Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstr. 190, CH-8057, Zürich, Switzerland.
Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, CH-8600, Dübendorf, Switzerland.
Nat Commun. 2020 Jul 17;11(1):3585. doi: 10.1038/s41467-020-17337-8.
The alarming declines of freshwater biodiversity call for efficient biomonitoring at fine spatiotemporal scales, such that conservation measures be grounded upon accurate biodiversity data. Here, we show that combining environmental DNA (eDNA) extracted from stream water samples with models based on hydrological first principles allows upscaling biodiversity estimates for aquatic insects at very high spatial resolution. Our model decouples the diverse upstream contributions to the eDNA data, enabling the reconstruction of taxa distribution patterns. Across a 740-km basin, we obtain a space-filling biodiversity prediction at a grain size resolution of 1-km long stream sections. The model's accuracy in matching direct observations of aquatic insects' local occurrence ranges between 57-100%. Our results demonstrate how eDNA can be used for high-resolution biodiversity assessments in rivers with minimal prior knowledge of the system. Our approach allows identification of biodiversity hotspots that could be otherwise overlooked, enabling implementation of focused conservation strategies.
淡水生物多样性的惊人减少呼吁在精细的时空尺度上进行有效的生物监测，以便保护措施建立在准确的生物多样性数据基础上。在这里，我们展示了将从溪流水样中提取的环境 DNA（eDNA）与基于水文学基本原理的模型相结合，可以以非常高的空间分辨率扩大对水生昆虫生物多样性的估计。我们的模型分离了对 eDNA 数据的多种上游贡献，从而能够重建分类群的分布模式。在一个 740 公里的流域中，我们在 1 公里长的溪流段的粒度分辨率上获得了空间填充的生物多样性预测。该模型在匹配水生昆虫局部出现范围的直接观测方面的准确性在 57-100%之间。我们的结果表明，在对系统知之甚少的河流中，eDNA 如何用于高分辨率的生物多样性评估。我们的方法可以识别可能被忽视的生物多样性热点，从而能够实施有针对性的保护策略。
