Understanding the relationships between ecosystem services and associated social-ecological drivers in a karst region: A case study of Guizhou Province, China

Abstract

<jats:p> Understanding the relationships among multiple ecosystem services could improve the landscape capacity to provide benefits to human society. However, the underlying mechanisms shaping ecosystem services relationships are still unclear although some studies have been conducted to explore how natural and socioeconomic factors influence the relationships among ecosystem services. In this study, the karst landscape in southwestern China, a vulnerable system with intensive human activities, was focused on, aiming to explore relationships between ecosystem services and associated social and ecological factors. The results showed that the distribution of eight individual ecosystem services were spatially heterogeneous and clustered based on the characteristics of the karst landscape. The relationships between provisioning services and regulating services, such as grain production and net primary productivity, as well as water yield and soil retention, were quite different in high karst coverage regions and low karst coverage regions. Among five ecosystem service bundles identified, ecosystem services in the urban development bundle were mainly determined by socioeconomic factors, while in the other four bundles of multifunction, grain production, habitat conservation, and carbon sequestration, ecosystem services were dominated by ecological factors. However, socioeconomic factors (i.e. population density and night-time light intensity) appeared to explain the overall ecosystem service delivery more than karst terrain. This study provided insights for sustainable ecosystem management in a vulnerable karst region through exploring social-ecological factors of the relationships among ecosystem services. </jats:p>

Eco-engineering controls vegetation trends in southwest China karst

“…The rapid expansion of human population and increasing demand for natural resources, such as agricultural land and forest products (Brandt et al 2017, Qiu et al 2020, threatens the eco-environment (Li et al 2017). Many studies have discussed that human disturbances are threatening biodiversity and lead to land degradation (Martínez-Ramos et al 2016, Nguyen andLiou 2019).…”

“…Recently, large parts of the greening trend are attributed to ecological conservation projects like the Grain for Green program (Brandt et al 2018, Zhao et al 2019b) aiming at recovering ecosystems, alleviate poverty , Wang et al 2019b and improving ecosystem services (Qiu et al 2020, Liu et al 2008. Methods looking into land use change (Liao et al 2018, rocky desertification control (Jiang et al 2014) and vegetation improvement (Qi et al 2013) were used to indicate the effectiveness of ecological engineering.…”

Spatiotemporal Variation in Ecosystem Services and Their Drivers among Different Landscape Heterogeneity Units and Terrain Gradients in the Southern Hill and Mountain Belt, China

“…Most previous studies have focused on the spatial distribution, mapping, and relationships of ESs at a single point in time [23,24]. Recently, an increasing number of researchers have investigated the complex relationships among ESs and how their relationships might change over longer timespans [25][26][27][28][29] by analyzing land cover/land use (LULC) changes [30][31][32], performing scenario analysis [33,34] or assessing ES bundle changes [35,36]. The overall research on ESs is in-depth and specific, with a variety of perspectives.…”

“…The fuzzy k-means (FKM) algorithm is a widely used clustering technique [42], and it is regarded as an effective method that can produce results with high within-cluster homogeneity and between-cluster heterogeneity [43]. In the field of ES research, FKM has been used to cluster ES bundles [35,[44][45][46], rural-urban classification [47,48], and major vegetation clustering [8]. Few studies have considered obtaining multi-LHUs using the FKM, which maximizes the cluster heterogeneity of the landscape.…”

Determinants of ecosystem processes and services in the karst critical zone in south-west China

“…The results imply that lithology has a significant influence on the function of forestation in preventing soil erosion in the karst critical zone. Qiu et al (2021) discuss the factors shaping the relationships between different ecosystem services and identify five ecosystem services bundles. In contrast to studies on individual ecosystem services, socio-economic factors rather than lithology appear to explain the overall ecosystem service delivery.…”