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Dhanabalan, S.; Neethirajan, N.; Natarajan, J.; Muthupandi, K.; Thangaraji, R.; Sethuraj, A., and Julin, A.S., 2024. Gillnet selectivity for the Coromandel flying fish, Hirundichthys coromandelensis (Hornell, 1923), along the Nagapattinam coast, southeast coast of India. Journal of Coastal Research, 40(6), 1137–1143. Charlotte (North Carolina), ISSN 0749-0208. The Coromandel flying fish, Hirundichthys coromandelensis, forms a seasonal fishery along the Nagapattinam coast of Tamil Nadu, southeast coast of India, from March to August. The peak fishing season for this fish in the small-meshed gillnets operated along the Nagapattinam coast was found to be the month of June. Small-meshed gillnets, locally known as “Kolavalai,” with different mesh sizes of 30 mm (net A), 32 mm (net B), and 34 mm (net C) were found to be operated for the capture of H. coromandelensis. Length frequency data collected from March 2023 to August 2023 were used to create gillnet selectivity curves for H. coromandelensis. The length at first capture (Lb) of this species was found to be 149 mm, 160.9 mm, and 172.8 mm for net A, net B, and net C, respectively. The mean selection length (Lc) was estimated as 179.9 mm, 191.9 mm, and 203.9 mm for net A, net B, and net C, respectively. Further, the escapement length (Ld) was found to be 211.8 mm, 223.7 mm, and 235 mm for net A, net B, and net C, respectively. The value of gill selection factor for H. coromandelensis was estimated as 5.99. The mesh sizes for the commercial exploitation of commercially significant length groups (195 mm) and fishable length groups (190 mm) were estimated as 32.5 mm and 31.7 mm, respectively. The selectivity study revealed that among the three different mesh sizes of gillnets analyzed, net A with a mesh size 30 mm needs to be discouraged, as it was found to capture mainly immature and maturing specimens of H. coromandelensis.

Climate change and deoxygenation are affecting fish stocks on a global scale, but disentangling the impacts of these stressors from the effects of overfishing is a challenge. This study was conducted to distinguish between climate change and mismanagement as possible causes for the drastic decline in spawning stock size and reproductive success in cod (Gadus morhua) and herring (Clupea harengus) in the Western Baltic Sea, when compared with the good or satisfactory status and reproductive success of the other commercial species in the area. Available data on water temperature, wind speed, and plankton bloom during the spawning season did not reveal conclusive correlations between years with good and bad reproductive success of cod or herring. Notably, the other commercial species in the area have very similar life history traits suggesting similar resilience against stress caused by climate change or fishing. The study concludes that severe, sustained overfishing plus inappropriate size selectivity of the main fishing gears have caused the decline in spawning stock biomass of cod and herring to levels that are known to have a high probability of impaired reproductive success. It is pointed out that allowed catches were regulated by management and adhered to by the fishers, meaning that unregulated fishing did not occur. Thus, mismanagement (quotas that were too high and gears that selected too small sizes) and not climate change appears to be the primary cause of the bad status of cod and herring in the Western Baltic Sea.

Through the advancement of observation systems, our vision has far extended its reach into the world of fishes, and how they interact with fishing gears—breaking through physical boundaries and visually adapting to challenging conditions in marine environments. As marine sciences step into the era of artificial intelligence (AI), deep learning models now provide tools for researchers to process a large amount of imagery data (i.e., image sequence, video) on fish behavior in a more time-efficient and cost-effective manner. The latest AI models to detect fish and categorize species are now reaching human-like accuracy. Nevertheless, robust tools to track fish movements in situ are under development and primarily focused on tropical species. Data to accurately interpret fish interactions with fishing gears is still lacking, especially for temperate fishes. At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e.g., Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.

