FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys

Maureaud, Aurore A.; Palacios-Abrantes, Juliano; Kitchel, Zoë; Mannocci, Laura; Pinsky, Malin L.; Fredston, Alexa; Beukhof, Esther; Forrest, Daniel L.; Frelat, Romain; Palomares, Maria L. D.; Pecuchet, Laurene; Thorson, James T.; van Denderen, P. Daniël; Mérigot, Bastien

doi:10.1038/s41597-023-02866-w

i

FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys

2024
By Maureaud, Aurore A. ; Palacios-Abrantes, Juliano ; Kitchel, Zoë ; ...

Details

Journal Title:

Scientific Data
Personal Author:

Maureaud, Aurore A. ; Palacios-Abrantes, Juliano ; Kitchel, Zoë ; Mannocci, Laura ; Pinsky, Malin L. ; Fredston, Alexa ; Beukhof, Esther ; Forrest, Daniel L. ; Frelat, Romain ; Palomares, Maria L. D. ; Pecuchet, Laurene ; Thorson, James T. ; van Denderen, P. Daniël ; Mérigot, Bastien
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center)
Description:

Scientific bottom-trawl surveys are ecological observation programs conducted along continental shelves and slopes of seas and oceans that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or weight in space and time, and contribute to fisheries management as well as population and biodiversity research. Bottom-trawl surveys are conducted all over the world and represent a unique opportunity to understand ocean biogeography, macroecology, and global change. However, combining these data together for cross-ecosystem analyses remains challenging. Here, we present an integrated dataset of 29 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,170 sampled fish taxa and 216,548 hauls collected from 1963 to 2021. We describe the processing steps to create the dataset, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this dataset is to support research, marine conservation, and management in the context of global change.
Keywords:

Computer Science Applications Education Information Systems Library And Information Sciences Statistics, Probability And Uncertainty Statistics And Probability Library And Information Sciences Statistics, Probability And Uncertainty Statistics And Probability Library And Information Sciences Statistics, Probability And Uncertainty Statistics And Probability
Source:

Scientific Data, 11(1)
DOI:

https://doi.org/10.1038/s41597-023-02866-w
ISSN:

2052-4463
Format:

PDF
Publisher:

Springer Science and Business Media LLC
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha256:010fbfc82a2f2fd4f59b9d474a39b0cc1218c67ed0c749f538c00d63177d046d
Download URL:

https://repository.library.noaa.gov/view/noaa/56442/noaa_56442_DS1.pdf
File Type:

[PDF - 5.55 MB ]

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