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.

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

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

Figures/Tables 8

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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

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