WeChat Mini Program
Old Version Features

Temporal and Spatial Variations in Squid Jigging Catch Efficiency in the Oyashio Extension Region

Fisheries Oceanography(2024)

Ocean Univ China

Cited 0|Views4
Abstract
The abundance of fishery resources significantly impacts the ratio of fishing effort to harvest. However, traditional statistics of fishery catches or commonly used catch per unit effort (CPUE) metrics cannot accurately capture the complexity of resource abundance in the ocean. To address this issue, we propose here a novel approach that integrates the actual fishery catch from vessel logs with fishing duration obtained through automatic identification system (AIS) positioning. This combined analysis eliminates confounding factors and introduces a novel metric called “catch efficiency (CE)” to evaluate fishing operations more accurately, thereby reflecting resource abundance in a more reasonable way. In this study, we focus on the CE of squid in the Oyashio Extension region in the Northwestern Pacific. Our analysis reveals significant temporal and spatial variations of CE, manifesting in both intensity and distribution patterns. Moreover, our findings establish a close relationship between CE and background water mass distribution, chlorophyll‐a concentration, and micronekton biomass. This implies that the resource abundance of squid can be inferred by considering the varying environmental factors within the fishing area.
More
Translated text
Key words
catch efficiency,CPUE,global fishing watch,Oyashio Extension region,resource abundance,squid jigging,water mass
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined