Ecosystem services are the direct or indirect benefits that humans obtain from nature. They can be divided into four categories: supply services, cultural services, regulation services, and support services. They are of great significance to the improvement of human well-being and are regarded as connecting society and ecosystems bridge. Since the launch of the Millennium Ecosystem Assessment (MA), ecosystem services have become a research hotspot in academia, and great progress has been made in how ecosystem service functions are transformed into economic value. However, under the dual drive of global warming and accelerated land use change, the ecological environment continues to deteriorate in exchange for short-term economic benefits. Therefore, as an ecosystem degrades, so does its ability to provide goods and services. In addition, in the process of landscape space planning, it is necessary to pay attention to the coordination and trade-off relationship between ecosystem services, and give priority to ecosystem services with economic value, in order to achieve both ecological benefits and socioeconomic benefits. Given the lack of economic value of some ecosystem services, such as aesthetic, cultural, and therapeutic values, managers and ecologists consider them of low importance and are often not considered in planning decisions. At the same time, due to the intangibility of the social value of ecosystem service functions and the dependence on the subjective perception of beneficiaries, it is difficult to evaluate or quantify them, which makes it difficult to incorporate the social value of ecosystem service functions into planning and resource management.
The SolVES model (Social Values for Ecosystem Services) is called the Ecosystem Service Social Value Model. It is a geographic information system application program jointly developed by the US Geological Survey and Colorado State University. The purpose of developing this model is mainly for ecosystem services. Spatial analysis and quantitative evaluation of social value in service functions. There are various types of social value evaluated by this model, such as: aesthetics, biodiversity, spirituality, entertainment, leisure and other social values. The evaluation results do not display the total value in the form of currency, but express the social value with a value index high and low. The model consists of three sub-modules, which are the social value module of ecosystem service function, the value mapping module and the value conversion mapping module. When evaluating the social value of ecosystem services in the study area, it is necessary to combine the social value module and the value mapping module, and collect the attitudes and opinions of ecosystem service product users towards the services or products provided by the ecosystem through emails, interviews, and questionnaires. Preferences, together with other socioeconomic survey data and natural environment data in the study area, run the model to estimate the social value of ecosystem services in the study area. The statistical data in the model will also generate a statistical table for analyzing the relationship between the value index and the natural environment. The value transfer module can transfer data to another research area that lacks survey data based on the existing research results, and generate a predicted value index map of the new research area. This module can be used independently and conveniently.
It will describe the SolVES model and its principles for evaluating the social value of ecosystem service functions. Through the study of this course, you will learn the principles and operation methods of the SolVES model: how to import the collected social survey data into the database (SQL), combine the The total amount of money allocated by the visitor to each social value type (ie weight index), how to use the built-in kernel density analysis tool to perform weighted kernel density analysis on the marked social value points, and get the "kernel density surface" and "maximum grid value "; How to use the "Average Nearest Neighbor Statistics" tool embedded in the SolVES model to count the clustering spatial distribution of social value points; How to use the model to divide the "kernel density surface" by the "maximum grid value" and calculate the result Standardized as "value index surface"; how to combine environmental variable data, start the MaxEnt maximum entropy model to predict the spatial distribution of social value points, and realize the mapping of social value.
At the same time, through this study, you will also learn the configuration and basic methods of the QGIS\PostgreSQL\ARCGIS\MAXENT\R language environment, and use the advantages of various platforms to expand and analyze the social value of ecosystem services: such as getting corresponding results based on different environmental variables social value distribution map, and analyze the distribution characteristics of social value in space; study the collinearity of environmental variables; study the influence of environmental variables on the spatial distribution of social value, and deeply analyze the relationship between each environmental variable and different types of social value The relationship among them; study the contribution of various environmental variables to social value, so as to further determine which environmental factors contribute more to social value, etc. This course will also summarize the current research results, research hotspots, advantages and disadvantages of the model based on relevant application cases, and look forward to its future development trend, in order to provide a reference for the SolVES model to be better applied to the social value assessment of ecosystem service functions.
Chapter 1: Theoretical Basis and Research Hotspots
1. Introduction to ecosystem services and ecosystem service values
2. Research methods of ecosystem service value
3. Research hotspots on the value of ecosystem services
Visual Analysis of Citespace Literature
VOSviewer document visual analysis
Chapter 2: SolVES 4.0 Model Runtime Environment Configuration
1. Introduction to SolVES 4.0 model functions
2. QGIS 3.8.2 installation and configuration
3. PostgreSQL 11.7 installation configuration
4. PostGIS 2.5.3 installation configuration
5. Maxent 3.4.1 installation configuration
Note: SolVES 4.0 was developed and tested on systems running Microsoft Windows 10 Enterprise Edition with 64-bit processors. The software listed above is required to run Solution 4.0. Additionally, the Java Runtime must be available on the system running Solution 4.0 in order to access and run Maxent 3.4.1.
