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Spatio-Temporal Variations in the Potential Habitat Distribution of Pacific Sardine (Sardinops sagax) in the Northwest Pacific Ocean

文献类型： 外文期刊

作者： Shi, Yongchuang 1 ; Kang, Bo 3 ; Fan, Wei 1 ; Xu, Lingling 4 ; Zhang, Shengmao 1 ; Cui, Xuesen 1 ; Dai, Yang 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Fisheries Remote Sensing, Shanghai 200090, Peoples R China

3.Marine Acad Zhejiang Prov, Hangzhou 310012, Peoples R China

4.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China

关键词： Sardinops sagax; habitat distribution; species distribution model; environmental variation; Northwest Pacific Ocean

期刊名称：FISHES （ 影响因子：2.3； 五年影响因子：2.4 ）

ISSN：

年卷期： 2023 年 8 卷 2 期

页码：

收录情况： SCI

摘要： Pacific sardine (Sardinops sagax) is a commercially important species and supports important fisheries in the Northwest Pacific Ocean (NPO). Understanding the habitat distribution patterns of Pacific sardine is of great significance for fishing ground prediction and stock management. In this study, both single-algorithm and ensemble distribution models were established through the Biomod2 package for Pacific sardine by combining the species occurrence data, sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS) and chlorophyll-a concentration (Chla) in the NPO during the main fishing season (June-November) from 2015 to 2020. The results indicated that the key environmental variables affecting the habitat distribution of Pacific sardine were the SSH and SST. The suitable habitat area for Pacific sardine showed significant monthly changes: the suitable habitat range in June was larger than that in July and August, while the suitable habitat range gradually increased from September to November. Furthermore, the monthly geometric centers of habitat suitability index (HSI) for Pacific sardine presented a counterclockwise pattern, gradually moving to the northeast from June, and then turning back to the southwest from August. Compared with single-algorithm models, the ensemble model had higher evaluation metric values and better spatial correspondence between habitat prediction and occurrence records data, which indicated that the ensemble model can provide more accurate prediction and is a promising tool for potential habitat forecasting and resource management.

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