Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM

Hua, Chuanxiang; Zhu, Quanmin; Shi, Yongchuang; Liu, Yu

doi:10.1007/s13131-019-1486-3

Cited by 21 publications

(15 citation statements)

References 28 publications

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“…Distribution models including environmental effects should thus be preferred to predict species distributions and abundances at local scales [ 111 , 112 ]. However, when dealing with gaps in the spatiotemporal distribution of marine resources GAMs are more convenient than GLMs because they can easily incorporate the nonlinear responses of catches to geographic factors by smoothing rather than stratifying [ 32 , 33 ]. On the other hand, GAMs are likely to cause overfitting, especially with small sample sizes, because they allow the use of several fixed effects in nonlinear smoothing functions, which often reduce the predictive ability [ 113 ].…”

Section: Discussionmentioning

confidence: 99%

“…Historically, many efforts have been made to solve the difficulties associated with CPUE standardization [ 7 ] promoting the flexibility and the availability of well-tested and user-friendly tools such as Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to perform calculation [ 31 ]. However, while considering the nonlinearity of predictors there is evidence that statistical models such as GAMs perform better than GLMs [ 32 ] even if the survey area is not well covered due to the lack of sampling locations or biased designs [ 33 ]. GAMs proved to be also helpful to understand the environmental processes underlying species distributions [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

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Accounting for environmental and fishery management factors when standardizing CPUE data from a scientific survey: A case study for Nephrops norvegicus in the Pomo Pits area (Central Adriatic Sea)

et al. 2022

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Abundance and distribution of commercial marine resources are influenced by environmental variables, which together with fishery patterns may also influence their catchability. However, Catch Per Unit Effort (CPUE) can be standardized in order to remove most of the variability not directly attributable to fish abundance. In the present study, Generalized Additive Models (GAMs) were used to investigate the effect of some environmental and fishery covariates on the spatial distribution and abundance of the Norway lobster Nephrops norvegicus within the Pomo/Jabuka Pits (Central Adriatic Sea) and to include those that resulted significant in a standardization process. N. norvegicus is a commercially important demersal crustacean, altering its catchability over the 24-h cycle and seasons according to its burrowing behavior. A historically exploited fishing ground for this species, since 2015 subject to specific fisheries management measures, is represented by the meso-Adriatic depressions, which are also characterized by particular oceanographic conditions. Both the species behaviour and the features of this study area influence the dynamics of the population offering a challenging case study for a standardization modelling approach. Environmental and catch data were obtained during scientific trawl surveys properly designed to catch N. norvegicus, thus improving the quality of the model input data. Standardization of CPUE from 2 surveys from 2012 to 2019 was conducted building two GAMs for both biomass and density indices. Bathymetry, fishing pressure, dissolved oxygen and salinity proved to be significant drivers influencing catch distribution. After cross validations, the tuned models were then used to predict new indices for the study area and the two survey series by means of informed spatial grids, composed by constant surface cells, to each of which are associated average values of environmental parameters and specific levels of fishing pressure, depending on the management measures in place. The predictions can be used to better describe the structure and the spatio-temporal distribution of the population providing valuable information to evaluate the status of such an important marine resource.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accounting for environmental and fishery management factors when standardizing CPUE data from a scientific survey: A case study for Nephrops norvegicus in the Pomo Pits area (Central Adriatic Sea)

et al. 2022

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“…Before modeling, the variance inflation factor (VIF) test was performed to exclude highly correlated explanatory variables to prevent covariance from affecting the accuracy of the model [14]. It is generally believed that the problem of multicollinearity exists when VIF > 10 [16,30]. In addition, since this study focused on exploring the impact of modeling accuracy at different approaches, we used "season" as a categorical variable and did not test for covariance when building the Yearly-GAM.…”

Section: Selection Of Explanatory Variablesmentioning

confidence: 99%

“…Although both modeling approaches have been widely used in fisheries in previous studies, there are differences in the fitting and predictive performance of these two modeling approaches, which need to be further explored. The generalized additive model (GAM), as a type of species distribution model, can explore the influencing factors of density as a whole or individually and can effectively handle the nonlinear relationship between response variables and explanatory variables [16]. It has the advantages of high accuracy and flexible application [17] and is useful in studying the relationship between fish resources and the environment.…”

Section: Introductionmentioning

confidence: 99%

Do Two Different Approaches to the Season in Modeling Affect the Predicted Distribution of Fish? A Case Study for Decapterus maruadsi in the Offshore Waters of Southern Zhejiang, China

