摘要: 目的分析黄土高原地区4个常见树种（樟子松、油松、柠条、华北落叶松）在当前和未来的潜在分布，揭示气候变化对植物空间分布格局的影响、�/sec> 方法基于19个气候因子结合来自Ho1dridge生命地带模型和Kira指标体系�?个指标：年生物温度（ABT）、潜在蒸散率（PER）、温暖指数（WI）、寒冷指数（CI）、干燥指数（HI），运用Maxent模型，模拟预测了4个树种在当前、未来（2041�?060年�?061�?080年）ssp126、ssp245、ssp585气候情景下的潜在地理分布。运用刀切图分析影响其分布的主要环境因子，并采用受试者工作特征曲线（ROC）下的面积（AUC）对预测结果进行检验、�/sec> 结果�?）Maxent模型可以较好地模拟黄土高�?个主要常见种的地理分布范围，各物种的10次平均AUC结果均大�?.8；（2）对于樟子松、油松、柠条来说，温度和水分共同限制其分布，而对于华北落叶松来说，影响其分布的主导因子是降水。温度季节性变化标准差、潜在蒸散率、最湿润季节降雨量、最干燥月降水都影响樟子松分布。年均温变化范围、温度季节性变化标准差、最冷月最低温度，最暖季度降水量、潜在蒸散率是影响油松分布的主导因子。影响柠条适宜区分布的主导因子为最暖月最高温度、温暖指数、等温性和潜在蒸散率、最冷季度降水量。影响华北落叶松分布的主导因子主要与降水有关，分别是最湿润月降雨量、最湿润季节降雨量、降水量变异系数和潜在蒸散率。（3）油松、柠条和华北落叶松的潜在适生区将向西北方向迁移，樟子松的潜在适生区向西南方向迁移。油松和华北落叶松的潜在适宜区面积呈现先扩大后缩小的趋势，而柠条和樟子松的潜在适生区将持续扩张，尤其是樟子松的高适生区占比将�?070s扩大�?0.97%、�/sec> 结论气候变化将使油松和华北落叶松丧失一部分的高适生区，但同时会使柠条和樟子松的高适生区扩张明显。在黄土高原退耕还林建设中，可优先考虑种植柠条和樟子松、�/sec>

关键诌�
• 气候变匕�/a> /
• 黄土高原地区/
• Maxent模型/
• 适生区分市�/a>

Abstract: ObjectiveThis paper aims to analyze the current and future potential distributions of four common tree species ( Pinus sylvestris, Pinus tabuliformis, C aragana korshinskiiand Larix gmeliniivar. principis- rupprechtii) in the Loess Plateau region of northern China, and to reveal the effects of climate change on spatial distribution patterns of plants. MethodFour dominant species on the Loess Plateau were studied based on 19 climate factors and 5 indicators from Holdridge life zone model and Kira index system: bio-temperature (ABT), potential evapotranspiration rate (PER), warmth index (WI), coldness index (CI) and humidity index (HI). The Maxent model was used to predict the potential geographical distribution of the four tree species under current and future scenarios (2041�?060, 2061�?080) SSP126, SSP245 and SSP585. The Jackknife method was used to analyze the main environmental factors affecting its distribution, and the predicted results were tested by the area under receiver operating characteristic curve (AUC). Result(1) Maxent model can well simulate the potential geographical distribution range of major established species in the Loess Plateau, and the average AUC of each species was greater than 0.8. (2) For Pinus sylvestris, Pinus tabuliformisand Caragana korshinskii, both temperature and precipitation limited their distribution, while for Larix gmeliniivar. principis-rupprechtii, precipitation was the main factor affecting their distribution. Temperature seasonality, potential evapotranspiration rate, precipitation of the driest month and precipitation of the wettest quarter were the main environmental factors affecting the distribution of Pinus sylvestris. Temperature annual range, temperature seasonality, minimum temperature of the coldest month, precipitation of the warmest quarter and potential evapotranspiration rate were the main factors affecting the distribution of Pinus tabuliformis. The main factors affecting the distribution of Caragana korshinskiiare maximum temperature of the warmest month, warmth index, isotherm, potential evapotranspiration rate and precipitation of the coldest season. The main factors affecting the distribution of Larix gmeliniivar. principis-rupprechtiiwere mainly related to precipitation, which were precipitation of the wettest month, precipitation of the wettest quarter, variation coefficient of precipitation, and potential evapotranspiration rate. (3) The potential suitable areas of Pinus tabuliformis, Caragana korshinskiiand Larix gmeliniivar. principis-rupprechtiiwill migrate to the northwest, while that of Pinus sylvestriswill migrate to the southwest. The potential suitable areas of Pinus tabuliformisand Larix gmeliniivar. principis-rupprechtiishowed a trend of expanding first and then decreasing, while the potential suitable areas of Caragana korshinskiiand Pinus sylvestriswould continue to expand, especially the proportion of highly suitable areas of Pinus sylvestriswould expand to 50.97% in 2070s. ConclusionClimate change will deprive P inus tabuliformisand Larix gmeliniivar. principis-rupprechtiiof some of their highly suitable areas, but at the same time, the highly suitable areas of Caragana korshinskiiand Pinus sylvestriswill expand significantly. Caragana korshinskiiand Pinus sylvestrisare preferred in the project of converting farmland to forest land on the Loess Plateau.

