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We explore the mutual dependencies and interactions among different groups of species of the plankton population, based on an analysis of the long-term field observations carried out by our group in the North–West coast of the Bay of Bengal. The plankton community is structured into three groups of species, namely, non-toxic phytoplankton (NTP), toxic phytoplankton (TPP) and zooplankton. To find the pair-wise dependencies among the three groups of plankton, Pearson and partial correlation coefficients are calculated. To explore the simultaneous interaction among all the three groups, a time series analysis is performed. Following an Expectation Maximization (E-M) algorithm, those data points which are missing due to irregularities in sampling are estimated, and with the completed data set a Vector Auto-Regressive (VAR) model is analyzed. The overall analysis demonstrates that toxin-producing phytoplankton play two distinct roles: the inhibition on consumption of toxic substances reduces the abundance of zooplankton, and the toxic materials released by TPP significantly compensate for the competitive disadvantages among phytoplankton species. Our study suggests that the presence of TPP might be a possible cause for the generation of a complex interaction among the large number of phytoplankton and zooplankton species that might be responsible for the prolonged coexistence of the plankton species in a fluctuating biomass.  相似文献
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森林作为陆地生态系统最大的碳库,对现在及未来的气候变化、碳平衡都具有重要影响。而对影响森林植被碳库的自然和非自然因素进行研究更是对增强森林的碳汇作用,继而改善生态环境状况意义重大。现有的森林动态模型虽然可以很好的模拟碳储量各影响因子之间的联系,但研究往往集中于小尺度从单一影响因素着手,且由于确定模型输入变量和参数的复杂性,使得这些模型在区域甚至更大尺度上的应用存在着一些困难。因此,运用VAR模型,以陕西省为例,构建森林植被碳储量与病虫害发生面积、木材产量、森林火灾面积、森林抚育面积、人工更新造林面积、降水和温度之间的动态关系,来验证该模型在省级尺度条件下的区域森林植被碳储量影响因素分析中的可行性。结果表明:各变量在5%的显著性水平下呈一阶单整序列并具有长期稳定的均衡关系,VAR模型也通过了平稳性检验满足运行的前提条件。通过脉冲响应和方差分解分析可知,森林病虫害、木材产量对陕西省森林植被碳储量呈现出很明显的负作用,并且贡献度很高,分别为5.61%和4.52%;森林抚育、人工更新造林对碳储量的影响存在一定的滞后期;火灾、温度和降水的冲击给碳储量带来的影响均不明显。模型较好的模拟了各影响因素对陕西省碳储量的影响,且具有一定的现实意义,因此,该模型可应用于省级尺度条件下的区域森林植被碳储量影响因素分析。  相似文献
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