草业学报 ›› 2024, Vol. 33 ›› Issue (10): 14-27.DOI: 10.11686/cyxb2023435
王鹏1(), 金正1, 余婷1, 秦康强1, 桑新亚1, 陶建平1,2, 罗唯学1,2()
收稿日期:
2023-11-16
修回日期:
2024-01-31
出版日期:
2024-10-20
发布日期:
2024-07-15
通讯作者:
罗唯学
作者简介:
Corresponding author. E-mail: luowx0305@swu.edu.cn基金资助:
Peng WANG1(), Zheng JIN1, Ting YU1, Kang-qiang QIN1, Xin-ya SANG1, Jian-ping TAO1,2, Wei-xue LUO1,2()
Received:
2023-11-16
Revised:
2024-01-31
Online:
2024-10-20
Published:
2024-07-15
Contact:
Wei-xue LUO
摘要:
姜黄属植物是我国重要的药用草本植物。然而,目前对其在中国的潜在分布规律仍然不清楚。为了研究姜黄属植物在中国当前的潜在空间分布特征及其对未来气候变化的响应模式,选取莪术、姜黄、郁金3个最具代表性的姜黄属植物作为研究对象,结合这3种姜黄属植物的现有分布记录和相关的气候、地形、土壤等环境因子,应用最大熵(maximum entropy,MaxEnt)模型和地理信息系统方法,预测上述3种姜黄属植物在当前和未来气候情景下的潜在适生区。研究结果表明,利用MaxEnt模型对3种姜黄属植物预测结果的受试者工作特征曲线下的面积(AUC)均大于0.95,预测结果具有较高的精度;3种姜黄属植物目前主要分布在云南、广西、广东、福建、台湾、海南等省份,其中姜黄的适生区面积最大,郁金的适生区面积最小;在未来2090s SSP245气候情景下,3种姜黄属植物均表现为向我国东北方向扩张的趋势,适生面积达到最大;在未来2090s SSP370和SSP585气候情景下,3种姜黄属植物仅有小部分地区发生适生区的收缩和扩张;此外,最干季度均温是莪术潜在分布的关键因子,姜黄和郁金受年平均降水量影响最大。综上,3种姜黄属植物主要分布在我国南部,并有向北扩张的趋势,相关保护部门可基于本研究开展姜黄属植物的种植和扩繁工作,有助于提升姜黄属植物的经济和生态效益。
王鹏, 金正, 余婷, 秦康强, 桑新亚, 陶建平, 罗唯学. 预测姜黄属植物在中国当前和未来气候情景下的潜在分布区变化[J]. 草业学报, 2024, 33(10): 14-27.
Peng WANG, Zheng JIN, Ting YU, Kang-qiang QIN, Xin-ya SANG, Jian-ping TAO, Wei-xue LUO. Prediction of the potential distribution of Curcuma in China under current and future climate scenarios[J]. Acta Prataculturae Sinica, 2024, 33(10): 14-27.
图1 姜黄属3种植物在中国的样本分布点基于自然资源部标准地图服务网站GS (2020) 4619号标准地图制作,底图边界无修改。Based on the standard map service website GS (2020) 4619 of the Ministry of Natural Resources, the boundary of the base map is not modified.
