Data for "Environmental DNA metabarcoding reflects rank-based abundance of fish"

Inferring organism abundance or biomass from sequence counts derived from metabarcoding data has been an exciting but contentious concept in the biomonitoring community for some time. Although demonstrating strong correlation with fish abundance measures has proven difficult, many researchers have assumed that quantitative metabarcoding data can at least provide broad-scale ranking of abundance. However, field validations of this assumption remain scarce. Here, we analyse read counts of eDNA data derived from 20 lakes with overlapping species composition to show a good relationship with within-species across-site rank abundance categories for fishes. The benefit of this approach is the potential for rapid assessment of rank abundance for multiple species, including smaller species which are often missed by conventional methods such as gillnetting. This approach will assist practitioners taking a species-based approach to freshwater habitat management in lakes worldwide.

长期以来，从元条形码（metabarcoding）测序数据的序列计数中推断生物丰度或生物量，一直是生物监测领域中一个备受关注但颇具争议的概念。尽管已有研究证明，要验证该数据与鱼类丰度指标间存在强相关性难度极大，但诸多研究者仍假定，定量元条形码数据至少可实现丰度的大范围排序。然而，针对这一假定的野外验证研究仍较为匮乏。本研究针对20个存在物种组成重叠的湖泊的环境DNA（eDNA）测序读段计数数据展开分析，结果显示，其与鱼类物种内跨位点的丰度排序类别间存在良好关联。该方法的优势在于，可快速评估多种鱼类的丰度排序，其中也包括传统刺网法等常规监测手段常遗漏的小型鱼类。该方法将为全球范围内采用物种导向型策略开展湖泊淡水生境管理的从业者提供助力。