"Targets-Plans-Decision": A framework to identify the size of protected areas based on the balance of ecological protection and economic development.

A framework called "Targets-Plans-Decision" (TPD) was established for identifying the size of adequate protected areas (PAs) based on the combination of species distribution probability prediction method, systematic conservation planning (SCP) theory, and protection efficiency analysis, to achieve a balance between ecological protection and economic development. The first step of the framework is to determine the protection targets. In this study, the protection targets were defined as important habitats and important ecosystems. The second step is to identify the PAs plans of different sizes. We adopted a SCP method, which takes into account cost-effectiveness while delineating the PAs. The third step is to determine the optimal size of the PAs through ecological protection efficiency analysis. We constructed the protection efficiency index (PEI), which is the protection cost per unit area. Then we used Kaya identity (a structural decomposition method) to decompose PEI. The decomposed PEI included value density (ecological value per unit area) and value cost (cost to realize unit ecological value). By analyzing the changes of the two, the optimal size of the PAs can be determined. We took Nanchang as an example to conduct an empirical study to verify the usability of the TPD framework. The result shows the TPD framework proposed in this study can effectively identify the optimal size of PAs and contribute to forming a cost-effective ecological protection plan. When the size of PAs was set to 70% of the important habitats and important ecosystems, it was most beneficial for ecological protection. Furthermore, the optimal protection efficiency plan recommended by the TPD framework improved the efficiency of ecological protection. The TPD framework can serve as a basis for the size identification of PAs and also support the delineation of PAs worldwide.

Can the establishment of ecological security patterns improve ecological protection? An example of Nanchang, China.

Establishing ecological security patterns provides new ideas for maintaining regional ecological security. Methods for establishing these patterns have been extensively investigated in several studies, but the ecological protection effects of these patterns need further examination. Nanchang is the capital city of Jiangxi province and a typical representative of rapidly developing cities. With the proposal of an ecological environment protection plan for Nanchang metropolitan area, the coordinated development of ecology, economy and society has become the local development goal. This study used Nanchang City as an example for the establishment of an ecological security pattern through the circuit theory. The ecological sources of a 1068.56 km2 location and 20 ecological corridors with a total area of 957.39 km2 were identified. Three development scenarios in 2015-2040 were set up, namely, unrestricted development (UD), core area protection (CP) and ecological security pattern restriction (ESPR) scenarios. The UD scenario followed the land expansion rate from 2010 to 2015. The CP scenario used a nature reserve as a forbidden conversion area. Under the ESPR scenario, ecological security pattern was regarded as a prohibited conversion area. The CLUMondo model was used in simulating land use and evaluating the ecological protection effects of the scenarios. Through comparison, we determined that the ecological security indices under UD, CP and ESPR were 0.230, 0.242 and 0.249, respectively, from the perspective of the overall ecological security of the region. In the evaluation of the landscape characteristics of EL, under ESPR, the landscape connectivity was the best. The detailed analysis results showed that the ecological security pattern not only could protect the regional ecological security on the regional scale but also had an outstanding protection effect on the local scale. In summary, compared with the UD and CP scenarios, ecological security patterns had a better effect on regional ecological protection.

Dataset of ecosystem services in Beijing and its surrounding areas.

This data article describes the multiple ecosystem services in Beijing and surrounding areas, including grain providing, water yield, carbon sequestration, soil retention, purified water service, cultural services, and habitat quality. These data are mainly from public data sets such as the Harmonized World Soil Database. These data can be used to improve the optimization of human well-being in the social-ecological system and further achieve regional sustainable development.

Identification of ecosystem service bundles and driving factors in Beijing and its surrounding areas.

In high-intensity human activity areas, such as metropolises, rapid changes in land use, agricultural intensification, and population urbanization have resulted in profound and complex transformations in socio-economic ecosystems. The study of ecosystem service (ES) bundle is conducive to various aspects, such as determination of the variation characteristics of ES; identification of the mechanism of interdependence within ES; and driving mechanism of socio-economic-ecological factors to ES to maintain the sustainable development of the region. The research areas include Beijing and its surrounding areas. Ten ES, including grain providing (GP), water yield (WY), carbon sequestration (CS), soil retention (SEC), purified water service, cultural services, and habitat quality (HQ) were selected for valuing and mapping. The ES paired trade-offs and synergetic relationship, bundle was determined, and the bundles' service types and spatial distribution characteristics were analyzed. Subsequently, GeoDetector was used for detecting the factors affecting the bundles' distribution. Results showed that WY, CS, SEC, and HQ were bounded by Tai-hang and Yanshan Mountains. Among the 45 pairs of ES, 38 pairs bore significant correlation. Multiple services had different degrees of positive and negative correlations with other services. For example, GP had a high positive correlation with WY while bearing a high negative correlation with HQ. Seven bundles include SEC, culture, urban, HQ, agriculture, water supply and purification, and water purification. Various factors played decisive roles in the bundles' spatial distribution. Among them, the investment capacity and demand for ecological protection depend on the level of GDP and POP. The formulation of agricultural planting plans is inseparable from TADEM. ASL is directly related to species richness. Results indicate that bundle research can identify the areas of the formation of co-occurrence of trade-offs and synergies and support the formulation of ES optimal management plans for different regions through further research of the driving mechanism.