Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling (PLS-SEM)
SHA Zongyao1*, XIE Yichun2, TAN Xicheng1, BAI Yongfei3, LI Jonathan4, LIU Xuefeng5
1 International Software School, Wuhan University, Wuhan 430079, China;
2 Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, USA;
3 Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
4 Department of Geography & Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo,Ontario N2L 3G1, Canada;
5 School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling (PLS-SEM)
SHA Zongyao1*, XIE Yichun2, TAN Xicheng1, BAI Yongfei3, LI Jonathan4, LIU Xuefeng5
1 International Software School, Wuhan University, Wuhan 430079, China;
2 Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, USA;
3 Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
4 Department of Geography & Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo,Ontario N2L 3G1, Canada;
5 School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
摘要 The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling (SEM) demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM (PLS-SEM) and geographically-weighted regression (GWR) modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass (AGB). The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.
Abstract：
The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling (SEM) demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM (PLS-SEM) and geographically-weighted regression (GWR) modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass (AGB). The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.
This study was supported by the National Natural Science Foundation of China (41371371) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050402).
SHA Zongyao,XIE Yichun,TAN Xicheng等. Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling (PLS-SEM)[J]. Journal of Arid Land, 2017, 9(4): 473-488.
SHA Zongyao,XIE Yichun,TAN Xicheng et al. Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling (PLS-SEM)[J]. Journal of Arid Land, 2017, 9(4): 473-488.
2017, Vol. 9 
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