## 6 Conclusions  
In this study, we quantified the ESV and kNDVI and integrated them into the ES identification framework. Modeling was completed using a Zonation model combined with two spatial priority rules, extracting ESs at different levels based on various thresholds. This framework was applied to the TRHR from 2000 to 2020. The results indicate that this method could reveal more precise spatio-temporal distributions of ESs, enhancing ecosystem integrity and providing technical modeling support for developing cross-scale spatial planning and management strategies for nature reserve boundaries.  
Firstly, the ESV was taken as an essential factor in ES identification, and multiple perspectives were considered in the ESV calculation, which solved the problem of focusing on a single perspective in the previous ES identification method. It could be concluded that most of the ESV showed a spatio-temporal increasing trend, with the southeast part gently decreasing.  
Secondly, representing vegetation productivity, the kNDVI was incorporated into the ES identification process, ensuring that the identification outcomes accurately capture the spatio-temporal dynamics of vegetation and carbon sinks. We found that the kNDVI of the TRHR showed longitudinal zonality and increased largely from 2000 to 2020.  
Thirdly, the ES identification framework is applied to the TRHR, and the ES has a certain degree of reliability. According to the proposed identification framework, different threshold ecological sources were detected, and most areas were distributed in the southeast. A significant spatio-temporal change in ecological sources has been observed since 2000 in the TRHR. Compared with the existing NRs in the TRHR, the ecological source identification framework had reliable accuracy and efficiency. The spatio-temporal variations of ecological sources provide a reference for ecological conservation in the TRHR. The framework proposed in our research could serve as a reference for building ecological networks in other ecologically fragile areas, as well as a step towards ecological security pattern construction. More thorough research should be concentrated on spatio-temporal distribution drivers of ESs and mechanisms of transition in the future. Meanwhile, the distinction between ESs and NRs should be examined to generate the highest benefits for different protection purposes. Additionally, a global perspective of ES identification needs to be expanded.  
All acronyms in this paper are listed in Table 3.  
**Author Contributions:** Conceptualization, X.F. and H.H.; methodology, X.F., H.H., Y.W. and Y.T.; validation, X.F. and Y.W.; formal analysis, X.F. and H.H.; investigation, X.F. and Y.W.; writing--original draft preparation, X.F.; writing--review and editing, H.H., Y.W. and L.L.; visualization, X.F. and L.L.; supervision, Y.T.; funding acquisition, H.H. and Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the MAJOR PROJECT OF CHINES HIGH-RESOLUTION EARTH OBSERVATION SYSTEM from National Geospatial Information Center, grant number: 00-Y30B01-9001-22/23.  
**Data Availability Statement:** All the data products in this study can be downloaded for free from the website links attached in the main text.  
**Acknowledgments:** The authors gratefully thank the editors and anonymous reviewers for their valuable advice in improving this manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.