Acoustic camera and net surveys reveal that nursery enhancement at living shorelines may be restricted to the marsh platform

Smith, Charles S.; Paxton, Avery B.; Donaher, Sarah E.; Kochan, David P.; Neylan, Isabelle P.; Pfeifer, Tessa; Hoeck, Rebecca V. Van; Taylor, James C.

doi:10.1016/j.ecoleng.2021.106232

Cited by 16 publications

(9 citation statements)

References 53 publications

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“…In this study, DIDSON enables full day observation (especially at night) of fish spawning and diminishes anthropogenic disturbances to fish spawning. It provides a new attempt to observe spawning behaviors in artificial structures (Pavlov et al, 2009;Langkau et al, 2016;Smith et al, 2021). Mastering the circadian rhythm of spawning and the composition of spawning populations is conducive to formulating protection and management strategies for artificial spawning grounds.…”

Section: Explore Effective Management and Protection Strategies On Sp...mentioning

confidence: 99%

“…Proper placement and regular maintenance can maximize the effectiveness of artificial spawning substrates (Nash et al, 1999). Artificial spawning substrate is an ideal material for studying fish reproduction behavior, and imaging sonar is considered to be the most effective, safe and environmentfriendly observation method for fish reproduction behavior (Pavlov et al, 2009;Langkau et al, 2016;Smith et al, 2021). The reproductive behavior of fish is a guideline for developing strategies for the protection and management of artificial spawning grounds.…”

Section: Introductionmentioning

confidence: 99%

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Application of artificial spawning substrates to support lacustrine fish recruitment and fisheries enhancement in a Chinese lake

Feng

Yuan²,

Zhang³

et al. 2023

Front. Ecol. Evol.

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Lake littoral zones with abundant submerged vegetation are considered to harbor diverse biotic communities and additionally serve as spawning grounds for important fishery resources. In recent decades, due to various human activities submerged vegetation in lakes have declined dramatically resulting in the function loss of spawning grounds. In order to enhance the recruitment of lacustrine fish (mainly Carassius auratus and Cyprinus carpio) and the proliferation of fishery resources, we explored a method of constructing artificial spawning ground based on artificial spawning substrate in lake. The results of present study indicate that the artificial substrates with complex and dense three-dimensional structure which have enough space to enable material exchange and oxygen circulation could be efficient to enhance fish recruitment. The water temperature of spawning peaks of artificial substrates were within the range of 20 ~ 25°C, and the water temperature provides a basis for the deployment period of artificial spawning grounds. The spawning substrate in the upper layer of water column was more attractive for fish to spawn. The above results provide guidance for the deployment of artificial spawning grounds from both temporal and spatial scales. Observations on the behavior of aggregations found that spawning activities mainly occurred at night and in the early morning. The abundance of larger individuals (>20 cm), as potential spawning groups, increased during this period. The above findings are informative to the construction and management of artificial spawning ground in lakes.

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Section: Explore Effective Management and Protection Strategies On Sp...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of artificial spawning substrates to support lacustrine fish recruitment and fisheries enhancement in a Chinese lake

Feng

Yuan²,

Zhang³

et al. 2023

Front. Ecol. Evol.

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show abstract

“…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%

“…The performances of MFLS are further evaluated by comparing them with other sampling methods. MFLS is recognized as more efficient than traditional sampling methods, such as fishing, netting and trapping in habitat abundance surveys (Kerschbaumer et al, 2020; Plumlee et al, 2020; Smith et al, 2021). Furthermore, Mora et al (2015) found that abundance estimates from DIDSON were more precise than that from the mark and recapture method.…”

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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“…Monitoring of fish stocks across a wide range of environments is a critical task for effective management. Fisheries scientists and managers monitor fish stocks by collecting data on population abundance, biomass and densities (Egerton et al 2018;Smith et al 2021), schooling behaviours (Trenkel et al 2011), predator-prey relationships (Becker and Suthers Abstract 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.…”

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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Acoustic camera and net surveys reveal that nursery enhancement at living shorelines may be restricted to the marsh platform

Cited by 16 publications

References 53 publications

Application of artificial spawning substrates to support lacustrine fish recruitment and fisheries enhancement in a Chinese lake

Application of artificial spawning substrates to support lacustrine fish recruitment and fisheries enhancement in a Chinese lake

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

Out of the shadows: automatic fish detection from acoustic cameras

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