TY - JOUR
T1 - System identification and landscape stability of stochastic competition ecosystem
AU - Zhang, Hongxia
AU - Lei, Youming
AU - Xu, Wei
N1 - Publisher Copyright:
© 2024 IOP Publishing Ltd and SISSA Medialab srl. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
PY - 2024/9/30
Y1 - 2024/9/30
N2 - Motivated by the challenging issue in analyzing the stochastic stability from observed state time series in a competition ecosystem, we take a systematic study from system identification to landscape stability. The vector field manifold is utilized to discuss dynamic properties of the deterministic competition ecosystem. For the stochastic state time series, we integrate the stochastic Koopman operator, the Kolmogorov backward equation and the extended dynamic mode decomposition methods to approximate the drift expression, while the Kramers-Moyal formula is used to identify the noise intensity. The depth and width indicators of the energy landscape are constructed to verify the effectiveness of the system identification method and to examine the impact of noise on system stability. Results demonstrate that proposed indicators can effectively assess the stochastic stability. Moreover, we find the environmental disturbance can induce global instability of the stochastic competition system, but its relative stability impact on each potential well will be altered by species competitiveness.
AB - Motivated by the challenging issue in analyzing the stochastic stability from observed state time series in a competition ecosystem, we take a systematic study from system identification to landscape stability. The vector field manifold is utilized to discuss dynamic properties of the deterministic competition ecosystem. For the stochastic state time series, we integrate the stochastic Koopman operator, the Kolmogorov backward equation and the extended dynamic mode decomposition methods to approximate the drift expression, while the Kramers-Moyal formula is used to identify the noise intensity. The depth and width indicators of the energy landscape are constructed to verify the effectiveness of the system identification method and to examine the impact of noise on system stability. Results demonstrate that proposed indicators can effectively assess the stochastic stability. Moreover, we find the environmental disturbance can induce global instability of the stochastic competition system, but its relative stability impact on each potential well will be altered by species competitiveness.
KW - energy landscape stability
KW - extended dynamic mode decomposition
KW - stochastic competition ecosystem
KW - stochastic Koopman operator
UR - http://www.scopus.com/inward/record.url?scp=85205915076&partnerID=8YFLogxK
U2 - 10.1088/1742-5468/ad7850
DO - 10.1088/1742-5468/ad7850
M3 - 文章
AN - SCOPUS:85205915076
SN - 1742-5468
VL - 2024
JO - Journal of Statistical Mechanics: Theory and Experiment
JF - Journal of Statistical Mechanics: Theory and Experiment
IS - 9
M1 - 093401
ER -