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Invasion and interaction determine population composition in an open evolving ecological system.
Park, Youngjai; Shimada, Takashi; Son, Seung-Woo; Park, Hye Jin.
Affiliation
  • Park Y; Department of Physics, Inha University, Incheon 22212, South Korea.
  • Shimada T; Department of Applied Physics, Hanyang University, Ansan 15588, South Korea.
  • Son SW; Asia Pacific Center for Theoretical Physics (APCTP), Pohang 37673, South Korea.
  • Park HJ; Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea.
Chaos ; 33(6)2023 Jun 01.
Article in En | MEDLINE | ID: mdl-37352503
ABSTRACT
It is well-known that interactions between species determine the population composition in an ecosystem. Conventional studies have focused on fixed population structures to reveal how interactions shape population compositions. However, interaction structures are not fixed but change over time due to invasions. Thus, invasion and interaction play an important role in shaping communities. Despite its importance, however, the interplay between invasion and interaction has not been well explored. Here, we investigate how invasion affects the population composition with interactions in open evolving ecological systems considering generalized Lotka-Volterra-type dynamics. Our results show that the system has two distinct regimes. One is characterized by low diversity with abrupt changes of dominant species in time, appearing when the interaction between species is strong and invasion slowly occurs. On the other hand, frequent invasions can induce higher diversity with slow changes in abundances despite strong interactions. It is because invasion happens before the system reaches its equilibrium, which drags the system from its equilibrium all the time. All species have similar abundances in this regime, which implies that fast invasion induces regime shift. Therefore, whether invasion or interaction dominates determines the population composition.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ecosystem / Models, Biological Language: En Journal: Chaos Journal subject: CIENCIA Year: 2023 Type: Article Affiliation country: South Korea

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ecosystem / Models, Biological Language: En Journal: Chaos Journal subject: CIENCIA Year: 2023 Type: Article Affiliation country: South Korea