Potential distribution of wild Camellia oleifera based on ecological niche modeling

doi:10.17520/biods.2016164

Abstract

Abstract:

Camellia oleifera is the dominant woody oil crop in China, and wild C. oleifera is a valuable genetic resource for C. oleifera breeding. Using distribution data of wild C. oleifera from the Chinese Virtual Herbarium (CVH, http://www.cvh.org.cn/), together with climate and soil data, ecological niche models were constructed with MaxEnt and genetic algorithm for rule-set prediction (GARP) models to predict the potential distribution of wild C. oleifera, and the major environmental factors affecting the distribution of wild C. oleifera were analyzed. Based on the presence probability of wild C. oleifera predicted by the models, the distribution regions of wild C. oleifera were divided into different suitable growing categories, which were then compared with actual distribution data of major C. oleifera production fields to evaluate reliability. Results indicated that the predictions of both MaxEnt and GARP models represented the distributions of C. oleifera well. The potential distribution range predicted by the GARP model was wider, while that predicted by the MaxEnt model was more accurate. Predictions of both the MaxEnt and GARP models showed that the potential distribution regions of wild C. oleifera were located mainly in China and partly in the Indo-China Peninsula. According to predictions of the MaxEnt model, the potential distribution regions of wild C. oleifera in China were matched with the distribution regions of subtropical evergreen broad-leaved forests, and the highly suitable growing regions could be divided into three large regions: (1) northeastern-southwestern trending Wuyi Mountain and adjacent mountainous regions; (2) eastern-western trending Nanling Mountain and adjacent mountainous regions; (3) northeastern-southwestern trending Wuling Mountain and adjacent mountainous regions. The analysis of the MaxEnt model showed that the major environmental factors affecting the distribution of wild C. oleifera were mean monthly diurnal temperature range, precipitation during the driest quarter, and precipitation during the warmest quarter. The vast majority of the regions with large growing areas of C. oleifera were located in the medium to highly suitable growing regions predicted by the MaxEnt model, suggesting that the division of suitable growing regions was reliable. The field investigations showed that the model predictions had high reference values for finding wild C. oleifera resources. Additionally, the study shows that using the plant distribution data from CVH and related environmental data to construct an ecological niche model can help to understand the geographic distribution of crop wild relatives.

http://jtp.cnki.net/bilingual/detail/html/SWDY201610004

Key words: wild Camellia oleifera, geographic distribution, ecological niche model, precipitation, temperature, MaxEnt model, genetic algorithm for rule-set prediction (GARP) model

Xiangyan Cui, Wenjuan Wang, Xiaoqiang Yang, Shu Li, Shengyuan Qin, Jun Rong. Potential distribution of wild Camellia oleifera based on ecological niche modeling[J]. Biodiv Sci, 2016, 24(10): 1117-1128.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.biodiversity-science.net/EN/10.17520/biods.2016164

