Tidal amplitude and fish abundance in the mouth region of a small estuary

Becker, Alistair; Whitfield, Alan K.; Cole, Victoria J.; Taylor, Matthew D.

doi:10.1111/jfb.13056

Cited by 14 publications

(7 citation statements)

References 10 publications

(12 reference statements)

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Section: Introductionmentioning

confidence: 99%

“…Although automatic analysis techniques have improved, manual processing is still the usual method to count and measure fish with DIDSON cameras within the scope of fish monitoring scientific surveys (Becker et al, 2016;Crossman et al, 2011;Doehring et al, 2011;Lilja et al, 2010;Ogburn et al, 2017;Viehman & Zydlewski, 2015), but also in uses by different kinds of operators (for instance fish farmers, stakeholders, consultants, operators of once-off surveys, etc.). Burwen et al (2007Burwen et al ( , 2010 found a strong linear relationship (r 2 = 0.9) between manual DIDSON-based lengths and true lengths for a variable number of swimming fish at different ranges (i.e., distance to the camera).…”

Section: Introductionmentioning

confidence: 99%

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Manual fish length measurement accuracy for adult river fish using an acoustic camera (DIDSON)

Daroux

Martignac

Nevoux

et al. 2019

Journal of Fish Biology

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Total lengths (LT) of 50 free‐swimming fish in a tank, silver carp Hypophthalmichthys molitrix and rainbow trout Oncorhynchus mykiss, were measured using a DIDSON (Dual‐frequency IDentification SONar) camera. Using Sound Metrics software, multiple measurements of each fish (LT, side aspect angle and distance from the camera) at different times were analysed by two experienced operators while a subset of data was analysed by two inexperienced operators. The main result showed high variability in intra‐fish LT measurements. The number of measurements required to minimise errors and to obtain robust fish measurements (true LT ± 3 cm) was estimated by a bootstrap method. Three to five measurements per fish were recommended for fish surveys in rivers. In this experimental study, aiming to reproduce river conditions, no evidence of fish position (side aspect angle and distance from the camera) effect was detected, but an operator effect (partially explained by training) was observed. General linear mixed models also showed that lengths of the smallest fish (LT < 57 cm) were overestimated and lengths of the largest fish (LT > 57 cm) were underestimated in comparison with their true lengths. In conclusion, we highlight that this technology, like any monitoring methods, returns imperfect observations. We advise DIDSON users to ensure that measurements are carried out correctly in order to draw accurate conclusion from this new technology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Manual fish length measurement accuracy for adult river fish using an acoustic camera (DIDSON)

Daroux

Martignac

Nevoux

et al. 2019

Journal of Fish Biology

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“…High counting accuracy is often achieved due to the high‐quality data obtained and the single swimming direction of migrating fishes. In addition, MFLS is also widely used in surveys of habitats, such as artificial reefs (Guo et al, 2018; Plumlee et al, 2020), estuary (Becker et al, 2016; Becker et al, 2017; Lankowicz et al, 2020), river (Hayes et al, 2015; Kerschbaumer et al, 2020; Mora et al, 2015), lake (Jing, Han, Wang, et al, 2018a), estuarine shoreline (Smith et al, 2021), offshore habitat (Artero et al, 2021; Tassetti et al, 2020; Van Hal et al, 2017), reservoir (Huang & Gong, 2020; Mo et al, 2015; Shen et al, 2018), etc., to study the fish stock and interaction of fishes with the habitats. The fish density is typically calculated to estimate abundance and studied for the habitat population distribution (Plumlee et al, 2020; Zhou et al, 2014).…”

Section: Applications In Fish Monitoringmentioning

confidence: 99%

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Wei

Duan

2022

Fish and Fisheries

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The demand for fish products, which provide crucial protein for humans, is rising as the global population grows. In contrast, fish stock is declining due to human activity, environmental changes and overfishing. Fish monitoring provides valuable support data for effective fishery management and ecosystem conservation. The common monitoring methods are based on manual sampling, which is time-consuming, laborious and intrusive. Imaging sonar is a hydroacoustic system that produces acoustic images similar to optical images by transmitting and receiving sound waves, allowing for in situ monitoring of fish non-intrusively in the dark and turbid water environments where optical cameras are limited. In the last decade, imaging sonar, especially high frequency multibeam forward-looking sonar and side-scan sonar, has been widely used in fish monitoring. We reviewed the literature from the previous decade on the use of these two types of imaging sonar in fish species identification, abundance estimation, length measurement and behaviour analysis, as well as the sonar imagery processing concerning fish. The review results show that these imaging sonars are efficient and effective tools for fish monitoring in complex environments. The challenges include(1) the recognition of small fish forming dense aggregations; (2) species identification, which limits their use in species-specific studies; (3) time-consuming massive data processing. Therefore, advanced algorithms for sonar imagery processing and integrations with other sampling technologies are needed for future development.

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“…Fisheries scientists have used acoustic cameras to monitor fish (and other animals) by detecting their direct acoustic image and/or their acoustic shadow (Horne 2000;Trenkel et al 2011;Martignac et al 2015). For example, acoustic cameras have been used in saltmarsh habitats to analyse predator-prey interactions (Boswell et al 2019) and fish movement in tidal passageways (Kimball et al 2010;Bennett et al 2020); in areas of high turbidity caused by sedimentation to estimate size and abundance of key demersal fish (Artero et al 2021); and, in intermittently closed estuaries to determine the abundance and the direction of fish movement, and the distribution of different sized fish (Becker et al 2016(Becker et al , 2017. Coupling of direct acoustic images and acoustic shadows has enabled identification of different species (Able et al 2014;Artero et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Out of the shadows: automatic fish detection from acoustic cameras

et al. 2022

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Efficacious monitoring of fish stocks is critical for efficient management. Multibeam acoustic cameras, that use sound-reflectance to generate moving pictures, provide an important alternative to traditional video-based methods that are inoperable in turbid waters. However, acoustic cameras, like standard video monitoring methods, produce large volumes of imagery from which it is time consuming and costly to extract data manually. Deep learning, a form of machine learning, can be used to automate the processing and analysis of acoustic data. We used convolutional neural networks (CNNs) to detect and count fish in a publicly available dual-frequency identification sonar (DIDSON) dataset. We compared three types of detections, direct acoustic, acoustic shadows, and a combination of direct and shadows. The deep learning model was highly reliable at detecting fish to obtain abundance data using acoustic data. Model accuracy for counts-per-image was improved by the inclusion of shadows (F1 scores, a measure of the model accuracy: direct 0.79, shadow 0.88, combined 0.90). Model accuracy for MaxN per video was high for all three types of detections (F1 scores: direct 0.90, shadow 0.90, combined 0.91). Our results demonstrate that CNNs are a powerful tool for automating underwater acoustic data analysis. Given this promise, we suggest broadening the scope of testing to include a wider range of fish shapes, sizes, and abundances, with a view to automating species (or ‘morphospecies’) identification and counts.

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Tidal amplitude and fish abundance in the mouth region of a small estuary

Cited by 14 publications

References 10 publications

Manual fish length measurement accuracy for adult river fish using an acoustic camera (DIDSON)

Manual fish length measurement accuracy for adult river fish using an acoustic camera (DIDSON)

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Out of the shadows: automatic fish detection from acoustic cameras

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