2022
DOI: 10.3390/f13030381
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Prediction of Potentially Suitable Distribution Areas for Prunus tomentosa in China Based on an Optimized MaxEnt Model

Abstract: Prunus tomentosa (Thunb.) Wall has high nutritional value and medicinal effects. It is widespread in China; however, most plants growing in the wild are near extinction in many places. Predicting the potential distribution of P. tomentosa under climate change is helpful for cultivating and protecting wild germplasm resources. We used two general circulation models (CCSM4 and MIROC-ESM) and two future climate scenarios (RCP4.5 and RCP8.5) to predict P. tomentosa’s present and future geographical distribution. A… Show more

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Cited by 12 publications
(4 citation statements)
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“…Zhang et al [10] also employed the AHP-GIS and MaxEnt models to compare the prediction results regarding the potential distribution patterns of Arabica coffee, discovering that the MaxEnt model outperformed the AHP-GIS model. The above findings confirm that, under the condition of limited distribution point data, the MaxEnt model can achieve better prediction results and higher accuracy than other models and be more suitable for predicting the potential geographical distribution of species [27].…”
Section: Introductionsupporting
confidence: 71%
“…Zhang et al [10] also employed the AHP-GIS and MaxEnt models to compare the prediction results regarding the potential distribution patterns of Arabica coffee, discovering that the MaxEnt model outperformed the AHP-GIS model. The above findings confirm that, under the condition of limited distribution point data, the MaxEnt model can achieve better prediction results and higher accuracy than other models and be more suitable for predicting the potential geographical distribution of species [27].…”
Section: Introductionsupporting
confidence: 71%
“…The screening process consisted of two main steps: (1) Utilising SPSS 23.0 software to examine the correlation amongst the 26 climate variables, with a threshold of 0.80 set for determination; and (2) Running MaxEnt 3.4.1 software with species distribution data and the 26 climate variables to determine the initial percentage contribution of each variable to the model. Following this, variables with a correlation coefficient above 0.80 and a lower contribution rate were excluded ( Fang et al 2022 ). Ultimately, eleven climate variable factors were selected for the subsequent prediction of Ch.…”
Section: Methodsmentioning
confidence: 99%
“…The OR provides information on model differences and overfitting and evaluates the data used at a specific threshold. The model parameters were optimal when the OR was < 5% and the DELTA AICc was minimal 24 , 25 .…”
Section: Methodsmentioning
confidence: 99%