气候变化下西南地区12种常见针叶树种适宜分布区预测

陈禹衡; 吕一维; 殷晓洁

doi:10.3969/j.issn.1000-2006.201808045

陈禹衡, 吕一维, 殷晓洁

南京林业大学学报（自然科学版） ›› 2019, Vol. 43 ›› Issue (6) : 113-120.

PDF(3173 KB)

国家林草科技领军期刊
中国精品科技期刊
中国高校百佳科技期刊
江苏省新闻出版政府奖期刊奖
RCCSE林学权威期刊（A+）
CSCD核心期刊
Scopus数据库收录期刊
中文核心期刊
SCD核心期刊

作者加群：102861116

微信公众号：南京林业大学学报

高级检索

PDF(3173 KB)

南京林业大学学报（自然科学版） ›› 2019, Vol. 43 ›› Issue (6) : 113-120. DOI: 10.3969/j.issn.1000-2006.201808045

研究论文

气候变化下西南地区12种常见针叶树种适宜分布区预测

陈禹衡 ¹ ,
吕一维 ² ,
殷晓洁 ¹^,^*

作者信息 +

Predicting habitat suitability of 12 coniferous forest tree species in southwest China based on climate change

CHEN Yuheng ¹ ,
LÜ Yiwei ² ,
YIN Xiaojie ¹^,^*

Author information +

文章历史 +

摘要

【目的】随气候变化加剧,未来西南地区针叶林分布存在诸多不确定性,进行未来气候下西南地区常见针叶树种适宜分布区研究,为该地区森林生态安全评估提供参考。【方法】基于最大熵模型(MaxEnt)模拟未来气候情景下西南地区常见的12种针叶树的气候适宜分布区。【结果】MaxEnt模型能够很好地模拟西南地区12个树种的潜在分布,AUC值均达0.9以上; 2070年MPI-ESM-LR模式RCP4.5情景下西南地区12种常见针叶树种气候适宜区面积分布变化显著,包括冷杉、三尖杉、杉木、干香柏、柏木、水杉、云南松、红豆杉和福建柏在内的9个树种气候适宜区与气候最适区面积增加11.1%~412.8%;银杉和油麦吊云杉的气候适宜区面积分别增加6.0%和32.8%,但气候最适区面积减少了0.8%和3.5%;云南油杉未来气候适宜区和气候最适区的面积则减少24.0%和29.1%。【结论】西南地区针叶林中广布树种的气候适宜区面积会得益于气候变化而扩大,但云南油杉和油麦吊云杉等一些西南特有乡土树种的气候适宜区面积则会因为气候变化而缩小,因此对特有乡土树种的保护应给予重视。

Abstract

【Objective】Coniferous forests in southwest China are important biodiversity hotspots, but they are under significant threat from ongoing climatic changes. In this study we explored the predicted changes in habitat suitability for 12 key tree species.【Method】We used MaxEnt models to predict habitat suitability for 12 coniferous tree species that are currently present in southwest China. 【Result】The AUC values (receiver operating characteristics, area under curve) of all 12 species were more than 0.9, suggesting our results are highly supported. We found that the climatically suitable area of all species under representative concentration pathways scenario 4.5 in 2070 will increase greatly. For 9 of the 12 species (Abies fabri, Cephalotaxus fortunei, Cunninghamia lanceolata, Cupressus duclouxiana, Metasequoia glyptostroboides, Pinus yunnanensis, Fokienia hodginsii, Taxus wallichiana var. chinensis and Cupressus funebris), both the total climatically suitable area and the area of optimal climate will increase 11.1% and 412.8% than the current areal, respectively. ForPicea brachytyla and Cathaya argyrophylla, the total climatically suitable area will increase by 6.0% and 32.8%, respectively, but the area of optimal conditions will decrease ( P. brachytyla by -0.8%; C. argyrophylla by -3.5%). However, for Keteleeria evelyniana, both the total climatically suitable area and the area of optimal climate area will respectively decrease by 24.0% and 29.1%.【Conclusion】Overall, our models show that climate change is expected to increase the range of various widely distributed coniferous tree species. However, for specialized coniferous species like Picea brachytyla and Keteleeria evelyniana, there will be a range decrease as conditions change. We suggest that more attention be paid to specialized forest conifer species, particularly in relation to expected climatic changes.

