基于MaxEnt模型的东北地区槭树潜在地理分布

doi:10.16258/j.cnki.1674-5906.2024.04.002

生态环境学报 ›› 2024, Vol. 33 ›› Issue (4): 509-519.DOI: 10.16258/j.cnki.1674-5906.2024.04.002

基于MaxEnt模型的东北地区槭树潜在地理分布

田叙辰¹^,²(), 魏洪玲¹^,², 解胜男¹^,², 储启名¹^,², 杨婧¹^,², 张颖¹^,², 肖思秋¹^,², 唐中华¹^,²^,³, 刘英¹^,²^,³, 李德文¹^,²^,³^,^*()

1.森林植物生态学教育部重点实验室，黑龙江哈尔滨 150000
2.东北林业大学化学化工与资源利用学院，黑龙江哈尔滨 150000
3.黑龙江省林源活性物质生态利用重点实验室，黑龙江哈尔滨 150000

收稿日期:2023-12-14 出版日期:2024-04-18 发布日期:2024-05-31
通讯作者: *李德文。E-mail: lidewen1@126.com
作者简介:田叙辰（1998年生），女，硕士研究生，研究方向为植物生理生态。E-mail: 3364871597@qq.com
基金资助:
国家科技基础资源调查专项(2019FY100500)

Potential Geographical Distribution of Acer in Northeast China Based on the MaxEnt Model

TIAN Xuchen¹^,²(), WEI Hongling¹^,², XIE Shengnan¹^,², CHU Qiming¹^,², YANG Jing¹^,², ZHANG Ying¹^,², XIAO Siqiu¹^,², TANG Zonghua¹^,²^,³, LIU Ying¹^,²^,³, LI Dewen¹^,²^,³^,^*()

1. Key Laboratory of Forest Plant Ecology, Ministry of Education, Harbin 150000, P. R. China
2. School of Chemistry, Chemical Engineering and Resource Utilization, Harbin 150000, P. R. China
3. Key Laboratory of Ecological Utilization of Active Substances of Forest Source in Heilongjiang Province, Harbin 150000, P. R. China

Received:2023-12-14 Online:2024-04-18 Published:2024-05-31

摘要/Abstract

摘要：

槭属植物是东北地区针阔混交林的重要组成成分，研究东北地区槭树的地理分布具有重要生态意义。为了解影响槭树潜在分布的关键气候因素，保护槭树的种质资源和可持续发展提供参考，基于野外调查数据，结合19个气候因子，应用 MaxEnt 模型模拟当代（1970－2000年）和未来（2030s、2050s）4种气候情景下槭树（东北槭Acer mandshuricum Maxim、色木槭A. mono Maxim、茶条槭A. ginnala Maxim和紫花槭A. pseudosieboldianum (Pax) Komarov）的潜在地理分布。结果表明，1）AUC值>0.9，表明模型具有较高的准确性。2）在当代气候情景下，4种槭树的地理分布区主要集中在长白山和小兴安岭地区，其中茶条槭分布最广，面积为3.79×10⁵ km²。东北槭与适生区分布相关性最高的气候因子为年降水量，色木槭为降水量季节性变化，茶条槭为最湿季度平均温度，紫花槭为最干季度降水量。3）在未来4种气候情景下，槭树的适生区中心呈向高纬度地区迁移的趋势，适生区分布面积均减少，其中色木槭向北迁移最远，迁移距离为338 km，茶条槭面积缩减最大，减少比例为89.1%。未来气候情景下，影响槭树适生区分布的主要因子为年降水量、降水量季节性变化和最干季度降水量。综上，降水因子是决定槭树分布格局的主要气候条件。气候因子间交互探测分析为非线性增强，表明降水和温度因子交互作用大于降水因子单独作用。该研究考虑了多种气候因素的作用，有助于全面了解影响槭树分布的关键因素，提供了不同气候情景下槭树的潜在分布的情况，为槭树的管理、保护和合理选址提供科学依据。

关键词: 槭树, MaxEnt 模型, 气候变化, 潜在适生区, 气候因素, 地理探测器

Abstract:

