Hänfling Bernd, Lawson Handley Lori, Read Daniel S, Hahn Christoph, Li Jianlong, Nichols Paul, Blackman Rosetta C, Oliver Anna, Winfield Ian J
Evolutionary and Environmental Genomics Group (@EvoHull), School of Biological, Biomedical and Environmental Sciences, University of Hull (UoH), Cottingham Road, Hull, HU6 7RX, UK.
Centre for Ecology & Hydrology (CEH), Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK.
Mol Ecol. 2016 Jul;25(13):3101-19. doi: 10.1111/mec.13660. Epub 2016 Jun 1.
Organisms continuously release DNA into their environments via shed cells, excreta, gametes and decaying material. Analysis of this 'environmental DNA' (eDNA) is revolutionizing biodiversity monitoring. eDNA outperforms many established survey methods for targeted detection of single species, but few studies have investigated how well eDNA reflects whole communities of organisms in natural environments. We investigated whether eDNA can recover accurate qualitative and quantitative information about fish communities in large lakes, by comparison to the most comprehensive long-term gill-net data set available in the UK. Seventy-eight 2L water samples were collected along depth profile transects, gill-net sites and from the shoreline in three large, deep lakes (Windermere, Bassenthwaite Lake and Derwent Water) in the English Lake District. Water samples were assayed by eDNA metabarcoding of the mitochondrial 12S and cytochrome b regions. Fourteen of the 16 species historically recorded in Windermere were detected using eDNA, compared to four species in the most recent gill-net survey, demonstrating eDNA is extremely sensitive for detecting species. A key question for biodiversity monitoring is whether eDNA can accurately estimate abundance. To test this, we used the number of sequence reads per species and the proportion of sampling sites in which a species was detected with eDNA (i.e. site occupancy) as proxies for abundance. eDNA abundance data consistently correlated with rank abundance estimates from established surveys. These results demonstrate that eDNA metabarcoding can describe fish communities in large lakes, both qualitatively and quantitatively, and has great potential as a complementary tool to established monitoring methods.
生物体通过脱落的细胞、排泄物、配子和腐烂物质不断地将DNA释放到其环境中。对这种“环境DNA”(eDNA)的分析正在彻底改变生物多样性监测。对于单一物种的靶向检测,eDNA优于许多已确立的调查方法,但很少有研究调查eDNA在多大程度上能反映自然环境中生物体的整个群落。我们通过与英国现有的最全面的长期刺网数据集进行比较,研究了eDNA是否能够获取大湖泊中鱼类群落准确的定性和定量信息。沿着深度剖面样带、刺网站点以及在英格兰湖区的三个大型深水湖泊(温德米尔湖、巴森斯韦特湖和德文特湖)的岸边采集了78个2升水样。通过对线粒体12S和细胞色素b区域进行eDNA宏条形码分析来检测水样。使用eDNA在温德米尔湖历史记录的16个物种中检测到了14个,而在最近的刺网调查中只检测到了4个物种,这表明eDNA在检测物种方面极其灵敏。生物多样性监测的一个关键问题是eDNA是否能够准确估计物种丰度。为了验证这一点,我们将每个物种的序列读数数量以及通过eDNA检测到某一物种的采样位点比例(即位点占有率)用作丰度的替代指标。eDNA丰度数据与既定调查中的等级丰度估计值始终相关。这些结果表明,eDNA宏条形码分析能够在定性和定量方面描述大湖泊中的鱼类群落,并且作为现有监测方法的补充工具具有巨大潜力。