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      Identifying climate refugia and its potential impact on Tibetan brown bear ( Ursus arctos pruinosus) in Sanjiangyuan National Park, China

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          Abstract

          Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear ( Ursus arctos pruinosus). We used the maximum entropy (MaxEnt) model to assess the current (1950–2000) and future (2041–2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China. Next, we selected Circuit model to simulate potential migration paths based on current and future climatically suitable habitat. Results indicate a total area of potential suitable habitat under the current climate scenario of approximately 31,649.46 km 2, of which 28,778.29 km 2 would be unsuitable by the 2050s. Potentially suitable habitat under the future climate scenario was projected to cover an area of 23,738.6 km 2. Climate refugia occupied 2,871.17 km 2, primarily in the midwestern and northeastern regions of Yangtze River Zone, as well as the northern region of Yellow River Zone. The altitude of climate refugia ranged from 4,307 to 5,524 m, with 52.93% lying at altitudes between 4,300 and 4,600 m. Refugia were mainly distributed on bare rock, alpine steppe, and alpine meadow. Corridors linking areas of potentially suitable brown bear habitat and a substantial portion of paths with low‐resistance value were distributed in climate refugia. We recommend various actions to ameliorate the impact of climate change on brown bears, such as protecting climatically suitable habitat, establishing habitat corridors, restructuring conservation areas, and strengthening monitoring efforts.

          Abstract

          Determining regions of refugia and climate connectivity enable the identification of the most effective areas to maintain brown bear populations and enhance connectivity against the background of climate change projection in this century.

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          Most cited references53

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          Predicting species distributions for conservation decisions

          Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.
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            Global response of terrestrial ecosystem structure and function to CO2and climate change: results from six dynamic global vegetation models

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              Circuit theory predicts gene flow in plant and animal populations.

              Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.
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                Author and article information

                Contributors
                xueyadong334@163.com
                lidiqiang_caf@163.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                14 November 2019
                December 2019
                : 9
                : 23 ( doiID: 10.1002/ece3.v9.23 )
                : 13278-13293
                Affiliations
                [ 1 ] Research Institute of Forest Ecology, Environment and Protection Chinese Academy of Forestry Beijing China
                [ 2 ] Key Laboratory of Biodiversity Conservation State Forestry and Grassland Administration Beijing China
                [ 3 ] Department of Biological Sciences Duquesne University Pittsburgh PA USA
                [ 4 ] Key Laboratory for Biodiversity Science and Ecological Engineering Ministry of Education College of Life Sciences Beijing Normal University Beijing China
                [ 5 ] Qilian Mountain National Park Qinghai Administration Xining China
                [ 6 ] Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing China
                [ 7 ] Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau Biology Chinese Academy of Sciences Xining China
                Author notes
                [*] [* ] Correspondence

                Diqiang Li and Yadong Xue, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China.

                Email: lidiqiang_caf@ 123456163.com (DL); xueyadong334@ 123456163.com (YX)

                Author information
                https://orcid.org/0000-0002-3225-6698
                https://orcid.org/0000-0003-3184-3070
                https://orcid.org/0000-0002-6878-6703
                Article
                ECE35780
                10.1002/ece3.5780
                6912912
                31871644
                9c52b1dd-02be-406f-8b5f-d6dbc8d6b5e6
                © 2019 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
                : 18 June 2019
                : 01 October 2019
                : 02 October 2019
                Page count
                Figures: 7, Tables: 2, Pages: 15, Words: 9405
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2017YFC0506405
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                December 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:16.12.2019

                Evolutionary Biology
                circuit model,climate refugia,corridor,habitat connectivity,ursus arctos pruinosus

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