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Journal ArticleDOI

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

01 Feb 2013-Ecological Engineering (Elsevier)-Vol. 51, Iss: 51, pp 83-87
TL;DR: In this paper, the authors reported the results of a study carried out in the Lesser Himalayan foothills in India (Dun valley) on potential distribution modeling for Malabar nut using Maxent model.
About: This article is published in Ecological Engineering.The article was published on 2013-02-01. It has received 409 citations till now. The article focuses on the topics: Population & Species distribution.
Citations
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Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper used the principle of maximum entropy (Maxent) to model the species' potential distribution area under paleoclimate, current and future climate background, and found that the Maxent model was highly accurate with a statistically significant AUC value of 0.998, which is higher than 0.5 of a null model.

214 citations


Cites methods from "Maxent modeling for predicting the ..."

  • ...With a reference to the classification proposed by Yang et al. (2013), five classes of potential habitats were regrouped: unsuitable habitat (0–0.2); barely suitable habitat (0.2–0.4); suitable habitat (0.4–0.6); highly suitable habitat (0.6–0.7); very highly suitable habitat (0.7–1.0)....

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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors reported the quantitative predictions of climate change on riparian species, such as Homonoia riparia (H. riparia) Lour, a species native to China, is a medicinal plant with high ecological and economic value.

198 citations

Journal ArticleDOI
01 May 2017-Catena
TL;DR: In this paper, the authors used maximum entropy (ME) as a machine learning model, with two sampling strategies: Mahalanobis distance (MEMD) and random sampling (MERS), to map landslide susceptibility over the Ziarat watershed in the Golestan Province, Iran.
Abstract: The aim of the current study is to map landslide susceptibility over the Ziarat watershed in the Golestan Province, Iran, using Maximum Entropy (ME), as a machine learning model, with two sampling strategies: Mahalanobis distance (MEMD) and random sampling (MERS). To this aim, a total of 92 landslides in the watershed were recorded as point features using a GPS (Global Positioning System) device, along with several field surveys and available local data. By reviewing landslide-related studies and using principal component analysis, 12 landslide-controlling factors were chosen namely altitude, slope percent, slope aspect, lithological formations, proximity (to faults, streams, and roads), land use/cover, precipitation, plan and profile curvature and the state-of-the-art topo-hydrological factor known as height above the nearest drainage (HAND). Two sampling methods were used to divide landslides into two sets of training (70%) and test (30%). The Area under the success rate curve (AUSRC) and the area under the prediction rate curve (AUPRC) were used to evaluate the results of the MEMD and MERS. The results showed that both MEMD and MERS strategies with the respective AUSRC values of 0.884 and 0.878, have good performance in modelling the landslide susceptibility in the study area. However, AUPRC test showed slightly different results in which MEMD with the value of 0.906 showed excellent predictive power in comparison with the MERS with the AUPRC value of 0.846. The higher AUPRC value in relation to AUSRC indicated the MEMD as the premier model in the current study. According to the MEMD, three landslide controlling factors including lithological formations, proximity to roads and precipitation with the respective contribution percentages of 25.1%, 23.3%, and 19.1%, contained more information in relation to the rest. Moreover, according to one-by-one factor removal test, lithological formations and proximity to faults were identified to have a unique information compared to the rest. According to the MEMD, about 13.8% of the study area is located within high to very high susceptibility classes which can be matter of great interest to decision makers and the local authorities for formulating land use planning strategies and implementing pragmatic measures.

166 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present an assessment on current and future habitat suitability distribution of Myristica dactyloides Gaertn (MD), a medicinally and ecologically important tree species by using a maximum entropy (MaxEnt) species distribution model.

157 citations


Cites background from "Maxent modeling for predicting the ..."

  • ..., 2012) and Justicia adhatoda the Malabar Nut (Yang et al., 2013)....

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  • ...85 were eliminated (Yang et al., 2013)....

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Journal ArticleDOI
TL;DR: This study model the potential invasion range of bushmint in India and investigates prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production).

151 citations

References
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Journal ArticleDOI
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).
Abstract: We 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). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright  2005 Royal Meteorological Society.

17,977 citations

Journal ArticleDOI
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.

13,120 citations

Journal ArticleDOI
E. T. Jaynes1
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
Abstract: Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum-entropy estimate. It is the least biased estimate possible on the given information; i.e., it is maximally noncommittal with regard to missing information. If one considers statistical mechanics as a form of statistical inference rather than as a physical theory, it is found that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle. In the resulting "subjective statistical mechanics," the usual rules are thus justified independently of any physical argument, and in particular independently of experimental verification; whether or not the results agree with experiment, they still represent the best estimates that could have been made on the basis of the information available.It is concluded that statistical mechanics need not be regarded as a physical theory dependent for its validity on the truth of additional assumptions not contained in the laws of mechanics (such as ergodicity, metric transitivity, equal a priori probabilities, etc.). Furthermore, it is possible to maintain a sharp distinction between its physical and statistical aspects. The former consists only of the correct enumeration of the states of a system and their properties; the latter is a straightforward example of statistical inference.

12,099 citations

Journal ArticleDOI
08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

7,089 citations

Journal ArticleDOI
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.

6,748 citations