垂钓
渔业
海洋学
太平洋
地理
栖息地
环境科学
地质学
生态学
生物
作者
Minghua Xue,Jianfeng Tong,Wen Ma,Zhenhong Zhu,Weiqi Wang,Shuo Lyu,Xinjun Chen
标识
DOI:10.1016/j.ecoinf.2024.102971
摘要
The Pacific saury (Cololabis saira) in the Northwest Pacific Ocean holds significant economic value. Understanding the relationship between its distribution and habitat is crucial for ecosystem-based fishery management. Acoustic data from fishing vessels provide a new option for describing the relative abundance of Pacific saury. We transformed opportunity acoustic data collected from Pacific saury fishing vessel during the summer fishing season of 2021 into an acoustic index that is suitable for habitat modeling. The acoustic index was used to develop a habitat suitability index (HSI) model. Meanwhile, generalized additive models were applied to select environmental variables for modeling, and boosting regression tree-weighted analyses were employed to determine the weights of these variables. Instead of traditional fishing effort or catch per unit effort (CPUE) based HSI models, this acoustic index-based model offered an innovative approach with immutable evidence for establishing the relationship between sea surface temperature (SST, °C), sea surface temperature gradient (SSTG, °C/°), sea surface height (SSH, m), and chlorophyll a concentration (Chl, mg/m3) with suitable habitats. The validity of the acoustic index in HSI modeling was tested with the catch data. This study showed that SSH had the most important impact on acoustical relative abundance, followed by SST, Chl and SSTG. The suitability index model determined the optimal habitat ranges for the four environmental variables. Cross-validation results indicated that the HSI model developed using the arithmetic mean method can better predict the distribution of suitable habitats than the geometric mean method. The HSI results matched the catch distribution well. This study contributes to further exploration of the marine variables' impact on suitable fish habitats based on acoustic indices and provides scientific references for ecosystem-based fishery management.
科研通智能强力驱动
Strongly Powered by AbleSci AI