Spatial patterns of net primary productivity and its driving forces: a multi-scale analysis in the transnational area of the Tumen River

Abstract

Analyzing the spatial patterns of net primary productivity (NPP) and its driving forces in transnational areas provides a solid basis for understanding regional ecological processes and ecosystem services. However, the spatial patterns of NPP and its driving forces have been poorly understood on multiple scales in transnational areas. In this study, the spatial patterns of NPP in the transnational area of the Tumen River (TATR) in 2016 were simulated using the Carnegie Ames Stanford Approach (CASA) model, and its driving forces were analyzed using a stepwise multiple linear regression model. We found that the total amount of NPP in the TATR in 2016 was approximately 14.53 TgC. The amount of NPP on the Chinese side (6.23 TgC) was larger than those on the other two sides, accounting for 42.88% of the total volume of the entire region. Among different land-use and land-cover (LULC) types, the amount of NPP of the broadleaf forest was the largest (11.22 TgC), while the amount of NPP of the bare land was the smallest. The NPP per unit area was about 603.21 gC/(m²·yr) across the entire region, while the NPP per unit area on the Chinese side was the largest, followed by the Russian side and the DPRK’s side. The spatial patterns of NPP were influenced by climate, topography, soil texture, and human activities. In addition, the driving forces of the spatial patterns of NPP in the TATR had an obvious scaling effect, which was mainly caused by the spatial heterogeneity of climate, topography, soil texture, and human activities. We suggest that effective land management policies with cooperation among China, the DPRK, and Russia are needed to maintain NPP and improve environmental sustainability in the TATR.

Keywords

transnational area of the Tumen River / NPP / spatial pattern / driving force / multiple scale

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Acknowledgments

We also want to express our respects and gratitude to the anonymous reviewers and editors for their professional comments and suggestions. This work was supported by in part by the National Natural Science Foundation of China (Grant Nos. 41771094, 41501195, and 41801184).

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