Exploring the spatial heterogeneity of ecosystem services and influencing factors on the Qinghai Tibet Plateau

• Published in: Ecological indicators Vol. 154; p. 110521
• Main Authors: Jia, Zixu, Wang, Xiaofeng, Feng, Xiaoming, Ma, Jiahao, Wang, Xiaoxue, Zhang, Xinrong, Zhou, Jitao, Sun, Zechong, Yao, Wenjie, Tu, You
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023; Elsevier
• Subjects: Constraint effects; Ecosystem services; Geographical detector model; Geographically weighted regression model; Influencing factors; Qinghai Tibet Plateau; Spatial heterogeneity; Pre; TES; ESs; LPI; Qinghai Tibet Plateau; HQ; QTP; Influencing factors; NDVI; NPP; SC; GDM; SF; Geographically weighted regression model; OLS; Resident; Ecosystem services; WY; GWR; Road; Geographical detector model; DEM; ES; Constraint effects; RSS; AICc; Spatial heterogeneity; Tem
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2023.110521

•The ecosystem services (ESs) were spatially heterogeneous.•The joint and individual effects of influencing factors on ESs were considered.•The local spatial characteristics of the influencing factors were explored.•The influencing factors had constraint effects on ESs. Identifying the spatial heterogeneity characteristics of ecosystem services (ESs) and their influencing mechanisms is crucial for regional ecological management and sustainable development. This study quantified net primary production (NPP), water yield (WY), soil conservation (SC), sand fixation (SF), habitat quality (HQ) and total ecosystem service (TES) on the Qinghai Tibet Plateau (QTP), and then analyzed the spatial–temporal characteristics of ESs. The geographical detector model (GDM), geographically weighted regression (GWR) model, and constraint lines were used to explore the effects of influencing factors on ESs. The results showed that (1) NPP, WY, SC, HQ and TES showed an increasing trend from 2000 to 2018, while SF showed a decreasing trend. NPP, WY, SC and TES showed a spatial distribution pattern of “high values in the southeast and low values in the northwest”, while the high values of SF were located in the northern QTP and the high values of HQ were located in the southern QTP. (2) NDVI was the key factor influencing NPP, SC, HQ and TES, and precipitation dominated WY, and SF was mainly influenced by Largest patch index (LPI). (3) In most of the study areas, the natural factors (precipitation, temperature, DEM, slope and NDVI) had positive effects on NPP, SC and TES and both positive and negative effects on WY, SF and HQ. The human factors (Resident, Road and LPI) had positive effects on HQ and both positive and negative effects on other ESs. There were mainly non-monotonic constraint effects of influencing factors on ESs. Overall, natural factors had a much stronger influence on ESs than human factors, and the interactions among factors were much stronger than their independent effects. The results of this study can provide a basis for ecological protection on the QTP.