https://www.biodiversity-science.net/EN/Y2016/V24/I10/1117

Figures/Tables 8

References 33

1	Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1, 73-81.
2	Che L, Cao B, Bai CK, Wang JJ, Zhang LL (2014) Predictive distribution and habitat suitability assessment of Notholirion bulbuliferum based on MaxEnt and ArcGIS. Journal of Ecology, 33, 1623-1628. (in Chinese with English abstract)
	[车乐, 曹博, 白成科, 王娟娟, 张琳琳 (2014) 基于MaxEnt和ArcGIS对太白米的潜在分布预测及适宜性评价. 生态学杂志, 33, 1623-1628.]
3	Chen LL, Yu Y, He XJ (2008) Historical invasion and expansion process of Alternanthera philoxeroides and its potential spread in China. Biodiversity Science, 16, 578-585. (in Chinese with English abstract)
	[陈立立, 余岩, 何兴金 (2008) 喜旱莲子草在中国的入侵和扩散动态及其潜在分布区预测. 生物多样性, 16, 578-585.]
4	Chen WJ (2006) Floristic Phytogeography of Evergreen Broad-leaved Forest (EBLF) in Mid-subtropical China. PhD dissertation, East China Normal University, Shanghai. (in Chinese with English abstract)
	[陈卫娟 (2006) 中亚热带常绿阔叶林植物区系地理研究. 博士学位论文, 华东师范大学, 上海.]
5	Chen XM, Lei YC, Zhang XQ, Jia HY (2012) Effects of sample size on accuracy and stability of maximum entropy model in predicting species distribution. Scientia Silvae Sinicae, 48(1), 53-59. (in Chinese with English abstract)
	[陈新美, 雷渊才, 张雄清, 贾宏炎 (2012) 样本量对MaxEnt模型预测物种分布精度和稳定性的影响. 林业科学, 48(1), 53-59.]
6	Chen YZ (2008) Oil Tea Camellia Superior Germplasm Resources. China Forestry Publishing House, Beijing. (in Chinese)
	[陈永忠 (2008) 油茶优良种质资源. 中国林业出版社, 北京.]
7	Chen YZ, Luo J, Wang R, Chen LS, Wang XN (2013) Current status and prospects for the development of oil tea industry in China. Grain Science and Technology and Economy, (1), 10-12. (in Chinese)
	[陈永忠, 罗健, 王瑞, 陈隆升, 王湘南 (2013) 中国油茶产业发展的现状与前景. 粮食科技与经济, (1), 10-12.]
8	Hajjar R, Hodgkin T (2007) The use of wild relatives in crop improvement: a survey of developments over the last 20 years. Euphytica, 156, 1-13.
9	Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.
10	Hu BL, Wan Y, Wu L, Li X, Zhang JW, Luo XD, Xie JK (2011) Research advances in genetic diversity and its ecological conservation of endangered Dongxiang wild rice (Oryza rufipogon Griff.). Chinese Agricultural Science Bulletin, 27, 5-11. (in Chinese with English abstract)
	[胡标林, 万勇, 邬磊, 李霞, 张金伟, 罗向东, 谢建坤 (2011) 濒危东乡野生稻遗传多样性及其生态保护学研究进展. 中国农学通报, 27, 5-11.]
11	Hu X, Wu FC, Guo W, Liu N (2014) Identification of potential cultivation region for Santalum album in China by the MaxEnt ecologic niche model. Scientia Silvae Sinicae, 50(5), 27-33. (in Chinese with English abstract)
	[胡秀, 吴福川, 郭微, 刘念 (2014) 基于MaxEnt生态学模型的檀香在中国的潜在种植区预测. 林业科学, 50(5), 27-33.]
12	Jing PF, Wu KY, Gong Y, Han LM, Cui LJ (2015) Prediction of potential geological distribution of Asarum in China by MaxEnt model. Plant Diversity and Resource, 37, 349-356. (in Chinese with English abstract)
	[景鹏飞, 武坤毅, 龚晔, 韩立敏, 崔浪军 (2015) 药用植物细辛在中国的潜在适生区分布. 植物分类与资源学报, 37, 349-356.]
13	Lei JC, Xu HG (2010) MaxEnt-based prediction of potential distribution of Solidago canadensis in China. Journal of Ecology and Rural Environment, 26, 137-141. (in Chinese with English abstract)
	[雷军成, 徐海根 (2010) 基于MaxEnt的加拿大一枝黄花在中国的潜在分布区预测. 生态与农村环境学报, 26, 137-141.]
14	Min TL (2000) Monograph of the Genus Camellia. Yunnan Science and Technology Press, Kunming. (in Chinese)
	[闵天禄 (2000) 世界山茶属的研究. 云南科技出版社, 昆明.]
15	Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102-117.
16	Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63-72.
17	Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
18	Raxworthy CJ, Martinez-Meyer E, Horning N, Nussbaum RA, Schneider GE, Ortega-Huerta MA, Peterson AT (2003) Predicting distributions of known and unknown reptile species in Madagascar. Nature, 426, 837-841.
19	Roura-Pascual N, Suarez A V, Gómez C, Pons P, Touyama Y, Wild AL, Peterson AT (2004) Geographical potential of Argentine ants (Linepithema humile Mayr) in the face of global climate change. Proceedings of the Royal Society of London B: Biological Sciences, 271, 2527-2535.
20	Sobek-Swant S, Kluza D A, Cuddington K, Lyons DB (2012) Potential distribution of emerald ash borer: what can we learn from ecological niche models using MaxEnt and GARP? Forest Ecology and Management, 281, 23-31.
21	USDA-FAS (2015) Oilseeds: world markets and trade, 07. 10.. (accessed on 2016-06- 15
22	Waltari E, Hijmans RJ, Peterson AT, Nyári ÁS, Perkins SL, Guralnick RP (2007) Locating Pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS ONE, 2, e563.
23	Wang LH, Yang JX, Huang CL (2013) Modelling geographic distribution of Malus hupehensis with MaxEnt. Journal of Anhui Agricultural University, 40, 383-386. (in Chinese with English abstract)
	[王雷宏, 杨俊仙, 黄成林 (2013) 用MaxEnt模拟湖北海棠(Malus hupehensis)地理分布. 安徽农业大学学报, 40, 383-386.]
24	Wang LH, Yang JX, Xu XN (2015) Analysis of suitable bioclimatic characteristics of Pseudolarix amabilis by using MaxEnt model. Scientia Silvae Sinicae, 51, 127-131. (in Chinese with English abstract)
	[王雷宏, 杨俊仙, 徐小牛 (2015) 基于MaxEnt分析金钱松适生的生物气候特征. 林业科学, 51(1), 127-131.]
25	Wang YS, Xie BY, Wan FH, Xiao QM, Dai LY (2007) Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models. Biodiversity Science, 15, 365-372. (in Chinese with English abstract)
	[王运生, 谢丙炎, 万方浩, 肖启明, 戴良英 (2007) ROC曲线分析在评价入侵物种分布模型中的应用. 生物多样性, 15, 365-372.]
26	Warren DL, Wright AN, Seifert SN, Shaffer HB (2014) Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Diversity and Distributions, 20, 334-343.
27	Wei SG, Dai YJ, Duan QY, Liu BY, Yuan H (2014) A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6, 249-263.
28	Yin XJ, Zhou GS, Sui XH, He QJ, Li RP (2013) Potential geographical distribution of Quercus wutaishanica forest and its dominant factors. Scientia Silvae Sinicae, 49(8), 10-14. (in Chinese with English abstract)
	[殷晓洁, 周广胜, 隋兴华, 何奇瑾, 李荣平 (2013) 辽东栎林潜在地理分布及其主导因子. 林业科学, 49(8), 10-14.]
29	Yu YB, Wang QL, Kell S, Maxted N, Ford-Lloyd BV, Wei W, Kang DM, Ma KP (2013) Crop wild relatives and their conservation strategies in China. Biodiversity Science, 21, 750-757. (in Chinese with English abstract)
	[于燕波, 王群亮, Kell S, Maxted N, Ford-Lloyd BV, 魏伟, 康定明, 马克平 (2013) 中国栽培植物野生近缘种及其保护对策. 生物多样性, 21, 750-757.]
30	Zhang HJ, Chen Y, Huang LJ, Ni HW (2011) Predicting potential geographic distribution of Mikania micrantha planting based on ecological niche models in China. Transactions of the Chinese Society of Agricultural Engineering, 27, 413-418. (in Chinese with English abstract)
	[张海娟, 陈勇, 黄烈健, 倪汉文 (2011) 基于生态位模型的薇甘菊在中国适生区的预测. 农业工程学报, 27, 413-418.]
31	Zhu GP, Liu GQ, Bu WJ, Gao YB (2013) Ecological niche modeling and its applications in biodiversity conservation. Biodiversity Science, 21, 90-98. (in Chinese with English abstract)
	[朱耿平, 刘国卿, 卜文俊, 高玉葆 (2013) 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, 21, 90-98.]
32	Zhu GP, Liu Q, Gao YB (2014) Improving ecological niche model transferability to predict the potential distribution of invasive exotic species. Biodiversity Science, 22, 223-230. (in Chinese with English abstract)
	[朱耿平, 刘强, 高玉葆 (2014) 提高生态位模型转移能力来模拟入侵物种的潜在分布. 生物多样性, 22, 223-230.]
33	Zhuang RL (2012) Oil-tea Camellia in China, 2nd edn. China Forestry Publishing House, Beijing. (in Chinese)
	[庄瑞林 (2012) 中国油茶, 第2版. 中国林业出版社, 北京.]

