立地尺度植被重建模型MARCO POLO的应用分析——以柴达木盆地为例

立地尺度植被定量重建是对利用孢粉进行古气候和古环境研究的一个重要补充。一些新的定量重建模型相继提出,但其适用性仍需进一步验证。以柴达木盆地24个样方表土孢粉及4个湖泊表层沉积孢粉为研究对象,利用MARCO POLO模型重建立地尺度植被盖度并分析该模型的适用性。研究结果表明,由于地形开阔且风力较大,干旱区的大型湖泊沉积物中往往容易富集超代表性孢粉,使这些孢粉的区域信号增强,从而降低孢粉类型筛选的正确率。若湖泊与样方间的距离较近,湖泊表层孢粉能够更好地反映样方所在区域的孢粉特征,则有利于提高孢粉类型筛选的正确率。MARCO POLO模型中的孢粉代表性R值是将孢粉含量转化为植被盖度的关键参数。由于受到孢粉自身生产、传播、保存以及外部环境等多方面因素的影响,不同样方的R值可能差异很大。因此在运行该模型前需保证R值能够准确反映立地尺度中各孢粉类型与植被盖度的关系。仅在以柴达木盆地为代表的干旱区对该模型进行检验,今后仍需要在不同植被或气候区进行深入研究以全面了解该模型的适用条件。

Problems associated with applicability of the stand-scale vegetation reconstruction model MARCO POLO: A case study in the Qaidam Basin

Stand-scale vegetation reconstruction is an important component of pollen-based paleoclimate and paleoenvironment research. Recently, several new quantitative reconstruction models have been reported, the applicability of which needs to be validated before these models can be widely used. In this study, our aim was to assess the applicability of the MARCO POLO model in an arid region (the Qaidam Basin). Top soil pollen collected from 24 plots and surface pollen collected from four lakes were used as "local" and "regional" pollen records, respectively. Our analyses demonstrated that lake size is one of the important factors that influence the reliability of reconstruction. Large lakes in arid regions tend to accumulate over-represented pollen types, leading to higher percentages of these pollen types in lake sediments than in soil samples. The over-represented pollen will probably be identified as regionally derived pollen by the model and incorrectly excluded from the "local" pollen records. Distance between lake and plot can also have a prominent effect. If they are close to each other, lake surface pollen can better reflect the vegetation composition of the plot, which will contribute to obtaining more robust results from present/absence analyses. Pollen representation (R) is a key parameter for the conversion of pollen percentage to vegetation coverage. R can vary considerably among different plots because it is strongly influenced by pollen productivity, dispersal, preservation, and many other external factors. Therefore, it is crucial to estimate R values correctly for the study region. To gain a more comprehensive understanding of the applicability of the MARCO POLO model, further studies need to be conducted in regions with diverse climates or vegetation.