Relationships Among Ecosystem Services In The Coupled Human And Natural Systems

Ecosystem services (ESs) are defined as the benefits that people obtain from ecosystems. The enhancement of human well-being depends on the sustainable supply of various ESs. In urbanized areas, human interference and land use transformation have had a profound impact on ecosystems. Intensive human activities have increasingly changed the structure and function of ecosystems, thus deeply affected the relationships of ESs. Relationships among multiple ESs are mainly manifested as the change in trade-offs or synergies. Trade-offs of ESs is defined as the situations in which one service increases at the cost of reducing the provision of another service. For instance, increasing the provision of food, fiber and wood often induces undesirable declines in other services such as carbon sequestration and water quality. The synergies arise when the sum of multiple services is enhanced, an example being that enhancing biodiversity may also improve the yield and quality of coffee. Understanding the relationships among ESs is the foundation of a rational ecosystem-based management (EBM).With the global urbanization, improving human well-being increasingly depends on the services provided by human-dominated ecosystems or artificial ecosystems in urbanized areas. Although many ecologists do not advocate using artificial (or called engineered) ecosystems to replace the natural ecosystems, artificial ecosystems will be a significant part of a future sustainable world. The service provided by artificial ecosystem have some different with services provided by natural ecosystem. The artificial ecosystem usually emphasizes the purpose of the service (such as grain yield), and thus strengthen the trade-off among ESs. For example, the expansion of farmland leads to the reduction of forest and grassland, thus reducing the regulating services. Some researchers used non-ecosystem service (socioeconomic) to name the service which was indirect provided by the natural capital. However, the boundary between ES and non-ES is not clear. In this paper, to address the issue on artificial ecosystems, we defined ecosystem services as the outputs provided by ecosystems in broad and directly or indirectly contribute to human well-being.The ESs were provide at different scales. There is seldom one, ideal scale at which to conduct an ecosystem assessment that will suit several purposes, additionally, the scaling rules may be complex and are often nonlinear. Therefore, it has important implication for theory and practice to explore the relationships among ESs at multi-scales. In this paper, the Yangtze River Delta Region (YRDR) and the Inner Mongolia Autonomous Region (IMAR) were chosen to study the relationships among ESs at multi-scales for its relatively high and low level of urbanization (proportion of built-up area). The difference urbanization intensity of these two regions provide a good research objects to investigate the impact of socio-economic and natural factors on the distribution of ESs.Considering general context of the study areas and our objective, this study selected ESs from natural, semi-natural, highly modified and artificial ecosystems, as they are typical and important in study area, covering provisioning, regulating, and cultural services in the MA category. This paper set up city level and county level in these two study areas. In the same region, the selection of ESs were consistent at two spatial scales. Based on the distribution of ESs, this study investigated the tradeoffs, synergies and spatial features of ESs, detected the ES bundles and discussed the impact of natural and socio-economic driver on the change of ES relationships.(1) For the 12 ESs in the YRDR with high urbanization level,7 of them were spatially clumped (crops, tea, water supply, carbon sequestration, soil protection, water conservation and forest recreation) and 5 ESs (livestock, aquatic products, industrial products, tourism and higher education) were random spatial distributed at the city scale; however, on the county scale, only one service showed random spatial distribution (higher education) and all the rest services were spatially clumped. For the 8 ESs in IMAR with low urbanization level,3 ESs were spatial agglomeration distribution (mutton, grass production and regulating service) and the rest 5 are random spatial distribution on the city scale (crops, pork, beef, water supply and tourism); however, on the county scale, only 1 service was random spatial distribution (tourism) and all the rest services were spatial agglomeration distribution. By comparing with other studies, we found that the aggregation degree of ESs increases with the decrease of research scale. Nearly all of the ecological services that depend on the land area (most of the provisioning services and all the regulating services) show spatial distribution pattern; but the land-independent service (industrial products and cultural services, etc.) tends to show a random distribution pattern. This indicates that artificial systems depends on the technology improvement, which makes the provisioning services less depending on the natural endowment gradually; but the regulating services still depends on natural resources as they are mainly relied on natural vegetation.(2) This study carried out the correlation analysis for each pairs of ESs in the study area. Of the 66 possible pairs of ESs in YRDR,31 pairs were significantly correlated at city scale,12 of the 31 pairs were trade-offs (mainly between provisioning services and other services), and the other 19 pairs were synergies (mainly relate to regulating services). In the county scale,47 pairs were significantly correlated,25 of the 47 pairs were trade-offs (mainly among regulating services and crops, livestock and industrial products), and the other 22 pairs were synergies (mainly between regulating services and water supply). Of the 28 possible pairs of ESs in IMAR,4 pairs were significantly correlated at city scale, and all of them were trade-offs (between crop with pork and beef; regulating service with water supply and grass production). In the county scale,13 pairs were significantly correlated,4 of the 13 pairs were trade-offs (between grass production and other provisioning services), and the other 9 pairs were synergies (mainly relate to crops and livestock products). Based on the results of this paper and previous studies, it revealed that with the increasing of scale (from town, county, to URC scale), the trade-offs of ESs are less and the synergies are more. The reason is that at larger scale, the study units could carry more ES entries than those at smaller scale. Some of the services are land- independent that do not compete with each other. The result also fits with the niche complementary effect in ecology, that a multi-species mixture may produce higher productivity than a mono culture through making full use of resources.