Socioeconomic contexts for the spatial variations of ecosystem services and the associated uncertainties
Ecosystem services are strongly underpinned by ecological processes and functions and influenced by socioeconomics in human-environmental systems. As the prerequisites for human well-being, ecosystem services can reflect the interactions of human and environmental systems. Being pervading the process of ecosystem service assessments, uncertainties should be uncovered and preferably reduced before the assessing results are adopted for the decision-making of regional environmental management. This study explores the interrelationships between ecosystem services and socioeconomic variables at regional scales, develops a methodological framework of uncertainty analysis and applies it to investigate the uncertainties emerged in the assessments of ecosystem services of the study areas. Chapter 1 provides a brief review of the fields concerning the ecosystem service issues addressed in this thesis. The introduction involves the basic concepts related to ecosystem services, the state of the art of ecosystem service quantification and mapping, the role of ecosystem services in human-environmental systems, ecosystem services’ linkages with socioeconomics as well as the uncertainties in ecosystem service assessments. After uncovering the respective research gaps, this chapter identifies and elucidates the objectives of the study and raises the associated four research questions. Chapter 2 explores the socioeconomic influences on biodiversity, ecosystem services and human well-being at the regional scale of Jiangsu, China on the basis of the DPSIR (Driver-Pressure-State-Impact-Response) conceptual model. Additionally, the study investigates the quantitative linkages between the five sectors of the DPSIR model. The results show that urbanization and industrialization in the urban areas can have positive influences on regional biodiversity, agricultural productivity, tourism services and rural residents’ living standards. Besides, the knowledge, technology and finance inputs for agriculture have positive impacts on these system components. Concerning regional carbon storage, non-cropland vegetation cover obviously plays a significant positive role. Contrarily, the expansion of farming land and the increase of total food production are two important negative influential factors of biodiversity, ecosystems’ food provisioning capacity, regional tourism income and the well-being of the rural population. Finally, the linkages of the DPSIR sectors in a network pattern are quantitatively evidenced. Chapter 3 characterizes the urban-rural gradients of ecosystem services and socioeconomics of Leipzig, Germany and Kunming, China. It further quantifies the linkages between the gradients of ecosystem services and socioeconomics and conducts gradient comparisons between different gradient patterns in the two study areas. The chapter ends with the revelation of the uncertainties in creating the gradients. The results show some similar regularities in the spatial patterns of ecosystem services and socioeconomic dimensions in both study areas. Habitat quality and f-evapotranspiration of Leipzig and habitat quality of Kunming demonstrate apparent trends of increases along all gradient patterns. However, the other ecosystem services present divergent spatial variability in different gradient patterns. Road density, urban fabric and population density show identical declining trends in both study areas except for the soaring of population density around the center of Leipzig. Differently, household size, housing area and unemployment rate in Leipzig present inconsistent spatial dynamics with considerable fluctuations. Regarding the gradient interrelations, road density, urban fabric and population density are strongly correlated with most ecosystem service types in both case study areas. In contrast, the gradients of household size, housing area and unemployment rate of Leipzig show inconsistent correlations with the ecosystem services gradients. The introduced uncertainty gradient method shows appropriateness to quantitatively capture the uncertainties in exploring ecosystem services and socioeconomic gradients in urban-rural areas. Chapter 4 addresses the spatial characteristics of ecosystem services and the respective socioeconomic influences in a heavily human-disturbed watershed in Southwest China. It firstly quantifies and maps five ecosystem services of nine river basins of the Dianchi Lake Watershed. The quantification is based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the biophysical and socioeconomic data. Thereafter, a confirmatory research is conducted by using a hypothesis-test methodology to investigate the socioeconomic causes of the spatial changes of the five ecosystem services. On the basis of the modeling results of nitrogen retention and water yield, this chapter exemplifies the distinctions between ecosystem services potential, flow and demand and performs a sensitivity analysis to test the influences of input data and parameter uncertainties on the modeling results. The hypothesis-test analysis reveals only a small number of socioeconomic influential factors, most of which are related to land use structure. The hypothesis-test methodology provided in this study is applicable in the investigation of socioeconomic influences on ecosystem services in the situation of socioeconomic data uncertainty and scarcity. Chapter 5 summarizes the sources of uncertainties in landscape analysis and ecosystem service assessments and proposes a methodology to analyze and reduce the uncertainties. The fundamental uncertainty origins of landscape analysis are landscape complexity and methodological uncertainties. The major uncertainty sources of ecosystem service assessments include the complexity of the natural system, respondents’ preferences and technical problems. Among these uncertainty source categories, initial data uncertainty pervades the whole assessment process and the limited knowledge about the complexity of ecosystems is the focal uncertainty origin. To analyze the uncertainties in assessments, systems analysis, scenario simulation and the comparison method are promising strategies. Lastly, we assume that the actions to reduce uncertainties should integrate continuous learning, expanding respondent numbers and sources, considering representativeness, improving and standardizing assessment methods and optimizing spatial and geobiophysical data. Chapter 6 reaches the general conclusions of this thesis. It firstly answers the four research questions asked in the introduction. In the answers, the close connections between ecosystem services and socioeconomics are confirmed, the applicability of the mainstreaming quantification methods is debated, the strength of ecosystem service mapping is illustrated and the necessity and possibility of uncertainty analysis are argued. In ending the entire thesis, chapter 6 further generally evaluates the ecosystem service approach and identifies and main obstacles and problems in the application of ecosystem services. Moreover, it proposes potential solutions to the overcome the impediments and finally calls for an optimistic attitude to propel ecosystem services research.