环境变量 Environmental variable	描述 Description
Bio1	年平均温度 Annual mean temperature
Bio2	昼夜温差月均值 Mean diurnal range (Mean of monthly (max.temp.–min.temp.))
Bio3	昼夜温差与年温差比值 Isothermality (Bio2 / Bio7) (× 100)
Bio4	温度季节变化 Temperature seasonality (Standard deviation × 100)
Bio5	最热月份最高温 Max temperature of warmest month
Bio6	最冷月份最低温 Min temperature of coldest month
Bio7	年温度变化范围 Temperature annual range (Bio5 - Bio6)
Bio8	最湿季平均温度 Mean temperature of wettest quarter
Bi 9	最干季平均温度 Mean temperature of driest quarter
Bio10	最暖季平均温度 Mean temperature of warmest quarter
Bio11	最冷季平均温度 Mean temperature of coldest quarter
Bio12	年均降水量 Annual precipitation
Bio13	最湿月降水量 Precipitation of wettest month
Bio14	最干月降水量 Precipitation of driest month
Bio15	降水量季节性变异系数 Coefficient of variation of precipitation seasonality
Bio16	最湿季降水量 Precipitation of wettest quarter
Bio17	最干季降水量 Precipitation of driest quarter
Bio18	最暖季降水量 Precipitation of warmest quarter
Bio19	最冷季降水量 Precipitation of coldest quarter
Alt	海拔 Altitude
OC	土壤有机碳 Soil organic carbon
TN	土壤总氮 Soil total N
pH	土壤pH值(以水作为浸出液体) Soil pH value (with water as the leaching liquid)
EXK	土壤可交换钾 Soil exchangeable potassium
SAND	土壤含沙量 Soil sand content
GRAV	土壤碎石含量 Soil gravel content
BD	土壤容重 Soil bulk density
VMC1	土壤含水量 Soil volumetric water content at -10 kPa

