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Assessing the impact of climate change on the distribution of Osmanthus fragrans using Maxent

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TLDR
Wang et al. as mentioned in this paper constructed Maxent models for current as well as future appropriate habitats for Osmanthus fragrans based on 89 occurrence records and 30 environmental variables, which indicated that UV-B seasonality (19.1%), precipitation seasonality, and mean diurnal temperature range (12.5%) were the most important factors used for interpreting the environmental demands for this species, mainly distributed in southwestern Jiangsu, southern Anhui, Shanghai, Zhejiang, Fujian, northern Guangdong, Guangxi, southern Hunan, southern Hubei, S
Abstract
Models that evaluate the potential geographic distribution of species can be used with a variety of important applications in conservation biology. Osmanthus fragrans has high ornamental, culinary, and medicinal value, and is widely used in landscaping. However, its preferred habitat and the environmental factors that determine its distribution remain largely unknown; the environmental factors that shape its suitability also require analysis. Based on 89 occurrence records and 30 environmental variables, this study constructed Maxent models for current as well as future appropriate habitats for O. fragrans. The results indicate that UV-B seasonality (19.1%), precipitation seasonality (18.8%), annual temperature range (13.1%), and mean diurnal temperature range (12.5%) were the most important factors used for interpreting the environmental demands for this species. Highly appropriate habitats for O. fragrans were mainly distributed in southwestern Jiangsu, southern Anhui, Shanghai, Zhejiang, Fujian, northern Guangdong, Guangxi, southern Hunan, southern Hubei, Sichuan, and Taiwan. Under climate change scenarios, the spatial extent of the area of suitable distribution will decrease, and the distribution center of O. fragrans will shift to the southwest. The results of this study will help land managers to avoid blindly introducing this species into inappropriate habitat while improving O. fragrans yield and quality.

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Global potential distribution prediction of Xanthium italicum based on Maxent model.

TL;DR: In this paper, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum, and the prediction result of this model was excellent.
Journal ArticleDOI

MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

TL;DR: In this article, the appropriate distribution area of C. lanceolata (Lamb.) Hook was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041-2060.
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The current and future potential geographical distribution of Nepeta crispa Willd., an endemic, rare and threatened aromatic plant of Iran: Implications for ecological conservation and restoration

TL;DR: In this article , the authors aimed to model the current and future potential geographical distributions and identify the most relevant environmental factors influencing the distribution of Nepeta crispa Willd. in western Iran.
Journal ArticleDOI

Predicting differential habitat suitability of Rhodomyrtus tomentosa under current and future climate scenarios in China

TL;DR: In this paper, the potential biogeographical range of Rhodomyrtus tomentosa in China was predicted by Maxent and QGIS modeling under current and three future climate change scenarios.
Journal ArticleDOI

Habitat distribution modeling of endangered medicinal plant Picrorhiza kurroa (Royle ex Benth) under climate change scenarios in Uttarakhand Himalaya, India

TL;DR: In this article , the authors used the maximum entropy (MaxEnt) model to predict the current and future potential habitat distribution of endangered medicinal plant Picrorhiza kurroa Royle ex Benth in Uttarakhand Himalaya.
References
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Journal ArticleDOI

Very high resolution interpolated climate surfaces for global land areas.

TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Journal ArticleDOI

Maximum entropy modeling of species geographic distributions

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI

Predictive habitat distribution models in ecology

TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
Journal ArticleDOI

Predicting species distribution: offering more than simple habitat models.

TL;DR: An overview of recent advances in species distribution models, and new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales are suggested.
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