Fig. 1 Distribution sites of three species of Curcuma in China
代号 Code | 中文名称 Name | 单位 Unit | 代号 Code | 中文名称 Name | 单位 Unit |
---|---|---|---|---|---|
bio1 | 年平均气温值 Annual mean temperature | 10-1 ℃ | bio15 | 降水季节性 Precipitation seasonality | mm |
bio2 | 昼夜温差月均值 Mean diurnal range | 10-1 ℃ | bio16 | 最湿季度降水量 Precipitation of wettest quarter | mm |
bio3 | 等温性Isothermality [(bio2/bio7)×100] | — | bio17 | 最干季度降水量 Precipitation of driest quarter | mm |
bio4 | 季节性温度 Temperature seasonality | — | bio18 | 最暖季度降水量 Precipitation of warmest quarter | mm |
bio5 | 最暖月最高温 Max temperature of warmest month | 10-1 ℃ | bio19 | 最冷季度降水量 Precipitation of coldest quarter | mm |
bio6 | 最冷月最低温 Min temperature of coldest month | 10-1 ℃ | topo_aspect | 坡向 Aspect | — |
bio7 | 温度年较差 Temperature annual range | 10-1 ℃ | topo_elev | 海拔 Elevation | m |
bio8 | 最湿季度均温 Mean temperature of wettest quarter | 10-1 ℃ | topo_slope | 坡度 Slope | — |
bio9 | 最干季度均温 Mean temperature of driest quarter | 10-1 ℃ | soil_BD | 土壤容重 Soil bulk density | kg·dm-3 |
bio10 | 最暖季度均温 Mean temperature of warmest quarter | 10-1 ℃ | soil_sand | 土壤沙粒含量 Soil sand content | % |
bio11 | 最冷季度均温 Mean temperature of coldest quarter | 10-1 ℃ | soil_TN | 土壤全氮 Soil total nitrogen | g·kg-1 |
bio12 | 年平均降水量 Annual precipitation | mm | soil_pH | 土壤pH Soil pH | — |
bio13 | 最湿月降水量 Precipitation of wettest month | mm | soil_ocd | 土壤有机碳密度 Soil organic carbon density | kg·m-3 |
bio14 | 最干月降水量 Precipitation of driest month | mm | soil_soc | 土壤有机碳含量 Soil organic carbon content | g·kg-1 |
表1 环境因子
Table 1 Environmental factors
代号 Code | 中文名称 Name | 单位 Unit | 代号 Code | 中文名称 Name | 单位 Unit |
---|---|---|---|---|---|
bio1 | 年平均气温值 Annual mean temperature | 10-1 ℃ | bio15 | 降水季节性 Precipitation seasonality | mm |
bio2 | 昼夜温差月均值 Mean diurnal range | 10-1 ℃ | bio16 | 最湿季度降水量 Precipitation of wettest quarter | mm |
bio3 | 等温性Isothermality [(bio2/bio7)×100] | — | bio17 | 最干季度降水量 Precipitation of driest quarter | mm |
bio4 | 季节性温度 Temperature seasonality | — | bio18 | 最暖季度降水量 Precipitation of warmest quarter | mm |
bio5 | 最暖月最高温 Max temperature of warmest month | 10-1 ℃ | bio19 | 最冷季度降水量 Precipitation of coldest quarter | mm |
bio6 | 最冷月最低温 Min temperature of coldest month | 10-1 ℃ | topo_aspect | 坡向 Aspect | — |
bio7 | 温度年较差 Temperature annual range | 10-1 ℃ | topo_elev | 海拔 Elevation | m |
bio8 | 最湿季度均温 Mean temperature of wettest quarter | 10-1 ℃ | topo_slope | 坡度 Slope | — |
bio9 | 最干季度均温 Mean temperature of driest quarter | 10-1 ℃ | soil_BD | 土壤容重 Soil bulk density | kg·dm-3 |
bio10 | 最暖季度均温 Mean temperature of warmest quarter | 10-1 ℃ | soil_sand | 土壤沙粒含量 Soil sand content | % |
bio11 | 最冷季度均温 Mean temperature of coldest quarter | 10-1 ℃ | soil_TN | 土壤全氮 Soil total nitrogen | g·kg-1 |
bio12 | 年平均降水量 Annual precipitation | mm | soil_pH | 土壤pH Soil pH | — |
bio13 | 最湿月降水量 Precipitation of wettest month | mm | soil_ocd | 土壤有机碳密度 Soil organic carbon density | kg·m-3 |
bio14 | 最干月降水量 Precipitation of driest month | mm | soil_soc | 土壤有机碳含量 Soil organic carbon content | g·kg-1 |
物种Species | 保留的环境因子Retained environmental factors |
---|---|
莪术C. phaeocaulis | bio3、bio4、bio9、bio12、bio15、soil_BD、soil_sand、soil_TN、topo_aspect、topo_slope |
姜黄C. longa | bio3、bio6、bio8、bio12、bio15、soil_BD、soil_pH、soil_sand、topo_aspect、topo_slope |
郁金C. aromatica | bio3、bio9、bio12、bio15、soil_pH、soil_TN、topo_aspect、topo_elev、topo_slope |
表2 3种姜黄属植物环境因子的筛选结果
Table 2 Environmental factors screening results of three Curcuma species
物种Species | 保留的环境因子Retained environmental factors |
---|---|
莪术C. phaeocaulis | bio3、bio4、bio9、bio12、bio15、soil_BD、soil_sand、soil_TN、topo_aspect、topo_slope |
姜黄C. longa | bio3、bio6、bio8、bio12、bio15、soil_BD、soil_pH、soil_sand、topo_aspect、topo_slope |
郁金C. aromatica | bio3、bio9、bio12、bio15、soil_pH、soil_TN、topo_aspect、topo_elev、topo_slope |
图5 当前气候下3种姜黄属植物在中国的适生区基于自然资源部标准地图服务网站 GS (2020) 4619 号标准地图制作,底图边界无修改。Based on the standard map service website GS (2020) 4619 of the Ministry of Natural Resources, the boundary of the base map is not modified.
