Enhanced spatiotemporal heterogeneity and the climatic and biotic controls of autumn phenology in northern grasslands

Author(s):  
Shilong Ren ◽  
Matthias Peichl
2021 ◽  
Vol 13 (9) ◽  
pp. 5013
Author(s):  
Dan Zhu ◽  
Degang Yang

Identifying how policy, socioeconomic factors, and environmental factors influence changes in human well-being (HWB) and conservation efficiency is important for ecological management and sustainable development, especially in the Giant Panda National Park (GPNP). In this study, we systematically analyzed the differences in the conservation status of the giant panda habitat and changes in HWB over 15 years in the GPNP, which includes six mountain sites, Minshan (MS), Qionglai (QLS), Xiaoxiangling (XXL), Liangshan (LS), Qinling (QL), and Daxiangling (DXL). Redundancy analyses were used to determine the factors contributing (policy, socioeconomic factors, and environmental factors) to HWB and giant panda habitat conservation (HC). In addition, using a structural equation model (SEM), we investigated the relationship between the aforementioned three factors and their direct and indirect effects on HWB and HC. The results indicated that there was spatiotemporal heterogeneity of HWB and HC in our study area. There was an increasing number of plant species as well as an increased number of giant panda in GPNP. Generally, HWB in 2015 showed an increasing trend compared with that in 2000. Socioeconomic factors (23.6%) have the biggest influence on HWB and HC, followed by policy (23.2%) and environmental factors (19.4%). Conservation policy had a significantly positive influence on HWB (0.52), while it negatively influenced HC (−0.15). Socioeconomic factors significantly negatively influenced HWB (−0.38). The formulation and implementation of policies to promote economic development will contribute to the protection of giant pandas and their habitat. Our results provide insight on the conservation status of the giant panda habitat, HWB, and factors influencing them in different mountain sites in the GPNP, as well as having implications for the future management of the GPNP.


1999 ◽  
Vol 84 (11) ◽  
pp. 1318-1331 ◽  
Author(s):  
Ji-Min Cao ◽  
Zhilin Qu ◽  
Young-Hoon Kim ◽  
Tsu-Juey Wu ◽  
Alan Garfinkel ◽  
...  

Author(s):  
Youyi Bi ◽  
Jian Xie ◽  
Zhenghui Sha ◽  
Mingxian Wang ◽  
Yan Fu ◽  
...  

Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.


2019 ◽  
Vol 11 (15) ◽  
pp. 4012 ◽  
Author(s):  
Jing Yang ◽  
Feng Shi ◽  
Yizhong Sun ◽  
Jie Zhu

While cellular automata (CA) has become increasingly popular in land-use and land-cover change (LUCC) simulations, insufficient research has considered the spatiotemporal heterogeneity of urban development strategies and applied it to constrain CA models. Consequently, we proposed to add a zoning transition rule and planning influence that consists of a development grade coefficient and traffic facility coefficient in the CA model to reflect the top-down and heterogeneous characteristics of spatial layout and the dynamic and heterogeneous external interference of traffic facilities on land-use development. Testing the method using Nanjing city as a case study, we show that the optimal combinations of development grade coefficients are different in different districts, and the simulation accuracies are improved by adding the grade coefficients into the model. Moreover, the integration of the traffic facility coefficient does not improve the model accuracy as expected because the deployment of the optimal spatial layout has considered the effect of the subway on land use. Therefore, spatial layout planning is important for urban green, humanistic and sustainable development.


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