Probabilistic modeling to estimate jellyfish ecophysiological properties and size distributions

Area of Science:

• Marine biology
• Computational oceanography
• Ecological modeling

Background:

• Ocean modeling advances have not fully captured complex biological components.
• Jellyfish, like Pelagia noctiluca, are abundant but poorly represented in models due to ecophysiological uncertainties.
• Accurate modeling of jellyfish is crucial for understanding their impact on biogeochemical processes.

Purpose of the Study:

• To introduce and apply the Statistical Model Checking Engine (SMCE) for improved ecophysiological modeling of marine organisms.
• To estimate key parameters for the jellyfish Pelagia noctiluca, integrating laboratory and in situ data.
• To provide a framework for incorporating uncertainties in ecological models for broader biogeochemical applications.

Main Methods:

• Utilized the Statistical Model Checking Engine (SMCE), a probability-based computational framework.
• Applied SMCE to estimate ecophysiological parameters for jellyfish growth, degrowth, and size.
• Integrated laboratory culturing observations and in situ patterns within a probabilistic approach.

Main Results:

• Successfully estimated optimal parameter sets for the ecophysiological model of Pelagia noctiluca.
• Demonstrated SMCE's capability to handle parameter uncertainties and fit diverse data types.
• Provided a more robust representation of jellyfish ecophysiology within an ecological model.

Conclusions:

• The Statistical Model Checking Engine (SMCE) offers a novel approach to overcome limitations in ecological modeling, particularly for underrepresented organisms.
• Accurate ecophysiological parameterization is essential for understanding the role of jellyfish in marine ecosystems and biogeochemical cycles.
• SMCE provides a versatile computational framework for future research on marine ecosystem dynamics and the impact of environmental change.