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黑河下游绿洲胡杨种群空间点格局及其潜在分布研究

A Study on The Spatial Point Pattern And Potential Distribution of Populus Euphratica Populations in The Oasis of Lower Reaches of Heihe River

【作者】 张兰

【导师】 张华;

【作者基本信息】 西北师范大学 , 自然地理学, 2015, 硕士

【摘要】 以黑河下游绿洲胡杨种群作为研究对象,通过野外样地观测与室内数据分析,采用空间点格局分析方法,将微观的空间分布格局与宏观的地理环境结合起来,对不同河流梯度和不同离河距离的胡杨种群在不同尺度下的空间分布格局进行了分析;结合高分辨率Quick Bird影像数据,分析了胡杨种群种内(胡杨种群不同龄级)、种间(胡杨种群与柽柳种群)间的空间关联性;应用最大熵模型和地理信息系统,选取158个胡杨种群实际分布点数据,结合与胡杨生长有关的6个气候因子、3个地形因子、2个土壤因子和1个水因子,对研究区胡杨种群分布进行了预测,并对其适宜生长区进行等级划分,分析了影响其生长分布的主要环境因子。结论如下:(1)黑河下游绿洲胡杨种群分布格局在不同空间尺度下存在明显变化,主要在中尺度5-12 m下表现为聚集分布,在小尺度0-2 m和大尺度38-40 m表现为随机分布,极少为均匀分布。这反映了胡杨种群主要在2 m以下和38 m以上尺度个体间的相互作用较弱。胡杨种群沿河流自上而下,主要表现为聚集分布,聚集强度和聚集规模呈先增大后减小的趋势。在不同的离河距离,胡杨种群也主要表现为聚集分布,且聚集的强度随离河距离增大而呈增大的趋势。沿河流向下,离河距离越远的区域,地下水河流补给量很少,胡杨分布较少,呈扩散趋势,表现为随机分布。在同一样带的同一离河距离,东河沿岸胡杨种群的聚集强度和聚集规模总体上小于西河。(2)基于高分辨率Quick Bird影像数据,对胡杨种群和柽柳种群不同尺度下的空间分布格局及空间关联性分析:胡杨和柽柳种群在较小尺度0-4 m范围内表现为均匀分布,且两者间具有显著的空间关联性,表明种内和种间在较小空间尺度范围内都存在激烈的竞争关系。当空间尺度大于某临界值时,胡杨和柽柳种群的空间分布格局却倾向于随机分布,且两者间的空间关联性也减弱,表明种群内部和种间的生态联系对空间尺度有着很强的依赖性。胡杨种群的聚集程度明显低于柽柳种群。胡杨种群各龄级在所研究的空间尺度范围内,主要呈随机分布。当L(t)>0时,最大聚集程度表现为幼龄树最强;随着植株的增大,胡杨种群的聚集分布程度下降,中龄树和老龄树主要呈随机分布。幼龄树与老龄树、中龄树与老龄树仅在尺度0-9 m范围内呈显著负关联,在其他空间尺度范围内呈不关联;幼龄树与中龄树在所研究的空间尺度0-50m范围内呈不关联。表明胡杨种群各龄级个体在空间分布上相互独立。(3)对胡杨种群的潜在分布区预测表明:胡杨种群的最适生区主要集中在黑河沿岸,面积为0.16×104 km2,而较适生区的面积为0.24×104 km2,胡杨种群核心适宜分布区的面积约占河流沿岸15 km缓冲区面积的38.32%,占黑河下游总面积的6.44%。影响胡杨种群分布的主要环境因子有地下水埋深(59.8%)、年均温(22.5%)、年均降水量(5.1%)、1月降水量(3.2%)、土壤有效含水量(2.4%)等。

【Abstract】 In this study, taking Populus euphratica population in the oasis of lower reaches of Heihe River as the research object, through field observation and indoor data analysis, using spatial point pattern analysis method, combined the micro spatial distribution pattern with macro processor geographical environment, to analyze spatial distribution pattern of P. euphratica population in different gradient and different distances from the riverat different scales; combining with the data of high resolution QuickBird image,analyzed the population spatial associations of intraspecies(P. euphratica population in differentage class) and interspecies(P. euphratica population and Tamarix chinensis population); application maximum entropy models and geographic information systems, selected 158 of P. euphratica population actual distribution point data, combined with the growth of P. euphratica six climate-related factors, three terrain factors, two soil factors and a water factor, to predicte the distribution of P. euphratica population in the study area and classifiy the level of appropriate growth areas, analysis of the main environmental factors affecting their growth distribution. Conclusions are as follows:(1)The distribution pattern of P. euphratica population in lower reaches of Heihe River at different spatial scales exist significant changes, mainly at mesoscale scale of 5-12 m were aggregated distribution, at small-scale of 0-2 m and large-scale of 38-40 m were randomly distributed, very few were uniformly distributed. It reflects that the interaction between individuals of P. euphratica population is weak mainly in less than scale of 2 m and greater than scale of 38 m.Along the river from top to bottom, P. euphratica population are mainly aggregated distribution, the aggregation intensities and scales show the first increase and then decrease. At different distances from the river, P. euphratica population are mainly aggregated distribution and aggregation intensities with distance from the river increases tended to increase. Flowing down the river, the farther the distance from the river lots, river recharge little, there is little P. euphratica can be grown in the lot, and showed a tendency to spread, P. euphratica population were random distributed. At the same belt with a defined distance from the river, the aggregation intensities and scales of P. euphratica population which along the East River on the whole are less than the West River’ s.(2)Based on high resolution QuickBird image data, analyzed the spatial distribution pattern and spatial correlation of P. euphratica and Tamarix chinensis populations at different scales: P. euphratica and Tamarix chinensis populations showed a uniform distribution within the range of 0-4 m smaller scale, and between them have significant spatial correlation, indicating that is fierce competition between intraspecies and interspecies in a small space scales. When the spatial scalelarger than a critical dimension tend to random distribution, the spatial correlation between the two is also reduced, indicating ecological linkages within and between species populations on the spatial scale has a strong dependence. The aggregation intensities of Tamarix chinensis population is higher than the P. euphratica population.P. euphratica population of each age class in the space-foot range studied mainly distributed randomly. When L(t)> 0, the maximum aggregation intensities of young age of trees was the strongest; increases with plants, the aggregation intensities of P. euphratica population decline, the middle and old age of P. euphratica population mainly distributed randomly. Young and old age of P. euphratica population, middleand old age of P. euphratica population only within the range of 0-9 m scale were significantly negatively associated, was not associated with spatial scales in the other; young and middle age of P. euphratica population were not associated within the studied spatial scales of 0-50 m. Shows that the P. euphratica population of each age class individuals are independent of each other in spatical distribution.(3)The predicted results of the potential distribution area of P. euphratica population show that: the most suitable areas of P. euphratica population mainly near the coast of Heihe River, where optimum area is 0.16 × 104 km2, representing suitable area is 0.24 × 104 km2, the core distribution area accounts for about 38.32% of buffer area along the river 15 km, accounting for about 6.44% of the total area of the lower reaches of Heihe River. The main environmental factors of affect the P. euphratica population distribution have groundwater level(59.8%), the average annual temperature(22.5%), the average annual rainfall(5.1%), January rainfall(3.2%), available soil water(2.4%) and so on.

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