Regression Analysis on Total Annual Mackerel and Horse Mackerel Egg Production

DOI

10.2991/978-94-6463-562-1_35How to use a DOI?

Keywords

Modeling and simulation; Model development and analysis; Model verification and validation

Abstract

The International Council for the Exploration of the Sea (ICES) plays a crucial role in marine science, focusing on the sustainable management and conservation of marine biodiversity. This paper presents a detailed analysis of data from the Mackerel and Horse Mackerel Egg Surveys (WGMEGS), conducted under the ICES. Specifically, we explore the 2010 datasets, which estimate the spawning-stock biomass of mackerel and horse mackerel in the Northeast Atlantic and North Sea. Our research is structured around three primary objectives: (1) Estimating the total annual egg production for mackerel and horse mackerel using statistical models, (2) Identifying key features among selected covariates that significantly impact egg production, and (3) Analyzing the differences between two surveys conducted in the same year. The outcomes of our study aim to provide insights that are critical for the effective management and conservation of these vital fish species, contributing to the broader goal of safeguarding marine biodiversity.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiamu Zhao
PY  - 2024
DA  - 2024/11/13
TI  - Regression Analysis on Total Annual Mackerel and Horse Mackerel Egg Production
BT  - Proceedings of the 2024 5th International Conference on Big Data and Social Sciences (ICBDSS 2024)
PB  - Atlantis Press
SP  - 383
EP  - 391
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-562-1_35
DO  - 10.2991/978-94-6463-562-1_35
ID  - Zhao2024
ER  -