导出引用

陈禹衡, 吕一维, 殷晓洁. 气候变化下西南地区12种常见针叶树种适宜分布区预测[J]. 南京林业大学学报（自然科学版）. 2019, 43(6): 113-120 https://doi.org/10.3969/j.issn.1000-2006.201808045

CHEN Yuheng, LÜ Yiwei, YIN Xiaojie. Predicting habitat suitability of 12 coniferous forest tree species in southwest China based on climate change[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2019, 43(6): 113-120 https://doi.org/10.3969/j.issn.1000-2006.201808045

中图分类号： Q948.5;S718

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]

冯建孟, 董晓东, 徐成东, 等. 取样尺度效应对滇西北地区种子植物物种多样性纬度分布格局的影响[J]. 生物多样性, 2009, 17(3):266-271. DOI: 10.3724/SP.J.1003.2009.08265.

https://doi.org/10.3724/SP.J.1003.2009.08265

10.3724/SP.J.1003.2009.08265

摘要

滇西北地区是全球25个生物多样性保护热点地区之一, 是验证生物多样性理论的理想场所。为探索取样尺度效应对植物物种多样性纬度分布格局的影响, 我们探讨了不同取样尺度下滇西北地区种子植物物种多样性的纬度分布格局及其影响因子。我们利用野外考察数据和文献资料建立了群落尺度下的源数据集和区域尺度(县域尺度)下的源数据集, 共建立、收集了68个植物群落样方和26个县域的种子植物物种丰富度; 采用二元相关性和多元逐步回归分析植物物种多样性纬度分布格局与气候、地理因子间的关系。结果表明, 从南到北, 物种多样性在群落尺度下呈单调递减格局, 在区域尺度下反而呈线性递增趋势; 在群落尺度下受到热量因子的显著影响, 在区域尺度下主要受单位面积海拔高差的影响。这一结果在一定程度上表明了取样的尺度效应对物种多样性纬度分布格局的显著影响。了解滇西北地区植物多样性的热点区域, 应该基于不同取样尺度下的分析, 以消除或减少植物多样性保护的盲点。在今后的相关研究中, 应关注不同的取样尺度下多样性的纬度分布格局可能的表现形式及其内在机制, 这或许可以减少或消除相关研究中的争议或不一致。

FENG J

, DONG X

, XU C

, et al. Effects of sampling scale on latitudinal patterns of species diversity in seed plants in northwestern Yunnan, China[J]. Biodiversity Science, 2009, 17(3):266-271.

https://doi.org/10.3724/SP.J.1003.2009.08265

http://pub.chinasciencejournal.com/article/getArticleRedirect.action?doiCode=10.3724/SP.J.1003.2009.08265

本文引用 [1]

[2]

杨庆媛. 西南丘陵山地区土地整理与区域生态安全研究[J]. 地理研究, 2003, 22(6):698-708. DOI: 10.3321/j.issn:1000-0585.2003.06.004.

10.3321/j.issn:1000-0585.2003.06.004

YANG Q

. A study on the issue of land consolidation and eco-security in hilly and mountainous regions of southwest China[J]. Geographical Research, 2003, 22(6):698-708.

本文引用 [1]

[3]	FINLAYSON M, CRUZ R D, DAVIDSON N, et al. Millennium ecosystem assessment: ecosystems and human well-being: wetlands and water synreport[R]. Washington DC: World Resources Institute, 2005. 本文引用 [1]

[4]	KERR R A. A stronger IPCC report[J]. Science, 2013, 342(6154):23. DOI: 10.1126/science.342.6154.23-b. 10.1126/science.342.6154.23-b 本文引用 [1]

[5]	Vié JEAN C, HILTON T C, STUART S N. Wildlife in a changing world : an analysis of the 2008 IUCN red list of threatened species[R]. Switzerland: International Union for Conservation of Nature, 2009. 本文引用 [1]

[6]	刘国华. 西南生态安全格局形成机制及演变机理[J]. 生态学报, 2016, 36(22):7088-7091.DOI: 10.5846/stxb201611212364. 10.5846/stxb201611212364 LIU G H. Formation and evolution mechanism of ecological security pattern in southwest China[J]. Acta Ecologica Sinica, 2016, 36(22):7088-7091. 本文引用 [1]

[7]

杨志香, 周广胜, 殷晓洁, 等. 中国兴安落叶松天然林地理分布及其气候适宜性[J]. 生态学杂志, 2014, 33(6):1429-1436. DOI: 10.13292/j.1000-4890.2014.0122.

