Modelling the spatial pattern of biodiversity utilizing the high-resolution tree cover data at large scale: Case study in Yunnan province, Southwest China

Liu, Shiliang; Dong, Yuan; Sun, Yixuan; Li, Junran; An, Yingli; Shi, Fangning

doi:10.1016/j.ecoleng.2019.05.001

Cited by 15 publications

(10 citation statements)

References 51 publications

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“…Geographically weighted regression (GWR) model, which is an extension of traditional regression model (e.g., ordinary least squares, OLS) (Ștefănescu et al., 2017 ; Tripathi et al., 2019a , 2019b ; Xue et al., 2020 ), has become one of the crucial spatial heterogeneity modeling tools (Lu et al., 2020 ). In recent years, many domestic and foreign scholars have carried out in‐depth and extensive research in various fields by using GWR model, including social environmental factors and regional economy, regional house prices and pollution (McCord et al., 2018 ; Xu et al., 2019 ), the impacts of environmental heterogeneity and land‐use change on wild animal distribution (Liu et al., 2019 ; Wang et al., 2020 ; Xue et al., 2020 ), and vegetation activity and climate change (Gao et al., 2019 ). However, there are few studies that in combination MaxEnt with GWR models to analyze the potential geographical distribution and explore environmental explanations for some rare and endangered plant species, especially in biodiversity hot spot areas.…”

Section: Introductionmentioning

confidence: 99%

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

Zhang

Zhao

et al. 2021

Ecology and Evolution

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Section: Introductionmentioning

confidence: 99%

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

Zhang

Zhao

et al. 2021

Ecology and Evolution

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“…Based on previous studies (Nieto et al, 2015;S , tefȃnescu et al, 2017;Zhang et al, 2019;Liu et al, 2019), we initially selected 24 environmental variables that may affect species distribution to model the current potential geographical distribution patterns (Table 1). Above all, we divided these variables into five groups according to their categories.…”

Section: Environmental Variablesmentioning

confidence: 99%

“…In addition, R 2 and AIC value can also reflect fitting goodness of the model. The higher R 2 , and the lower AIC value, indicating the better fitting effect of the model (Li et al, 2017;Liu et al, 2019). When the difference in the AIC value ([?…”

Section: Evaluation Of the Gwr Modelmentioning

confidence: 99%

“…Geographically weighted regression (GWR) model, which is an extension of traditional regression model (e.g., Ordinary least squares, OLS) (S , tefȃnescu et al, 2017;Tripathi et al, 2019a;Tripathi et al, 2019b;Xue et al, 2020), has become one of the crucial spatial heterogeneity modeling tools (Lu et al, 2020). In recent years, many domestic and foreign scholars have carried out in-depth and extensive research in various fields by using GWR model, including social environmental factors and regional economy, regional house prices and pollution (McCord et al, 2018;Xu et al, 2019), the impacts of environmental heterogeneity and land use change on wild animal distribution (Liu et al, 2019;Wang et al, 2020;Xue et al, 2020), vegetation activity and climate change (Gao et al, 2019). However, there are few studies that in combination MaxEnt with GWR models to analyze the potential geographical distribution and explore environmental explanations for some key species, especially in biodiversity hotspot areas.…”

Section: Introductionmentioning

confidence: 99%

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Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modelling: A case study of Northwest Yunnan, China

Zhang²,

Zhao³

et al. 2020

Preprint

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In recent decades, due to the effect of climate change and the interference of human activities, the species habitat index fallen 2%. Studying the geographical distribution pattern and predicting the potential geographical distribution of species are of great significance for developing scientific and effective biodiversity conservation strategies. The purpose of this research is to predict the potential geographical distribution of 25 rare and endangered plant species in Northwest Yunnan, China on the grid map with a resolution of 0.05° × 0.05° and analyze the explanation capabilities of various environmental factors on the potential geographical distribution patterns of these species, and explore the main restrictive environmental factors. Initially, we employed the ecological niche model MaxEnt to predict the potential geographical distribution of target species. Following that, we overlaid the potential geographical distribution of each species, and we obtained the potential geographical distribution pattern of species richness on the spatial scale of the ecological niche model with a resolution of 0.05° × 0.05°. Ultimately, we also adopted geographically weighted regression (GWR) model to investigate the explanation capabilities of various environmental parameters on the potential distribution patterns. The results showed that the average AUC value of each species was between 0.80 and 1.00, which indicated that the simulation precision of the MaxEnt model for each species was good or excellent. Besides, the potential distribution areas of these species were between 826.33 km2 and 44,963.53 km2. In addition, the average contribution values of the annual precipitation (Bio12), precipitation of coldest quarter (Bio19) and population density (Pop) were 25.92%, 15.86% and 17.95%, respectively. Moreover, the goodness of fit R2 and AIC value of the water model were 0.88 and 7,703.82, respectively, which indicated the water factor largely influenced the potential distribution of these species. The results of this study would be helpful for implementing long-term conservation and reintroduction for these species.

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“…Therefore, local parameters can be estimated instead of global parameters, which can better describe the relationships between local variables [33]. The GWR model has been widely used to explain changes in regional vegetation and urban surface environments or urban expansion [29,31,[34][35][36]. Therefore, in this study, the GWR model was selected to study the relationships between changes in landscape ecological risk and the driving factors.…”

Section: Introductionmentioning

confidence: 99%

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Bao

et al. 2019

IJERPH

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Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

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Modelling the spatial pattern of biodiversity utilizing the high-resolution tree cover data at large scale: Case study in Yunnan province, Southwest China

Cited by 15 publications

References 51 publications

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modelling: A case study of Northwest Yunnan, China

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

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