Environmental adaptability and interspecific relationships of demersal fishes in the coastal waters of Shandong in summer explored by HMSC models

Xu Tianheng; Zhang Chongliang; Xue Ying; Xu Binduo; Ji Yupeng; Ren Yiping

doi:10.12284/hyxb2023106

Volume 45 Issue 8

Aug. 2023

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Xu Tianheng，Zhang Chongliang，Xue Ying, et al. Environmental adaptability and interspecific relationships of demersal fishes in the coastal waters of Shandong in summer explored by HMSC models[J]. Haiyang Xuebao，2023, 45(8)：86–95 doi: 10.12284/hyxb2023106

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Environmental adaptability and interspecific relationships of demersal fishes in the coastal waters of Shandong in summer explored by HMSC models

doi: 10.12284/hyxb2023106

Xu Tianheng^{1, 2
,},
Zhang Chongliang^{1, 2
,
,},
Xue Ying^{1, 2},
Xu Binduo^{1, 2},
Ji Yupeng^{1, 2},
Ren Yiping^{1, 2}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China

Received Date: 2023-02-03
Rev Recd Date: 2023-04-20

Available Online: 2023-08-24

Publish Date: 2023-08-31

Abstract

Abstract

Traditional species distribution models rarely incorporate interspecific relationships into the modeling framework, which hinders their predictions of habitat distributions. In recent years, joint species distribution models (JSDMs) have drawn increasing attentions, but their practical applications remain rare in the marine realm. In this study, we used the HMSC (hierarchical modelling of species communities) method to study their relationships between 17 demersal fish species and environmental factors and the interspecific correlation. The model was built on the basis of bottom trawling data collected in the coastal waters of Shandong in summer, 2017, including the environmental data of water depth, bottom water temperature and bottom water salinity. Five variants of HMSC models were developed with respect to the linear or nonlinear relationships between species and the environmental variables and the exists of random effects, and WAIC and other indicators as well as cross-validation were used to evaluate the performances of fitting and prediction of these models. The results showed that the optimal model was the one incorporating nonlinear relationships and random effects (Model 5). The nonlinear models were generally superior to the linear models, and including the interspecific relationships in the model could improve model fitting performances. Temperature was the main factor influencing the distribution of demersal fishes in the coastal waters of Shandong, accounting for 51.4% of the mean explained variance, followed by water depth and random effects, which accounted for 35.7% and 12.8% explained variance, respectively. There were significant linear positive correlations between most demersal fishes and water depth, and significant nonlinear relationships with water temperature. There were significant interspecific correlations among the demersal fishes, which could be roughly divided into three groups according to the sign of the correlations, indicating that the interspecies relationships played an important role in shaping species distributions. This study suggested that the abiotic factors and biotic factors should be integrated in species distribution modeling, and our results might provide a guideline for the prediction of habitat distribution of fishery resources.

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References(54)

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