Seasonal-Spatial Distribution Variations and Predictions of Loliolus beka and Loliolus uyii in the East China Sea Region: Implications from Climate Change Scenarios

被引:2
|
作者
Xu, Min [1 ,2 ]
Feng, Wangjue [3 ]
Liu, Zunlei [1 ,2 ]
Li, Zhiguo [4 ]
Song, Xiaojing [1 ,2 ]
Zhang, Hui [1 ,2 ]
Zhang, Chongliang [3 ]
Yang, Linlin [1 ,2 ]
机构
[1] Minist Agr & Rural Affairs, Key Lab East China Sea Fishery Resources Exploitat, Shanghai 200090, Peoples R China
[2] Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China
[3] Ocean Univ China, Fisheries Coll, Qingdao 266003, Peoples R China
[4] Xiangshan Cty Fisheries Bur, Ningbo 315700, Peoples R China
来源
ANIMALS | 2024年 / 14卷 / 14期
关键词
climate warming; species distribution model; Loliolus uyii; Loliolus beka; sdmTMB; SSP1-2.6; SSP2-4.5; SSP5-8.5; Cephalopoda; Loliginidae; IMPACTS;
D O I
10.3390/ani14142070
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Simple Summary: Loliolus spp., such as Loliolus beka (Sasaki, 1929) and Loliolus uyii (Wakiya and Ishikawa, 1921), represent a dominant fishing bycatch in the East China Sea. However, their seasonal-spatial distribution characteristics are unclear. Here, the generalized linear mixed-effects model sdmTMB was used to evaluate ways of handling merged distribution data for L. beka and L. uyii to predict the variation in their spatial distributions under different climate change scenarios. Model predictions were validated using cross-validation, while model performance was assessed using T-tests. We speculated that the range and density of both species will vary over time, with some areas in the mid-East China Sea becoming potential habitats. In addition, we assumed that individuals of both species in the northern survey area might migrate to the northern Yellow Sea and Bohai Sea to spawn, whereas those in the central and southern Yellow Sea might migrate to coastal areas. During autumn, newborn larvae in the northern survey area would migrate to the southern Yellow Sea, whereas those in the central and southern areas would gradually migrate to offshore areas, resulting in a distribution ranging from coastal sea areas to the central survey area. Thus, this study provides novel insights into modeling habitat distributions for data-limited species and theoretical support for adapting fisheries resource management to climate change. Global climate change profoundly impacts the East China Sea ecosystem and poses a major challenge to fishery management in this region. In addition, closely related species with low catches are often not distinguished in fishery production and relevant data are commonly merged in statistics and fishing logbooks, making it challenging to accurately predict their habitat distribution range. Here, merged fisheries-independent data of the closely related squid Loliolus beka (Sasaki, 1929) and Loliolus uyii (Wakiya and Ishikawa, 1921) were used to explore the construction and prediction performance of species distribution models. Data in 2018 to 2019 from the southern Yellow and East China Seas were used to identify the seasonal-spatial distribution characteristics of both species, revealing a boundary line at 29.00 degrees N for L. uyii during the autumn, with the highest average individual weight occurring during the summer, with both larvae and juveniles occurring during the autumn. Thus, the life history of L. uyii can be divided into winter-spring nursery and summer-autumn spawning periods. L. beka showed a preference for inshore areas (15-60 m) during the summer and offshore areas (32.00-78.00 m) during the winter. High-value areas of both species included inshore areas of the southern Yellow and mid-East China Seas during the autumn, enlarging during the spring to include central areas of the survey region, before significantly decreasing during the summer. Therefore, this study provides both a novel perspective for modeling biological habitat distribution with limited data and a scientific basis for the adjustment of fishery resource management and conservation measures in the context of climate change.
引用
收藏
页数:20
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