Tropical freshwater ecosystems are some of the world's most biodiverse and productive systems where determining what sustainable exploitation of inland fisheries looks like is particularly challenging. One of the greatest obstacles to sustainable management is collecting and using quality data on fish production and yield. The biodiversity and hydro‐ecology of these systems often under open‐access governance, add to the complexity of managing them. This paper describes an integrated citizen‐science, earth observation, environmental DNA and independent survey approach to collecting fish and fisheries data, using the Cambodian Mekong as a case study.

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An understanding of the genetic composition of populations across management boundaries is vital to developing successful strategies for sustaining biodiversity and food resources. This is especially important in ecosystems where habitat fragmentation has altered baseline patterns of gene flow, dividing natural populations into smaller subpopulations and increasing potential loss of genetic variation through genetic drift. River systems can be highly fragmented by dams built for flow regulation and hydropower. We used reduced-representation sequencing to examine genomic patterns in an exploited catfish, Hemibagrus spilopterus, in a hotspot of biodiversity and hydropower development—the Mekong River basin. Our results revealed the presence of 2 highly divergent coexisting genetic lineages which may be cryptic species. Within the lineage with the greatest sample sizes, pairwise FST values, principal component analysis, and a STRUCTURE analysis all suggest that long-distance migration is not common across the Lower Mekong Basin, even in areas where flood-pulse hydrology has limited genetic divergence. In tributaries, effective population size estimates were at least an order of magnitude lower than in the Mekong mainstream indicating these populations may be more vulnerable to perturbations such as human-induced fragmentation. Fish isolated upstream of several dams in one tributary exhibited particularly low genetic diversity, high amounts of relatedness, and a level of inbreeding (GIS = 0.51) that has been associated with inbreeding depression in other outcrossing species. Our results highlight the importance of assessing genetic structure and diversity in riverine fisheries populations across proposed dam development sites for the preservation of these critically important resources.

Carbon dioxide (CO2) supersaturation in lakes and rivers worldwide is commonly attributed to terrestrial–aquatic transfers of organic and inorganic carbon (C) and subsequent, in situ aerobic respiration. Methane (CH4) production and oxidation also contribute CO2 to freshwaters, yet this remains largely unquantified. Flood pulse lakes and rivers in the tropics are hypothesized to receive large inputs of dissolved CO2 and CH4 from floodplains characterized by hypoxia and reducing conditions. We measured stable C isotopes of CO2 and CH4, aerobic respiration, and CH4 production and oxidation during two flood stages in Tonle Sap Lake (Cambodia) to determine whether dissolved CO2 in this tropical flood pulse ecosystem has a methanogenic origin. Mean CO2 supersaturation of 11,000 ± 9,000 μatm could not be explained by aerobic respiration alone. 13C depletion of dissolved CO2 relative to other sources of organic and inorganic C, together with corresponding 13C enrichment of CH4, suggested extensive CH4 oxidation. A stable isotope-mixing model shows that the oxidation of 13C depleted CH4 to CO2 contributes between 47 and 67% of dissolved CO2 in Tonle Sap Lake. 13C depletion of dissolved CO2 was correlated to independently measured rates of CH4 production and oxidation within the water column and underlying lake sediments. However, mass balance indicates that most of this CH4 production and oxidation occurs elsewhere, within inundated soils and other floodplain habitats. Seasonal inundation of floodplains is a common feature of tropical freshwaters, where high reported CO2 supersaturation and atmospheric emissions may be explained in part by coupled CH4 production and oxidation.

Striped catfish Pangasianodon hypopthalmus (Sauvage, 1878) is a flagship catfish species of the Mekong River region, a commercially valuable food fish that is important in freshwater fisheries, and a popular aquaculture species in many Asian countries. The species was assessed as “Endangered” by the International Union for Conservation of Nature (IUCN) due to range contraction and declining abundance, though the status of the species’ wild population in Cambodia, a critical habitat for the species, is not well understood. Here, we assess the population status of the striped catfish in Cambodia using multiple sources, including time-series catch data and length frequency distribution data from a commercial fishery (stationary trawl bagnet or dai) operated in the Tonle Sap River from 1998/99 to 2017/18 and larval drift data monitored in the Mekong River in Phnom Penh from 2004 to 2018. We found that there was a significant decline (R2 = 0.54, p = 0.0002) in the catch (metric tonnes) of the striped catfish from the commercial dai fishery over the last two decades. Similarly, length-based indicator analysis indicates that striped catfish mean length and abundance have both declined over the study period, raising concerns about the sustainability of river catfish fisheries. Moreover, long-term larval drift monitoring in Mekong River shows that there was a marginally significant decline in the quantity of striped catfish larvae/juvenile drifting downstream to the lower floodplain over the last decade. Changes in flood index (extent and duration of flood) in the Tonle Sap floodplain affected by the Mekong’s flow are likely key factors driving the decline of the wild populations of the striped catfish. Both larval fish abundance and floodplain fish harvests have a significant positive relationship with Mekong flow and flood extent. Indiscriminate fishing exacerbates pressures on striped catfish stocks. Therefore, actions such as maintaining natural seasonal flows (flood timing, extent, and duration) to the Tonle Sap floodplain and protecting migratory fish stocks from overharvest and habitat fragmentation are essential to the persistence of stocks of striped catfish and other large-bodied migratory fishes that utilize both the Cambodian Mekong and Tonle Sap floodplains.

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.