Potential geographical distribution of the edangred plant Isoetes under human activities using MaxEnt and GARP

Yang, Jingtian; Huang, Yi; Jiang, Xue; Chen, Hao; Liu, Mei; Wang, Rulin

doi:10.1016/j.gecco.2022.e02186

Cited by 46 publications

(47 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a Jackknife test was performed, comparing the model's training gain using a single variable to that obtained using all the other variables. This test served to determine the relative importance of each variable (Yang et al, 2022).…”

Section: Environmental Variables Selectionmentioning

confidence: 99%

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Zhang,

Li,

Mason

et al. 2024

Earth Surf Processes Landf

View full text Add to dashboard Cite

Nebkhas are distinctive biogeomorphological landforms prevalent in global drylands and coastal environments. They play a crucial role in supporting local biodiversity and preventing land desertification and often serve as an indicator of local environmental change. Despite their significance, the environmental factors that affect their geographical distribution and how they respond to climate change have not been fully explored. This study represents a novel application of machine learning models to quantifying the biogeomorphological niche of Nitraria nebkhas in northern China and simulating their geographical distribution under future climate change conditions. Findings underscore that climatic variables influence the growth of formative shrub species on nebkhas, whereas climate, soil and geomorphological conditions, along with their spatial configuration, determine the probability of nebkha occurrence. Predictions under medium and high greenhouse gas emission scenarios indicate a northward shift in the potential distribution of nebkhas in northern China by the end of the century, accompanied by a decrease in the south due to rising temperatures. Given the potential impact of nebkha field degradation on biodiversity and soil hydrological conditions, adaptive land use strategies should be designed to protect nebkhas and mitigate the impact of climate change. Our study not only provides valuable insights for informing policy‐making and conservation initiatives but also serves as an example for quantifying the niche of biogeomorphological landforms and simulating their dynamics by integrating machine learning approaches into empirical geomorphological studies.

show abstract

Section: Environmental Variables Selectionmentioning

confidence: 99%

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Zhang,

Li,

Mason

et al. 2024

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…SSP1-2.6 (the lowly greenhouse gas emission scenario), SSP2-4.5 (the moderately greenhouse gas emission scenario) and SSP5-8.5 (the highly greenhouse gas emission scenario) in the Shared Socio-economic Pathways (SSPs) scenarios that can more scientifically describe future climate change were selected [33]. In order to reduce the influence of multi-collinearity among 19 bioclimatic variables, Pearson correlation analysis method was used [31,34]. Firstly, MaxEnt was used to calculate the percent contribution of 19 environmental variables, and the variables whose percent contribution rate was greater than 0 were retained (S1 Table ).…”

Section: Environmental Datamentioning

confidence: 99%

“…The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the model prediction results. The evaluation criteria of AUC value were 0.5-0.6 (failure), 0.6-0.7 (poor), 0.7-0.8 (average), 0.8-0.9 (good), 0.9-1.0 (excellent) [39][40][41].…”

Section: Plos Onementioning

confidence: 99%

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Xia

Kazim²,

Nasir³

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Climatic variables are important conditions for plant growth, development and reproduction. Citrus medica L. var. sarcodactylis Swingle (Rutaceae: Citrus) is one of the traditional bulk Chinese medicinal materials in China with the effects of bacteriostasis, anti-inflammatory, anti-oxidation, anti-cancer cells, regulating the immun. Analyzing the impact of climate change on geographical distribution of C. medica L. var. sarcodactylis can provide strong support for its production layout and agricultural zoning. In our paper, MaxEnt and ArcGIS were applied to simulate the suitable areas of C. medica L. var. sarcodactylis in China from the perspectives of bioclimate, soil, topographic factors and human activities, and the future climate scenarios generated by global climate models (GCMs) were selected to predict its suitable areas in 2050s and 2090s. Results showed that, 1) Under current climate condition, areas of the total, most, moderately and poorly suitable habitats of C. medica L. var. sarcodactylis in China were 177.36×104 km2, 22.27×104 km2, 51.96×104 km2 and 103.13×104 km2 respectively. The range of the most suitable habitat was the narrowest, which was located in the middle east of Sichuan, western Chongqing in the upstream of the Yangtze River Basin, southern Guizhou and western Guangxi in the upstream of the Pearl River Basin, central and southern Yunnan and Southeast Tibet in the Middle-Lower reaches of the Southwest River Basin and western Taiwan. 2) Under the future climate change scenarios, the total suitable area showed a significant increase trend in 2090s, and the change of most, moderately and poorly suitable habitats showed no obvious law. 3) Under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the centroid of the most suitable habitat of C. medica L. var. sarcodactylis would move to the northwest, southeast and southwest respectively.

show abstract

“…Since its introduction in 2004, the MaxEnt model has been widely used by scholars at home and abroad, and its prediction effect has been highly evaluated in the industry. Therefore, it has been widely used in the prediction of potential distribution areas of many tree species, such as Magnolia wilsonii [15], Dipteronia sinensis [16], Blumea balsamifera [17], Isoetes [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Potential Suitable Distribution of Endangered Medicinal of <i>Paeonia rockii</i> under Climate Change Scenarios

Lan¹,

Chen²,

Pan³

et al. 2023

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Paeonia rockii has been a traditional medicinal material from time immemorial. It is an important plant resource of ethnic medicine and chinese herbal medicine. It has unique effects on heat and cool blood, promoting blood circulation and dispersing blood stasis, analgesia and promoting menstruational flow. It is also a first-class protected plant. With the increasing demand for the bark and roots of P. rockii in China's medicinal market, the continuous high-intensity and disorderly excavation has led to a sharp decline in wild resources. P. rockii is in imminent danger and needs to be saved and protected urgently. In this study, the distribution records of P. rockii in China, combined with environmental factors, are used to simulate the current potential distribution area of P. rockii through MaxEnt model. The environmental factors affecting the distribution of P. rockii are obtained by using the contribution rate of environmental factors and a jackknife test analysis, and the appropriate value of environmental factors is determined by using the response curve. The results show that the AUC value of test set data is 0.985 and that of training set data is 0.987. The prediction accuracy of the Maxent model is extremely high and the simulation effect is very good. At present, the high suitable areas of P. rockii are mainly distributed in the shape of cake in Southern Ningxia, southern Gansu and southern Shanxi, accounting for 15.58% of the total suitable areas. The most important environmental factor affecting the geographical distribution of P. rockii is the altitude; Under the future climate change scenario, the total suitable area of P. rockii shows a decreasing trend, among which the area of high suitable area shows

show abstract

Potential geographical distribution of the edangred plant Isoetes under human activities using MaxEnt and GARP

Cited by 46 publications

References 52 publications

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model

Simulation of Potential Suitable Distribution of Endangered Medicinal of <i>Paeonia rockii</i> under Climate Change Scenarios

Contact Info

Product

Resources

About