Fig.5 Suitable habitat areas of three Curcuma species in China under the current climate
物种 Species | 总适生区 Total suitable habitat | 非适生区 Unsuitable habitat | 低适生区 Lowly suitable habitat | 中适生区 Moderately suitable habitat | 高适生区 Highly suitable habitat |
---|---|---|---|---|---|
莪术C. phaeocaulis | 100.89 | 858.94 | 30.54 | 23.46 | 46.89 |
姜黄C. longa | 155.31 | 804.52 | 67.67 | 30.76 | 56.88 |
郁金C. aromatica | 98.95 | 860.88 | 30.96 | 22.91 | 45.08 |
表3 当前气候下3种姜黄属植物在中国的适生区面积
Table 3 Suitable habitat areas of three Curcuma species in China under current climate (×104 km2)
物种 Species | 总适生区 Total suitable habitat | 非适生区 Unsuitable habitat | 低适生区 Lowly suitable habitat | 中适生区 Moderately suitable habitat | 高适生区 Highly suitable habitat |
---|---|---|---|---|---|
莪术C. phaeocaulis | 100.89 | 858.94 | 30.54 | 23.46 | 46.89 |
姜黄C. longa | 155.31 | 804.52 | 67.67 | 30.76 | 56.88 |
郁金C. aromatica | 98.95 | 860.88 | 30.96 | 22.91 | 45.08 |
图6 2090s时期不同气候情景下3种姜黄属植物在中国的适生区基于自然资源部标准地图服务网站 GS(2020)4619 号标准地图制作,底图边界无修改。Based on the standard map service website GS(2020)4619 of the Ministry of Natural Resources, the boundary of the base map is not modified.
Fig.6 Suitable habitat areas of three Curcuma species in China under different climatic scenarios in the 2090s
图7 2090s时期不同气候情景下3种姜黄属植物在中国的适生区空间格局变化基于自然资源部标准地图服务网站 GS(2020)4619 号标准地图制作,底图边界无修改。Based on the standard map service website GS(2020)4619 of the Ministry of Natural Resources, the boundary of the base map is not modified.
Fig.7 Suitable habitat spatial pattern changes of three Curcuma species under different climate scenarios in China in the 2090s
1 | Ge Y W, Gao H M, Wang Z M. Advances in study of genus Curcuma. China Journal of Chinese Materia Medica, 2007, 32(23): 2461-2467. |
葛跃伟, 高慧敏, 王智民. 姜黄属药用植物研究进展. 中国中药杂志, 2007, 32(23): 2461-2467. | |
2 | Xie Z W. National herbal medicine assembly. Beijing: Peoples Medical Publishing House, 1996. |
谢宗万. 全国中草药汇编(上册). 北京: 人民卫生出版社, 1996. | |
3 | Commission Chinese Pharmacopoeia. Pharmacopoeia of the People’s Republic of China. Beijing: Peoples Medical Publishing House, 1988. |
国家药典委员会. 中华人民共和国药典. 北京: 人民卫生出版社, 1988. | |
4 | Lestari M L, Indrayanto G. Curcumin. Profiles Drug Subst Excip Relat Methodol, 2014, 39: 113-204. |
5 | Liu J, Qiao M, Peng C, et al. Curcumanes E and F, two rare sesquiterpenoids with a dicyclo [3.3.1] nonane moiety, from Curcuma longa and their vasorelaxant activities. Frontiers in Chemistry, 2022, 10: 995950. |
6 | Abbas W, Khan R A, Baig M T, et al. Role of Curcuma longa in type 2 diabetes and its associated complications. Journal of Pharmaceutical Research International, 2021, 33(42B): 369-376. |
7 | Sirisidthi K, Kosai P, Jiraungkoorskul K, et al. Antithrombotic activity of turmeric (Curcuma longa): A review. Indian Journal of Agricultural Research, 2016, 50(2): 101-106. |
8 | Rukkumani R, Balasubashini M S, Vishwanathan P, et al. Comparative effects of curcumin and photo-irradiated curcumin on alcohol-and polyunsaturated fatty acid-induced hyperlipidemia. Pharmacological Research, 2002, 46(3): 257-264. |
9 | Costantino M, Corno C, Colombo D, et al. Curcumin and related compounds in cancer cells: New avenues for old molecules. Frontiers in Pharmacology, 2022, 13: 889816. |
