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The performance of single-and multi-proxy transfer functions (testate amoebae, bryophytes, vascular plants) for reconstructing mire surface wetness and pH

机译:单一和多代理转移功能(测试变形虫,苔藓植物,维管植物)的性能,用于重建泥潭表面湿度和pH值

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摘要

Peatlands are widely exploited archives of paleoenvironmental change. We developed and compared multiple transfer functions to infer peatland depth to the water table (DWT) and pH based on testate amoeba (percentages, or presence/absence), bryophyte presence/absence, and vascular plant presence/absence data from sub-alpine peatlands in the SE Swiss Alps in order to 1) compare the performance of single-proxy vs. multi-proxy models and 2) assess the performance of presence/absence models. Bootstrapping cross-validation showing the best performing single-proxy transfer functions for both DWT and pH were those based on bryophytes. The best performing transfer functions overall for DWT were those based on combined testate amoebae percentages, bryophytes and vascular plants; and, for pH, those based on testate amoebae and bryophytes. The comparison of DWT and pH inferred from testate amoeba percentages and presence/absence data showed similar general patterns but differences in the magnitude and timing of some shifts. These results show new directions for paleoenvironmental research, 1) suggesting that it is possible to build good-performing transfer functions using presence/absence data, although with some loss of accuracy, and 2) supporting the idea that multi-proxy inference models may improve paleoecological reconstruction. The performance of multi-proxy and single-proxy transfer functions should be further compared in paleoecological data.
机译:泥炭地是被广泛利用的古环境变化档案。我们开发并比较了多种传递函数,以根据遗留的睾丸阿米巴(百分比或存在/不存在),苔藓植物的存在/不存在以及脉管植物存在/不存在的数据来推断泥炭地的地下水位(DWT)和pH在瑞士瑞士阿尔卑斯山中进行以下操作:1)比较单代理模型与多代理模型的性能,以及2)评估存在/不存在模型的性能。自举交叉验证表明,对于DWT和pH而言,最佳性能的单代理传递函数是基于苔藓植物的交叉验证。总的来说,DWT的最佳传递函数是基于组合的睾丸变形虫百分比,苔藓植物和维管植物的传递函数。对于pH,基于睾丸变形虫和苔藓植物的pH。由睾丸阿米巴百分比和存在/不存在数据推断出的DWT和pH值的比较显示出相似的一般模式,但某些变化的幅度和时机不同。这些结果显示了古环境研究的新方向,1)暗示尽管存在一些准确性的损失,但仍可以使用存在/不存在的数据来建立性能良好的传递函数,以及2)支持多代理推理模型可以改善的想法古生态重建。在古生态数据中应进一步比较多代理和单代理传递函数的性能。

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