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      The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

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          Abstract

          Many kinds of medicinal ingredients occur in Cirsium lineare that have good clinical efficacy, conferring on this species its high medicinal development value. However, with a rapidly changing global climate, it is increasingly imperative to study the factors affecting the habitat distribution and survival of species. We predicted the current and future distribution areas of suitable habitats for C. lineare, analyzed the importance of environmental variables in influencing habitat shifts, and described the alterations to suitable habitats of C. lineare in different periods (modern, 2050s, and 2070s) and scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show that, under the current climate, the total suitable area of C. lineare is about 2,220,900 km 2, of which the highly suitable portion amounts to ca. 292,600 km 2. The minimum temperature of the coldest month, annual precipitation, and mean daily temperature range are the chief environmental variables affecting the distribution of habitat for C. lineare. In the same period, with rising greenhouse gas emission concentrations, the total suitable area will increase. In general, under future climate change, the suitable habitat for C. lineare will gradually migrate to the west and north, and its total suitable area will also expand. The results of this experiment can be used for the conservation and management of the wild resources of C. lineare. We can choose suitable growth areas to protect the medicinal resources of C. lineare through in situ conservation and artificial breeding.

          Abstract

          We predicted the distribution of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change by optimizing the Maxent model. The experimental results show that under future climate change, the suitable habitat for C. lineare will gradually migrate to the west and north, and the total suitable area will also expand.

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          A statistical explanation of MaxEnt for ecologists

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            The next generation of scenarios for climate change research and assessment.

            Advances in the science and observation of climate change are providing a clearer understanding of the inherent variability of Earth's climate system and its likely response to human and natural influences. The implications of climate change for the environment and society will depend not only on the response of the Earth system to changes in radiative forcings, but also on how humankind responds through changes in technology, economies, lifestyle and policy. Extensive uncertainties exist in future forcings of and responses to climate change, necessitating the use of scenarios of the future to explore the potential consequences of different response options. To date, such scenarios have not adequately examined crucial possibilities, such as climate change mitigation and adaptation, and have relied on research processes that slowed the exchange of information among physical, biological and social scientists. Here we describe a new process for creating plausible scenarios to investigate some of the most challenging and important questions about climate change confronting the global community.
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              Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

              MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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                Author and article information

                Contributors
                yqssyx2008@163.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                23 January 2024
                January 2024
                : 14
                : 1 ( doiID: 10.1002/ece3.v14.1 )
                : e10848
                Affiliations
                [ 1 ] College of Pharmacy Anhui University of Chinese Medicine Hefei China
                [ 2 ] Dexing Research and Training Center of Chinese Medical Sciences Dexing China
                [ 3 ] The Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine Hefei China
                Author notes
                [*] [* ] Correspondence

                Qing‐Shan Yang, College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China.

                Email: yqssyx2008@ 123456163.com

                Author information
                https://orcid.org/0009-0006-5962-9294
                Article
                ECE310848 ECE-2023-05-00863.R2
                10.1002/ece3.10848
                10805606
                38264336
                5f7841e9-71ea-42ff-af1a-9d6481c9ad7c
                © 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 October 2023
                : 27 May 2023
                : 22 December 2023
                Page count
                Figures: 10, Tables: 1, Pages: 13, Words: 8178
                Funding
                Funded by: Major increase or decrease in expenditure projects at the central level of China
                Award ID: 2060302
                Funded by: Special subsidies for public health services of traditional Chinese medicine
                Award ID: Finance Agency [2017] Number 66
                Categories
                Botany
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.6 mode:remove_FC converted:23.01.2024

                Evolutionary Biology
                cirsium lineare (thunb.) sch.‐bip,climate change,environment variables,habitat distribution

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