Distance-decay equations of antibiotic resistance genes across freshwater reservoirs.

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR). This is because our model is based on the 'law of limiting factors', which reduces influence of unmeasured factors that reduce the efficacy of the LSR method. DD equations generated from the 99th QR model for ARGs were 'Sall = 90.03e-0.01Dall' in water and 'Sall = 92.31e-0.011Dall' in sediment. The 99th QR model was less impacted by uneven sample sizes, resulting in a better quantification of ARGs dissemination. Within an individual reservoir, the 99th QR model demonstrated that there is no dispersal limitation of ARGs at this smaller spatial scale. The QR method not only allows for construction of robust DD equations that better display dissemination of organisms and genes across ecosystems, but also provides new insights into the biogeography exhibited by key parameters, as well as the interactions between organisms and environment.

Data-driven discoveries on widespread contamination of freshwater reservoirs by dominant antibiotic resistance genes.

The propagation of antibiotic resistance genes (ARGs) in freshwater reservoirs threatens ecosystem security and human health, and has attracted increasing attention. A series of recent research articles on ARGs provides a unique opportunity for data-driven discoveries in this emerging field. Here, we mined data from a total of 290 samples from 60 reservoirs worldwide with a data-driven framework (DD) developed to discover geographical distribution, influencing factors and pollution hotspots of ARGs in freshwater reservoirs. Most data came from Asia and Europe where nine classes of ARGs were most frequently detected in reservoirs with multi-drug resistance and sulfonamide resistance genes prevailing. Factors driving distribution of reservoir ARGs differed between reservoir waters and sediments, and interactions among these factors had linear or nonlinear enhancement effects on the explanatory power of ARG distribution. During the cold season, small-sized reservoir waters rich in organic carbon, mobile genetic elements (MGEs) and antibiotics had a higher pollution potential of ARGs; during the spring drought, sediments in large reservoirs located in densely populated areas were more conducive to dissemination of ARGs due to their richness in antibiotics and MGEs. Thus, distribution pattern of ARG pollution hotspots in reservoir waters and sediments varies greatly depending on the differences of internal and external factors. From the "One Health" perspective, this widespread contamination of freshwater reservoirs by ARGs we discovered through the DD framework should be a push to promote integrated research across regions and disciplines. Especially the human - food-chain - ecosystem interface needs an improved understanding of ARG contamination mechanisms and targeted monitoring and evaluation systems should be developed to maintain all ecosystem services in freshwater reservoirs as well as to safeguard human health.

Global meta-analysis of microplastic contamination in reservoirs with a novel framework.

Microplastic contamination in reservoirs is receiving increasing attention worldwide. However, a holistic understanding of the occurrence, drivers, and potential risks of microplastics in reservoirs is lacking. Building on a systematic review and meta-analysis of 30 existing publications, we construct a global microplastic dataset consisting of 440 collected samples from 43 reservoirs worldwide which we analyze through a framework of Data processing and Multivariate statistics (DM). The purpose is to provide comprehensive understanding of the drivers and mechanisms of microplastic pollution in reservoirs considering three different aspects: geographical distribution, driving forces, and ecological risks. We found that microplastic abundance varied greatly in reservoirs ranging over 2-6 orders of magnitude. Small-sized microplastics (< 1 mm) accounted for more than 60% of the total microplastics found in reservoirs worldwide. The most frequently detected colors, shapes, and polymer types were transparent, fibers, and polypropylene (polyester within aquatic organisms), respectively. Geographic location, seasonal variation and land-use type were main factors influencing microplastic abundance. Detection was also dependent on analytical methods, demonstrating the need for reliable and standardized methods. Interaction of these factors enhanced effects on microplastic distribution. Microplastics morphological characteristics and their main drivers differed between environmental media (water and sediment) and were more diverse in waters compared to sediments. Similarity in microplastic morphologies decreased with increasing geographic distance within the same media. In terms of risks, microplastic pollution and potential ecological risk levels are high in reservoirs and current policies to mitigate microplastic pollution are insufficient. Based on the DM framework, we identified temperate/subtropical reservoirs in Asia as potential high-risk areas and offer recommendations for analytical methods to detect microplastics in waters and sediments. This framework can be extended and applied to other multi-scale and multi-attribute contaminants, providing effective theoretical guidance for reservoir ecosystems pollution control and management.

Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications.

Investigation of seasonal variations of water quality parameters is essential for understanding the mechanisms of structural changes in aquatic ecosystems and their pollution control. Despite the ongoing rise in scientific production on spatiotemporal distribution characteristics of water quality parameters, such as total nitrogen (TN) in reservoirs, attempts to use published data and incorporate them into a large-scale comparison and trends analyses are lacking. Here, we propose a framework of Data extraction, Data grouping and Statistical analysis (DDS) and illustrate application of this DDS framework with the example of TN in reservoirs. Among 1722 publications related to TN in reservoirs, 58 TN time-series data from 19 reservoirs met the analysis requirements and were extracted using the DDS framework. We performed statistical analysis on these time-series data using Dynamic Time Warping (DTW) combined with agglomerative hierarchical clustering as well as Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Three patterns of seasonal TN dynamics were identified. In Pattern V-Sum, TN concentrations change in a "V" shape, dropping to its lowest value in summer; in Pattern P-Sum, TN increases in late summer/early fall before decreasing again; and in Pattern P-Spr, TN peaks in spring. Identified patterns were driven by phytoplankton growth and precipitation (Pattern V-Sum), nitrate wet deposition and agricultural runoff (Pattern P-Sum), and anthropogenic discharges (Pattern P-Spr). Application of the DDS framework has identified a key bottleneck in assessing the dynamics of TN - low data accessibility and availability. Providing an easily accessible data sharing platform and increasing the accessibility and availability of raw data for research will facilitate improvements and expand the applicability of the DDS framework. Identification of additional spatiotemporal patterns of water quality parameters can provide new insights for more comprehensive pollution control and management of aquatic ecosystems.

Linking reservoir ecosystems research to the sustainable development goals.

Reservoirs account for about 10% of the freshwater stored in lakes worldwide. These reservoirs are home to 'reservoir ecosystems', that is, the aquatic and non-aquatic interactive ecosystems associated with artificial lakes where water is stored, typically behind a dam, for human purposes. While reservoir ecosystems provide various ecosystem services for sustainable development, their significance in research and policy has not been well understood and not well defined in the 2030 United Nation's (UN) Agenda for Sustainable Development. To advance understanding of reservoir ecosystems and their impact on policy, here we provide an overview of research on reservoir ecosystems and link it to UN SDGs and their Targets. Based on 5280 articles published in the last three decades, we applied network visualization to construct a framework for research addressing reservoir ecosystems. The framework covers four major themes: (1) ecosystem structure and function, (2) environmental pollution and stress effects, (3) climate impacts and ecological feedbacks, and (4) ecosystem services and management. We have found that sustainable reservoir ecosystems synergistically support 121 Targets of UN SDGs (71% of all). Reservoir ecosystems have both negative and positive implications for 15 targets (9%) and negative trade-offs for only 3 targets (2%). Thirty SDG Targets (18%) are unrelated to sustainable reservoir ecosystems. The synergies and trade-offs exist in three fields, securing basic material needs (SDGs 2, 6, 7, 14 and 15), pursuing common human well-being (SDGs 1, 3, 4, 5, 8 and 10), and coordinating sustainable governance policies (SDGs 9, 11, 12, 13, 16 and 17). Exploring these linkages allows better integration of reservoir ecosystems into the UN SDGs framework and guides sustainable management of reservoir ecosystems for sustainable development.

Sustainability management of short-lived freshwater fish in human-altered ecosystems should focus on adult survival.

