Data from: Plant DNA metabarcoding of lake sediments: how does it represent the contemporary vegetation

Metabarcoding of lake sediments have been shown to reveal current and past biodiversity, but little is known about the degree to which taxa growing in the vegetation are represented in environmental DNA (eDNA) records. We analysed composition of lake and catchment vegetation and vascular plant eDNA at 11 lakes in northern Norway. Out of 489 records of taxa growing within 2 m from the lake shore, 17-49% (mean 31%) of the identifiable taxa recorded were detected with eDNA. Of the 217 eDNA records of 47 plant taxa in the 11 lakes, 73% and 12% matched taxa recorded in vegetation surveys within 2 m and up to about 50 m away from the lakeshore, respectively, whereas 16% were not recorded in the vegetation surveys of the same lake. The latter include taxa likely overlooked in the vegetation surveys or growing outside the survey area. The percentages detected were 61, 47, 25, and 15 for dominant, common, scattered, and rare taxa, respectively. Similar numbers for aquatic plants were 88, 88, 33 and 62%, respectively. Detection rate and taxonomic resolution varied among plant families and functional groups with good detection of e.g. Ericaceae, Roseaceae, deciduous trees, ferns, club mosses and aquatics. The representation of terrestrial taxa in eDNA depends on both their distance from the sampling site and their abundance and is sufficient for recording vegetation types. For aquatic vegetation, eDNA may be comparable with, or even superior to, in-lake vegetation surveys and may therefore be used as an tool for biomonitoring. For reconstruction of terrestrial vegetation, technical improvements and more intensive sampling is needed to detect a higher proportion of rare taxa although DNA of some taxa may never reach the lake sediments due to taphonomical constrains. Nevertheless, eDNA performs similar to conventional methods of pollen and macrofossil analyses and may therefore be an important tool for reconstruction of past vegetation.已有研究表明，湖泊沉积物的元条形码（Metabarcoding）技术可揭示当前与过往的生物多样性，但对于植被中生长的类群在环境DNA（eDNA）记录中的表征程度，目前仍知之甚少。我们对挪威北部11个湖泊的湖泊及集水区植被组成，以及维管植物eDNA开展了分析。在距湖岸2米范围内生长的489条类群记录中，17%~49%（平均31%）的可识别类群通过eDNA被成功检测。 在11个湖泊的47个植物类群对应的217条eDNA记录中，分别有73%和12%的记录与距湖岸2米范围内、以及距湖岸约50米范围内的植被调查类群相匹配；另有16%的记录未在对应湖泊的植被调查中被检出，此类类群可能是在植被调查中被遗漏，或是生长于调查区域之外。 不同优势度等级的类群检测占比分别为：优势类群61%、常见类群47%、散生类群25%、稀有类群15%；水生植物的对应检测占比则依次为88%、88%、33%和62%。 检测率与分类学分辨率在不同植物科与功能群间存在差异，其中杜鹃花科（Ericaceae）、蔷薇科（Rosaceae）、落叶乔木、蕨类、石松类植物以及水生类群的eDNA检测效果良好。 环境DNA记录中陆地类群的表征情况，同时受其距采样点的距离与自身丰度的影响，且该技术足以实现植被类型的记录。对于水生植被而言，eDNA技术可媲美甚至优于湖泊原位植被调查方法，因此可作为生物监测的有效工具。 若要重建陆地植被群落，需通过技术改进与更密集的采样来提升稀有类群的检出比例——不过部分类群的DNA可能因埋藏学限制而无法抵达湖泊沉积物中。尽管存在上述局限，eDNA技术的整体表现与传统的花粉及大化石分析方法相当，因此可作为重建过往植被的重要技术手段。