ArticleScientific reports2021
The critical role of natural history museums in advancing eDNA for biodiversity studies: a case study with Amazonian fishes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 38 citations in OpenAlex.
- Assessing fish diversity in the Amazon: The impact of primers and reference libraries in eDNA surveys.PloS one · 2026Article
- Assessing fish diversity in small streams and ponds of the Peruvian Amazon using environmental DNA metabarcoding.ZooKeys · 2026Article
- Unlocking natural history collections to improve eDNA reference databases and biodiversity monitoring.Bioscience · 2025Article
- Fast-tracking bespoke DNA reference database generation from museum collections for biomonitoring and conservation.Molecular ecology resources · 2025Article
- Hydrological Connectivity Enhances Fish Biodiversity in Amazonian Mining Ponds: Insights From eDNA and Traditional Sampling.Molecular ecology · 2025Article
- Using eDNA for mammal inventories still needs naturalist expertise, a meta-analysis.Ecology and evolution · 2023Article
- Trapped DNA fragments in marine sponge specimens unveil North Atlantic deep-sea fish diversity.Proceedings. Biological sciences · 2023Article
- Application of a deep learning image classifier for identification of Amazonian fishes.Ecology and evolution · 2023Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ichthyological surveys have traditionally been conducted using whole-specimen, capture-based sampling with varied but conventional fishing gear. Recently, environmental DNA (eDNA) metabarcoding has emerged as a complementary, and possible alternative, approach to whole-specimen methodologies. In the tropics, where much of the diversity remains undescribed, vast reaches continue unexplored, and anthropogenic activities are constant threats; there have been few eDNA attempts for ichthyological inventories. We tested the discriminatory power of eDNA using MiFish primers with existing public reference libraries and compared this with capture-based methods in two distinct ecosystems in the megadiverse Amazon basin. In our study, eDNA provided an accurate snapshot of the fishes at higher taxonomic levels and corroborated its effectiveness to detect specialized fish assemblages. Some flaws in fish metabarcoding studies are routine issues addressed in natural history museums. Thus, by expanding their archives and adopting a series of initiatives linking collection-based research, training and outreach, natural history museums can enable the effective use of eDNA to survey Earth's hotspots of biodiversity before taxa go extinct. Our project surveying poorly explored rivers and using DNA vouchered archives to build metabarcoding libraries for Neotropical fishes can serve as a model of this protocol.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.