Environmental DNA (eDNA) metabarcoding approaches to surveillance have great potential for advancing biodiversity monitoring and fisheries management. For eDNA metabarcoding, having a genetic reference sequence identified to fish species is vital to reduce detection errors. Detection errors will increase when there is no reference sequence for a species or when the reference sequence is the same between different species at the same sequenced region of DNA. These errors will be acute in high biodiversity systems like the Mekong River Basin, where many fish species have no reference sequences and many congeners have the same or very similar sequences. Recently developed tools allow for inspection of reference database coverage and the sequence similarity between species. These evaluation tools provide a useful pre-deployment approach to evaluate the breadth of fish species richness potentially detectable using eDNA metabarcoding. Here we combined established species lists for the Mekong River Basin, resulting in a list of 1345 fish species, evaluated the genetic library coverage across 23 peer-reviewed primer pairs, and measured the species specificity for one primer pair across four genera to demonstrate that coverage of genetic reference libraries is but one consideration before deploying an eDNA metabarcoding surveillance program. This analysis identifies many of the eDNA metabarcoding knowledge gaps with the aim of improving the reliability of eDNA metabarcoding applications in the Mekong River Basin. Genetic reference libraries perform best for common and commercially valuable Mekong fishes, while sequence coverage does not exist for many regional endemics, IUCN data deficient, and threatened fishes.

Predictive models are widely used to investigate relationships between the distribution of fish diversity, abundance, and the environmental conditions in which they inhabit, and can guide management actions and conservation policies. Generally, the framework to model such relationships is established; however, which models perform best in predicting fish diversity and abundance remain unexplored in the Mekong River Basin. Here, we evaluated the performance of six single statistical models namely Generalized Linear Model, Classification and Regression Tree, Artificial Neural Network, k-Nearest Neighbor, Support Vector Machine and Random Forest in predicting fish species richness and abundance in the Lower Mekong Basin. We also identified key variables explaining variability and assessed the variable’s sensitivity in prediction of richness and abundance. Moreover, we explored the usefulness of an ensemble modeling approach and investigated if this approach improved model performance. Our results indicated that, overall, the six single statistical models successfully predicted the fish species richness and abundance using 14 geo-hydrological, physicochemical and climatic variables. The Random Forest model consistently out-performed all single statistical models for predicting richness (R² = 0.85) and abundance (R² = 0.77); whereas, Generalized Linear Model performed the worst of all models (R² = 0.60 and 0.56 for richness and abundance). The most important predictors of variation in both richness and abundance included water level, distance from the sea and alkalinity. Additionally, dissolved oxygen, water temperature and total nitrate were important predictors of species richness, while conductivity was important for fish abundance. We found that species richness increased with increasing water level, dissolved oxygen and water temperature, but decreased with increasing distance from the sea, alkalinity and total nitrate. Fish abundance increased with conductivity, but decreased with increasing distance from the sea, water level and alkalinity. Finally, our results highlighted the usefulness of ensemble modeling (R² = 0.90 and 0.85 for richness and abundance) for providing better predictive power than any of the six single statistical models. Our results can be used to support Mekong River management, particularly fisheries in the context of contemporary regional and global changes.

The Mekong River is one of the world’s largest rivers and has an annual captured fish production of about 2.3 million tonnes, equivalent to around 11 billion USD. Although the Mekong provides important ecological and socioeconomic benefits to millions of people, it is facing intensive change due to anthropogenic stressors. Therefore, it is necessary to understand the changes to the spatiotemporal fish communities to inform sustainable fisheries management. Here, we aimed to characterize patterns of the fish communities and identify the ecological status of each fish community using daily catch data from 2007 to 2018 at 25 monitoring sites in the Lower Mekong Basin (LMB). The collected data were classified by a self-organizing map into four main groups. Group 4 represented the lower Vietnam Mekong Delta (VMD), while groups 1, 2, and 3 were subdivided into subgroups 1a (upper LMB), 1b (upper and middle LMB), 2a (Mekong River below the Khone Falls and Sesan River), 2b (Mekong River below the Khone Falls and Sekong, Sesan and Srepok (3S) Rivers), 3a (Floodplain-Tonle Sap and Songkhram) and 3b (upper VMD). Among the 571 species recorded, 119 were identified as indicator species. Based on the abundance and biomass comparison curves, the fish community of 2b was in a healthier condition with a positive W-statistic value while the rest had a negative W-statistic value. The highest species richness and diversity were observed in 3a and 2b, so these subgroups deserve high management and conservation priority. Likewise, 1a should also be considered as a high priority area since it harbors several endangered and long-distant migratory fishes. It was also noticed that the fish communities of groups 3 and 4, located far from the hydropower dams, remained mostly unchanged compared to those of groups 1 and 2, close to the mainstream and tributary dams in the upper LMB and 3S Rivers.

Inland fisheries management in Cambodia has undergone two major policy reforms over the last two decades. These reforms led to the abolishment of a century-old commercial fishing lot system in 2012 and the establishment of new fish sanctuary and community fishing areas. However, the status of fisheries and fish assemblages following the reforms is not well understood. Here, we investigated the temporal changes in fish catch weight and fish assemblage structure for the period 1995–2000 before fishing lot abolishment (BLA) and for the period 2012–2015 after the removal of all fishing lots (after lot abolishment-ALA) using time-series fish catch data recorded from the Tonle Sap Lake (TSL), one of the world largest inland fisheries. We found (i) mean catch trends vary seasonally, with stable catch trends during the BLA and decreasing catch trends during the ALA and (ii) significant shifts in fish assemblage composition, notably a shift from large-bodied, migratory, and/or predatory species during the BLA toward more short-distance migratory and/or floodplain, small-bodied species during the ALA. Fishing lot abolishment coincided with substantial changes to floodplain habitats and increases in fishing pressure, threatening TSL fish stocks. Flow alterations caused by dams and climate change may exacerbate the problem. Therefore, to realize the fisheries reform objectives, it is imperative to strengthen the fisheries’ governance and management system, including effective law enforcement, institutional strengthening, improved planning, cooperation, and coordination as well as clearly defined roles and responsibilities among concerned stakeholders at all levels.