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.
Highlights
While Ocean modeling has made significant advances over the last decade, its complex biological component is still oversimplified
The jellyfish carbon mass (CM) prediction was constrained by two forcing variables: temperature and zooplanktonic biomass
In the last few years, computer sciences promoted the use of the Statistical Model Checking Engine (SMCE) method for verifying large software models that are out of reach of standard verification methods
Summary
While Ocean modeling has made significant advances over the last decade, its complex biological component is still oversimplified. 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 probabilitybased computational framework that considers a set of parameters as a whole. 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. Overall, considering a predictive goal at both physiological and ecosystem levels, the SMCE produces a global set of parameter values that guarantees that the model matches experimental observations despite slight parameter variations. P. noctiluca is used as a scaling-up example of how one can apply state of the art verification methods in computer sciences to better estimate parameters of an ecophysiological model
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