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1000
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Abstract/Summary
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<jats:p>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 groups 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 also be used to understand spatial ecosystem connectivity, identifying both the (geographically varying) connectivity length-scales and the clusters of spatial locations that are connected. We show that the biogeochemical length-scales vary significantly between variables and are not directly transferable. We also find that the spatial pattern of length-scales is similar across each variable, as long as a specific scaling factor for each variable is taken into account. The clusters indicate geographical regions within which there is a large exchange of information within the ecosystem, while information exchange across the boundaries between these regions is limited. The results of this study describe how information is expected to propagate through the shelf sea ecosystem, and how it can be used in multiple future applications such as stochastic noise modelling, data assimilation, or machine learning.
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1000
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Hinweis
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DeepGreen-ID: eb363d752de14a5ea35c1a8670ba7bc4 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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