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Probabilistic modeling to estimate jellyfish ecophysiological properties and size distributions

Authors
  • Ramondenc, Simon1
  • Eveillard, Damien2
  • Guidi, Lionel1, 3
  • Lombard, Fabien1
  • Delahaye, Benoît2
  • 1 Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefranche, LOV, Villefranche-sur-Mer, F-06230, France , Villefranche-sur-Mer (France)
  • 2 Université de Nantes, CNRS, LS2N, Nantes, F-44322, France , Nantes (France)
  • 3 University of Hawaii, Department of Oceanography, Honolulu, HI, 96822, USA , Honolulu (United States)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Apr 08, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-62357-5
Source
Springer Nature
License
Green

Abstract

While Ocean modeling has made significant advances over the last decade, its complex biological component is still oversimplified. In particular, modeling organisms in the ocean system must integrate parameters to fit both physiological and ecological behaviors that are together very difficult to determine. Such difficulty occurs for modeling Pelagia noctiluca. This jellyfish has a high abundance in the Mediterranean Sea and could contribute to several biogeochemical processes. However, gelatinous zooplanktons remain poorly represented in biogeochemical models because uncertainties about their ecophysiology limit our understanding of their potential role and impact. To overcome this issue, we propose, for the first time, the use of the Statistical Model Checking Engine (SMCE), a probability-based computational framework that considers a set of parameters as a whole. Contrary to standard parameter inference techniques, SMCE identifies sets of parameters that fit both laboratory-culturing observations and in situ patterns while considering uncertainties. Doing so, we estimated the best parameter sets of the ecophysiological model that represents the jellyfish growth and degrowth in laboratory conditions as well as its size. Behind this application, SMCE remains a computational framework that supports the projection of a model with uncertainties in broader contexts such as biogeochemical processes to drive future studies.

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