10 | Pintatum A, Maneerat W, Logie E, et al. In vitro anti-inflammatory, anti-oxidant, and cytotoxic activities of four Curcuma species and the isolation of compounds from Curcuma aromatica rhizome. Biomolecules, 2020, 10(5): 799. |
11 | Zhu G P, Liu G Q, Bu W J, et al. Ecological niche modeling and its applications in biodiversity conservation. Biodiversity Science, 2013, 21(1): 90-98. |
朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用. 生物多样性, 2013, 21(1): 90-98. | |
12 | Sahlean T C, Gherghel I, Papeş M, et al. Refining climate change projections for organisms with low dispersal abilities: A case study of the Caspian whip snake. PLoS One, 2014, 9(3): e91994. |
13 | Rupprecht F, Oldeland J, Finckh M. Modelling potential distribution of the threatened tree species Juniperus oxycedrus: How to evaluate the predictions of different modelling approaches? Journal of Vegetation Science, 2011, 22(4): 647-659. |
14 | Booth T H. Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important. Austral Ecology, 2018, 43(8): 852-860. |
15 | Saqib Z, Malik R N, Husain S Z. Modelling potential distribution of Taxus wallichiana in Palas Valley, Pakistan. Pakistan Journal of Botany, 2006, 38(3): 539-542. |
16 | Bao R, Li X, Zheng J. Feature tuning improves MaxEnt predictions of the potential distribution of Pedicularis longiflora Rudolph and its variant. PeerJ, 2022, 10: e13337. |
17 | Li X, Zhang C F, He S, et al. Research progress analysis on the comprehensive application of MaxEnt model. Journal of Green Science and Technology, 2020(14): 14-17. |
李响, 张成福, 贺帅, 等. MaxEnt模型综合应用研究进展分析. 绿色科技, 2020(14): 14-17. | |
18 | Ma M X, Zhang H, Gao J X, et al. Different methods comparison of delineating the ecological protection red line for biodiversity conservation. Acta Ecologica Sinica, 2019, 39(19): 6959-6965. |
马孟枭, 张慧, 高吉喜, 等. 生物多样性维护生态保护红线划定方法对比. 生态学报, 2019, 39(19): 6959-6965. | |
19 | Huang Y, Zeng Y, Jiang P, et al. Prediction of potential geographic distribution of endangered relict tree species Dipteronia sinensis in China based on MaxEnt and GIS. Polish Journal of Environmental Studies, 2022, 31(4): 3597-3609. |
20 | Li Y, Li M, Li C, et al. Optimized MaxEnt model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China. Forests, 2020, 11(3): 302. |
21 | Singh M, Rajasekaran A, Kumar L. Modeling potential hotspots of invasive Prosopis juliflora (Swartz) DC. in India. Ecological Informatics, 2021, 64: 101386. |
22 | Guo Y, Li X, Zhao Z, et al. Predicting the impacts of climate change, soils and vegetation types on the geographic distribution of Polyporus umbellatus in China. Science of the Total Environment, 2019, 648: 1-11. |
23 | Xin X G, Wu T W, Zhang J, et al. Introduction of BCC models and its participation in CMIP6. Climate Change Research, 2019, 15(5): 533-539. |
辛晓歌, 吴统文, 张洁, 等. BCC模式及其开展的CMIP6试验介绍. 气候变化研究进展, 2019, 15(5): 533-539. | |
24 | Wu T, Lu Y, Fang Y, et al. The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 2019, 12(4): 1573-1600. |
25 | Zhang L X, Chen X L, Xin X G. Short commentary on CMIP6 scenario model intercomparison project (ScenarioMIP). Climate Change Research, 2019, 15(5): 519-525. |