10.13292/j.1000-4890.2014.0122

YANG Z

, ZHOU G

, YIN X

, et al. Geographic distribution of Larix gmelinii natural forest in China and its climatic suitability [J]. Chinese Journal of Ecology, 2014, 33(6):1429-1436.

本文引用 [1]

[8]	张艳武, 张莉, 徐影. CMIP5模式对中国地区气温模拟能力评估与预估[J]. 气候变化研究进展, 2016, 12(1):10-19. DOI: 10.12006/j.issn,1673-1719.2015.113. 10.12006/j.issn ZHANG Y W, ZHANG L, XU Y. Simulations and projections of the surface air temperature in China by CMIP5 models[J]. Climate Change Research, 2016, 12(1):10-19. 本文引用 [1]

[9]

初祁, 徐宗学, 刘文丰, 等. 24个CMIP5模式对长江流域模拟能力评估[J]. 长江流域资源与环境, 2015, 24(1):81-89. DOI: 10.11870/cjlyzyyhj201501011.

10.11870/cjlyzyyhj201501011

CHU

, XU Z

, LIU W

, et al. Assessment on 24 global climate models in the CMIP5 over the Yangtze River[J]. Resources and Environment in the Yangtze Basin, 2015, 24(1):81-89.

本文引用 [1]

[10]	PHILLIPS S J, DUDÍK M. Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2):161-175. DOI: 10.1111/j.0906-7590.2008.5203.x. https://doi.org/10.1111/j.0906-7590.2008.5203.x 10.1111/j.0906-7590.2008.5203.x 本文引用 [1]

[11]	PHILLIPS S J, ANDERSON R P, SCHAPIRE R E. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3/4):231-259. DOI: 10.1016/j.ecolmodel.2005.03.026. https://doi.org/10.1016/j.ecolmodel.2005.03.026 10.1016/j.ecolmodel.2005.03.026 本文引用 [3]

[12]

唐继洪, 程云霞, 罗礼智, 等. 基于MaxEnt模型的不同气候变化情景下我国草地螟越冬区预测[J]. 生态学报, 2017, 37(14):4852-4863.DOI: 10.5846/stxb201604040608.

10.5846/stxb201604040608

TANG J

, CHENG Y

, LUO L

, et al. MaxEnt-based prediction of overwintering areas of Loxostege sticticalis in China under different climate change scenarios [J]. Acta Ecologica Sinica, 2017, 37(14):4852-4863.

本文引用 [2]

[13]

殷晓洁, 周广胜, 隋兴华, 等. 辽东栎林潜在地理分布及其主导因子[J]. 林业科学, 2013, 49(8):10-14. DOI: 10.11707/j.1001-7488.20130802.

10.11707/j.1001-7488.20130802

YIN X

, ZHOU G

, SUI X

, et al. Potential geographical distribution of Quercus wutaishanica forest and its dominant factors [J]. Scientia Silvae Sinicae, 2013, 49(8):10-14.

本文引用 [1]

[14]

韩阳阳, 王焱, 项杨, 等. 基于MaxEnt生态位模型的松材线虫在中国的适生区预测分析[J]. 南京林业大学学报(自然科学版), 2015, 39(1):6-10.DOI: 10.3969/j.issn.1000-2006.2015.01.001.

10.3969/j.issn.1000-2006.2015.01.001

HAN Y

, WANG

, XIANG

, et al. Prediction of potential distribution of Bursaphelenchus xylophilus in China based on MaxEnt ecological niche model [J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2015, 39(1):6-10.

本文引用 [1]

[15]

王雷宏, 杨俊仙, 徐小牛. 基于MaxEnt分析金钱松适生的生物气候特征[J]. 林业科学, 2015, 51(1):127-131. DOI: 10.11707/j.1001-7488.20150115.

