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author Zhang, Yan
Zhang, Heng
Akashi, Hiroshi
Albouy, Camille P
Andres, Kara J
Barquín, José
Brantschen, Jeanine
Connon, Richard E
Craine, Joseph M
Gleeson, Deirdre
Goldenberg-Vilar, Alejandra
González-Ferreras, Alexia M
Hatzenbuhler, Chelsea
Hupało, Kamil
Hyde, Josephine
Iwasaki, Wataru
Johnson, Mark D
Katz, Aron D
Kuzovlev, Vyacheslav V
Larson, Courtney E
Lecaudey, Laurène A
Leese, Florian
Leray, Matthieu
Li, Feilong
Macher, Till-Hendrik
Mauvisseau, Quentin
Morán-Luis, María
Nester, Georgia
Quintero, Helio
Ravelomanana, Tsilavina
Chacko, Merin Reji
Saccò, Mattia
Sales, Naiara
Schenekar, Tamara
Schletterer, Martin
Schmidt, Saskia
Schulte, Nicholas O
Schütz, Robin
Sperry, Jinelle H
Stevens, Emma R
Stinson, Sarah A
Weiss, Steven
Xia, Fei
Zhang, Hui
Zhang, Song
Zhong, Wenjun
Zong, Shuo
Pellissier, Loïc
Zhang, Xiaowei
Altermatt, Florian
author_facet Zhang, Yan
Zhang, Heng
Akashi, Hiroshi
Albouy, Camille P
Andres, Kara J
Barquín, José
Brantschen, Jeanine
Connon, Richard E
Craine, Joseph M
Gleeson, Deirdre
Goldenberg-Vilar, Alejandra
González-Ferreras, Alexia M
Hatzenbuhler, Chelsea
Hupało, Kamil
Hyde, Josephine
Iwasaki, Wataru
Johnson, Mark D
Katz, Aron D
Kuzovlev, Vyacheslav V
Larson, Courtney E
Lecaudey, Laurène A
Leese, Florian
Leray, Matthieu
Li, Feilong
Macher, Till-Hendrik
Mauvisseau, Quentin
Morán-Luis, María
Nester, Georgia
Quintero, Helio
Ravelomanana, Tsilavina
Chacko, Merin Reji
Saccò, Mattia
Sales, Naiara
Schenekar, Tamara
Schletterer, Martin
Schmidt, Saskia
Schulte, Nicholas O
Schütz, Robin
Sperry, Jinelle H
Stevens, Emma R
Stinson, Sarah A
Weiss, Steven
Xia, Fei
Zhang, Hui
Zhang, Song
Zhong, Wenjun
Zong, Shuo
Pellissier, Loïc
Zhang, Xiaowei
Altermatt, Florian
Zhang, Yan
Zhang, Heng
Akashi, Hiroshi
Albouy, Camille P
Andres, Kara J
Barquín, José
Brantschen, Jeanine
Connon, Richard E
Craine, Joseph M
Gleeson, Deirdre
Goldenberg-Vilar, Alejandra
González-Ferreras, Alexia M
Hatzenbuhler, Chelsea
Hupało, Kamil
Hyde, Josephine
Iwasaki, Wataru
Johnson, Mark D
Katz, Aron D
Kuzovlev, Vyacheslav V
Larson, Courtney E
Lecaudey, Laurène A
Leese, Florian
Leray, Matthieu
Li, Feilong
Macher, Till-Hendrik
Mauvisseau, Quentin
Morán-Luis, María
Nester, Georgia
Quintero, Helio
Ravelomanana, Tsilavina
Chacko, Merin Reji
Saccò, Mattia
Sales, Naiara
Schenekar, Tamara
Schletterer, Martin
Schmidt, Saskia
Schulte, Nicholas O
Schütz, Robin
Sperry, Jinelle H
Stevens, Emma R
Stinson, Sarah A
Weiss, Steven
Xia, Fei
Zhang, Hui
Zhang, Song
Zhong, Wenjun
Zong, Shuo
Pellissier, Loïc
Zhang, Xiaowei
Altermatt, Florian
collection PubMed - marine biology