张丽霞, 陈晓龙, 辛晓歌. CMIP6情景模式比较计划(ScenarioMIP)概况与评述. 气候变化研究进展, 2019, 15(5): 519-525. | |
26 | Jiang T, Lyu Y R, Huang J L, et al. New scenarios of CMIP6 model (SSP-RCP) and its application in the Huaihe River Basin. Advances in Meteorological Science and Technology, 2020, 10(5): 102-109. |
姜彤, 吕嫣冉, 黄金龙, 等. CMIP6模式新情景(SSP-RCP)概述及其在淮河流域的应用. 气象科技进展, 2020, 10(5): 102-109. | |
27 | Zhang L, Wei Y Q, Wang J N, et al. The potential geographical distribution of Lycium ruthenicum Murr under different climate change scenarios. Chinese Journal of Applied and Environmental Biology, 2020, 26(4): 969-978. |
张亮, 魏彦强, 王金牛, 等. 气候变化情景下黑果枸杞的潜在地理分布. 应用与环境生物学报, 2020, 26(4): 969-978. | |
28 | Wang Y J, Gao T, Shi J. Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt. Journal of Beijing Forestry University, 2021, 43(9): 59-69. |
王艳君, 高泰, 石娟. 基于MaxEnt模型对舞毒蛾全球适生区的预测及分析. 北京林业大学学报, 2021, 43(9): 59-69. | |
29 | Fielding A H, Bell J F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 1997, 24(1): 38-49. |
30 | Swets J A. Measuring the accuracy of diagnostic systems. Science, 1988, 240(4857): 1285-1293. |
31 | Hanley J A, Mcneil B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982, 143(1): 29-36. |
32 | Pearson R G, Raxworthy C J, Nakamura M, et al. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar: Predicting species distributions with low sample sizes. Journal of Biogeography, 2007, 34(1): 102-117. |
33 | Liu Y, Shi J. Predicting the potential global geographical distribution of two Icerya species under climate change. Forests, 2020, 11(6): 684. |
34 | Zhang Y B, Liu Y L, Qin H, et al. Prediction on spatial migration of suitable distribution of Elaeagnus mollis under climate change conditions in Shanxi Province, China. Chinese Journal of Applied Ecology, 2019, 30(2): 496-502. |
张殷波, 刘彦岚, 秦浩, 等. 气候变化条件下山西翅果油树适宜分布区的空间迁移预测. 应用生态学报, 2019, 30(2): 496-502. | |
35 | Shi C Y, Lai W F, Wen G W, et al. Prediction of the potentially suitable area of Fraxinus mandshurica based on MaxEnt model. Journal of Northwest Forestry University, 2022, 37(2): 149-156. |
施晨阳, 赖文峰, 文国卫, 等. 基于Maxent模型预测水曲柳的潜在适生区. 西北林学院学报, 2022, 37(2): 149-156. | |
36 | Zhao G, Cui X, Sun J, et al. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models. Ecological Indicators, 2021, 132: 108256. |
37 | Erofeeva E A. Plant hormesis and Shelford’s tolerance law curve. Journal of Forestry Research, 2021, 32(5): 1789-1802. |
38 | Kandiannan K, Chandaragiri K K, Anandaraj M. Models to elucidate crop-weather association in turmeric (Curcuma longa L.). Italian Journal of Agrometeorology-Rivista Italiana di Agrometeorologia, 2015, 20(2): 49-58. |
39 | Gao Y, Giese M, Brueck H, et al. The relation of biomass production with leaf traits varied under different land-use and precipitation conditions in an Inner Mongolia steppe. Ecological Research, 2013, 28(6): 1029-1043. |
40 | Zhang J, Han X. N2O emission from the semi-arid ecosystem under mineral fertilizer (urea and superphosphate) and increased precipitation in Northern China. Atmospheric Environment, 2008, 42(2): 291-302. |
41 | Cui Y P, Liu J Y, Hu Y F, et al. Estimating and analyzing the optimum temperature for vegetation growth in China. Journal of Natural Resources, 2012, 27(2): 281-292. |