环境变量 Environmental variable	描述 Description
Bio1	年平均温度 Annual mean temperature
Bio2	昼夜温差月均值 Mean diurnal range (Mean of monthly (max.temp.–min.temp.))
Bio3	昼夜温差与年温差比值 Isothermality (Bio2 / Bio7) (× 100)
Bio4	温度季节变化 Temperature seasonality (Standard deviation × 100)
Bio5	最热月份最高温 Max temperature of warmest month
Bio6	最冷月份最低温 Min temperature of coldest month
Bio7	年温度变化范围 Temperature annual range (Bio5 - Bio6)
Bio8	最湿季平均温度 Mean temperature of wettest quarter
Bi 9	最干季平均温度 Mean temperature of driest quarter
Bio10	最暖季平均温度 Mean temperature of warmest quarter
Bio11	最冷季平均温度 Mean temperature of coldest quarter
Bio12	年均降水量 Annual precipitation
Bio13	最湿月降水量 Precipitation of wettest month
Bio14	最干月降水量 Precipitation of driest month
Bio15	降水量季节性变异系数 Coefficient of variation of precipitation seasonality
Bio16	最湿季降水量 Precipitation of wettest quarter
Bio17	最干季降水量 Precipitation of driest quarter
Bio18	最暖季降水量 Precipitation of warmest quarter
Bio19	最冷季降水量 Precipitation of coldest quarter
Alt	海拔 Altitude
OC	土壤有机碳 Soil organic carbon
TN	土壤总氮 Soil total N
pH	土壤pH值(以水作为浸出液体) Soil pH value (with water as the leaching liquid)
EXK	土壤可交换钾 Soil exchangeable potassium
SAND	土壤含沙量 Soil sand content
GRAV	土壤碎石含量 Soil gravel content
BD	土壤容重 Soil bulk density
VMC1	土壤含水量 Soil volumetric water content at -10 kPa