contents Integrated Reanalysis of Global Riverine Fish eDNA Datasets Shows Robustness and Congruence of Biodiversity Conclusions. Zhang, Yan Zhang, Heng Akashi, Hiroshi Albouy, Camille P Andres, Kara J Barquín, José Brantschen, Jeanine Connon, Richard E Craine, Joseph M Gleeson, Deirdre Goldenberg-Vilar, Alejandra González-Ferreras, Alexia M Hatzenbuhler, Chelsea Hupało, Kamil Hyde, Josephine Iwasaki, Wataru Johnson, Mark D Katz, Aron D Kuzovlev, Vyacheslav V Larson, Courtney E Lecaudey, Laurène A Leese, Florian Leray, Matthieu Li, Feilong Macher, Till-Hendrik Mauvisseau, Quentin Morán-Luis, María Nester, Georgia Quintero, Helio Ravelomanana, Tsilavina Chacko, Merin Reji Saccò, Mattia Sales, Naiara Schenekar, Tamara Schletterer, Martin Schmidt, Saskia Schulte, Nicholas O Schütz, Robin Sperry, Jinelle H Stevens, Emma R Stinson, Sarah A Weiss, Steven Xia, Fei Zhang, Hui Zhang, Song Zhong, Wenjun Zong, Shuo Pellissier, Loïc Zhang, Xiaowei Altermatt, Florian Biodiversity Animals Fishes DNA, Environmental DNA Barcoding, Taxonomic Rivers Computational Biology Ecosystem The analysis of environmental DNA (eDNA) has revolutionized biodiversity assessments in aquatic ecosystems, enabling non-invasive monitoring of fish communities across diverse regions. However, the global comparability of these eDNA datasets remains ambiguous due to heterogeneous sampling protocols and bioinformatic workflows across studies, making it difficult to assess how robust and comparable the biodiversity patterns inferred from these datasets actually are. Here, we conducted a meta-analysis of 58 riverine fish eDNA metabarcoding studies, covering 1818 sampling sites worldwide, to evaluate the robustness of eDNA-derived biodiversity patterns. We found that species richness estimates and metrics of community structure derived under a common bioinformatic workflow were overall consistent with those of original analyses, despite the relatively high variability in bioinformatic analyses in the respective original studies. Contrastingly, congruence of species identity varied more extensively across datasets, mostly reflecting different completeness and regional relevance of reference databases. Restricting taxonomic assignment to basin-specific species pools improved species identification accuracy, while datasets lacking publicly accessible or well-curated reference data were more prone to mismatches. Year of sampling had a positive effect on taxonomic congruence, such that more recent studies showed increased robustness, also reflecting improved reference database coverage and enhanced species-level identification over time and overall method congruence in more recent years. Overall, the suitability and potential of eDNA for global biodiversity monitoring is corroborating overall robust biodiversity estimates, irrespective of the bioinformatic approaches. Our study underlines the effectiveness and need for further harmonization of bioinformatic workflows and strengthened region-specific reference databases for improved taxonomic resolution and comparability across studies.
format Artículo científico
id pubmed_41999115
institution PubMed
language en
publishDate 2026
publisher Molecular ecology
record_format pubmed
spellingShingle Integrated Reanalysis of Global Riverine Fish eDNA Datasets Shows Robustness and Congruence of Biodiversity Conclusions.
