Most techniques for pollen-based quantitative climate reconstruction use modern assemblages as a reference data set. We examine the implication of methodological choices in the selection and treatment of the reference data set for climate reconstructions using Weighted Averaging Partial Least Squares (WA-PLS) regression and records of the last glacial period from Europe. We show that the training data set used is important because it determines the climate space sampled. The range and continuity of sampling along the climate gradient is more important than sampling density. Reconstruction uncertainties are generally reduced when more taxa are included, but combining related taxa that are poorly sampled in the data set to a higher taxonomic level provides more stable reconstructions. Excluding taxa that are climatically insensitive, or systematically overrepresented in fossil pollen assemblages because of known biases in pollen production or transport, makes no significant difference to the reconstructions. However, the exclusion of taxa overrepresented because of preservation issues does produce an improvement. These findings are relevant not only for WA-PLS reconstructions but also for similar approaches using modern assemblage reference data. There is no universal solution to these issues, but we propose a number of checks to evaluate the robustness of pollen-based reconstructions.

中文翻译：

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方法决策对使用 WA-PLS 进行气候重建的影响

大多数基于花粉的定量气候重建技术都使用现代组合作为参考数据集。我们使用加权平均偏最小二乘法 (WA-PLS) 回归和欧洲末次冰期的记录来检查方法选择在气候重建参考数据集的选择和处理中的意义。我们表明使用的训练数据集很重要，因为它决定了采样的气候空间。沿气候梯度采样的范围和连续性比采样密度更重要。当包括更多的分类群时，重建的不确定性通常会降低，但是将数据集中采样不佳的相关分类群与更高的分类水平组合可以提供更稳定的重建。排除对气候不敏感的分类群，或由于花粉生产或运输中的已知偏差而在化石花粉组合中系统性地过度代表，对重建没有显着影响。然而，由于保存问题而排除过多的分类群确实会产生改善。这些发现不仅与 WA-PLS 重建有关，而且与使用现代组合参考数据的类似方法有关。这些问题没有通用的解决方案，但我们提出了一些检查来评估基于花粉的重建的稳健性。这些发现不仅与 WA-PLS 重建有关，而且与使用现代组合参考数据的类似方法有关。这些问题没有通用的解决方案，但我们提出了一些检查来评估基于花粉的重建的稳健性。这些发现不仅与 WA-PLS 重建有关，而且与使用现代组合参考数据的类似方法有关。这些问题没有通用的解决方案，但我们提出了一些检查来评估基于花粉的重建的稳健性。