Coupling strength of human-natural systems mediates the response of ecosystem services to land use change.

Addressing the dynamics of human-natural systems (HNS) driven by land use change (LC) is a key challenge for the sustainable development of ecosystem services (ES). However, how changes to the HNS coupling relationships affect ES is rarely reported. We used network analysis methods to construct an HNS correlation network in the Loess Plateau based on the correlation between the main components of HNS, such as ES, human factors, landscape pattern, vegetation cover, climate change and geomorphic characteristics, and quantitatively described the HNS coupling relationships through key network attributes. We analyzed the variation in HNS network attributes and their relationships with ES along an LC intensity gradient. The results show that carbon storage and soil conservation in the Loess Plateau increased by 0.56% and 0.26%, respectively, during the study period, while the habitat quality and water yield decreased by 0.11% and 0.18%, respectively. An increase in LC intensity reduces connectivity and density in the HNS network, which results in looser connections among HNS components. Importantly, we found that HNS network attributes explained 85% of ES variation across different LC intensity gradients and that connectivity and density had the strongest explanatory power. This means that LC mainly affects ES dynamics by changing the coupling strength of HNS. Our research offers a new perspective for linking LC-HNS-ES, which will help guide practitioners toward establishing and maintaining the sustainability of human well-being amidst changing HNS.

Unraveling the dynamics, heterogeneity, determinants of eco-efficiency in Beijing-Tianjin-Hebei urban agglomeration, China.

Eco-efficiency has been considered a valuable gauge for evaluating how efficient economic activities are in regard to resource inputs and eco-environmental pressures. Even though Ecosystem services (ESs) are inseparable from sustainable eco-environment, a paucity of literature has considered ESs in eco-efficiency research lines. Therefore, this study aims to construct a novel eco-efficiency evaluation framework by integrating ESs as natural capital input and measure it utilizing the Epsilon-based measure model for the county-level cities in Beijing-Tianjin-Hebei urban agglomeration (BTHUA) during the period 2005-2015. The spatial econometric technique is further performed to acquire quantitative evidence about whether ESs and other determinants impact eco-efficiency. The results revealed that eco-efficiency increased continuously in the whole BTHUA and BTHUA's optimized development functional areas, whereas eco-efficiency of BTHUA's sub-regions showed a significant temporal diversity. The average eco-efficiency values of cities in key development functional areas and restricted development functional areas showed the V-shaped trend (declining before 2010 and then rising). Interestingly, this study found that ESV economic loss may result in eco-efficiency decline for cities located in key development functional areas. From the spatial heterogeneity perspective, the city with high EE is mainly located in eastern BTHUA, whereas cities in the northern plateau areas, southwestern, and western BTHUA have relatively low EE. Furthermore, there existed a significant spatial autocorrelation and a spatial agglomeration heterogeneity, which suggests that the low-low correlation regions gradually being the most dominant spatial pattern. The results of spatial econometric model verified that water yield has the strongest positive effect on EE while soil erosion will lead to declining EE. This paper potentially provides new insights for future policy design of urban agglomeration sustainable deployment.

The hidden risk: Changes in functional potentials of microbial keystone taxa under global climate change jeopardizing soil carbon storage in alpine grasslands.

Climate change is endangering the soil carbon stock of alpine grasslands on the Qinghai-Tibetan Plateau (QTP), but the limited comprehension regarding the mechanisms that sustain carbon storage under hydrothermal changes increases the uncertainty associated with this finding. Here, we examined the relative abundance of soil microbial keystone taxa and their functional potentials, as well as their influence on soil carbon storage with increased precipitation across alpine grasslands on the QTP, China. The findings indicate that alterations in precipitation significantly decreased the relative abundance of the carbon degradation potentials of keystone taxa, such as chemoheterotrophs. The inclusion of keystone taxa and their internal functional potentials in the two best alternative models explained 70% and 63% of the variance in soil organic carbon (SOC) density, respectively. Moreover, we found that changes in chemoheterotrophs had negative effects on SOC density as indicated by a structural equation model, suggesting that some specialized functional potentials of keystone taxa are not conducive to the accumulation of carbon sink. Our study offers valuable insights into the intricate correlation between precipitation-induced alterations in soil microbial keystone taxa and SOC storage, highlighting a rough categorization is difficult to distinguish the hidden threats and the importance of incorporating functional potentials in SOC storage prediction models in response to changing climate.