Gao

Qin

et al. 2022

Fishes

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The relationships between environmental factors and fish density are closely related, and species distribution models (SDMs) have been widely used in exploring these relationships and predicting the spatial distribution of fishery resources. When exploring the prediction of the spatial distribution of species in different seasons, the method of choosing the appropriate approach to the season will help to improve the predictive performance of the model. Based on data collected from 2015 to 2020 during a survey off southern Zhejiang, the Tweedie-GAM was used to establish the relationship between the density of Decapterus maruadsi and environmental factors at different modeling approaches. The results showed that water temperature, salinity and depth were the main factors influencing D. maruadsi, and they operated through different mechanisms and even resulted in opposite trends of density in different seasons. Spatially, the two modeling approaches also differed in predicting the spatial distribution of D. maruadsi, with the seasonal model showing a higher density trend in inshore waters than in offshore waters in spring but showing the opposite trend in summer and autumn, which was more consistent with the actual spatial distribution of the resource. By analyzing the effects of two different approaches on the prediction of fishery resources, this study aims to provide research ideas and references for improving the predictive performance of SDMs.

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“…Thus, it may be more populated and robust for modeling and the spatial distribution of species ( Leathwick, Elith & Hastie, 2006 ; Schmiing et al, 2013 ). Meanwhile, GAMs are regarded as informative tools in fisheries management, and they have been widely used in recent years ( Auth et al, 2011 ; Choi, Min & Soh, 2021 ; Hua et al, 2019 ; Knutsen et al, 2007 ; Liu et al, 2019 ). While quantitative relationships between fishing grounds and environmental factors have used GAMs ( Arcos, Cubillos & Núez, 2001 ; Chen & Tian, 2007 ; Cornic & Rooker, 2018 ; Feng et al, 2021 ; Maxwell et al, 2012 ; Yu et al, 2019 ; Hou et al, 2021 ), few studies have investigated relationships between T. japonicus fishing grounds and environmental factors in the Beibu Gulf.…”

Section: Introductionmentioning

confidence: 99%

Relationships between environmental variables and spatial and temporal distribution of jack mackerel (Trachurus japonicus) in the Beibu Gulf, South China Sea

Feng

Shi

Hou

et al. 2021

PeerJ

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The jack mackerel (Trachurus japonicus) is both a dominant pelagic fish species and an important fishing target in the Beibu Gulf, South China Sea. However, the resource status of this species fluctuates dramatically, and it has recently been added to a “red list” of threatened species of the International Union for Conservation of Nature (IUCN). Despite its economic importance and decreasing population status, limited research on its spatiotemporal distribution has been undertaken over the last decades. In order to evaluate the most crucial factors that influence the spatiotemporal variability of T. japonicus and to determine GAM performance and predictability, we analyze catch per unit effort (CPUE) of T. japonicus from Beibu Gulf over four seasons (months) from 2013 to 2014. A generalized additive model (GAMs) is populated with water depth and remotely sensed sea surface temperature (SST), sea surface salinity (SSS), sea surface chlorophyll-a concentration (Chl-a) and sea level anomaly (SLA). The CPUE of T. japonicus varies seasonally, with higher CPUE in summer and autumn than in spring and winter, and the highest CPUE in summer. GAM results explain 57% of the deviation explained in CPUE, with the most important variables being SLA, Month, Depth, SSS, and SST , each explaining 21.2%, 18.7%, 10.7%, 5.1%, and 1.3% of the variation in CPUE, respectively. This species occurs mainly between 50 and 75 m depth, SSS values 32.3–33.5 PSU and SST 25–30.5 °C. High CPUE sites occur near SLA ≤ 0 m, on the edge of cold eddies, and there is a certain catch near the sea surface with SLA ≥ 0 m. The spatial and temporal distribution of T. japonicus is affected by the season and the marine hydrological environment. This study might contribute to a better understanding of the distributional patterns of T. japonicus as well as provide a basis for sustainable management in the Beibu Gulf.

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Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM

Cited by 21 publications

References 28 publications

Accounting for environmental and fishery management factors when standardizing CPUE data from a scientific survey: A case study for Nephrops norvegicus in the Pomo Pits area (Central Adriatic Sea)

Accounting for environmental and fishery management factors when standardizing CPUE data from a scientific survey: A case study for Nephrops norvegicus in the Pomo Pits area (Central Adriatic Sea)

Do Two Different Approaches to the Season in Modeling Affect the Predicted Distribution of Fish? A Case Study for Decapterus maruadsi in the Offshore Waters of Southern Zhejiang, China

Relationships between environmental variables and spatial and temporal distribution of jack mackerel (Trachurus japonicus) in the Beibu Gulf, South China Sea

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