Brandão-Dias Pedro F P, Hallack Daniel M C, Snyder Elise D, Tank Jennifer L, Bolster Diogo, Volponi Sabrina, Shogren Arial J, Lamberti Gary A, Bibby Kyle, Egan Scott P
Department of BioSciences, Rice University, Houston, Texas, USA.
Department of Civil and Environmental Engineering, University of Notre Dame, Notre Dame, Indiana, USA.
Mol Ecol Resour. 2023 May;23(4):756-770. doi: 10.1111/1755-0998.13751. Epub 2023 Jan 12.
Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish-derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium-sized particles (1-10 μm) decayed more slowly than other size classes (i.e., <1 and > 10 μm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target-specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single-target applications.
环境DNA(eDNA)分析是一种用于远程检测目标生物的强大工具。然而,从eDNA数据中获取定量和纵向信息具有挑战性,需要深入了解eDNA生态学。值得注意的是,如果eDNA的各种大小成分以不同速率衰减,并且我们能够在一个样本中分离它们,那么它们不断变化的比例可用于获取目标生物的纵向动态信息。为了检验这种可能性,我们进行了一项水生中宇宙实验,在该实验中,我们通过连续过滤分离鱼类来源的eDNA成分,以评估各种eDNA颗粒大小随时间的衰减率和变化比例。然后,我们将四种替代数学衰减模型应用于数据,构建一个预测框架来解释来自各种颗粒大小的eDNA数据。我们发现中等大小的颗粒(1 - 10微米)比其他大小类别(即<1微米和>10微米)衰减得更慢,因此随着时间的推移,它们在eDNA颗粒中所占比例越来越大。我们还观察到鲤鱼和虹鳟样本之间不同的eDNA颗粒大小分布(PSD),这表明需要特定目标的检测方法来确定起始eDNA的PSD。此外,我们发现有证据表明不同大小的eDNA颗粒并非独立衰减,随着时间的推移,颗粒大小的转换会补充较小的颗粒。尽管如此,一个颗粒大小独立衰减的简约数学模型最能解释这些数据。基于这些结果,我们提出了一个通过应用连续过滤利用eDNA数据辨别目标距离和丰度的框架,从理论上讲,该框架具有宏条形码和单目标应用。