Zhang, Yan
Zhang, Heng
Akashi, Hiroshi
Albouy, Camille P
Andres, Kara J
Barquín, José
Brantschen, Jeanine
Connon, Richard E
Craine, Joseph M
Gleeson, Deirdre
Goldenberg-Vilar, Alejandra
González-Ferreras, Alexia M
Hatzenbuhler, Chelsea
Hupało, Kamil
Hyde, Josephine
Iwasaki, Wataru
Johnson, Mark D
Katz, Aron D
Kuzovlev, Vyacheslav V
Larson, Courtney E
Lecaudey, Laurène A
Leese, Florian
Leray, Matthieu
Li, Feilong
Macher, Till-Hendrik
Mauvisseau, Quentin
Morán-Luis, María
Nester, Georgia
Quintero, Helio
Ravelomanana, Tsilavina
Chacko, Merin Reji
Saccò, Mattia
Sales, Naiara
Schenekar, Tamara
Schletterer, Martin
Schmidt, Saskia
Schulte, Nicholas O
Schütz, Robin
Sperry, Jinelle H
Stevens, Emma R
Stinson, Sarah A
Weiss, Steven
Xia, Fei
Zhang, Hui
Zhang, Song
Zhong, Wenjun
Zong, Shuo
Pellissier, Loïc
Zhang, Xiaowei
Altermatt, Florian
Biodiversity
Animals
Fishes
DNA, Environmental
DNA Barcoding, Taxonomic
Rivers
Computational Biology
Ecosystem
Integrated Reanalysis of Global Riverine Fish eDNA Datasets Shows Robustness and Congruence of Biodiversity Conclusions. Zhang, Yan Zhang, Heng Akashi, Hiroshi Albouy, Camille P Andres, Kara J Barquín, José Brantschen, Jeanine Connon, Richard E Craine, Joseph M Gleeson, Deirdre Goldenberg-Vilar, Alejandra González-Ferreras, Alexia M Hatzenbuhler, Chelsea Hupało, Kamil Hyde, Josephine Iwasaki, Wataru Johnson, Mark D Katz, Aron D Kuzovlev, Vyacheslav V Larson, Courtney E Lecaudey, Laurène A Leese, Florian Leray, Matthieu Li, Feilong Macher, Till-Hendrik Mauvisseau, Quentin Morán-Luis, María Nester, Georgia Quintero, Helio Ravelomanana, Tsilavina Chacko, Merin Reji Saccò, Mattia Sales, Naiara Schenekar, Tamara Schletterer, Martin Schmidt, Saskia Schulte, Nicholas O Schütz, Robin Sperry, Jinelle H Stevens, Emma R Stinson, Sarah A Weiss, Steven Xia, Fei Zhang, Hui Zhang, Song Zhong, Wenjun Zong, Shuo Pellissier, Loïc Zhang, Xiaowei Altermatt, Florian Biodiversity Animals Fishes DNA, Environmental DNA Barcoding, Taxonomic Rivers Computational Biology Ecosystem The analysis of environmental DNA (eDNA) has revolutionized biodiversity assessments in aquatic ecosystems, enabling non-invasive monitoring of fish communities across diverse regions. However, the global comparability of these eDNA datasets remains ambiguous due to heterogeneous sampling protocols and bioinformatic workflows across studies, making it difficult to assess how robust and comparable the biodiversity patterns inferred from these datasets actually are. Here, we conducted a meta-analysis of 58 riverine fish eDNA metabarcoding studies, covering 1818 sampling sites worldwide, to evaluate the robustness of eDNA-derived biodiversity patterns. We found that species richness estimates and metrics of community structure derived under a common bioinformatic workflow were overall consistent with those of original analyses, despite the relatively high variability in bioinformatic analyses in the respective original studies. Contrastingly, congruence of species identity varied more extensively across datasets, mostly reflecting different completeness and regional relevance of reference databases. Restricting taxonomic assignment to basin-specific species pools improved species identification accuracy, while datasets lacking publicly accessible or well-curated reference data were more prone to mismatches. Year of sampling had a positive effect on taxonomic congruence, such that more recent studies showed increased robustness, also reflecting improved reference database coverage and enhanced species-level identification over time and overall method congruence in more recent years. Overall, the suitability and potential of eDNA for global biodiversity monitoring is corroborating overall robust biodiversity estimates, irrespective of the bioinformatic approaches. Our study underlines the effectiveness and need for further harmonization of bioinformatic workflows and strengthened region-specific reference databases for improved taxonomic resolution and comparability across studies.
title Integrated Reanalysis of Global Riverine Fish eDNA Datasets Shows Robustness and Congruence of Biodiversity Conclusions.
topic Biodiversity
Animals
Fishes
DNA, Environmental
DNA Barcoding, Taxonomic
Rivers
Computational Biology
Ecosystem
url https://pubmed.ncbi.nlm.nih.gov/41999115/