摘要: 目的分析极小种群濒危植物梓叶槭在中国当代和未来的潜在分布区，揭示未来气候变化条件下梓叶槭的分布动态、�/sec> 方法以梓叶槭为研究对象，基于现有的梓叶槭分布位点、气候数据集和海拔数据，利用优化的MaxEnt模型和GIS技术，模拟当前�?050s�?041�?060年）�?090s�?081�?100年）（SSP126、SSP245、SSP370和SSP585）气候情景下梓叶槭的分布格局，划分适生等级，采用受试者工作曲线（ROC）下的面积（AUC），评价模拟的精度。以刀切法分析气候变量贡献率，找出制约梓叶槭分布的主导气候变量。基于分布面积比＇�i>N _a）、生境变化程度（ N _e）比较梓叶槭在不同气候条件下的地理分布动态、�/sec> 结果梓叶槭主要适生区分布在我国西南地区�?种气候情景下训练集与测试集AUC值均大于0.995，表明模型模拟精度极高。最暖季降雨量、温度季节性变化标准差、海拔贡献率最高，分别�?6.1%�?8.2%�?0.9%、�/sec> 结论气候变化背景下梓叶槭将丧失大量高适生区，生境破碎化趋势严重，中高强度排放情景SSP370对梓叶槭潜在分布区影响较小。本研究可为濒危物种梓叶槭的就地与迁地保护提供依据、�/sec>

关键诌�
• 梓叶�?/a> /
• MaxEnt/
• 潜在分布匹�/a> /
• 气候因孏�/a>

Abstract: ObjectiveThis paper aims to analyze the potential distribution areas of extremely small population of endangered plant Acer catalpifoliumin China today and in the future, reveal the distribution dynamics of A. catalpifoliumunder future climate change. MethodTaking A. catalpifoliumas the research object, based on the existing A. catalpifoliumdistribution sites, climate data and altitude data, using the MaxEnt model and GIS technology to simulate the current, 2050s (2041�?060) and 2090s (2081�?100) (SSP126, SSP245, SSP370 and SSP585) distribution pattern of A. catalpifoliumunder climate scenarios, classify the fitness level and use the area under the receiver operating characteristic curve (ROC) (AUC) to evaluate the accuracy of simulation, analyze the contribution rate of climate variables with the knife-cut method to find out the dominant climate variables that restrict the distribution of A. catalpifolium; compare the geographic distribution of A. catalpifoliumunder different climatic conditions based on the distribution area ratio ( N _a ) and the degree of habitat change ( N _e) dynamic. ResultThe main suitable areas for A. catalpifoliumwere distributed in southwestern China. The AUC values of the training set and the test set under the nine climatic scenarios were both greater than 0.995, indicating that the model simulation accuracy was extremely high. The warmest season rainfall, temperature seasonal variation standard deviation and altitude had the highest contribution rates, which were 56.1%, 18.2% and 10.9%, respectively. ConclusionUnder the background of climate change, A. catalpifoliumwill lose a large number of highly suitable areas, and the habitat fragmentation will be more serious than the trend. The medium-to-high intensity emission scenario SSP370 has little impact on the potential distribution area of A. catalpifolium. This study can provide a basis for the in-situ and ex-situ conservation of the endangered species of A. catalpifolium.

Key words:
• Acer catalpifolium/
• MaxEnt/
• potential distribution area/
• climate factor

�?nbsp; 1梓叶槭分布点位图

Figure 1.Location point map ofAcer catalpifolium

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 2不同参数设置下梓叶槭的MaxEnt模型评估结果

AUC为受试者工作特征曲线下面积。L 为线性；H 为铰链型；Q 为二次型；P 为乘积型；T 为阈值型。下同。AUC，the area under the receiver operating characteristic curve. L, linearity; H, hinge type; Q, quadratic type; P, product type; T, threshold type. The same below.

Figure 2.MaxEnt model evaluation results ofA. catalpifoliumunder different parameter settings

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�?nbsp; 3当前气候下梓叶槭在中国的适宜生境分布

Figure 3.Distribution of suitable habitats forA. catalpifoliumin China under current climate

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 4不同时期不同气候背景下梓叶槭适宜性生境分市�/p>

A ~ D. 2050s SSP126、SSP245、SSP370和SSP585气候情景；E ~ H. 2090s SSP126、SSP245、SSP370和SSP585气候背景。A−D: SSP126, SSP245, SSP370 and SSP585 climate scenarios in 2050s; E−H: SSP126, SSP245, SSP370 and SSP585 climate backgrounds in 2090s.