Chapter 3: SolVES 4.0 Model Running
1. Create a new project
2. Data Analysis Tools
l Principles of survey data analysis
l Selection and setting of survey data analysis parameters
3. Transfer value tools
l Transfer value calculation principle
l Selection of transfer value tool parameters
4. Analysis of model running results
Users generate composite reports that map social value and related environmental indicators based on the results of current projects or previously completed projects.
The map layout contains selected value index maps, including study area boundaries and selected backgrounds. The map title includes project name, survey group, and social value type.
Continuous data is displayed in the form of a line graph. Categorical data are displayed as scatterplots. The x-axis labels of the scatterplot have integer values for the specified categories.
AUC values, mean nearest neighbor statistics, and maximum index scores are included in the map layout for all results generated by the Analyze Survey Data tool.
5. S olVES model performance test
lMaxent model principle
lMaxent model parameters and operation
lMaxEnt result analysis:
Based on the AUC data generated by the MaxEnt model, the performance of the model operation is tested for reliability and fitness, and the area under the Maxent curve and the variable contribution are interpreted and adjusted.
lTransfer value result analysis:
When the study area has a similar biophysical and social context to the main study area, but survey data are not available, a value transfer mapping model can be used, accessed through the transfer value tool, relying on Maxent's Solve the statistical models generated in the analysis.
Chapter Four: Data Acquisition and Storage
1. Table data type and format and loading
The "id" field and the "geom" field (in the case of vector data) are managed by PostgreSQL and should not be included when preparing user-supplied data for loading into the solver database.
2. Spatial data loading
3. Acquisition of social survey data
l Understand the characteristics and satisfaction of the interviewees
l Ask respondents to assign social value and mark corresponding social value points.
l Collect the social background and demographic characteristics data of the interviewees
4. Source and processing of spatial data
Geospatial data includes Shapefiles and raster datasets of the study area:
lSTUDY_AREA class Shapefile needs to study area boundary elements;
lSURVEY_POINTS data is based on questionnaire digitization;
l The raster dataset is the extraction of environmental elements in the research area;
l For SURVERY_POINTS data and STUDY_AREA data, use the kernel density analysis tool of ArcGIS to operate on the two data, and output the overall spatial distribution map of social value.
4. SolVES model processing data
Perform average nearest neighbor analysis on the SURVERY_POINTS data to obtain the average nearest neighbor ratio (R-ratio) and standard deviation (Z-score), highlighting the importance of each value in the region;
l Use the SolVES model to statistically integrate the hypothetical scores assigned to each social value by the respondents collected from the questionnaire, and generate a spatially explicit map with a 10-point value index (VI) to determine the value of each social value. The importance of the value type.
l Group the respondents, import the corresponding socio-demographic characteristics data and spatial data into the model, use the value index output by normalized calculation as the weight, and output the spatial distribution map of each social value in the entire research area, and A graph of the relationships that exist between these value distributions and the environmental variables.
Chapter Five: Correlation Analysis of Environmental Variables and Social Value
1. Multicollinearity test of environmental variables
lR environment configuration and basic syntax
l Correlation analysis
l Variance inflation factor analysis
2. Statistical analysis
l Statistical analysis of landscape types using ArcGIS zonal statistical tools
3. Spatial distribution of main social value types
4. Analysis of the impact of environmental variables on social value
l Conduct a correlation analysis of the selected landscape types under the social value points marked by the interviewees, get the public's favorite landscape type, and analyze the influencing factors of the landscape type and social value;
5. Analysis of the contribution rate of environmental variables
l According to the contribution rate of environmental variables obtained from MaxEnt operation, the contribution of each environmental variable can be obtained
6. Spatial autocorrelation analysis
l Judge the relationship between the distance variable and VI according to Moran's I, p value and z score.
7. Analysis of the effectiveness of social value transfer of ecosystem services
l Transfer error analysis
l Difference analysis
l Cartographic analysis
Chapter 6: Extended case analysis
l National park biodiversity value assessment from the perspective of social-ecological coupling analysis
l Cluster analysis of social ecological management and ecosystem services
l Analysis of coastal ecosystem services
l Importance of human well-being to understanding social preferences for ecosystem services
