Back

Spatial and temporal variation of biomass density beneath drifting fish‐aggregating devices in the western and central Pacific Ocean.

  • Published In: Fisheries Management & Ecology, 2024, v. 31, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Tianjiao; Xin, Jia; Song, Liming; Yuan, Hongchun 3 of 3

Abstract

We analyzed three‐dimensional (3‐D) spatial and temporal variation of biomass density associated with drifting fish aggregating devices (dFADs) in the western and central Pacific Ocean. Detection depth of dFAD echosounder buoys was divided into three layers, for estimation of biomass density in each layer based on detected water volume. Temporal variation and spatial distribution of biomass density in each layer were compared. Similarity of biomass density gravity center shifts in each layer were assessed using the dynamic time warping (DTW) regularization algorithm. Biomass density varied regularly over ~1 month, with the 2nd and 3rd layers delayed ~14 days compared to the 1st layer. Biomass distribution range and density values were higher in the 1st layer than the 2nd and 3rd layers, but locations of maximum biomass density were close. The distribution of biomass density gravity centers between adjacent water layers were similar, with gravity centers of the 2nd and 3rd layers shifted over time in relationd to the 1st layer. Our study demonstrated that the aggregation behavior of species from different water layers attracted by dFADs were related, and emphasized the necessity for ecosystem‐based fisheries management. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Fisheries Management & Ecology. 2024/10, Vol. 31, Issue 5, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2024
  • ISSN:0969-997X
  • DOI:10.1111/fme.12711
  • Accession Number:180986893
  • Copyright Statement:Copyright of Fisheries Management & Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.