Naturalis Biodiversity Center, Leiden, The Netherlands.
IBED, University of Amsterdam, Amsterdam, The Netherlands.
Mol Ecol Resour. 2024 Oct;24(7):e14000. doi: 10.1111/1755-0998.14000. Epub 2024 Jul 23.
Describing living community compositions is essential to monitor ecosystems in a rapidly changing world, but it is challenging to produce fast and accurate depiction of ecosystems due to methodological limitations. Morphological methods provide absolute abundances with limited throughput, whereas metabarcoding provides relative abundances of genes that may not correctly represent living communities from environmental DNA assessed with morphological methods. However, it has the potential to deliver fast descriptions of living communities provided that it is interpreted with validated species-specific calibrations and reference databases. Here, we developed a quantitative approach to retrieve from metabarcoding data the assemblages of living large benthic foraminifera (LBF), photosymbiotic calcifying protists, from Indonesian coral reefs that are under increasing anthropogenic pressure. To depict the diversity, we calculated taxon-specific correction factors to reduce biological biases by comparing surface area, biovolume and calcite volume, and the number of mitochondrial gene copies in seven common LBF species. To validate the approach, we compared calibrated datasets of morphological communities from mock samples with bulk reef sediment; both sample types were metabarcoded. The calibration of the data significantly improved the estimations of genus relative abundance, with a difference of ±5% on average, allowing for comparison of past morphological datasets with future molecular ones. Our results also highlight the application of our quantitative approach to support reef monitoring operations by capturing fine-scale processes, such as seasonal and pollution-driven dynamics, that require high-throughput sampling treatment.
描述生物群落组成对于监测快速变化世界中的生态系统至关重要，但由于方法学的限制，很难快速准确地描绘生态系统。形态学方法提供绝对丰度，但通量有限，而代谢组学提供的基因相对丰度可能无法正确反映通过形态学方法评估的环境 DNA 中的生物群落。然而，如果与经过验证的物种特异性校准和参考数据库相结合进行解释，它有可能快速描述生物群落。在这里，我们开发了一种定量方法，从印度尼西亚珊瑚礁的代谢组学数据中提取受人为压力影响日益增大的大型底栖有孔虫（LBF）和光合钙化原生生物的生物群落。为了描绘多样性，我们通过比较七种常见 LBF 物种的表面积、生物体积和方解石体积以及线粒体基因拷贝数，计算了分类群特异性校正因子，以减少生物偏差。为了验证该方法，我们比较了模拟样本和大块珊瑚礁沉积物的形态群落的校准数据集；这两种样本类型都进行了代谢组学分析。数据的校准显著提高了属相对丰度的估计，平均差异为±5%，从而可以比较过去的形态数据集和未来的分子数据集。我们的结果还强调了我们的定量方法在支持珊瑚礁监测操作方面的应用，该方法可以捕捉到需要高通量采样处理的精细尺度过程，如季节性和污染驱动的动态变化。