Acer L is an important component of mixed forests in Northeast China and has important ecological significance when studying the geographic distribution of Acer L. To protect germplasm resources and ensure the sustainable development of Acer, the potential distribution of Acer trees under the influence of key climatic factors was studied. Based on data from the maple field survey combined with 19 climatic factors, the MaxEnt model was applied to simulate the potential geographic distribution of the four Acer species (Acer mandshuricum Maxim, A. mono Maxim, A. ginnala Maxim, and A. pseudosieboldianum (Pax) Komarov) in Northeast China under current (1970-2000) and future (2030s, 2050s) conditions. The results showed that (1) the model AUC values were > 0.9, indicating that the model had a higher degree of accuracy. (2) Under the current climatic conditions, the potential distribution of Acer was primarily concentrated in the Changbai and Xiaoxing’an Mountains. A. ginnala Maxim was the most widely distributed, with a maximum area of 3.79×10⁵ km². The climate factor with the highest correlation with suitable area distribution was annual precipitation for A. mandshuricum Maxim, precipitation seasonality for A. mono Maxim, mean temperature of wettest quarter for A. ginnala Maxim, and precipitation of driest quarter for A. pseudosieboldianum (Pax) Komarov. (3) Under future climatic conditions, the center of the suitable area of Acer showed a tendency to migrate to high latitudes, and the potential distribution area was reduced. The migration of A. mono Maxim was the farthest to the north (338 km), and the area reduction of A. ginnala Maxim was the largest (89.1%). The main factors affecting the distribution of Acer in suitable areas are annual precipitation, seasonal precipitation, and precipitation in the driest quarter under future climatic conditions. In summary, precipitation is the main climatic condition that determines the distribution pattern of the Acer. The interaction between climate factors is a nonlinear enhancement, which shows that the interaction between precipitation and temperature factors is greater than that between precipitation factors alone, and that the key factors for Acer distribution were understood among climatic factors, and provided the potential distribution of Acer under different climatic conditions, which could provide a scientific basis for Acer management, conservation, and rational site selection.

Key words: Acer species, MaxEnt model, climate change, potential habitat, climatic factors, geoprobes

中图分类号:

Q948
X173

田叙辰, 魏洪玲, 解胜男, 储启名, 杨婧, 张颖, 肖思秋, 唐中华, 刘英, 李德文. 基于MaxEnt模型的东北地区槭树潜在地理分布[J]. 生态环境学报, 2024, 33(4): 509-519.

TIAN Xuchen, WEI Hongling, XIE Shengnan, CHU Qiming, YANG Jing, ZHANG Ying, XIAO Siqiu, TANG Zonghua, LIU Ying, LI Dewen. Potential Geographical Distribution of Acer in Northeast China Based on the MaxEnt Model[J]. Ecology and Environment, 2024, 33(4): 509-519.

图/表 14

参考文献 43

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变量	变量名称	单位	东北槭	色木槭	茶条槭	紫花槭
BIO1	年平均气温	℃	1.26‒4.79	-0.53‒6.35	-0.81‒6.35	1.52‒6.35
BIO2	平均气温日较差	℃	12.6‒12.7	11.9‒13.6	11.9‒13.9	11.9‒12.7
BIO3	等温性	－	25.3‒26.9	24.1‒25.1	23.8‒25.1	25.1‒26.4
BIO4	气温季节性变动系数	－	1256‒1355	1273‒1569	1273‒1653	1273‒1296
BIO5	最热月份最高温度	℃	22.8‒26.5	25.2‒27.6	26.1‒27.6	23.3‒27.6
BIO6	最冷月份最低温度	℃	-24.3‒ -23.4	-31.1‒ -19.9	-32.2‒ -19.9	-24.9‒ -19.9
BIO7	气温年较差	℃	47.1‒49.9	47.5‒56.3	47.5‒58.3	47.5‒48.2
BIO8	最湿季度平均温度	℃	16.1‒20.4	17.6‒21.2	18.2‒21.2	16.7‒21.2
BIO9	最干季度平均温度	℃	-17.0‒ -12.8	-21.0‒ -10.0	-20.9‒ -10.4	-15.4‒ -10.5
BIO10	最暖季度平均温度	℃	16.1‒20.4	17.6‒21.2	18.2‒21.2	16.7‒21.2
BIO11	最冷季度平均温度	℃	-12.8‒ -17.0	-21.0‒ -10.0	-22.3‒ -10.4	-15.4‒10.5
BIO12	年降水量	mm	791‒824	635‒852	518‒852	758‒852
BIO13	最湿月份降水量	mm	190‒211	154‒213	126‒213	182‒213
BIO14	最干月份降水量	mm	6.00‒8.00	5.00‒9.00	4.00‒9.00	7.00‒9.00
BIO15	降水量季节性变动系数	－	97.9‒99.1	98.3‒102	99.2‒98.3	95.6‒98.3
BIO16	最湿季度降水量	mm	350‒604	331‒606	323‒555	358‒606
BIO17	最干季度降水量	mm	11.0‒39.0	10.0‒39.0	13.0‒38.0	13.0‒39.0
BIO18	最暖季度降水量	mm	350‒604	331‒606	323‒555	358‒606
BIO19	最冷季度降水量	mm	11.0‒39.0	10.0‒39.0	13.0‒38.0	13.0‒39.0

