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Atlantis Ecosystem Model Summit 2022: Report from a workshop

•The second international Atlantis Ecosystem Modeling Summit was convened in May 2022.•Atlantis can be coupled to species distribution models and R to expand its use-cases.•Identified solutions for attaining high resolution ocean forecasts and hindcasts.•Accentuated paths for using Atlantis to infor...

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Bibliographic Details
Published in:Ecological modelling 2023-09, Vol.483, p.110442, Article 110442
Main Authors: Perryman, Holly A., Kaplan, Isaac C., Blanchard, Julia L., Fay, Gavin, Gaichas, Sarah K., McGregor, Vidette L., Morzaria-Luna, Hem Nalini, Porobic, Javier, Townsend, Howard, Fulton, Elizabeth A.
Format: Article
Language:English
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Summary:•The second international Atlantis Ecosystem Modeling Summit was convened in May 2022.•Atlantis can be coupled to species distribution models and R to expand its use-cases.•Identified solutions for attaining high resolution ocean forecasts and hindcasts.•Accentuated paths for using Atlantis to inform ecosystem-based fisheries management.•Artificial Intelligence offers ways for aiding parameter estimation and calibration. Ecosystem models can play a supportive and informative role in the implementation of integrated approaches to marine resource management. Atlantis, an end-to-end biogeochemical marine ecosystem model, is capable of exploring a wide range of ecosystem aspects and interactions. To aid the testing and development of Atlantis as a central tool for integrated ecosystem management and advance global partnerships for living marine resource management, the second International Atlantis Ecosystem Modeling Summit gathered 84 participants from 13 countries in May 2022. The main outcomes of the 2022 Atlantis Summit include: fortifying the sense of global community, training 37 participants, disseminating tools currently available and tools needed for processing Atlantis inputs/outputs, and identifying limitations and use-cases for moving forward with research pertaining to climate change, fisheries management, and synergies with artificial intelligence and machine learning.
ISSN:0304-3800
1872-7026
DOI:10.1016/j.ecolmodel.2023.110442