Small-scale reef fisheries are important commercial and subsistence activities that support the livelihoods of millions of people in tropical regions. Tropical marine fisheries typically target a diversity of species caught with a matching diversity of fishing gears and practices. Here, we explored how multiple fishing gears select for distinct functional traits of fish assemblages inside a large multiple use marine environmental protected area off northeastern Brazil. In 1833 landing interviews with local fishers, we identified 101 species, which were categorized according to six traits: body size, schooling behavior, mobility, position in the water column, diet and period of activity. Our research is the first to explore the broad patterns of gear selectivity with regards to fish functional traits for different habitat types. While gears used in reef habitats were highly selective of sedentary and benthic species that form schools with few individuals, gears used in coastal lagoons were selective of highly mobile pelagic species that form large schools. We found a low competitive interaction between different gear types, meaning there was a low overlap in trait selectivity between fishing gears. We also found direct associations between gears and fish functional traits: hooks and line targeted species that exhibit limited mobility capabilities, making these species more vulnerable to local levels of fishing effort. In contrast, nets and fish corrals targeted mobile species that exhibited a greater diversity of functional traits. Some of our results contrasted with the current literature on the topic, with differences highlighting the need for more research to clarify global patterns of trait selectivity by gear type. Our results have implications for fisheries management in northeastern Brazil: gear bans and effort caps are commonly used management measures that can foster fisheries sustainability by minimizing impacts to fish assemblage functions.

Since 2015, the European Union gradually implemented the landing obligation (LO). This prohibits at-sea discarding of species under total allowable catch management. Spatiotemporal avoidance strategies and increasing fishing gear selectivity are two complementary levers that could help fishers in reducing the amount of discards. The objective of this paper is to analyse discarding practices of demersal mixed fisheries in the central part of the Celtic Sea to inform on potential spatial avoidance strategies of unwanted catches in a multi-species context.This study provides the first international and fine scale discard maps based on combined observer at-sea data from Ireland, France and the UK, the main countries fishing in the area. Using a suite of multivariate analyses, we identified areas with similar discard profiles, accounting for the multi-species nature of the fisheries. The maps were also derived separately for the three countries to examine national versus general patterns. Strong spatial segregation in effort between the countries, combined with nationally distinct quotas constraints, fisheries targets and market preferences, resulted in limited differences in the species composition of discards, but considerable differences in spatial discard patterns between countries. In theory, the maps based on discards below and above the minimum conservation reference size could inform fishers on areas to avoid but in practice, the spatial ubiquity of some species involved and strong technical interactions between fishing gears limit the possibility of avoiding discards. Some species trade-offs could be identified that might help to minimize adverse impacts of the implementation of the LO.

We performed a preliminary evaluation of a mobile sampling platform with adjustable push net and live box (Platform) against two common methods for sampling small-bodied fish (i.e., 10–100 mm) in two distinct lentic habitats. Nearshore (NS) littoral habitat was sampled by Platform and beach seine, and open water (OW) pelagic habitat by Platform and Kodiak trawl. Our goal was to evaluate the Platform’s ability to describe fish assemblage structure across habitat types in contrast to common techniques restricted to single habitat types that are less comparable due to gear-specific bias. Platform sample speed had a significant positive effect on recapture efficiency of both nearly neutrally buoyant objects and marked fish. Marked fish recapture efficiencies were similar for Platform in NS and OW, indicating similar efficiency across habitat types. Platform capture efficiency was similar to beach seine and greater than Kodiak trawl. With similar sampling time, the Platform collected more individuals and taxa in NS relative to beach seine and in OW relative to Kodiak trawl. Greater taxa detection by the Platform suggests that it may be effective at detecting species that are numerically rare in specific habitats when compared to these methods. Fish CPUE was significantly greater NS regardless of technique. However, by using the Platform, there is greater confidence that this difference was reliable and not a gear selectivity artifact. Overall, this preliminary study demonstrates the Platform’s potential to collect standardized data across NS and OW habitats, track ontogenetic habitat shifts, and detect differences in small-bodied fish taxa richness, relative abundance, and density between NS and OW habitats. Continued experimentation beyond a single reservoir and fish size range is required before consensus can be established regarding the utility of this new push net design.

