Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills

Yang, Xueqing; Kushwaha, S. P. S.; Saran, Sameer; Xu, Jianchu; Roy, P. S.

doi:10.1016/j.ecoleng.2012.12.004

Cited by 501 publications

(309 citation statements)

References 22 publications

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“…In a geographical space, ENM (ecological niche modelling) predictive high output values (high threshold) at grid cells shows relatively high probability of finding a species under suitable environment [14,31,32].…”

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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“…The results revealed that the predicted potential distribution areas through Maxent almost always appear as over predicted in some area compared to the realized niche of the species. Because Maxent considers only niche-based presence data, it estimates the species fundamental niche (different from occupied niche) rather than realized niche (Kumar & Stohlgren, 2009;Yang et al, 2013). This study is based only on a set of existing data.…”

Section: Model Performance Predictions and Suitable Potential Habitamentioning

confidence: 99%

Comparing Suitable Habitat Models to Predict Rare and Endemic Plant Species Distributions: What are the Limits of the Niche of Cola lorougnonis (Malvaceae) in Cote d'Ivoire?

Vroh

Yao²,

Kpangui³

et al. 2016

ENRR

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Cola lorougnonis is an endemic, rare, and treated species. It was recently recorded in Dékpa forest near Agbaou (a village of Divo region, Côte d'Ivoire). In the same forest, 20 other rare and endemic plant species were recorded. Accurate modeling of geographical distributions of these species is crucial to various applications in ecology and biodiversity conservation. The present study analyzed suitable habitat models for the 21 species. The main objective was to test geographical predictions for the focused species: Cola lorougnonis. We used Maxent modelling method for predicting potential suitable habitats combining environmental variables and species records. We evaluated Maxent predictions using the area under the receiver-operating characteristic curve (AUC). For each species, the map of distribution was engineered using DIVA-GIS. We compared the suitable habitat areas among species. Principal Canonical Analysis allowed the ordination of species according to environmental variables. AUC values allowed to get 11 species with excellent distribution models, 8 species with good distribution models, and 2 species with predictive models considered as acceptable. Cola lorougnonis (AUC = 0.99) and Drypetes singroboensis (AUC = 0.96) have the same focused area: moist semi-deciduous forest in Côte d'Ivoire. They are more sensitive to changes in rainfall of both warmest and coldest seasons. The State of Côte d'Ivoire has to undertake monitoring, assessment and reporting of conservation status facilitation for all habitats where these species could be found within the territory. Additional studies focusing on the investigation areas and niche models onto future conditions of climate could be considered.

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“…The AUC values above 0.9 indicated high model accuracy, AUC values from 0.7 to 0.9 indicated moderate model accuracy and AUC values from 0.5 to 0.7 indicated low model accuracy [18][19][20]. Generated maps of potential distribution of the studied species were characterized by a range of values from 0 to 1, where the values >0.6 represented "high potential", 0.4-0.6 "good potential", 0.2-0.4 "moderate potential", and <0.2 "least potential" class [17]. The Jackknife test was used to estimate the importance of the climatic variables predicting the distribution of the studied species.…”

Section: Model Buildingmentioning

confidence: 99%

“…The analyzes were carried out for the logistic output format with maximum of 500 iterations and the maximum of background points 10 000 [17]. The area under the receiving operator curve (AUC) was used to evaluate the resulting models.…”

Section: Model Buildingmentioning

confidence: 99%

Modeling the potential distribution of three lichens of the Xanthoparmelia pulla group (Parmeliaceae, Ascomycota) in Central Europe

2015

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The paper presents models of potential geographical distribution of Xanthoparmelia delisei, X. loxodes, and X. verruculifera in Central Europe. The models were developed with MaxEnt (maximum entropy algorithm) based on 224 collection localities and bioclimatic variables. The applied method enabled to identify the areas where climatic conditions are the most suitable for modeled species outside their known localities. According to obtained model, high potential distribution of the X. delisei and X. loxodes was found in the northern and northeastern Poland, when areas most suitable for X. verruculifera were placed in the south, especially in the Carpathians. Model also suggests that potential distribution of X. delisei could be wider than known data on its occurrence and extend to Lithuania, Belarus and the Czech Republic. MaxEnt modeling of X. loxodes showed the widest potential distribution for this species in Central Europe with the best regions in Lithuania. Potential distribution in all models was strongly influenced by precipitation-related variables. All the modelled species prefer areas where precipitation in the coldest quarter is very low.

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Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills

Cited by 501 publications

References 22 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

Comparing Suitable Habitat Models to Predict Rare and Endemic Plant Species Distributions: What are the Limits of the Niche of Cola lorougnonis (Malvaceae) in Cote d'Ivoire?

Modeling the potential distribution of three lichens of the Xanthoparmelia pulla group (Parmeliaceae, Ascomycota) in Central Europe

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