Key words:
• climate change/
• the Loess Plateau region/
• Maxent model/
• distribution of suitable area

�?nbsp; 1当前4种主要常见树种在黄土高原地区分布现状

Figure 1.Current distribution of four common tree species in the Loess Plateau region

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 2刀切法分析环境变量重要�?/p>

Figure 2.Importance analysis of environmental variables based on Jackknife test

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 3不同时期不同气候背景下樟子松适生区分市�/p>

Figure 3.Distribution of suitable areas ofPinus sylvestrisin different periods and climates

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油松适生区分市�/p>

Figure 4.Distribution of suitable areas ofPinus tabuliformisin different periods and climates

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 5不同时期不同气候背景下柠条适生区分市�/p>

Figure 5.Distribution of suitable areas ofCaragana korshinskiiin different periods and climates

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 6不同时期不同气候背景下华北落叶松适生区分市�/p>

Figure 6.Distribution of suitable areas ofLarix gmeliniivar.principis-rupprechtiiin different periods and climates

下载: 全尺寸图牆�/a> 幻灯牆�/a>

�?nbsp; 1筛选后的环境变野�/p>

Table 1.Filtered environment variables

树种 Tree species	环境变量 Environment variable
樟子�Pinus sylvestris	bio4，bio8，bio13，bio14，bio16，bio18，PER，ABT
油松Pinus tabuliformis	bio3，bio4，bio6，bio7，bio13，bio18，PER，WI
柠条Caragana korshinskii	bio3，bio5，bio8，bio13，bio16，bio19，PER，WI
华北落叶�Larix gmeliniivar.principis-rupprechtii	bio3，bio4，bio13，bio15，bio16，bio17，PER，WI
注：bio3~8分别为等温性、温度季节性变化标准差、最暖月最高温度、最冷月最低温度、年均温变化范围、最湿季度平均温；bio13~19分别为最湿润月降雨量、最干燥月降雨量、降水量变异系数、最湿润季节降雨量、最干燥季节降雨量、最暖季度降水量、最冷季度降水量；PER为潜在蒸散率、ABT为年生物温度、WI为温暖指数。Notes: bio3�? are isothermality, temperature seasonality, max. temperature of the warmest month, min. temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter; 13�?9 are precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, precipitation of the coldest quarter; PER means potential evapotranspiration rate; ABT means bio-temperature; WI means warmth index.

下载: 导出CSV

�?nbsp; 24种常见树种的环境变量对模型预测的贡献玆�/p>

Table 2.Contribution rate of environmental variables of four common tree species to model prediction %

环境变量 Environment variable	变量描述 Variable description	樟子杽�br/>Pinus sylvestris	油松 Pinus tabuliformis	柠条 Caragana korshinskii	华北落叶杽�br/>Larix gmeliniivar.principis-rupprechtii
bio3	等温� Isothermality		4.1	8.5	5.9
bio4	温度季节性变化标准差 Standard deviation of seasonal variation of temperature	48.8	16.8		12.7
bio5	最暖月最高温� Max. temperature of the warmest month			20.3
bio6	最冷月最低温� Min. temperature of the coldest month		10.1
bio7	年均温变化范� （bio5~bio6� Annual average temperature variation range（bio5−bio6（�/td>		18.5
bio8	最湿季度平均温 Mean temperature of the wettest quarter	2.0		2.8
bio13	最湿润月降雨量 Precipitation of the wettest month	1.0	1.1	6.9	31.2
bio14	最干燥月降雨量 Precipitation of the driest month	10.1
bio15	降水量变异系� Precipitation variation coefficient				14.7
bio16	最湿润季节降雨� Precipitation of the wettest quarter	0.3		7.5	25.0
bio17	最干燥季节降雨� Precipitation of the driest quarter				1.1
bio18	最暖季度降水量 Precipitation of the warmest quarter	4.5	34.0
bio19	最冷季度降水量 Precipitation of the coldest quarter			13.5
ABT	年生物温� Annual bio-temperature	0.4
PER	潜在蒸散玆�br/>Potential evapotranspiration rate	32.9	11.6	27.4	9.0
WI	温暖指数 Warmth index		3.8	13.2	0.4

下载: 导出CSV

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