Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

Solanki, H. U.; Bhatpuria, Dhyey; Chauhan, Prakash

doi:10.1080/10106049.2015.1120357

Cited by 43 publications

(23 citation statements)

References 38 publications

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“…The CPUE follows a continuous distribution, logarithmic transformation was used to normalize the asymmetrical distribution, and a factor of 0.1 is commonly used in CPUE standardizations (Supplementary Material 2.1). GAM is modeled using the Gaussian distribution family (Solanki et al, 2017). The GAM Equation 14 https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/ and the GLM Equation (2) implemented were presented as follows:…”

Section: Model Constructmentioning

confidence: 99%

Impact of Climate Change on Wintering Ground of Japanese Anchovy (Engraulis japonicus) Using Marine Geospatial Statistics

Liu

Alabia

et al. 2020

Front. Mar. Sci.

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The distribution and fluctuations in abundance of small pelagic species such as anchovy are largely affected by climate change. We hypothesized that the future projected rise in temperature will result to a northward shift of Japanese anchovy (Engraulis japonicus) habitat and a subsequent increase in relative abundance. To test this hypothesis, we explored the link between Japanese anchovy abundance and environmental conditions using machine-learning and statistical models. The models were fitted with catch per unit effort (CPUE) as the response variable and remotely sensed data of sea surface temperature (SST), sea surface chlorophyll-a (Chl-a), assimilated information of sea surface salinity (SSS), meridional and zonal ocean currents, and depth as environmental covariates. Our results showed that the abundance of E. japonicus was significantly influenced by environmental factors. In particular, salinity front and SST highlight strong relationships with winter CPUE distribution. Based on these models, the results reinforced our hypothesis and showed that the warming ocean will drive a substantial shift in Japanese anchovy habitat in the China seas. SST and CPUE showed negative correlations with the El Niño Southern Oscillation (ENSO) index. These findings underpin ramifications of the climate-driven habitat shift of small pelagic fish species on the regional marine ecosystem in the China seas.

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Section: Model Constructmentioning

confidence: 99%

Impact of Climate Change on Wintering Ground of Japanese Anchovy (Engraulis japonicus) Using Marine Geospatial Statistics

Liu

Alabia

et al. 2020

Front. Mar. Sci.

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“…The spatial distribution of Loligo spp. and Tachysurus in the Arabian Sea was well predicted based on the data of sea surface temperature (SST), chlorophyll a (Chl a) concentration, photosynthetically active radiation (PRA) and sea level anomalies (SLA) in the study of Solanki (2017). The interaction between upwelling index, year and latitude had a great influence on the spatial distribution of sardines in Mauritanian waters (Bacha et al, 2017;Abdullah and Rahim, 2018).…”

Section: Introductionmentioning

confidence: 89%

Environmental Factors Affecting the Spatiotemporal Distribution of Decapterus Maruadsi in the Western Guangdong Waters, China

Yu¹

2019

Appl. Ecol. Env. Res.

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Decapterus maruadsi is a small pelagic fish and is of great significance to the ecosystems in the northern South China Sea. We collected and analyzed the data recorded by the fishing vessels in the western Guangdong waters in winter and summer and remote sensing data including sea surface temperature (SST), chlorophyll a concentration (Chl a) and sea level anomalies (SLA) from 2011 to 2015, using generalized additive model (GAM). Results showed that the catch per unit effort (CPUE) of D. maruadsi in the western Guangdong waters had a significant positive linear correlation with longitude, and the D. maruadsi populations were concentrated in the east of the study area. The total explanation of D. maruadsi CPUE in GAMs was 47.40%, among which, the factors year, month, lunar phase, latitude, Chl a, SLA and water depth explained 9.30%, 13.50%, 6.50%, 1.90%, 1.70%, 4.60%, and 9.90%, respectively. The D. maruadsi populations in the study were concentrated in the area of latitude 21°N, Chl a 0.1-0.5 mg•m -3 , SLA -0.05 m and water depth 80 m. Results of this work will help in understanding the environmental factors affecting D. maruadsi distribution in the western Guangdong waters.

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“…GAMs were applied to investigate the influence of environmental variables on the abundance and distribution of fishery resources. GAMs have been successfully applied in the prediction of spatial distribution of fishing ground [12,13] and diversity of fish community [14,15].…”

Section: Introductionmentioning

confidence: 99%

Environmental Influence on the Spatiotemporal Variability of Fishing Grounds in the Beibu Gulf, South China Sea

Wang

Yao

Chen

et al. 2020

JMSE

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The spatiotemporal distribution of fishing grounds in the Beibu Gulf and its relationship with marine environment were analyzed using the survey data of light falling-net vessels and satellite remote sensing data including sea surface temperature (SST), chlorophyll a concentration (Chl a) and net primary production (NPP), based on the generalized additive model (GAM) and the center of gravity (COG) of fishing grounds. The results showed that the total deviance explained by GAM for the catch per unit effort (CPUE) in the Beibu Gulf was 42.9%, in which SST was the most important influencing factor on CPUE, with a relative contribution of 40%; followed by latitude, Chl a, month and NPP, with relative contributions of 25.2%, 19%, 10.4% and 5.4%, respectively. Fishing grounds in the Beibu Gulf were mainly distributed in waters with SST of 27–29 °C, Chl a of 0.5–1.5 mg m−3 and NPP of 500–700 mg m−2 d−1. Light falling-net fishing grounds were concentrated in waters with latitude of 18.5° N and 20–20.25° N. There was a significant correlation between the mean latitude of optimum NPP and the latitudinal COG of CPUE, with the R2 being 0.91. These were connected with environmental factors such as the northeast monsoon that began in autumn and winter, warm pools near 19° N and local upwelling in the Beibu Gulf.

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Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

Cited by 43 publications

References 38 publications

Impact of Climate Change on Wintering Ground of Japanese Anchovy (Engraulis japonicus) Using Marine Geospatial Statistics

Impact of Climate Change on Wintering Ground of Japanese Anchovy (Engraulis japonicus) Using Marine Geospatial Statistics

Environmental Factors Affecting the Spatiotemporal Distribution of Decapterus Maruadsi in the Western Guangdong Waters, China

Environmental Influence on the Spatiotemporal Variability of Fishing Grounds in the Beibu Gulf, South China Sea

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