(3) In the same study area, the relationships between ESs of two scales would be totally different. There was a trade-off between grass production and crop production at the county scale of IMAR, but no significant relationship between these two services at the city scale. In addition, there was no significant relationship between the regulating service and grass production in IMAR at the county scale, whereas these two services at the city scale showed significant synergy. The trend of more synergies than trade-offs among ESs at the city scale demonstrated that land sparing was more suitable than land sharing on management. Although land release could result in the trade-offs between ESs at a smaller scale, the release of some production land to provide regulating services and cultural services was promoting the synergies among ESs at a larger scale through intensive management to improve the yield per unit area. In the IMAR, intensive farming which belongs to the land sparing could be improving the economic outcome per land area and reducing the pressures on natural grassland. Due to the use of the straw (and other materials) of the food production processes, complementary effect was appeared between the herbivore livestock production, which is different with the pig production, and the grain production.(4) The relationships among ESs in the high urbanization areas have showed more trade-offs, while in the low urbanization areas, the relationships among services have showed more synergies. This is due to the development of the economy resulting in the intense transformation and interference of nature, furthermore, the enhancement of the artificial ESs has strengthened the trade-offs between the services. While in the areas with low intensity of urbanization, the impacts of the transformation of natural ecosystems are still at a low level.(5) The relationship between ESs could completely opposite at different intensity of urbanization. Such as the regulating service and crops was trade-off in the YRDR, but in the IMAR was synergy. However, some ESs showed a consistent relationship. For example, synergies between crops and livestock, regulating service and water supply were found at different scales in each study area. More importantly, these synergies have also been confirmed in other studies. This demonstrated the complexity of the relationships among ESs. The mechanisms behind ESs that create synergies and trade-offs are not fixed, including interaction, space incompatibility and other social feedbacks. Empirically studies at multi-scale in different landscape are needed to understand the dynamic of relationships among ESs that could imporve the ecosystem management efficiency.(6) In order to explore the influence of natural condition and the management strategy on ESs further, all of the counties in IMAR were divided into three subsets, including pastoral region, semi-pastoral region and non-pastoral region, to study the relationships among ESs respectively. The study found that land-dependent services (grass production, crops) were always trade-offs in three subsets. From the pastoral region to semi-pastoral region and to non-pastoral region, the synergy between mutton and grass production shifted to trade-off, while the relationship between mutton and crop shifted to synergy from no significant correlation. The increase of rainfall will strengthen competition between farmland and grassland, because the water is the limiting factors of ecosystem in the IMAR. These findings suggested that the synergies or trade-offs between ESs are not fixed, the relationship between ESs could be opposite under different natural conditions and management patterns. And understanding the relationship between ESs can help determine the leverage point of ecosystem management, and thus to improve the pertinence of ecosystem management.(7) The relationship of ESs is exhibited not only in form of trade-off or synergy between the two ESs, but also in the space consistency of a set of ESs. Based on the framework of ES bundles, we mapped the distribution of ESs in space, and then,4 types of ES bundles were detected in the Yangtze River Delta in the city scale including the Plain-city bundle, the Mountain-city bundle, the Island-city bundle, and the Mega-city bundles; 4 types of ES bundles in the county scale were found including the Mountain bundle, the Island bundle, the Urban bundle and the Plain bundle. More importantly, the spatial distributions of the ES bundles in the city and county scale were consistent. Through cluster analysis,12 cities in IMAR were divided into 4 ES bundles in the city scale, including the Grassland-city bundle, the Forest-city bundle, the Agriculture-city bundle and the Desert-city bundle. While in the county scale,89 counties are divided into 5 ES bundles, including the Agriculture Feedlot bundle, the Tourism bundle, the Intensive Culture bundle, the Forestry bundle and the Grassland bundle, respectively. Spatial overlay analysis indicates that there is a significant correspondence between the ESs bundles at two scales. In this paper, we showed a spatial agglomeration of the distribution of ES bundles in two research areas at two scales. The distribution of the service bundles in scale of town, county and nation in previous studies also present spatial agglomerations. Combining these studies, we found that the spatial clustering of the ES bundles is scale-independent.(8) In this study, the similarity of ES bundles was not only reflected in the correspondence of the spatial distribution at two scales, but also in the mechanism of forming ES bundles. Cluster analysis and principal component analysis showed that the socio-economic factor were primary driver of ES bundles in high level of urbanization area, while the natural condition were primary driver in low level of urbanization area. It can be speculated that, with the development of China and global economy, the main driver of ES bundles will shift to socio-economic factor.