Figure 4.Distribution of suitable habitats forA. catalpifoliumin different periods and climates

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 5梓叶槭分布对6个环境因子的响应曲线

Figure 5.Response curves ofA. catalpifoliumdistribution to 6 environmental factors

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 1环境变量信息

Table 1.Environment variable information

类型 Type	变量 Variable	描述 Description
气候因孏�br/>Climate factor	Bio2	月均气温日较�?br/>Daily range of monthly average temperature/ℂ�/td>
	Bio3	等温�?br/>Isothermality/ℂ�/td>
	Bio4	温度季节性变化标准差 SD of seasonal temperature change
	Bio7	气温年较�?br/>Annual temperature range/ℂ�/td>
	Bio11	最冷季均温 Mean temperature of the coldest quarter/ℂ�/td>
	Bio12	年降水量 Annual precipitation/mm
	Bio15	降水量变异系�?br/>Variation coefficient of precipition
	Bio18	最热季降水野�br/>Precipitation of the warmest quarter/mm
	Bio19	最冷季降水野�br/>Precipitation of the coldest quarter/mm
地形因子 Topographic factor	Alt	海拔 Altitude/m

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�?nbsp; 2适生区划刅�/p>

Table 2.Division of suitable distribution area

生境适宜性指� Habitat suitability index (HSI)	评价等级 Evaluation level		生境适宜性指� HSI	评价等级 Evaluation level
HSI < 0.2	非适生� Unsuitable area		0.4 � HSI < 0.6	中适生� Medium-suitable area
0.2 � HSI < 0.4	低适合生区 Low-suitable area		HSI � 0.6	高适生� High-suitable area

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�?nbsp; 3不同参数组合下梓叶槭MaxEnt模型的赤池化信息量准则（AICc（�/p>

Table 3.Akaike information criteria (AICc) for MaxEnt model ofA. catalpifoliumunder different parameter combinations

参数组合 Parameter combination	不同正则化参数下梓叶槭MaxEnt模型的AICc� AICc value ofA. catalpifoliumMaxEnt model under different regularization parameters
	0.5	1	1.5	2	2.5	3	3.5	4
L	1701.19	1773.05	1818.27	1812.36	1806.35	1800.20	1794.03	1787.76
H	1557.06	1412.83	1334.19	1293.49	1319.24	1278.62	1249.96	1252.36
L + Q	1465.43	1127.27	1113.02	1108.63	1116.15	1132.10	1154.42	1166.25
L + H + Q	1869.88	1544.06	1385.27	1195.73	1116.06	1096.02	1098.19	1096.88
L + H + Q + P	1610.69	1307.60	1134.10	1105.80	1096.47	1119.45	1125.41	1130.05
L + H + Q + P + T	3615.42	1213.23	1108.74	1103.12	1097.55	1119.45	1125.41	1130.05

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�?nbsp; 4当代�?050s�?090s梓叶槭在各省高适生区面�?/p>

Table 4.Contemporary and future areas ofA. catalpifoliumin each province in 2050s and 2090s km ²

地区 Area	当代 Current	2050s					2090s
		SSP126	SSP245	SSP370	SSP585		SSP126	SSP245	SSP370	SSP585
中国 China	193 339	73 589	109 951	175 007	122 267		74 423	65 586	102 581	90 359
四川 Sichuan	112 362	53 723	74 792	109 322	80 072		53 011	47 383	65 395	36 302
贵州 Guizhou	41 161	3 314	11 175	20 910	3 297		2 273	2 481	503	–�/td>
云南 Yunnan	9 492	2 308	4 113	6 334	2 412		1 475	1 596	1 926	694
陕西 Shaanxi	10 105	4 098	6 806	15 331	18 769		7 778	3 907	13 821	10 383
重庆 Chongqing	16 188	3 865	6 014	13 519	5 494		2 288	2 530	503	69
西藏 Tibet	313	3 455	3 698	3 959	5 348		4 080	5 747	11 598	30 298

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�?nbsp; 5气候变化下梓叶槭分布动�?/p>

Table 5.Distribution dynamics ofA. catalpifolium

气候情�?br/>Climate scenario	当前与其他时期分布面积比 Distribution area ratio in current and other periods (Na)	生境变化程度 Habitat change extent (Ne)/%	生境变化趋势 Habitat change trend
当代 Current	1	0	不变 No change
2050s SSP126	1.28	24.9	收缩 Contraction
2050s SSP245	1.19	19.6	收缩 Contraction
2050s SSP370	0.97	7.2	扩张 Expansion
2050s SSP585	1.09	19.1	收缩 Contraction
2090s SSP126	1.30	27.9	收缩 Contraction
2090s SSP245	1.34	31.7	收缩 Contraction
2090s SSP370	1.00	31.6	不变 No change
2090s SSP585	1.05	55.7	收缩 Contraction

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�?nbsp; 6主要气候因子对梓叶槭分布的贡献率和重要倻�/p>

Table 6.Contribution rates and important values of major climatic factors to the distribution ofA. catalpifolium

代号 Code	环境因子 Environmental factor	贡献� Contribution rate/%	重要� Importance value
Bio18	最暖季降水� Precipitation of the warmest quarter	56.1	0.8
Bio4	温度季节性变化标准差 SD of temperature seasonal change	18.2	1.5
Alt	海拔 Altitude	10.9	0.8
Bio19	最冷季降雨� Precipitation of the coldest quarter	3.8	1.1
Bio11	最冷季均温 Mean temperature of the coldest quarter	3.6	57.7
Bio2	月均气温日较� Daily range of monthly average temperature	2.8	0
合计 Total		95.4	61.9

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