English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Reordered hierarchical complexity in ecosystems with delayed interactions

Authors

Qin,  Bo-Wei
External Organizations;

Sheng,  Wenbo
External Organizations;

Qian,  Xuzhe
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Hastings,  Alan
External Organizations;

Lai,  Ying−Cheng
External Organizations;

Lin,  Wei
External Organizations;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Qin_2025_pgaf214.pdf
(Publisher version), 8MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Qin, B.-W., Sheng, W., Qian, X., Kurths, J., Hastings, A., Lai, Y., Lin, W. (2025): Reordered hierarchical complexity in ecosystems with delayed interactions. - PNAS Nexus, 4, 7, pgaf214.
https://doi.org/10.1093/pnasnexus/pgaf214


Cite as: https://publications.pik-potsdam.de/pubman/item/item_33315
Abstract
It was once believed that large ecosystems with random interactions are unstable, limiting their complexity. Thus, large community size or numerous interactions are rare in nature. Later, a strict hierarchical complexity was revealed: competitive and mutualistic communities have the least complexity, followed by random ones, and then predator–prey communities. Recently, a hierarchy of recovery times for ecosystems with identical complexity was found, influenced by discrete time delays. A key question is whether this hierarchical complexity holds under noninstantaneous interactions. We surprisingly show that it does not. Specifically, the complexity of predator–prey communities is significantly affected by time delays, reordering the hierarchy at a critical threshold. These changes exhibit nonmonotonic behavior with continuous time delays, another realistic interaction type. We validated our findings in various realistic ecosystems. Our results indicate that incorporating factors like time delays and their appropriate forms can lead to correct and even deeper understanding about complexity of large ecosystems and other biophysical systems.