Comparative performance of modelling approaches for predicting fish species richness in the Yangtze River Estuary

Dai, Libin; Hodgdon, Cameron T.; Tian, Shu; Chen, Jinhui; Gao, Chunxia; Han, Dongyan; Kindong, Richard; Ma, Qiuyun; Wang, Xuefang

doi:10.1016/j.rsma.2020.101161

Cited by 8 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Salinity is one of the important environmental factors for fish development and distribution. It can change the fish stock by changing the osmotic pressure of fish eggs and affecting the development of embryos [14,43,44]. However, few studies have focused on how salinity affects the presence and distribution of E. japonicus.…”

Section: Relationship Between E Japonicus Resource Density and Enviro...mentioning

confidence: 99%

“…Many scholars use the presence-absence models to explore the relationship between fishery resource density and environmental factors [10][11][12][13]. As a kind of SDM, GAM can deal with the non-linear relationship between response variables and explanatory variables, such as spatial, temporal, and environmental variables [14], which comes with good stability and more flexibility for exploring the relationship between fishery resource density and environmental factors [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Relationship between Engraulis japonicus Resources and Environmental Factors Based on Multi-Model Comparison in Offshore Waters of Southern Zhejiang, China

Gao

Tang

et al. 2022

JMSE

Self Cite

View full text Add to dashboard Cite

In order to accurately explore the relationship between the density of Engraulis japonicus and environmental factors, five types of models, including Tweedie-Generalized Additive Model (GAM), two-stage GAM, Ad hoc-GAM, and Generalized Additive Mixing Model (GAMM), were compared based on the survey data in offshore waters of southern Zhejiang, China from 2015 to 2021 in this study. The results showed the best goodness of fit for two-stage GAM when processing the data of E. japonicus resource density. The deviance explained of GAM1 and GAM2 were 19.9 and 53.8%, respectively. According to this study, water temperature and salinity are important environmental factors affecting the distribution of E. japonicus, which are also closely related to latitude. In general, the resource density of E. japonicus decreases gradually with the increase in water temperature. When the salinity was between 26 ppt and 34 ppt, the resource density was higher. Also, there were some differences in the spatial distribution of E. japonicus in different seasons. The relationship between the resource density of E. japonicus and environmental factors was analyzed through various models to provide a scientific basis for the conservation management of E. japonicus in offshore waters of southern Zhejiang, China.

show abstract

Section: Relationship Between E Japonicus Resource Density and Enviro...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relationship between Engraulis japonicus Resources and Environmental Factors Based on Multi-Model Comparison in Offshore Waters of Southern Zhejiang, China

Gao

Tang

et al. 2022

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Selection Of Explanatory Variablesmentioning

confidence: 99%

“…When predicting the distribution patterns of target species resources across seasons, calculations are often carried out in two ways. The first approach is to model the entire dataset and take into account the 'season' (or month) as an influencing factor [2,14] (Yearly-GAM), and then use the best model to predict the distribution of the target species in each season. The second approach is to build models in separate seasons [3,15] and then use the best models for different seasons to separately make predictions to derive the spatial distribution characteristics of the target species in the corresponding season.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…As a brackish and fresh-water confluence area, the environmental elements of the Changjiang River estuary undergo drastic changes over a relatively small area [16]. Therefore, the spatial interpolation method is often used to estimate the distribution of marine environmental elements in studies predicting the temporal and spatial distribution and richness of fish in this region [17,18]. Coilia nasus is one of the main economic fish in the Changjiang River, which is an amphidromous fish [15].…”

Section: Introductionmentioning

confidence: 99%

Influence of Spatial Scale Selection of Environmental Factors on the Prediction of Distribution of Coilia nasus in Changjiang River Estuary

Meng

Gong

Wang

et al. 2021

Fishes

Self Cite

View full text Add to dashboard Cite

An estuary region is a complex environment with a transition from fresh to brackish to salt water, and in which some environmental factors change dramatically over small ranges. Therefore, it is important to understand the impact of the selection of spatial scale on the prediction of the distribution of estuarine species. As the largest estuary in China, the Changjiang River estuary is the spawning ground, feeding ground, and migration channel for many species. Based on Coilia nasus, an important economic fish species in the Changjiang River estuary, this study uses the two-stage generalized additive model (GAM) to investigate the potential differences in the response of species’ spatial distribution when environmental factors are assessed at different spatial scales (1′ × 1′, 2′ × 2′, 3′ × 3′, 4′ × 4′, 5′ × 5′). The results showed the following: (1) according to the analysis of the variance inflation factor (VIF), the values of all environmental factors were less than three and we found no correlation among the environmental variables selected. (2) The first stage GAM retained six variables, including year, month, latitude (Lat), water depth (Depth, m), bottom salinity (Sal, mg/L), and chemical oxygen demand (COD, mg/L). The second stage GAM retained four variables, including Year, Lat, pH, and chlorophyll a (Chl-a, μg/L). (3) The mean value of the Chla for the 3′×3′ spatial scale was significantly lower than that of the other spatial scales, and the mean value of Sal for the 5′×5′ spatial scale was higher than that of the other spatial scales. (4) In terms of the spatial distribution of abundance, the distribution patterns of C. nasus predicted by all scales were not very similar, and the distribution patterns predicted by the 5′ × 5′ scale, in the autumn of 2012, were significantly different from those at other scales. Therefore, the selection of spatiotemporal scales may affect predictions of the spatial distributions of species. We suggest that potential spatiotemporal scale effects should be evaluated in future studies.

show abstract

Comparative performance of modelling approaches for predicting fish species richness in the Yangtze River Estuary

Cited by 8 publications

References 38 publications

Relationship between Engraulis japonicus Resources and Environmental Factors Based on Multi-Model Comparison in Offshore Waters of Southern Zhejiang, China

Relationship between Engraulis japonicus Resources and Environmental Factors Based on Multi-Model Comparison in Offshore Waters of Southern Zhejiang, China

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

Influence of Spatial Scale Selection of Environmental Factors on the Prediction of Distribution of Coilia nasus in Changjiang River Estuary

Contact Info

Product

Resources

About