编号 Code	地点 Location	纬度 Latitude (°N)	经度 Longitude (°E)	海拔 Altitude (m)	预测分布概率 Predicted probability of presence (MaxEnt/GARP)	频率 Frequency	生境 Habitat	人为干扰 Human disturbances	备注 Remarks
1	海南澄迈 Chengmai, Hainan	19.58	110.02	98	0.20/0.99	中 Medium	路边、疏林 Roadside, open forest	强 Strong	可能为栽培 Possibly cultivated
2	广东罗浮山 Luofu Mountain, Guangdong	23.28	114.02	1,125-1,212	0.25/0.65	中 Medium	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild
3	广东南岭 Nanling Mountain, Guangdong	24.90	113.06	547-861	0.60/1.00	高 High	路边、疏林 Roadside, open forest	弱 Weak	野生 Wild
4	江西井冈山 Jinggang Mountain, Jiangxi	26.55	114.17	412-978	0.61/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	低海拔可能为栽培区 Possibly cultivated at low altitudes
5	江西马头山 Matou Mountain, Jiangxi	27.73	117.16	506-515	0.62/1.00	中 Medium	路边、疏林 Roadside, open forest	弱 Weak	野生 Wild
6	江西庐山 Lushan Mountain, Jiangxi	29.60	115.98	256-874	0.56/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild
7	浙江天目山 Tianmu Mountain, Zhejiang	30.32	119.44	411-463	0.41/1.00	极低 Extremely low	路边、疏林 Roadside, open forest	强 Strong	野生 Wild
8	河南白云山 Baiyun Mountain, Henan	31.66	115.07	137	0.48/1.00	极高 Extremely high	路边、疏林 Roadside, open forest	极强 Very strong	栽培 Cultivated
9	贵州梵净山 Fanjing Mountain, Guizhou	27.91	108.63	983-1,332	0.61/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild

编号 Code	地点 Location	纬度 Latitude (°N)	经度 Longitude (°E)	海拔 Altitude (m)	预测分布概率 Predicted probability of presence (MaxEnt/GARP)	频率 Frequency	生境 Habitat	人为干扰 Human disturbances	备注 Remarks
1	海南澄迈 Chengmai, Hainan	19.58	110.02	98	0.20/0.99	中 Medium	路边、疏林 Roadside, open forest	强 Strong	可能为栽培 Possibly cultivated
2	广东罗浮山 Luofu Mountain, Guangdong	23.28	114.02	1,125-1,212	0.25/0.65	中 Medium	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild
3	广东南岭 Nanling Mountain, Guangdong	24.90	113.06	547-861	0.60/1.00	高 High	路边、疏林 Roadside, open forest	弱 Weak	野生 Wild
4	江西井冈山 Jinggang Mountain, Jiangxi	26.55	114.17	412-978	0.61/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	低海拔可能为栽培区 Possibly cultivated at low altitudes
5	江西马头山 Matou Mountain, Jiangxi	27.73	117.16	506-515	0.62/1.00	中 Medium	路边、疏林 Roadside, open forest	弱 Weak	野生 Wild
6	江西庐山 Lushan Mountain, Jiangxi	29.60	115.98	256-874	0.56/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild
7	浙江天目山 Tianmu Mountain, Zhejiang	30.32	119.44	411-463	0.41/1.00	极低 Extremely low	路边、疏林 Roadside, open forest	强 Strong	野生 Wild
8	河南白云山 Baiyun Mountain, Henan	31.66	115.07	137	0.48/1.00	极高 Extremely high	路边、疏林 Roadside, open forest	极强 Very strong	栽培 Cultivated
9	贵州梵净山 Fanjing Mountain, Guizhou	27.91	108.63	983-1,332	0.61/1.00	高 High	路边、疏林 Roadside, open forest	中度 Medium	野生 Wild

