Page 30 - 《广西植物》2023年第3期
P. 30

4 3 0                                  广  西  植  物                                         43 卷
              ( 1. College of Life Sciencesꎬ Guangxi Normal Universityꎬ Guilin 541006ꎬ Guangxiꎬ Chinaꎻ 2. Guangxi Key Laboratory of Plant Conservation
                 and Restoration Ecology in Karst Terrainꎬ Guangxi Institute of Botanyꎬ Guangxi Zhuang Autonomous Region and Chinese Academy of
               Sciencesꎬ Guilin 541006ꎬ Guangxiꎬ Chinaꎻ 3. Nonggang Karst Ecosystem Observation and Research Station of Guangxiꎬ Chongzuo 532499ꎬ
                Guangxiꎬ Chinaꎻ 4. College of Tourism and Landscape Architectureꎬ Guilin University of Technologyꎬ Guilin 541006ꎬ Guangxiꎬ China )

                 Abstract: Excentrodendron tonkinense is a constructive species of karst seasonal rainforest and a karst obligate plantꎬ
                 which is also one of the national secondary key protected wild plant and an IUCN endangered plantꎬ with high ecological
                 economic value. In order to explore how the potential suitable areas of E. tonkinense change in the context of global
                 change and its key driving factorsꎬ we used the maximum ̄entropy (MaxEnt) model to analyze the potential geographical
                 distribution changes in China under future climate scenarios (SSP1 ̄2.6 and SSP5 ̄8.5)ꎬ and tested the influence of the
                 karst geological background distribution on predicting the suitable areas of karst obligate plants. The results were as
                 follows: (1) In the case of adding karst geological background dataꎬ the average AUC value of the prediction model for
                 the suitable area was 0.997ꎬ which had a good prediction effect. And the model prediction results were strictly limited to
                 the karst regionꎬ consistent with the characteristics of karst obligate plant E. tonkinense. (2) According to the fitting
                 results of the modelꎬ the karst geological backgroundꎬ the precipitation of the warmest quarter (800-950 mm)ꎬ and the
                 minimum temperature of the coldest month (7-11 ℃) were the key factors limiting the distribution of E. tonkinense. (3)
                 With the increase of temperature in the futureꎬ the potential suitable areas for E. tonkinense would continue to expand in
                 higher latitude karst areasꎻ large areas of stable habitats existed in parts of southwestern Guangxi and southeastern
                 Yunnan. In conclusionꎬ the karst geological distribution is essential as predicting the potential geographic distribution of
                 karst obligate plants such as E. tonkinenseꎻ if the temperature continues to rise in the futureꎬ its potential suitable areas
                 will expand to high latitudesꎬ and the degree of endangerment may be affected by climateꎬ which means that it is not
                 obvious under the influence of climate changeꎻ parts of Southwest Guangxi and Southeast Yunnan are suitable areas for
                 the conservation and utilization of E. tonkinense under the climate change scenarios in the future. The results provide
                 some scientific reference for the introductionꎬ cultivationꎬ sustainable managementꎬ protection and utilization of E.
                 tonkinense.
                 Key words: MaxEnt modelꎬ Excentrodendron tonkinenseꎬ karst obligate plantsꎬ climate changeꎬ suitable area




                物种的分布受到多种环境因子的影响ꎬ气候                                物种 分 布 模 型 ( species distribution modelsꎬ
            是其中 最 重 要 的 非 生 物 因 子 ( O’ Connor et al.ꎬ          SDMs) 是结合物种的分布点数据和 相 关 环 境 变
            2019)ꎮ 联 合 国 政 府 间 气 候 变 化 专 门 委 员 会               量ꎬ依据特定的算法推测出该物种的基础生态位

            (Intergovernmental Panel on Climate Changeꎬ IPCC)  (李国庆等ꎬ2013)ꎮ 由于 SDMs 模型无需野外实
            正式发布的第六次气候变化评估报告( AR6) 表                           验数据即可快速预测气候变化下物种的分布范
            明ꎬ自 1850—1900 年以来ꎬ全球地表平均温度已                        围ꎬ因此在濒危物种保护中得到广泛应用( Guisan
            上升约 1 ℃ ꎬ未来 20 年的平均温度升高预计达到                        et al.ꎬ 2005ꎻTariq et al.ꎬ 2021)ꎮ 一系列的方法被
            或超过 1.5 ℃ ( 周天军等ꎬ2021)ꎮ 气候变化会影                     提出来用于构建 SDMs 模型ꎬ如广义线性模型、广
            响植物的维持和分布ꎬ尤其对一些濒危或分布范                              义加性模型、随机森林模型、生物气候模型、最大
            围狭窄的植物而言ꎬ全球变暖等气候变化过程会                              熵模型等ꎬ此类模型不仅能预测物种当前的潜在

            使其原生境无法提供稳定的生存条件( Bennett et                       分布区ꎬ还能预测物种在未来气候变化下的潜在
            al.ꎬ 2019)ꎬ进而导致物种改变其地理分布范围以                        分布格局( Guisan et al.ꎬ 2002ꎻ 李国庆等ꎬ2013)ꎮ
            适应新的环境ꎮ 如果无法适应新的环境ꎬ大量的                             在 诸 多 物 种 分 布 模 型 中ꎬ 最 大 熵 ( maximum ̄
            物种就可能会濒临灭绝( Urbanꎬ 2015)ꎮ 因此ꎬ预                     entropyꎬ MaxEnt)模型因其以很小的样本量即可获
            测未来不同气候情景下物种的潜在生境变化及可                              得很高精确度的预测结果且不受分布信息缺失的
            能的灭绝风险ꎬ可以为濒危物种的引种回归、保护                             约束而成为濒危物种潜在适生区预测的有力工具
            措施的制定和可持续利用提供理论依据ꎮ                                 (应凌霄等ꎬ2016)ꎮ
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