Stability-instability transition in tripartite merged ecological networks.

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Abstrakt

Tytuł:: Stability-instability transition in tripartite merged ecological networks.
Autorzy:: Emary C; School of Mathematics, Statistics and Physics, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK. .
Malchow AK; Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
Źródło:: Journal of mathematical biology [J Math Biol] 2022 Aug 12; Vol. 85 (3), pp. 20. Date of Electronic Publication: 2022 Aug 12.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Publication: Berlin : Springer Verlag
Original Publication: Wien, New York, Springer-Verlag.
MeSH Terms:: Ecosystem*
Food Chain*
Symbiosis
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Contributed Indexing:: Keywords: Community matrix; Ecological network; Phase transition; Population dynamics; Random eigenvalues; Random matrices
Entry Date(s):: Date Created: 20220812 Date Completed: 20220816 Latest Revision: 20220927
Update Code:: 20240104
PubMed Central ID:: PMC9374642
DOI:: 10.1007/s00285-022-01783-7
PMID:: 35960362
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Although ecological networks are typically constructed based on a single type of interaction, e.g. trophic interactions in a food web, a more complete picture of ecosystem composition and functioning arises from merging networks of multiple interaction types. In this work, we consider tripartite networks constructed by merging two bipartite networks, one mutualistic and one antagonistic. Taking the interactions within each sub-network to be distributed randomly, we consider the stability of the dynamics of the network based on the spectrum of its community matrix. In the asymptotic limit of a large number of species, we show that the spectrum undergoes an eigenvalue phase transition, which leads to an abrupt destabilisation of the network as the ratio of mutualists to antagonists is increased. We also derive results that show how this transition is manifest in networks of finite size, as well as when disorder is introduced in the segregation of the two interaction types. Our random-matrix results will serve as a baseline for understanding the behaviour of merged networks with more realistic structures and/or more detailed dynamics.
(© 2022. The Author(s).)

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