ArticleScientific reports2025
Understanding the trade-off and synergy effects of landscape multifunctional and its driving mechanism in rapidly urbanizing areas.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Understanding landscape multifunction trade-offs and synergies is fundamental to achieve the regional sustainable management and improving human well-being. Taking the Zhejiang Greater Bay Area as an example, this paper quantitatively evaluates residents' carrying function (RC), food production function (FP), habitat maintenance function (HM), water conservation function (WC) and landscape aesthetic function (LA) in 2022. On the basis of constructing the multifunctional landscape model of the Bayesian belief networks, the key nodes that affect the landscape function are identified by analyzing the importance of nodes. Joint probability distribution, probabilistic reasoning and scenario simulation were used to explore the synergistic and trade-off relationship of landscape multifunction and its driving factors. The results show that: (1) The spatial heterogeneity of RC, FP, HM, WC and LA in the Greater Bay Area is significant. The distribution of RC and FP is relatively consistent, with high values concentrated in the northeastern plain and coastal areas, and low values distributed in the mountainous and hilly areas of the northwest and southwest. The distribution of HM, WC and LA is relatively consistent, showing a spatial pattern of high hills in the northwest and southwest mountains and low in the northeast plain and coastal areas. (2) There is a synergistic relationship between HM, WC and LA. There is a trade-off relationship between RC-WC, FP-HM and FP-LA. (3) Land use type and NDVI are the main factors affecting the synergistic relationship of landscape multifunctional. Population density and altitude are the main factors affecting the trade-off relationship. It is found that different drivers generate the same synergy (or trade-off) in different states, while the same drivers generate different synergy (or trade-off) in different states. This study has important theoretical and practical value for understanding the complex relationship between landscape multifunctionality and the differences in driving factors, and for proposing countermeasures and measures to improve landscape ecosystem management and human well-being.
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