Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Functional Plant Ecology

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1668073

Analysis of Interrelated Characteristics between Ecosystem Services and Ecosystem Health in the Guangdong–Hong Kong–Macao Greater Bay Area

Provisionally accepted
Xiaojia  WangXiaojia Wang1Yushang  WangYushang Wang2Langxi  SongLangxi Song2Seping  DaiSeping Dai3*Chuanfu  ZangChuanfu Zang2*
  • 1Other
  • 2South China Normal University, Guangzhou, China
  • 3Guangzhou Institute of Forestry and Landscape Architecture, Guangzhou, China

The final, formatted version of the article will be published soon.

Ecosystem health (EH) underpins the capacity of vegetation ecosystems to provide essential ecosystem services (ESs), which together are fundamental to regional sustainability. In regions undergoing rapid urbanization, the interrelationships between EH and ESs become increasingly complex, yet they remain largely unexplored in previous studies. This study integrates the VOR and InVEST models to quantify EH and four key ESs in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2000 to 2020, and further analyzes their interrelationships using a bivariate spatial autocorrelation model and the XGBoost-SHAP approach. The results indicate that: (1) From 2000 to 2020, low-value areas of most ESs and EH expanded, regions of EH deterioration accounted for 71.75% of the study area, indicating the profound impact of rapid urbanization. (2) EH showed strong positive global spatial correlations with CS and NPP, but weak negative spatial correlations with FP and WY. (3) Interrelationships between ESs and EH can be divided into stable synergy type and dynamic trade-off type based on their differing ecological processes, climate factors can significantly impact the interrelationships primarily by affecting the dynamic trade-off type. This study integrates spatial analysis and machine learning approaches to examine the relationships between EH and ESs, thereby advancing the understanding of ecosystem states and functions and providing a theoretical basis for formulating ecological restoration targets.

Keywords: Ecosystem health, ecosystem services, Interrelated Characteristics, Machinelearning, the Guangdong–Hong Kong–Macao Greater Bay Area

Received: 17 Jul 2025; Accepted: 11 Sep 2025.

Copyright: © 2025 Wang, Wang, Song, Dai and Zang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Seping Dai, Guangzhou Institute of Forestry and Landscape Architecture, Guangzhou, China
Chuanfu Zang, South China Normal University, Guangzhou, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.