Predicting potential fishing grounds of ribbonfish (<i>Trichiurus lepturus</i>) in the north-eastern Arabian Sea, using remote sensing data

Azeez, P Abdul; Raman, Mini; Rohit, Prathibha; Shenoy, Latha; Jaiswar, A. K.; Koya, K. Mohammed; Divu, D

doi:10.1080/01431161.2020.1809025

Cited by 16 publications

(7 citation statements)

References 60 publications

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“…(Abdul Azeez et al, 2021). It influences spawning, the development of eggs and larvae, and the general distribution of fish (Gordoa et al, 2000), and it plays a key role in the spatial distribution of marine fish species (Hidayat et al, 2021).…”

Section: Influence Of Marine Environmental Variability On the Catch R...mentioning

confidence: 99%

“…Salinity levels exceeding 35.8 PSU result in oligotrophic conditions and decreased primary production, which cannot meet the nutritional needs of pelagic fish species (Coletto et al, 2019). The prey species aggregate near the fronts and eddies associated with the SSH, which attracts pelagic predatory fish species (Abdul Azeez et al, 2021). In the present study, the optimal SSH range was lower in spring and summer (0.5-0.68 m) and higher in autumn and winter (0.57-0.74 m) for the greater amberjack, and the SSH value were higher in the La Niña years and lower in the El Niño years.…”

Section: Influence Of Marine Environmental Variability On the Catch R...mentioning

confidence: 99%

“…This information can assist in improving ecosystem management (Saraux et al, 2014), fish stocks and abundance (Azevedo and Silva, 2020), and prey abundance and dispersion (Garrison et al, 2002) and in habitat conservation (Ward et al, 2015) and fishery management (Kai et al, 2017). Satellite-derived environmental variables and temporal and spatial distribution patterns can be utilized to locate and predict potentially suitable fish habitats and distribution (Mansor et al, 2001;Zhang et al, 2017;Lee et al, 2018;Abdul Azeez et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Habitat changes and catch rate variability for greater amberjack in the Taiwan Strait: The effects of El Niño–southern oscillation events

Mammel

Lee²,

Naimullah³

et al. 2023

Front. Mar. Sci.

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El Niño–Southern Oscillation (ENSO) is a crucial oceanographic phenomenon that leads to interannual fluctuations in the climate and ecosystem productivity of tropical and subtropical areas. These fluctuations affect the suitability of habitats for many commercial fish species. However, detailed information on the effects of this major phenomenon and the resulting environmental changes on the habitat and catch rates of the economically and ecologically crucial species of the greater amberjack (Seriola dumerili) in the Taiwan Strait (TS) is lacking. In this study, we employed a weighted habitat suitability index (HSI) modeling method and used remotely sensed marine environmental data as well as data from recorders in Taiwanese fishing vessels (in 2014–2019) to understand the effects of ENSO events on the habitat suitability and catch rates for greater amberjack in the TS. Analysis of variance revealed that environmental factors substantially influenced greater amberjack habitats and catch rates during ENSO events across seasons. The catch rates were high in spring and summer in the southern and northern TS and in autumn and winter in the southern TS. The catch rates were higher in spring, summer, and autumn (>9.0 kg/h) in El Niño years, and in winter, the catch rates were higher in normal years (>12.0 kg/h) and lower in La Niña years. The predicted HSI for the southern and northern TS revealed that greater amberjack populations were predominantly distributed at 20–24°N and 24–28°N, respectively. Opposite habitat suitability was synchronously found in spring and summer during ENSO events, with higher HSI values recorded in spring in El Niño and normal years and higher HSI values recorded in summer in La Niña years. In winter, the HSI values of the southern and northern TS were higher in El Niño and normal years and substantially lower in La Niña years. Habitat suitability was extremely low in autumn. These findings imply that ENSO events play a key role in regulating environmental conditions and affect the catch rates and habitat suitability for the greater amberjack in the TS.

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Section: Influence Of Marine Environmental Variability On the Catch R...mentioning

confidence: 99%

Section: Influence Of Marine Environmental Variability On the Catch R...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Habitat changes and catch rate variability for greater amberjack in the Taiwan Strait: The effects of El Niño–southern oscillation events

Mammel

Lee²,

Naimullah³

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

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“…The juveniles and sub-adults aggregate in coastal waters for feeding and the adults migrate offshore to spawn (Prabhu, 1950;Narasimham, 1972;James et al, 1986;Ghosh et al, 2009;Avinash et al, 2014). Azeez et al (2021a) observed the influence of environmental factors on the abundance of the species on the north-west coast of India. However, there is limited information on the spatial structure and distribution of ribbonfish in the WCI, other than the study conducted by Azeez et al (2016;2023) along the north-west coast.…”

Section: Introductionmentioning

confidence: 99%

Spatial structure and distribution of ribbonfish Trichiurus lepturus Linnaeus, 1758 along the eastern Arabian Sea

P. Abdul Azeez,

Prathibha Rohit,

K. M. Rajesh

et al. 2024

Indian J. Fish.

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Ribbonfish Trichiurus lepturus is one of the most important pelagic fish resources in India.The west coast of India (WCI) is considered to be the major region where the species isabundant and forms a sizeable fishery. Determining the spatio-temporal abundance of fishis a key requirement for sustainable resource harvest. This paper illustrates the spatialstructure and distribution of ribbonfish along the WCI using the geostatistical technique,semi-variogram modelling. The spatio-temporal distribution map was generated by kriginginterpolation using the global neighbourhood in the study area. The results show that theareas of higher abundance of ribbonfish shifted between seasons, abundance along thenorthern region was high during the post-monsoon, while later shifting southward up to17°N, peaking between 18°N and 19°N. Furthermore, summertime peak abundance tendedto be more coastal in the Mumbai region. Ribbonfish abundance was relatively lower alongthe southern region i.e., below 15°N year-round. This finding provides additional informationto fishery managers and policymakers which will help improve the sustainability of fisheriescoupled with traditional fisheries management measures Keywords:Arabian Sea, GIS, Geo-statistics, Species distributionmodelling, Spatiotemporal management

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“…The external environmental factors of marine fish survival are the main factors affecting the abundance and distribution of fishery resources [27,28]. It has been proved that the spatial and temporal distribution of fishing grounds, the living habits of marine fishes and marine environmental factors are very closely related [29,30]. Chen et al (2005) verified the relationship between marine environmental factors and the distribution of albacore fishing grounds in the Indian Ocean, and the results showed that SST, Chl-a and SSS had significant effects on the distribution of immature albacore, with SST having a particularly prominent effect on the distribution of mature albacore fishing grounds [31].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Albacore (Thunnus alalunga) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

Zhang

Fan

et al. 2023

Applied Sciences

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To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (ACC), fishing ground forecast precision (P) and recall (R) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (ACC = 86.92%), with an overall improvement of 4.39~19.48% over the machine learning models. (2) A better forecast accuracy (R2 of 81.82–98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (R1 of 47.37–96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of 10° S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades of fishing grounds (0.037).

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Predicting potential fishing grounds of ribbonfish (Trichiurus lepturus) in the north-eastern Arabian Sea, using remote sensing data

Cited by 16 publications

References 60 publications

Habitat changes and catch rate variability for greater amberjack in the Taiwan Strait: The effects of El Niño–southern oscillation events

Habitat changes and catch rate variability for greater amberjack in the Taiwan Strait: The effects of El Niño–southern oscillation events

Spatial structure and distribution of ribbonfish Trichiurus lepturus Linnaeus, 1758 along the eastern Arabian Sea

Forecasting Albacore (Thunnus alalunga) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

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