Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, People's Republic of China.
Observation and Research Station of Shenongjia Dajiuhu Wetland Earth Critical Zone, MNR, China University of Geosciences, Wuhan 430078, People's Republic of China.
Biol Lett. 2024 Jun;20(6):20240062. doi: 10.1098/rsbl.2024.0062. Epub 2024 Jun 26.
Diatom cell-size composition is an indicator of aquatic environmental changes but has been rarely investigated, especially in semi-terrestrial peatlands. In this study, both taxonomic composition and cell-size composition of diatoms were analysed in 41 samples from two montane peatlands, northeastern China. Redundancy analyses revealed that diatom taxonomic composition was significantly related to the depth to the water table (DWT) and Ca, while cell-size composition was significantly associated with DWT and Si. DWT was the most important factor and its sole effect explained 26.2% and 17.9% of the total variance in taxonomic composition and cell-size composition, respectively. Accordingly, diatom-based water-table transfer functions were developed based on taxonomic composition and cell-size composition, respectively. The maximum-likelihood (ML) model based on diatom taxonomic composition had the best performance, with a correlation coefficient value () of 0.78 and the root mean squared error of prediction (RMSEP) of 6.66 cm. The ML model based on cell-size composition had similar performance, with an of 0.78 and the RMSEP of 6.87 cm, suggesting that diatom cell-size composition can be a new quantitative means to track past water-table changes. This method requires further appraisal with palaeoecological data but offers a new option that deserves exploration.
硅藻细胞大小组成是水生环境变化的指示物,但很少被研究,特别是在半陆生泥炭地中。本研究分析了中国东北两个高山泥炭地的 41 个样本中的硅藻分类组成和细胞大小组成。冗余分析表明,硅藻分类组成与水位埋深(DWT)和 Ca 显著相关,而细胞大小组成与 DWT 和 Si 显著相关。水位埋深是最重要的因素,其单独作用分别解释了分类组成和细胞大小组成总方差的 26.2%和 17.9%。因此,分别基于分类组成和细胞大小组成开发了基于硅藻的水位传递函数。基于硅藻分类组成的最大似然(ML)模型表现最佳,相关系数()值为 0.78,预测均方根误差(RMSEP)为 6.66cm。基于细胞大小组成的 ML 模型具有相似的性能,其值为 0.78,RMSEP 为 6.87cm,表明硅藻细胞大小组成可以成为一种新的定量手段来追踪过去的水位变化。这种方法需要进一步结合古生态学数据进行评估,但提供了一种值得探索的新选择。