Multi-scenario ecosystem risk identification and early warning coupling ‘pattern-service-stability’

A case study of the Poyang Lake Plain, China

Bibliographic Data

ID15549717
AuthorsXiaofang Yang (0009-0005-3815-9468, Jiangxi Agricultural University, corresponding author), Zhijun Luo (0000-0002-4938-8101, Jiangxi Agricultural University, corresponding author), Songkai Luo (0009-0001-6542-7123, Jiangxi Agricultural University, corresponding author)
Year2025
Volume20
Issue8
Pages084030-084030
Publication date2025-06-23
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ade72f
OpenAlexW4411545055
LanguageEN
Citations received1
References cited40

Synthesizing the multidimensional characteristics of landscape ecosystems provides critical insights for ecological risk assessment and complex human–nature interdependencies. Through the synergetic integration of multi-temporal datasets encompassing landscape ecological risk (LER), ecosystem service value (ESV) and ecosystem stability sustainability (ESS), we formulate a novel three-dimensional analytical framework based on ‘pattern-service-stability’. This methodological advancement enables the quantification of dynamic ecosystem risk in the Poyang Lake Plain from 2000 to 2035 through multi-scenario simulation. The results reveal that (1) changes in forest, water and impervious land exhibit significant scenario dependence. The ecological protection (EP) scenario preserved higher forest–water proportions, while the urban growth (UG) scenario exhibited accelerated construction expansion, with the natural development scenario maintaining transitional states. (2) LER remains consistently low across periods, characterised by stability and minimal inter-scenario variations. The ESV shows a decreasing trend, with high ESV areas decreasing abruptly and becoming concentrated in Poyang Lake’s southern bay. The low ESV increases significantly in the UG scenario, while the EP scenario maintains a higher service capacity. The ESS displays pronounced spatial heterogeneity. Extreme values of ESS tend to be flat, while high-grade ESS dominates under the EP scenario. (3) The interaction among LER, ESV and ESS has significant spatio-temporal heterogeneity and policy sensitivity. The lakeshore is mostly characterised by ‘low LER-high ESS’, while near-lake zone is concentrated in ‘high LER-low ESS’, with the ESV in each zone mostly in an intermediate state. (4) The growth rate and spatial variation of the ecosystem risk index (ERI) were small during 2000–2023, but ERI was predicted to surge under the UG scenario. (5) The ecosystem risk evolution paths are different in each zone and roughly conform to a linear fit, with the lakeshore exhibiting accelerated risk escalation. The gravity centre of each risk early warning unit is located in Nanchang County. This spatially explicit diagnostic framework enables dynamic ecosystem risk early warning management and resilience enhancement

Archaeology · Biology · Business · China · Coupling (piping · Ecosystem · Ecosystem services · Environmental resource management · Geography · Identification (biology · Machine learning · Service (business · Stability (learning theory · Telecommunications · Warning system · Computer Science · Ecosystem dynamics and resilience · Engineering · Environmental Science · Ecology

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