One       Date:  2019-12-10       Impact factor: 3.240

  7 in total

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