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      Influence of the Environment on the Distribution and Quality of Gentiana dahurica Fisch.

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          Abstract

          Gentiana dahurica Fisch. is a characteristic medicinal plant found in Inner Mongolia, China. To meet the increase in market demand and promote the development of medicinal plant science, we explored the influence of the environment on its distribution and the quantity of its active compounds (loganic acid and 6’- O- β-D-glucosylgentiopicroside) to find suitable cultivation areas for G. dahurica. Based on the geographical distribution of G. dahurica in Inner Mongolia and the ecological factors that affect its growth, identified from the literature and field visits, a boosted regression tree (BRT) was used to model ecologically suitable areas in the region. The relationship between the content of each of active compound in the plant and ecological factors was also established for Inner Mongolia using linear regression. The results showed that elevation and soil type had the most significant influence on the distribution of G. dahurica—their relative contribution was 30.188% and 28.947%, respectively. The factors that had the greatest impact on the distribution of high-quality G. dahurica were annual precipitation, annual mean temperature, and temperature seasonality. The results of BRT and linear regression modeling showed that suitable areas for high-quality G. dahurica included eastern Ordos, southern Baotou, Hohhot, southern Wulanchabu, southern Xilin Gol, and central Chifeng. However, there were no significant correlations between the contents of loganic acid and 6’- O- β-D-glucosylgentiopicroside and the ecological factors. This study explored the influence of the environment on the growth and quantity of active compounds in G. dahurica to provide guidance for coordinating the development of medicinal plant science.

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          Maximum entropy modeling of species geographic distributions

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            A working guide to boosted regression trees.

            1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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              Novel methods improve prediction of species’ distributions from occurrence data

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                27 September 2021
                2021
                : 12
                : 706822
                Affiliations
                [1] 1Baotou Medical College, Inner Mongolia , Baotou, China
                [2] 2State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medical, China Academy of Chinese Medical Sciences , Beijing, China
                [3] 3State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS) , Beijing, China
                [4] 4College of Resources and Environment, University of Chinese Academy of Sciences , Beijing, China
                [5] 5Inner Mongolia Medical University , Hohhot, China
                [6] 6Inner Mongolia Hospital of Traditional Chinese Medicine , Hohhot, China
                [7] 7Inner Mongolia Key Laboratory of Characteristic Geoherbs Resources Protection and Utilization , Baotou, China
                Author notes

                Edited by: Ji Zhang, Yunnan Academy of Agricultural Sciences, China

                Reviewed by: Adrien Favre, Senckenberg Museum, Germany; Jinniu Wang, Chengdu Institute of Biology, Chinese Academy of Sciences (CAS), China

                *Correspondence: Fangyu Ding, dingfy@ 123456igsnrr.ac.cn

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2021.706822
                8503573
                35399196
                8d225f4f-4a15-4bbf-9b56-67c2a43bfb43
                Copyright © 2021 Zhang, Jiang, Yang, Ma, Ding, Hao, Chen, Zhang, Zhang and Li.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 May 2021
                : 06 September 2021
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 42, Pages: 11, Words: 7957
                Funding
                Funded by: Ministry of Agriculture and Rural Affairs of the People's Republic of China, doi 10.13039/501100011798;
                Funded by: Ministry of Finance, doi 10.13039/501100005045;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                gentiana dahurica fisch.,environment,species distribution model,boosted regression trees,medicinal plant

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