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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

Optimising sampling of fish assemblages on intertidal reefs using remote underwater video.

Katherine R Erickson^1,2, Ana B Bugnot^2,3, Will F Figueira^2,4

¹Centre for Marine Science and Innovation, University of New South Wales, Sydney, NSW, Australia.

|May 30, 2023

Summary

This study optimized remote underwater video (RUV) methods for assessing fish on oyster reefs. Systematic sampling and real-time recording of species richness and MaxN_T provide the most accurate fish assemblage data.

Keywords:

Cryptic fish Intertidal reef Sampling effort Unbaited underwater camera

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Published on: July 2, 2018

Area of Science:

Marine Ecology
Fisheries Science
Conservation Biology

Background:

Assessing fish assemblages in complex intertidal habitats is challenging.
Traditional methods like trapping are destructive and costly.
Remote underwater video (RUV) offers a less invasive alternative but requires optimized data processing.

Purpose of the Study:

To identify the optimal subsampling method for analyzing fish assemblages on intertidal oyster reefs using RUV footage.
To evaluate the impact of subsampling effort and method (systematic vs. random) on fish assemblage metrics.
To determine the best approach for calculating species richness, MaxN_T, and MeanCount_T.

Main Methods:

Utilized RUV footage from intertidal oyster reefs.
Applied bootstrapping techniques to determine optimal subsampling strategies.
Quantified accuracy and precision of systematic versus random sampling methods.
Assessed three fish assemblage metrics: species richness, MaxN_T, and MeanCount_T.

Main Results:

Systematic sampling was more accurate and precise than random sampling for assessing fish assemblages.
MaxN_T and species richness should be recorded in real time for optimal accuracy.
An optimal sampling interval of 60 seconds was identified for the MeanCount_T metric.

Conclusions:

Provides methodological recommendations for using RUV to effectively assess fish assemblages in shallow intertidal habitats.
Highlights the superiority of systematic sampling over random sampling for RUV data analysis.
Offers efficient data processing strategies for RUV-based fish community assessments.