10.11707/j.1001-7488.20150115

WANG L

, YANG J

, XU X

. Analysis of suitable bioclimatic characteristics of Pseudolarix amabilis by using MaxEnt model [J]. Scientia Silvae Sinicae, 2015, 51(1):127-131.

本文引用 [1]

[16]

马松梅, 张明理, 张宏祥, 等. 利用最大熵模型和规则集遗传算法模型预测孑遗植物裸果木的潜在地理分布及格局[J]. 植物生态学报, 2010, 34(11):1327-1335. DOI: 10.3773/j.issn.1005-264x.2010.11.010.

https://doi.org/10.3773/j.issn.1005-264x.2010.11.010

10.3773/j.issn.1005-264x.2010.11.010

摘要

利用野外调查的16个居群分布点和7个环境因子图层, 选择最大熵模型(MAXENT)和规则集遗传算法模型(GARP),在地理和环境空间上模拟了第三纪孑遗植物裸果木(Gymnocarpos przewalskii)在中国西北地区的潜在分布。结果表明: (1)裸果木的潜在适生区全部集中在西北荒漠区, 其中最佳适生区主要集中在3个区域, 一是河西走廊中部和玉门以西、宁夏北部及内蒙古乌拉特后旗; 二是塔里木盆地西北缘; 三是柴达木盆地西北缘两片极小的高度适生区。裸果木的生态位被确定在一个较广的干旱环境空间: 适生区极端最高气温基本上在29.2–36.8 ℃之间, 极端最低气温在–18.3至–13.4 ℃之间;年平均降水量40–200 mm; 潜在蒸发率在3–15之间。(2) MAXENT和GARP模型都较好地预测了裸果木的潜在分布, 但GARP产生了相对较大、较连续的潜在分布区, 部分过预测了破碎化生境; 而MAXENT预测到的潜在分布区, 在不同区域具有不同的环境适生性指数, 而且成功地排除了不合理的破碎化分布, 从而更直观地展示了裸果木的潜在分布格局和生态位要求。

MA S

, ZHANG M

, ZHANG H

, et al. Predicting potential geographical distributions and patterns of the relic plant Gymnocarpos przewalskii using Maximum Entropy and genetic algorithm for rule-set prediction [J]. Chinese Journal of Plant Ecology, 2010, 34(11):1327-1335.

本文引用 [1]

[17]

王伟, 田荣荣, 那立妍, 等. 基于MaxEnt生态软件划分澳洲坚果的潜在地理适生区[J]. 林业科学研究, 2017, 30(3):444-449. DOI: 10.13275/j.cnki.lykxyj.2017.03.012.

10.13275/j.cnki.lykxyj.2017.03.012

WANG

, TIAN R

, NA L

, et al. Predicting potential geographic suitable regions of Macadamia integrifolia based on MaxEnt [J]. Forest Research, 2017, 30(3):444-449.

本文引用 [1]

[18]	DOWLING. Using MaxEnt modeling to predict habitat of mountain pine beetle in response to climate change[D]. Los Angeles: University of Southern California, 2015. 本文引用 [1]

[19]

王运生, 谢丙炎, 万方浩, 等. ROC曲线分析在评价入侵物种分布模型中的应用[J]. 生物多样性, 2007, 15(4):365-372. DOI: 10.3321/j.issn:1005-0094.2007.04.005.

10.3321/j.issn:1005-0094.2007.04.005

WANG Y

, XIE B

, WANG F

, et al. Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models[J]. Biodiversity Science, 2007, 15(4):365-372.

https://doi.org/10.1360/biodiv.060280

http://www.biodiversity-science.net/EN/abstract/abstract9455.shtml

本文引用 [1]

[20]

李灿, 刘贤安, 王娟, 等. 基于MaxEnt的四川省红豆杉潜在分布区分析及适宜性评价[J]. 四川林业科技, 2017, 38(5):1-7, 32. DOI: 10.16779/j.cnki.1003-5508.2017.05.001.

10.16779/j.cnki.1003-5508.2017.05.001

, LIU X

, WANG

, et al. Predictive distribution and habitat suitability assessment of Taxus chinensis based on MaxEnt in Sichuan Province [J]. Journal of Sichuan Forestry Science and Technology, 2017, 38(5):1-7, 32.

