Preprints
https://doi.org/10.5194/egusphere-2023-475
https://doi.org/10.5194/egusphere-2023-475
17 Apr 2023
 | 17 Apr 2023
Status: this preprint is open for discussion.

Ecosystem connections in the shelf sea environment using complex networks

Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta

Abstract. We use complex network theory to better represent and understand the ecosystem connectivity in a shelf-sea environment. The baseline data used for the analysis are obtained from a state-of-the art coupled marine physics-biogeochemistry model simulating the North-West European Shelf (NWES). The complex network built on model outputs is used to identify the functional types of variables behind the biogeochemistry dynamics, suggesting how to simplify our understanding of the complex web of interactions within the shelf-sea ecosystem. We demonstrate that complex networks can be also used to understand spatial ecosystem connectivity, both identifying the (geographically varying) connectivity lengthscales and the clusters of spatial locations that are connected. These clusters indicate geographic regions where there is a substantial flow of information between the degrees of freedom within the ecosystem, while information exchange across the boundaries of these regions is limited. The results of this study help to understand how natural, or antrophogenic, perturbations propagate through the shelf-sea ecosystem, and can be used in multiple future applications such as stochastic noise modelling, data assimilation, or machine learning.

Ieuan Higgs et al.

Status: open (until 23 Jun 2023)

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Ieuan Higgs et al.

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Short summary
A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on a ecosystem-ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will effect the shelf-sea ecosystem, and can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.