Estuarine biodiversity plays a vital role in supporting ecosystem functions yet remains threatened by climate change and anthropogenic activity. Tracking and identifying estuarine biodiversity trends helps management ensure long-term provisions of human and environmental benefits by contributing to the estimation of habitat loss and the monitoring of restoration and conservation progress. However, results obtained using different sampling gears and different biodiversity metrics may lead researchers to reach different conclusions, which can lead to uncertainty in the actual state of the ecosystem-level biodiversity. Sampling benthic biodiversity in complex estuarine habitats, such as oyster reefs, is particularly challenging because no one gear type captures entire target assemblages, and differences in gear efficiency on these complex habitats make comparisons across gear types challenging. We investigated how estimates of oyster reef-associated benthic taxa abundance, richness, Pielou's evenness, and Shannon-Wiener diversity differed across three reefs in Louisiana between suction sampler and substrate tray sampling gears ( = 6), and how gear influenced comparisons across reefs (3 reefs × 6 replicates × 2 gears). Abundance and richness were higher, and Pielou's evenness was lower, in trays compared to suction samples at all reefs. Shannon-Wiener diversity was similar in suction samples and trays at two out of three reefs. Amphipod taxa were numerically dominant in trays, skewing the distribution of abundances and driving the reef assemblage differences between gears. Abundance and Shannon-Wiener diversity were similar across reefs within each gear. However, there were significant differences in richness across reefs in tray samples only, while evenness differed across reefs only in suction samples. Our results highlight that gear choices, along with biodiversity metrics tracked, can result in different conclusions in biodiversity trends, ultimately affecting conservation decisions and management.

Although gear efficiency is an important parameter in stock assessments, estimating its value experimentally is often time-consuming and laborious. The effort required is huge, especially when the survey gear previously used for a stock assessment is changed for some reason. To address the problem, we built a state-space model that can be used to estimate the gear efficiency of any new equipment relative to the original one using a dataset obtained from comparative trawl surveys. This approach is much easier to use than the direct experimental estimation used to calculate absolute gear efficiency. Using this model, we successfully estimated the relative gear efficiency of a new surface trawl net (NST-660), employed in the Japanese survey for Pacific saury, relative to the reference one (NST-99), whose gear efficiency is already known. The estimated 2.5–50–97.5 percentiles of the relative gear efficiency of NST-660 to NST-99 was 0.873, 1.59, and 2.91, respectively. The plausibility of the model assumption was validated through model diagnostics.

In length-based stock assessments for data-poor fisheries, commercial fishing data is often used due to its cost-effectiveness and accessibility. However, factors such as gear selectivity, seasonal closures, minimum conservation reference size (MCRS) regulations, and market-driven harvesting practices can render commercial catch data unrepresentative of the true population structure. Reliance on such data without correction or complementary sampling may lead to biased stock assessments, undermining effective fisheries management. This study investigates how length data from commercial fisheries and experimental sampling influence the estimation of biological reference points (BRPs) and explores differences in catch compositions between these data sources. Between June 2021 and May 2022, a study was conducted at Lake Eğirdir (Isparta, Türkiye), involving 10 different stations. A total of 400 fyke nets were deployed, consisting of 200 experimental and 200 commercial ones. The experimental fyke nets had a stretched mesh size of 18 mm, while the commercial ones had a mesh size of 34 mm. These nets were evenly distributed, with 40 nets at each station. TropFishR package was used for estimating of the life history parameters and stock assessment. The comparison of catch composition was performed using “ Length-dependent catch comparison” and “catch ratio” analyses through the SELNET program. As a result, it has been determined that, due to the potential size selectivity feature, the commercial fyke net is unsuccessful in catching small-sized individuals (<4 cm carapace length), while the experimental fyke net is unsuccessful in catching advanced-sized crayfish (>6.5 cm carapace length). The estimated BRPs showed significant differences depending on the data source, and there were also differences of up to 56% in the recommended total allowable catch ( TAC ) amounts. As a result, it is considered that relying solely on commercial or experimental fyke nets for crayfish may be misleading, and it would be more appropriate to use both for successful sampling and stock assessment.

In the single‐species literature, it is widely acknowledged that conserving young fish for future harvesting is beneficial. This finding holds great significance in fisheries economics and has garnered substantial attention over the years. In this study, a full‐blown age‐structured predator–prey model is developed and used to demonstrate that multispecies considerations may shift the optimal selection of predators towards smaller individuals, providing valuable counteractive insights. These new results offer a fresh perspective highly relevant to regulation and choice of selectivity patterns. Recommendations for Resource Managers Single‐species analyses often suggest that it is optimal to design gear restrictions to spare young fish for future harvest. However, this study reveals that the dynamics of predator–prey interactions may challenge this notion. Specifically, our findings demonstrate that targeting only large predators can result in disadvantages, including increased prey mortality, decreased utilization of growth potential, and lower catch per unit effort for the prey. As a result, managers should exercise caution when contemplating changes in gear restrictions for predator species that are integral to a predator–prey system involving high‐value prey.