变量	变量名称	单位	东北槭	色木槭	茶条槭	紫花槭
BIO1	年平均气温	℃	1.26‒4.79	-0.53‒6.35	-0.81‒6.35	1.52‒6.35
BIO2	平均气温日较差	℃	12.6‒12.7	11.9‒13.6	11.9‒13.9	11.9‒12.7
BIO3	等温性	－	25.3‒26.9	24.1‒25.1	23.8‒25.1	25.1‒26.4
BIO4	气温季节性变动系数	－	1256‒1355	1273‒1569	1273‒1653	1273‒1296
BIO5	最热月份最高温度	℃	22.8‒26.5	25.2‒27.6	26.1‒27.6	23.3‒27.6
BIO6	最冷月份最低温度	℃	-24.3‒ -23.4	-31.1‒ -19.9	-32.2‒ -19.9	-24.9‒ -19.9
BIO7	气温年较差	℃	47.1‒49.9	47.5‒56.3	47.5‒58.3	47.5‒48.2
BIO8	最湿季度平均温度	℃	16.1‒20.4	17.6‒21.2	18.2‒21.2	16.7‒21.2
BIO9	最干季度平均温度	℃	-17.0‒ -12.8	-21.0‒ -10.0	-20.9‒ -10.4	-15.4‒ -10.5
BIO10	最暖季度平均温度	℃	16.1‒20.4	17.6‒21.2	18.2‒21.2	16.7‒21.2
BIO11	最冷季度平均温度	℃	-12.8‒ -17.0	-21.0‒ -10.0	-22.3‒ -10.4	-15.4‒10.5
BIO12	年降水量	mm	791‒824	635‒852	518‒852	758‒852
BIO13	最湿月份降水量	mm	190‒211	154‒213	126‒213	182‒213
BIO14	最干月份降水量	mm	6.00‒8.00	5.00‒9.00	4.00‒9.00	7.00‒9.00
BIO15	降水量季节性变动系数	－	97.9‒99.1	98.3‒102	99.2‒98.3	95.6‒98.3
BIO16	最湿季度降水量	mm	350‒604	331‒606	323‒555	358‒606
BIO17	最干季度降水量	mm	11.0‒39.0	10.0‒39.0	13.0‒38.0	13.0‒39.0
BIO18	最暖季度降水量	mm	350‒604	331‒606	323‒555	358‒606
BIO19	最冷季度降水量	mm	11.0‒39.0	10.0‒39.0	13.0‒38.0	13.0‒39.0

槭树	交互因子	q值	交互结果
东北槭	BIO12∩BIO10	0.82	非线性增强
东北槭	BIO12∩BIO9	0.78	非线性增强
东北槭	BIO12∩BIO11	0.77	非线性增强
色木槭	BIO15∩BIO1	0.69	非线性增强
色木槭	BIO12∩BIO10	0.68	非线性增强
色木槭	BIO15∩BIO10	0.67	非线性增强
茶条槭	BIO15∩BIO1	0.80	非线性增强
茶条槭	BIO15∩BIO8	0.77	双因子增强
茶条槭	BIO15∩BIO10	0.77	双因子增强
紫花槭	BIO17∩BIO2	0.75	非线性增强
紫花槭	BIO17∩BIO10	0.73	非线性增强
紫花槭	BIO17∩BIO3	0.69	非线性增强

槭树	交互因子	q值	交互结果
东北槭	BIO12∩BIO10	0.82	非线性增强
东北槭	BIO12∩BIO9	0.78	非线性增强
东北槭	BIO12∩BIO11	0.77	非线性增强
色木槭	BIO15∩BIO1	0.69	非线性增强
色木槭	BIO12∩BIO10	0.68	非线性增强
色木槭	BIO15∩BIO10	0.67	非线性增强
茶条槭	BIO15∩BIO1	0.80	非线性增强
茶条槭	BIO15∩BIO8	0.77	双因子增强
茶条槭	BIO15∩BIO10	0.77	双因子增强
紫花槭	BIO17∩BIO2	0.75	非线性增强
紫花槭	BIO17∩BIO10	0.73	非线性增强
紫花槭	BIO17∩BIO3	0.69	非线性增强

槭树	气候情景	交互因子	q值	交互结果
东北槭	SSP1-2.6	BIO12∩BIO10	0.75	非线性增强
东北槭	SSP2-4.5	BIO12∩BIO10	0.83	非线性增强
东北槭	SSP3-7.0	BIO12∩BIO10	0.71	非线性增强
东北槭	SSP5-8.5	BIO12∩BIO10	0.75	非线性增强
色木槭	SSP1-2.6	BIO12∩BIO10	0.72	非线性增强
色木槭	SSP2-4.5	BIO12∩BIO10	0.72	非线性增强
色木槭	SSP3-7.0	BIO15∩BIO1	0.67	非线性增强
色木槭	SSP5-8.5	BIO15∩BIO10	0.67	非线性增强
茶条槭	SSP1-2.6	BIO15∩BIO10	0.73	非线性增强
茶条槭	SSP2-4.5	BIO5∩BIO2	0.72	非线性增强
茶条槭	SSP3-7.0	BIO15∩BIO1	0.67	非线性增强
茶条槭	SSP5-8.5	BIO15∩BIO8	0.74	非线性增强
紫花槭	SSP1-2.6	BIO17∩BIO10	0.71	非线性增强
紫花槭	SSP2-4.5	BIO17∩BIO10	0.77	非线性增强
紫花槭	SSP3-7.0	BIO17∩BIO2	0.69	非线性增强
紫花槭	SSP5-8.5	BIO17∩BIO11	0.71	非线性增强

基于MaxEnt模型的东北地区槭树潜在地理分布

Potential Geographical Distribution of Acer in Northeast China Based on the MaxEnt Model

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