本文引用 [1]

[21]

GARCIA

, LASCO

, INES

, et al. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines[J]. Applied Geography, 2013, 44:12-22. DOI: 10.1016/j.apgeog.2013.07.005.

https://doi.org/10.1016/j.apgeog.2013.07.005

10.1016/j.apgeog.2013.07.005

本文引用 [1]

[22]	麻亚鸿. 基于最大熵模型(MaxEnt)和地理信息系统(ArcGis)预测藓类植物的地理分布范围——以广西花坪自然保护区为列[D]. 上海: 上海师范大学, 2013. MA Y H. Applying MaxEnt and ArcGIS to predict mosses geographic distribution range: a case study of Huaping Nature Reserve, Guangxi[D]. Shanghai: Shanghai Normal University, 2013. 本文引用 [1]

[23]

张东方, 张琴, 郭杰, 等. 基于MaxEnt模型的当归全球生态适宜区和生态特征研究[J]. 生态学报, 2017, 37(15):5111-5120. DOI: 10.5846/stxb201605030837

10.5846/stxb201605030837

ZHANG D

, ZHANG

, GUO

, et al. Research on the global ecological suitability and characteristics of regions with Angelica sinensis based on the MaxEnt model [J]. Acta Ecologica Sinica, 2017, 37(15):5111-5120.

本文引用 [2]

[24]

唐继洪, 程云霞, 罗礼智, 等. 基于Maxent模型的不同气候变化情景下我国草地螟越冬区预测[J]. 生态学报, 2017, 37(14):4852-4863. DOI: 10.5846/stxb201604040608.

10.5846/stxb201604040608

TANG J

, CHENG Y

, LUO L

, et al. MaxEnt-based prediction of overwintering areas of Loxostege sticticalis in China under different climate change scenarios [J]. Acta Ecologica Sinica, 2017, 37(14):4852-4863.

本文引用 [1]

[25]	BRADLEY E. The Jackknife, the Bootstrap and other resampling plans[J]. Siam Monograph, 1982, 38(384). DOI: 10.1137/1.9781611970319. 10.1137/1.9781611970319 本文引用 [1]

[26]

刘清亮, 李垚, 方升佐. 基于MaxEnt模型的青钱柳潜在适宜栽培区预测[J]. 南京林业大学学报(自然科学版), 2017, 41(4):25-29. DOI: 10.3969/j.issn.1000-2006.201608010.

10.3969/j.issn.1000-2006.201608010

LIU Q

, LI

, FANG S

. MaxEnt model-based identification of potential Cyclocarya paliurus cultivation regions [J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2017, 41(4):25-29.

本文引用 [1]

[27]

WILSON C

, ROBERTS

, REID

. Applying species distribution modelling to identify areas of high conservation value for endangered species: a case study using Margaritifera margaritifera (L.)[J]. Biological Conservation, 2011, 144(2):821-829. DOI: 10.1016/j.biocon.2010.11.014.

https://doi.org/10.1016/j.biocon.2010.11.014

10.1016/j.biocon.2010.11.014

本文引用 [1]

[28]	WEBER T C. Maximum entropy modeling of mature hardwood forest distribution in four US states[J]. Forest Ecology and Management, 2011, 261(3):779-788. DOI: 10.1016/j.foreco.2010.12.009. https://doi.org/10.1016/j.foreco.2010.12.009 10.1016/j.foreco.2010.12.009 本文引用 [1]

[29]	郭樑, 李莲芳, 孙昂, 等. 水分对云南松苗木生长的影响[J]. 种子, 2014, 33(6):64-68. DOI: 10.16590/j.cnki.1001-4705.2014.06.083. 10.16590/j.cnki.1001-4705.2014.06.083 GUO L, LI L F, SUN A, et al. Effect of the moisture on seedling growth of Pinus yunnanensis [J]. Seed, 2014, 33(6):64-68. 本文引用 [1]

[30]	KAESLIN E, REDMOND I, DUDLEY N. Wildlife in a changing climate[R]. Washington DC: Food and Agriculture Organization of the United Nations, 2012. 本文引用 [1]