Development of an environmental DNA method for monitoring freshwater fish communities using metabarcoding

Monitoring current global biodiversity decline is essential to maintain ecosystem functioning, especially freshwater ecosystems. However, the conventional physical, acoustic and visual-based methods for monitoring biodiversity have some limitations such as morphological identification bias, recording small-bodied, rare and/or elusive species, and destructive impacts on the environment. The significant ―game-changer‖ in biodiversity monitoring is environmental DNA (eDNA) metabarcoding, which refers to the simultaneous identification of a multitude of species from environmental samples. However, developing, validating and improving eDNA-based metabarcoding monitoring methods is not trivial. Firstly, for further eDNA metabarcoding studies focusing on freshwater fish communities, two marker-specific reference databases were compiled and two metabarcoding primer pairs were rigorously tested. Subsequently, a PCR-based metabarcoding approach is applied to investigate (1) the effect of filtration method on the efficiency of eDNA capture and quantification, (2) the spatial and temporal distribution of eDNA in fish ponds, and (3) the potential of eDNA as a tool for biodiversity monitoring in diverse lakes with characterised fish faunas. The results show that the 0.8 μm filters are advocated for turbid and eutrophic water such as ponds to reduce the filtration time, the 0.45 μm filters are appropriate for clear water sampling to obtain consistent results, and the 0.45 μm Sterivex enclosed filters are suitable in situations where on-site filtration is required. Furthermore, eDNA distribution in ponds was highly localised in space and time, and 10 shore samples distributed along the full perimeter of lakes is adequate for capturing the majority of species. Lastly, this thesis provides further evidence that eDNA metabarcoding could be a powerful monitoring tool for freshwater fish communities, considerably outperforming other established survey techniques whether in species detection, relative abundance estimate or characterisation ecological fish communities. These outcomes constitute a significant advance towards a standardised and efficient assessment procedure for the ecological monitoring of aquatic ecosystems

Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter

Author Posting. © Acoustical Society of America, 2013. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 133 (2013): 1377-1386, doi:10.1121/1.4790354.This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.This work was supported by the National High Technology Research and Development Program of China (Grant No. 2012AA090901), the National Natural Science Foundation of China (Grant No. 61171147), and the State Key Laboratory of Acoustics, Chinese Academy of Sciences (Grant No. SKLOA201102)