Fish populations globally are susceptible to endangerment through exploitation and habitat loss. We present theoretical simulations to explore how reduced adult survival (age truncation) might affect short-lived freshwater fish species in human-altered contemporary environments. Our simulations evaluate two hypothetical "average fish" and five example fish species of age 1 or age 2 maturity. From a population equilibrium baseline representing a natural, unaltered environment we impose systematic reductions in adult survival and quantify how age truncation affects the causes of variation in population growth rate. We estimate the relative contributions to population growth rate arising from simulated temporal variation in age-specific vital rates and population structure. At equilibrium and irrespective of example species, population structure (first adult age class) and survival probability of the first two adult age classes are the most important determinants of population growth. As adult survival decreases, the first reproductive age class becomes increasingly important to variation in population growth. All simulated examples show the same general pattern of change with age truncation as known for exploited, longer-lived fish species in marine and freshwater environments. This implies age truncation is a general potential concern for fish biodiversity across life history strategies and ecosystems. Managers of short-lived, freshwater fishes in contemporary environments often focus on supporting reproduction to ensure population persistence. However, a strong focus on water management to support reproduction may reduce adult survival. Sustainability management needs a focus on mitigating adult mortality in human-altered ecosystems. A watershed spatial extent embracing land and water uses may be necessary to identify and mitigate causes of age truncation in freshwater species. Achieving higher adult survival will require paradigm transformations in society and government about water management priorities.

Biodiversity increases the productivity and stability of phytoplankton communities.

Global biodiversity losses provide an immediate impetus to elucidate the relationships between biodiversity, productivity and stability. In this study, we quantified the effects of species richness and species combination on the productivity and stability of phytoplankton communities subject to predation by a single rotifer species. We also tested one mechanism of the insurance hypothesis: whether large, slow-growing, potentially-defended cells would compensate for the loss of small, fast-growing, poorly-defended cells after predation. There were significant effects of species richness and species combination on the productivity, relative yield, and stability of phytoplankton cultures, but the relative importance of species richness and combination varied with the response variables. Species combination drove patterns of productivity, whereas species richness was more important for stability. Polycultures containing the most productive single species, Dunaliella, were consistently the most productive. Yet, the most species rich cultures were the most stable, having low temporal variability in measures of biomass. Polycultures recovered from short-term negative grazing effects, but this recovery was not due to the compensation of large, slow-growing cells for the loss of small, fast-growing cells. Instead, polyculture recovery was the result of reduced rotifer grazing rates and persisting small species within the polycultures. Therefore, although an insurance effect in polycultures was found, this effect was indirect and unrelated to grazing tolerance. We hypothesize that diverse phytoplankton assemblages interfered with efficient rotifer grazing and that this "interference effect" facilitated the recovery of the most productive species, Dunaliella. In summary, we demonstrate that both species composition and species richness are important in driving patterns of productivity and stability, respectively, and that stability in biodiverse communities can result from an alteration in consumer functioning. Our findings underscore the importance of predator-prey dynamics in determining the relationships between biodiversity, productivity and stability in producer communities.

Damaging UV radiation and invertebrate predation: conflicting selective pressures for zooplankton vertical distribution in the water column of low DOC lakes.

In nature most organisms have to manage conflicting demands of food gathering, predator avoidance, and finding a favorable abiotic environment (oxygen, temperature, etc.) in order to maximize their fitness. In the vertical water column of lakes with high solar ultraviolet radiation (UV) and invertebrate predators, zooplankton face two particularly strong and conflicting selective pressures. During daylight hours invertebrate predators often induce an upward vertical migration of zooplankton prey while potentially damaging UV forces a downward migration. We used 2.2 m long columns suspended vertically in a lake to conduct 2x2 factorial experiments to examine patterns of depth selection behavior by zooplankton in the presence and absence of both the invertebrate predator Chaoborus and UV. We hypothesized that Chaoborus and UV both affect the distribution of zooplankton and a combination of both factors would lead to a narrowing of depth distribution. We found that when Chaoborus were present zooplankton tended to be distributed at shallower depths in the columns, while in the presence of UV they exhibited a deeper distribution. Chaoborus themselves were always found near the bottom of the columns regardless of the UV treatment. Simultaneous exposure to predators and UV resulted in a peak of zooplankton (especially Daphnia catawba) distribution at intermediate depths. In a significant number of cases, depth range was narrowed in response to Chaoborus, UV, or both.