Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Keystone Species01:39

Keystone Species

25.0K
Measures of species biodiversity, such as richness (i.e., the number of species present) and evenness (i.e., their relative abundance), describe an ecological community’s structure. Many factors affect community structure, including abiotic factors (e.g., sunlight and nutrients), disturbances (e.g., fire or flood), species interactions (e.g., predation or competition), and chance events (e.g., foreign species invasion). Certain species—such as keystone species—also play a...
25.0K
Statistical Significance01:50

Statistical Significance

22.3K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.3K
Osmoregulation in Fishes02:32

Osmoregulation in Fishes

53.3K
When cells are placed in a hypotonic (low-salt) fluid, they can swell and burst. Meanwhile, cells in a hypertonic solution—with a higher salt concentration—can shrivel and die. How do fish cells avoid these gruesome fates in hypotonic freshwater or hypertonic seawater environments?
53.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

993
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
993
What is a Species?01:17

What is a Species?

50.6K
Overview
50.6K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Community-based eDNA metabarcoding for monitoring fish biodiversity and food webs in the Peace-Athabasca Delta.

PeerJ·2026
Same author

Optimization of environmental DNA-based methods: a case study for detecting brook trout (<i>Salvelinus fontinalis</i>).

PeerJ·2026
Same author

Synthesized double stranded gene fragments are not suitable for qPCR endogenous control spike-ins.

BMC research notes·2025
Same author

The Pathway to Increase Standards and Competency of eDNA Surveys (PISCeS) 2023 conference-Towards standardization and data management in the field of eDNA.

Genome·2025
Same author

The productivity-stability trade-off in global food systems.

Nature ecology & evolution·2024
Same author

Twenty-three years of PCR-based seafood authentication assay development: What have we learned?

Comprehensive reviews in food science and food safety·2024

Related Experiment Video

Updated: Feb 14, 2026

DNA-based Fish Species Identification Protocol
09:15

DNA-based Fish Species Identification Protocol

Published on: April 28, 2010

30.2K

Beyond Species Richness: A Statistically Rigorous Comparison of eDNA and Conventional Fish Sampling Methods.

Eric A Bonk1, Emelia Myles-Gonzalez2, Nathan Zeinstra1

  • 1Department of Integrative Biology, University of Guelph, Guelph N1G 2W1, Ontario, Canada.

Environmental Science & Technology
|February 12, 2026
PubMed
Summary

Comparing fish sampling methods, this study found environmental DNA (eDNA) metabarcoding offers higher species richness. However, rigorous statistical analysis is crucial, as COI metabarcoding outperformed 12S, and seine netting proved redundant alongside COI.

Keywords:
accuracy, diversity ,environmental DNAcongruencyconsistencyeDNAeDNA metabarcodingelectrofishingfish sampling comparisonfish sampling method comparisongill nettinghoop nettingmethodological comparisonminnow trappingseine netting

More Related Videos

Comparison of Methods for Isolating Entomopathogenic Fungi from Soil Samples
07:16

Comparison of Methods for Isolating Entomopathogenic Fungi from Soil Samples

Published on: January 6, 2022

5.4K
Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria
07:25

Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria

Published on: July 20, 2017

12.1K

Related Experiment Videos

Last Updated: Feb 14, 2026

DNA-based Fish Species Identification Protocol
09:15

DNA-based Fish Species Identification Protocol

Published on: April 28, 2010

30.2K
Comparison of Methods for Isolating Entomopathogenic Fungi from Soil Samples
07:16

Comparison of Methods for Isolating Entomopathogenic Fungi from Soil Samples

Published on: January 6, 2022

5.4K
Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria
07:25

Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria

Published on: July 20, 2017

12.1K

Area of Science:

  • Aquatic ecology and biodiversity assessment.
  • Molecular ecology and environmental DNA (eDNA) techniques.

Background:

  • Comparative performance of fish sampling methods is unclear, hindering accurate biodiversity assessments.
  • Increasing popularity of eDNA metabarcoding necessitates rigorous comparisons with conventional methods.
  • Previous studies often lack simultaneous comparison of multiple conventional techniques against eDNA.

Purpose of the Study:

  • To statistically compare the performance of various fish sampling methods, including eDNA metabarcoding and conventional techniques.
  • To evaluate methods using accuracy, consistency, diversity, and congruency metrics beyond simple species richness.
  • To provide statistically robust insights into the reliability and complementarity of different sampling strategies.

Main Methods:

  • Conducted simultaneous sampling using 12S and COI metabarcoding, electrofishing, hoop netting, seine netting, minnow trapping, and gill netting.
  • Collected samples from 10 sites across four waterbodies between July 8-13, 2023.
  • Employed a novel integrative statistical approach utilizing accuracy, consistency, diversity, and congruency metrics for rigorous comparison.

Main Results:

  • COI metabarcoding demonstrated higher accuracy than seine netting and outperformed 12S on three out of four metrics.
  • Hoop netting showed accuracy against seine and gill netting; seine netting was consistent with COI and hoop netting.
  • eDNA (COI + 12S) revealed higher species richness, and COI was congruent with four conventional methods, though 12S results raised validity concerns.

Conclusions:

  • COI metabarcoding is a reliable marker, often outperforming 12S and rendering seine netting redundant.
  • eDNA (12S + COI) provides a more comprehensive biodiversity picture but requires multiple markers for robust comparison.
  • Researchers must adopt rigorous statistical analyses, moving beyond superficial metrics, to validate and strengthen conclusions on sampling method efficacy.