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Predicting Shifts in the Suitable Climatic Distribution of Walnut (Juglans regia L.) in China: Maximum Entropy Model Paves the Way to Forest Management

Xiang Xu, +5 more
- 28 Feb 2018 - 
- Vol. 9, Iss: 3, pp 103
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TLDR
Predicting the influences of future climate change on the potentially suitable climatic distribution of an important woody oil plant species in China based on given climate change scenarios and the maximum entropy (MaxEnt) model has found that the current suitable environmental habitat of J. regia is mainly distributed in central and southwestern China.
Abstract
Cultivation of woody oil plants in environmentally suitable habitats is a successful ecological solution for oil development and forest management. In this study, we predicted the influences of future climate change on the potentially suitable climatic distribution of an important woody oil plant species (walnut; Juglans regia L.) in China based on given climate change scenarios and the maximum entropy (MaxEnt) model. The MaxEnt model showed that the minimum temperature of the coldest month and annual precipitation were the most important determinant variables limiting the geographical distribution of J. regia. We have found that the current suitable environmental habitat of J. regia is mainly distributed in central and southwestern China. Results of the MaxEnt model showed that global warming in the coming half-century may lead to an increase in the area size of environmentally suitable habitats for J. regia in China, indicating more lands available for artificial cultivation and oil production. However, those suitable habitat gains may be practically inaccessible due to over-harvest and urban development, and effective management strategies are urgently needed to establish those forests. This research will provide theoretical suggestions for the protection, cultivation management, and sustainable utilization of J. regia resources to face the challenge of global climate change.

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Identifying high-priority conservation areas for avian biodiversity using species distribution modeling

TL;DR: In this article, the authors used tree-cavity nesting birds (Black Woodpecker (Dryocopus martius), Great Spotted Woodpeckers (Dendrocopos major), Eurasian Nuthatch (Sitta europaea), European Green Woodpeech (Picus viridis) and Lesser Spotted woodpecker(Dendropos minor) to identify high-priority areas for conservation in Hyrcanian forests, Iran.
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Past and future climatic indicators for distribution patterns and conservation planning of temperate coniferous forests in southwestern China

TL;DR: In this paper, the authors investigated the distribution of temperate coniferous forests during the Quaternary and future climatic fluctuations in China and showed that the mean temperature of the driest quarter, followed by the temperature annual range and the precipitation of the warmest quarter were the most important variables controlling forests distribution.
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Climate change produces winners and losers: differential responses of amphibians in mountain forests of the Near East

TL;DR: In this paper, the impact of climate change on the geographic distribution of three typical species of forest-dwelling amphibians, the Balkan Crested Newt (Triturus ivanbureschi), the Anatolian Crested Newts (T. anatolicus), and the Southern CrestedNewt (Tarelinii), was projected.
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Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?

TL;DR: The results obtained from independent sample t-test on AUCratio values derived from the correlative models showed that it had more satisfactory results when they were getting from the data of scenario II than the scenario I, and therefore was attempted to compare the performance of bioclimatic variables derived from precipitation and temperature instrumental records.
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AHP-GIS and MaxEnt for delineation of potential distribution of Arabica coffee plantation under future climate in Yunnan, China

TL;DR: Based on climate, terrain and soil data, the MaxEnt model and AHP-GIS technology were used to evaluate and analyze the increase and decrease range of Arabica coffee ecological suitability planting areas and most suitable areas in different periods, determine the most significant environmental factors affecting ecological applicability, and determine the distribution transformation under climate change scenario (SSPs370) as mentioned in this paper.
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).
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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.
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Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
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A statistical explanation of MaxEnt for ecologists

TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
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