崔耀平, 刘纪远, 胡云锋, 等. 中国植被生长的最适温度估算与分析. 自然资源学报, 2012, 27(2): 281-292. | |
42 | Yu J, Cao G C, Rong Z L, et al. Prediction of potential distribution of Ophiocordyceps sinensis in China based on MaxEnt model. Ecological Science, 2023, 42(2): 202-210. |
喻洁, 曹广超, 戎战磊, 等. 基于MaxEnt模型的冬虫夏草中国潜在适生区预测. 生态科学, 2023, 42(2): 202-210. | |
43 | Li S, Mo S H, Hu X H, et al. Prediction of potential suitable areas of endangered plant Abies ziyuanensis based on MaxEnt and ArcGlS. Chinese Journal of Ecology, 2024, 43(2): 533-541. |
李莎, 莫舜华, 胡兴华, 等. 基于MaxEnt和ArcGIS预测濒危植物资源冷杉潜在适生区分析. 生态学杂志, 2024, 43(2): 533-541. | |
44 | Guo F L, Xu G B, Lu M Z, et al. Prediction of potential suitable distribution areas for Populus euphratica using the MaxEnt model. Scientia Silvae Sinicae, 2020, 56(5): 184-192. |
郭飞龙, 徐刚标, 卢孟柱, 等. 基于MaxEnt模型分析胡杨潜在适宜分布区. 林业科学, 2020, 56(5): 184-192. | |
45 | Guo F, Gu Z, Jia X L, et al. Research progress of the medicinal plant turmeric. Journal of Anhui Agricultural Sciences, 2022, 50(16): 14-19. |
郭芳, 顾哲, 贾训利, 等. 药用植物姜黄的研究进展. 安徽农业科学, 2022, 50(16): 14-19. | |
46 | Xu S, Kan Y C. Correlation of soil compactness and water content under different fertility levels. Chinese Agricultural Science Bulletin, 2022, 38(36): 94-100. |
徐爽, 阚雨晨. 不同肥力水平的土壤紧实度与含水量的相关度分析. 中国农学通报, 2022, 38(36): 94-100. | |
47 | Liu Y C, Si C C, Liu H J, et al. Effects of soil compactness on population structure and yield of sweet potato. Shandong Agricultural Sciences, 2019, 51(10): 99-103. |
刘永晨, 司成成, 柳洪鹃, 等. 土壤紧实度对甘薯群体结构及产量的影响. 山东农业科学, 2019, 51(10): 99-103. | |
48 | Ou Z G, Liu F Z, Li J X, et al. Resource distribution of Curcuma longa and its development utilization. Guizhou Agricultural Sciences, 2006(4): 126-127. |
欧珍贵, 刘凡值, 李家兴, 等. 姜黄资源概况及其开发利用. 贵州农业科学, 2006(4): 126-127. | |
49 | Wu X M, Ye D M, Bai Y E, et al. Distribution pattern and future change of Picea meyeri in China based on MaxEnt model. Acta Botanica Boreali-Occidentalia Sinica, 2022, 42(1): 162-172. |
吴晓萌, 叶冬梅, 白玉娥, 等. 基于MaxEnt模型的中国白杄分布格局及未来变化. 西北植物学报, 2022, 42(1): 162-172. | |
50 | Chen Y G, Le X G, Chen Y H, et al. Identification of the potential distribution area of Cunninghamia lanceolata in China under climate change based on the MaxEnt model. Chinese Journal of Applied Ecology, 2022, 33(5): 1207-1214. |
陈禹光, 乐新贵, 陈宇涵, 等. 基于MaxEnt模型预测气候变化下杉木在中国的潜在地理分布. 应用生态学报, 2022, 33(5): 1207-1214. | |
51 | Kozak K H, Graham C H, Wiens J J. Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology & Evolution, 2008, 23(3): 141-148. |
52 | Bertrand R, Lenoir J, Piedallu C, et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature, 2011, 479(7374): 517-520. |
53 | Garcia-Robledo C, Baer C S. Positive genetic covariance and limited thermal tolerance constrain tropical insect responses to global warming. Journal of Evolutionary Biology, 2021, 34(9): 1432-1446. |
54 | Zhou H Y, Huang H H, Chen W L, et al. Protection, exploitation and utilization of wild Chinese medicine resources. South China Agriculture, 2022, 16(14): 148-150. |
周宏英, 黄宏辉, 陈炜玲, 等. 野生中药资源的保护与开发利用. 南方农业, 2022, 16(14): 148-150. |
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