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.

尽管海洋建模在过去十年中取得了长足的进步，但其复杂的生物组成部分仍然过于简化。特别是，对海洋系统中的生物进行建模时必须整合参数，以适应很难共同确定的生理和生态行为。这样的困难发生在对夜蛾Pelagia noctiluca的建模中。这种水母在地中海具有很高的丰度，并可能有助于一些生物地球化学过程。但是，凝胶状浮游动物在生物地球化学模型中的代表性仍然很差，因为其生态生理的不确定性限制了我们对其潜在作用和影响的理解。为了克服这个问题，我们首次建议使用统计模型检查引擎（SMCE），一种基于概率的计算框架，将一组参数视为一个整体。与标准参数推断技术相反，SMCE在考虑不确定性的同时，确定了既适合实验室培养观察结果又适合原位模式的参数集。这样做，我们估算了代表实验室环境中水母生长和退化以及其大小的生态生理模型的最佳参数集。在此应用程序的背后，SMCE仍然是一个计算框架，它支持在更广泛的上下文中（例如，驱动未来研究的生物地球化学过程）具有不确定性的模型的投影。