Spatial Trade-offs And Synergies Among Ecosystem Services In The Three Parallel Rivers Region

The long-term human well-being is greatly depending on the sustainable supply of various ecosystem services (ESs). ESs are the benefits that people obtain from natural or emi-natural ecosystems, including various products or services, e.g. fresh air and healthy food. Under the joint influences of both physical and human factors, complex interactions occur among ESs, which generally exhibit as trade-offs and synergies. Trade-offs occur when the provisions of several ESs are in the opposite trend, while synergies occur when the provisions of several ESs grow together. However, managing multiple ESs across landscape is still a huge challenge because such spatial relationships among ESs are not well understood by far. Thus, identifying the trade-offs and synergies among ESs has been a key topic in the fields of geography and ecology. Mountain is an important geomorphic type on the earth, which can afford many important ESs for the local human society, and the complexity of relationships among ESs in the mountain ecosystem is extremely high. Identifying the spatial relationships among ESs in a typical mountain area can not only afford a new case for international research, but also provide important guidance for the sustainable development and ecological civilization construction of mountain areas in China.With the Three Parallel Rivers Region (TPRR)-a global biodiversity hotspot-as the study area, this study aim to identify the spatial relationships among ESs. We selected 8 types of ESs in the 4 different ES categories, namly provisioning services (food supply, livestock-raising and water supply), regulating services (carbon stock, carbon sequestration and soil retention), surpporting services (habitat surpport) and cultural services (natural recreation). First, we mapped the geographical distribution of the 8 ESs at township scale by implementing numerous spatial models in GIS. Second, we used Spearman correlation coefficient as well as hotspots analyses to reveal the pairwise relationships between different ESs. Third, we used the principal component analysis (PCA) to quantitatively analyze the variation in all 8 ESs across the landscape and to determine the main factors that may have important influences on the relationships among ESs. Finally, we identified 4 different ES bundles by K-means clustering method, and then proposed management recommendations for each bundles according to their current socio-economic and natural environmental context.The major results and conclusions are as following:(1) The spatial distributions of 8 ESs had their own features, both in the horizontal and vertical direction, which expressed similarity and dissimilarity among the spatial patterns of ESs. Six of the 8 ESs, except food supply and livestock-rasing, had a significance in maintaining regional ecology security, which indicates the necessity of protecting these ESs in TPRR.(2) Fifteen of the 28 pairwise relationships between ESs showed a medium or greater correlation (|Spearman’s r|≥0.30, p<0.05). The correlations between provining services and other 3 ES categories were mainy negative. But the correlation between regulating services and other 3 ES categories were mostly positive. There was a significant positive relationship between habitat support and natural recreation service (Spearman’s r=0.564, p<0.01). Trade-offs and synergies will occur in the same ES categories. Learning spatail relationships among ESs is important for ES management, for example, when we try to ehance the surppotting services, it may beneifit serveral other ES types. By comparing our results with previous studies, we found several special pairwise relationships between ESs were scale-free, like food supply and livestock-rasing and so on, but others were not.(3) We found, when trade-offs occur between two ESs, one ES’s hotspots will be more possibly the other’s coldspots, when synergies occur between two ESs, their hotspots will show spatial similarity.79.7% of the 153 townships could produce at least one ES no matter at higher or lower level across TPRR. But only 7.2% of the 153 townships could produce 4 or more ESs in the upper 20th percentile, these townships need priority protection. Only 5.9% of the 153 townships could produce 4 or more services in the lowest 20th percentile, these townships need ecological restoration according to their practical demand. There was no township that could afford all 8 higher or lower ESs simultaneously across the whole TPRR.(4) By comparing the loadings of different ESs and the spatial distributions of the scores of the first three principal components on different townships, we conclude that the interaction between elevation and human activity and its gradient variation across the space was the main factor influencing the relationships among ESs. Variations of climate conditions caused by the special geomorphologic pattern were the additional important factor.(5) Considering the major factors influencing the relationships among ESs, we identified 4 different ES bundles in TPRR. Radar grapies showed the supply capacity difference of ESs among and within bundles. We found that some certain ES bundles were repeated among different area for their similar social-economical and physical context, for example, the second bundle in TPRR; and as the studies expand, more and more novel bundles were found around the world, for example, the first bundle in TPRR. At the end of the article, we put forward several management strategies for each ES bundles in consideration of their current social-economical and physical context.