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Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.
Hosoda, Kazufumi; Seno, Shigeto; Kamiura, Rikuto; Murakami, Naomi; Kondoh, Michio.
Afiliación
  • Hosoda K; RIKEN Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan.
  • Seno S; Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka 565-0871, Japan.
  • Kamiura R; Institute for Transdisciplinary Graduate Degree Programs, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
  • Murakami N; Life and Medical Sciences Area, Health Sciences Discipline, Kobe University, Tomogaoka 7-10-2, Suma-ku, Kobe, Hyogo 654-0142, Japan.
  • Kondoh M; Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
Entropy (Basel) ; 25(12)2023 Dec 05.
Article en En | MEDLINE | ID: mdl-38136504
ABSTRACT
The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.
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