Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China

Yuan, Hai‐Sheng; Wei, Ying; Wang, Xugao

doi:10.1016/j.funeco.2015.06.001

Cited by 107 publications

(66 citation statements)

References 35 publications

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“…In ecological niche modelling (ENM) environmental parameters are compared within the geographical space. ENM techniques are based on statistical methods (e.g., generalized additive models (GAMs) and generalized linear models (GLMs)) and scientist-developed machine learning methods (e.g., artificial neural networks (ANNs) and maximum entropy (Maxent)) that are based on different statistical techniques [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model

et al. 2016

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Abstract:The potential distribution of Olea ferruginea was predicted by Maxent model for present and the upcoming hypothetical (2050) climatic scenario. O. ferruginea is an economically beneficial plant species. For predicting the potential distribution of O. ferruginea in Pakistan, Worldclim variables for current and future climatic change scenarios, digital elevation model (DEM) slope, and aspects with the occurrence point were used. Pearson correlation was used to reject highly correlated variables. A total of 219 sighting points were used in the Maxent modeling. The area under curve (AUC) value was higher than 0.98. The approach used in this study is considered useful in predicting the potential distribution of O. ferruginea species, and can be an effective tool in the conservation and restoration planning for human welfare. The results show that there is a significant impact under future bioclimatic scenarios on the potential distribution of O. ferruginea in Pakistan. There is a significant decrease in the overall distribution of O. ferruginea due to loss of habitats under current distribution range, but this will be compensated by gain of habitat at higher altitudes in the future climate change scenario (habitat shift). It is recommended that the areas predicted suitable for the O. ferruginea may be used for plantation of this species while the deforested land should be restored for human welfare.

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Section: Introductionmentioning

confidence: 99%

Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model

et al. 2016

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“…Over the past two decades, numerous SDMs from a range of methods, such as genetic algorithm for rule-set production (GARP)27, random forest (RF)28, ecological niche factor analysis (ENFA)29, artificial neural network (ANN)30, and maximum entropy (MaxEnt)31, have been developed. The prediction accuracy of MaxEnt is stable and reliable, even with incomplete data and small sample sizes16313233. In addition, MaxEnt requires only species presence data, and both continuous and categorical environmental data can be used as input variables.…”

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confidence: 98%

“…Although climate change is a global phenomenon, regional and local climate change are greater concerns for rare plants81314. Over the past few decades, many studies have focused on the effect climate change has on the spatial distribution of plant species, but few studies have examined the regional distribution of endangered ectomycorrhizal mushrooms1516, and even fewer have considered soil and vegetation factors.…”

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confidence: 99%

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Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios

Guo

Zhao

et al. 2017

Sci Rep

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Effective conservation and utilization strategies for natural biological resources require a clear understanding of the geographic distribution of the target species. Tricholoma matsutake is an ectomycorrhizal (ECM) mushroom with high ecological and economic value. In this study, the potential geographic distribution of T. matsutake under current conditions in China was simulated using MaxEnt software based on species presence data and 24 environmental variables. The future distributions of T. matsutake in the 2050s and 2070s were also projected under the RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 climate change emission scenarios described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change (IPCC). The areas of marginally suitable, suitable and highly suitable habitats for T. matsutake in China were approximately 0.22 × 106 km2, 0.14 × 106 km2, and 0.11 × 106 km2, respectively. The model simulations indicated that the area of marginally suitable habitats would undergo a relatively small change under all four climate change scenarios; however, suitable habitats would significantly decrease, and highly suitable habitat would nearly disappear. Our results will be influential in the future ecological conservation and management of T. matsutake and can be used as a reference for studies on other ectomycorrhizal mushroom species.

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“…The analysis in the paper was completed using MaxEnt version 3.3.3 k (http://biodiversityinformatics.amnh.org/open_source/maxent/). The MaxEnt model is a presence‐only model of the ecological niche and limited data can contribute to high‐accuracy prediction by using this model . As validated by the abovementioned studies, the MaxEnt model can make accurate predictions only based on 50 data points .…”

Section: Methodsmentioning

confidence: 88%

Impact of meteorological and geographical factors on the distribution of leishmaniasis's vector in mainland China

Gao

Huang

Zheng

et al. 2019

Pest Management Science

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BACKGROUND Phlebotomine sandfly (Diptera: Psychodidae) is best known for its role as the vector of the leishmaniasis. Leishmaniasis is a global disease, currently known to be affecting 88 countries and 12 million people worldwide. RESULTS This study explored the impacts of climate factors and landscape on the local abundance of Phlebotomine sandfly in mainland China. A presence‐only modelling method was used to evaluate this species' habitat preferences from environmental factors. Jackknife analysis revealed that several biologically meteorological variables, including the maximum temperature of the warmest quarter, the precipitation in the driest month, the daily average temperature and daily precipitation, would significantly affect the existence of this species. Moreover, the existence of Phlebotomine sandfly was significantly associated with grassland and forest. CONCLUSION The results will improve scientific understanding of the risks of the spread of leishmaniasis over current infected areas and can be used to design more detailed surveillance programmes and more evidence‐based control planning for Phlebotomine sandfly and leishmaniasis. © 2019 Society of Chemical Industry

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Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China

Cited by 107 publications

References 35 publications

Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model

Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model

Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios

Impact of meteorological and geographical factors on the distribution of leishmaniasis's vector in mainland China

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