A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015.

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai-Tibet Plateau in 1982-2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982-2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

The Mitigation Effect of Park Landscape on Thermal Environment in Shanghai City Based on Remote Sensing Retrieval Method.

The mitigation effects of park green space on Urban Heat Island (UHI) have been extensively documented. However, the relative effects of the configuration of park components on land surface temperature (LST) inside the park and indicators (i.e., park cooling intensity and distance) surrounding the park is largely unknown. Therefore, the main objective of this study is to explore the quantitative impacts of configuration and morphology features under different urban park scales on the cooling effect. In this study, based on Landsat-8 OLI/TIRS images on 3 August 2015 and 16 August 2020 during summer daytime, the LSTs of Shanghai City were retrieved by atmospheric correction method. Then, the relationships of park landscape features with LSTs in the park and typical indicators representing cooling efficiency of 24 parks on different grades were analyzed. The results showed that the average temperature in urban parks was, respectively, 1.46 °C and 1.66 °C lower than that in the main city of Shanghai in 2015 and 2020, suggesting that urban parks form cold islands in the city. The landscape metrics of park area (PA), park perimeter (PP), green area (GA) and water area (WA), were key characteristics that strong negatively affect the internal park LSTs. However, the park perimeter-to-area ratio (PPAR) had a significant positive power correlation with the park LSTs. Buffer zone analysis showed that LST cools down by about 0.67 °C when the distance from the park increases by 100 m. The Maximum Cooling Distance (MCD) for 2015 and 2020 had a significant correlation with PA, PC, PPAR, GA and WA, and increased sharply within the park area of 20 ha. However, the medium park group had the largest Maximum Cooling Intensity (MCI) in both periods, followed by the small park group. There could be a trade-off relationship between the MCD and MCI in urban parks, which is worth pondering to research. This study could be of great significance for planning and constructing park landscapes, alleviating Urban Heat Island effect and improving urban livability.

Analyzing Land-Use Change Scenarios for Ecosystem Services and their Trade-Offs in the Ecological Conservation Area in Beijing, China.

It is generally believed that land-use changes can affect a variety of ecosystem services (ES), but the relationships involved remain unclear due to a lack of systematic knowledge and gaps in data. In order to make rational decisions for land-use planning that is grounded in a systematic understanding of trade-offs between different land-use strategies, it is very important to understand the response mechanisms of various ecosystem services to changes in land-use. Therefore, the objective of our study is to assess the effects of land-use change on six ecosystem services and their trade-offs among the ecosystem services in the ecological conservation area (ECA) in Beijing, China. To do this, we projected future land-use in 2030 under three different scenarios: Business as Usual (BAU), Ecological Protection (ELP), and Rapid Urban Development (RUD), using GeoSOS-FLUS model. Then, we quantified six ecosystem services (carbon storage, soil conservation, water purification, habitat quality, flood regulation, and food production) in response to land-use changes from 2015 to 2030, using a spatially explicit InVEST model. Finally, we illustrated the trade-offs and/or synergistic relationships between each ecosystem service quantified under each of the different scenarios in 2030. Results showed that built-up land is projected to increase by 281.18 km2 at the cost of water bodies and cultivated land from 2015 to 2030 under the RUD scenario, while forest land is projected to increase by 152.38 km2 under the ELP scenario. The carbon storage, soil conservation, habitat quality, and the sum of ecosystem services (SES) would enrich the highest level under the ELP scenario. Land-use strategies that follow the ELP scenario can better maintain the ecosystem services and sustainable development of natural and social economic systems.