[1]	Jiali Lian, Jing Chen, Xueqin Yang, Ying Zhao, Xu Luo, Cui Han, Yaxin Zhao, Jianping Li. Responses of desert steppe plant diversity and microbial diversity to precipitation change [J]. Biodiv Sci, 2024, 32(6): 24044-.
[2]	Zhu Yao, Xue Wei, Jinhao Ma, Xiao Ren, Yuying Wang, Lei Hu, Pengfei Wu. Short-term effects of warming and wetting on the soil nematode communities in the alpine meadow [J]. Biodiv Sci, 2024, 32(5): 23483-.
[3]	Kexin Cao, Jingwen Wang, Guo Zheng, Pengfeng Wu, Yingbin Li, Shuyan Cui. Effects of precipitation regime change and nitrogen deposition on soil nematode diversity in the grassland of northern China [J]. Biodiv Sci, 2024, 32(3): 23491-.
[4]	Wei Liu, Ruge Wang, Tianqiao Fan, Nayiman Abudulijiang, Xinhang Song, Shuping Xiao, Ning Guo, Lingying Shuai. Habitat suitability for the Aviceda leuphotes in Mingxi County, Fujian Province [J]. Biodiv Sci, 2023, 31(7): 22660-.
[5]	Jiang Wang, Yifan Zhao, Yanfu Qu, Caiwen Zhang, Liang Zhang, Chuanwu Chen, Yanping Wang. A dataset of the morphological, life-history, and ecological traits of snakes in China [J]. Biodiv Sci, 2023, 31(7): 23126-.
[6]	Chang An, Yixue Zhuang, Ping Zheng, Yanxiang Lin, Chengzi Yang, Yuan Qin. A checklist of vascular plants in Fujian Province, China [J]. Biodiv Sci, 2023, 31(6): 22537-.
[7]	Xinyang Zhou, Yutao Wang, Jianping Li. Response of plant community composition to precipitation changes in typical grasslands in the Loess Plateau [J]. Biodiv Sci, 2023, 31(3): 22118-.
[8]	Qiongyue Zhang, Zhuodi Deng, Xuebin Hu, Zhifeng Ding, Rongbo Xiao, Chen Xiu, Zhenghao Wu, Guang Wang, Donghui Han, Yuke Zhang, Jianchao Liang, Huijian Hu. The impact of urbanization on regional bird distribution and habitat connectivity in the Guangdong-Hong Kong-Macao Greater Bay Area [J]. Biodiv Sci, 2023, 31(3): 22161-.
[9]	Fang Du, Xiaoying Rong, Peng Xu, Benfeng Yin, Yuanming Zhang. Bacterial diversity and community assembly responses to precipitation in the Gurbantunggut Desert [J]. Biodiv Sci, 2023, 31(2): 22492-.
[10]	Meng Honghu, Song Yigang. Biogeographic patterns in Southeast Asia: Retrospectives and perspectives [J]. Biodiv Sci, 2023, 31(12): 23261-.
[11]	Taohua Yuan, Meijun Li, Liuyi Ren, Rongxin Huang, Yi Chen, Xinxiang Bai. A dataset on the diversity and geographical distributions of wild Impatiens in China [J]. Biodiv Sci, 2022, 30(5): 22019-.
[12]	Yuxi Zhong, Chuanwu Chen, Yanping Wang. A dataset on the life-history and ecological traits of Chinese lizards [J]. Biodiv Sci, 2022, 30(4): 22071-.
[13]	Yunfeng Song, Chuanwu Chen, Yanping Wang. A dataset on the life-history and ecological traits of Chinese amphibians [J]. Biodiv Sci, 2022, 30(3): 22053-.
[14]	Chenchen Ding, Dongni Liang, Wenpei Xin, Chunwang Li, Eric I. Ameca, Zhigang Jiang. A dataset on the morphological, life-history and ecological traits of the mammals in China [J]. Biodiv Sci, 2022, 30(2): 21520-.
[15]	Yushan Xiao, Changrao Yang, Guo Zheng, Pengfeng Wu, Shixiu Zhang, Shuyan Cui. Effects of precipitation regime on the structure of soil micro-food web in the grassland of northern China [J]. Biodiv Sci, 2022, 30(12): 22208-.