Determination of the limiting nutrient of phytoplankton is critical to the lake eutrophication management. The average value of total nitrogen/total phosphorus (TN/TP) ratio is widely used to determine the limiting nutrient; while it suffers from the risk of the incorrect description of data and neglecting dynamics of the nutrient limitation. A probabilistic method was thereby proposed in this study to explore dynamics of nutrient limitation, including (a) indicator definition as the probability of TN/TP ratio failing in Redfield ratio line (PFR), indicating the possibility of TN limitation, to improve a probabilistic measure for the nutrient limitation; (b) Bayesian ANOVA analysis for posterior distributions of different treatments; and (c) dynamics determination as PFRs to show dynamics of nutrient limitation. Lake Xingyun in Southwestern China was taken as a case to explore the interannual and seasonal dynamics of the nutrient limitation. According to modeling results, we deducted that (a) for the interannual dynamics, the limiting nutrient shifted from TP to TN; and (b) for the seasonal dynamics, TN and TP were co-limiting. Deductions were further confirmed by the observed data. With the proposed probabilistic method, the co-limitation of TN and TP was identified for the seasonal dynamics; while using the average ratio solely denied the possibility of co-limitation. The current study also revealed that, due to neglecting the interannual and seasonal dynamics of nutrient limitation, the average ratio might mislead the eutrophication management strategy by recommending reducing TN and TP concentration together. The proposed probabilistic method demonstrated that TN was the limiting nutrient during the growing season of the phytoplankton in recent years and actions should focus on the TN concentration reduction. (C) 2017 Elsevier B.V. All rights reserved.

Lake eutrophication has become a worldwide challenge, and the empirical chlorophyll a-total phosphorus (Chla-TP) relationship provides a management target for TP concentrations. Neglecting the dynamics of the relationship at the lake-specific scale would mislead the eutrophication control strategy. The Bayesian hierarchical model (BHM) is a flexible tool to explore dynamics of the Chla-TP relationship and improves the overall estimation accuracy by partial pooling of data. In this study, we used the BHMto show the spatial and seasonal dynamics of the Chla-TP relationship in one of themost eutrophic lakes in China (Lake Dianchi). We defined an indicator (the Chla/TP ratio, CPR), to represent the susceptibility of Chla to TP. We conducted a model selection process and used the CPR-TP curves to show the spatial and seasonal dynamics of the ChlaTP relationships. We determined that the wind caused the spatial dynamics due to the horizontal transport of phytoplankton, while the water temperature and the percentage of soluble reactive phosphorus led to the seasonal dynamics via increasing the growth rate of phytoplankton. These findings helped the eutrophication control in Lake Dianchi. We found that compared with the strategy to decrease the TP concentration, decreasing the susceptibility is expected to be more effective. Finally, we concluded that exploring the dynamics of the Chla-TP relationship provided an important basis for eutrophication control at the lake-specific scale.

Denitrification community in wetland plays an important role in nitrogen removal. The present study investigated the seasonal and spatial dynamics of denitrification rate and nirS-denitrifier communities and the potential influential factors in a large wetland system treating polluted river water. Wetland denitrification rate and the abundance, richness, diversity and composition of nirS-denitrifier community were found to vary with season and sampling site. Both wetland denitrification rate and denitrifier community were related to plant type. Wetland soils and sediments differed greatly in either denitrification rate or denitrifier community structure. Wetland generally had lower denitrification rate and denitrifier abundance in summer than in spring and winter. Denitrification rate showed no direct correlation to denitrifier abundance but was positively correlated to denitrifier diversity. Denitrification rate could be mediated by denitrifier community structure. Moreover, Spearman rank correlation analysis suggested that denitrification rate was significantly correlated to sediment/soil ammonia, nitrate, nitrite, total phosphorus and pH, while denitrifier abundance was significantly correlated to total phosphorus and temperature. Nitrite, total nitrogen, total organic carbon, and the ratio of total organic carbon to total nitrogen showed significant correlations with wetland denitrifier diversity, while ammonia, nitrate, total nitrogen and total phosphorus might have important roles in shaping wetland denitrifier community structure. In addition, for each wetland sediment or soil, 0.8-46.2% of the retrieved nirS sequences could be related to the sequences from cultivated denitrifiers. Dechloromonas-like denitrifiers were more abundant in wetland sediments than in wetland soils.

Plants play an important role as sinks for or indicators of semivolatile organic pollutants, however most studies have focused on terrestrial plants and insufficient information has been obtained on aquatic plants to clarify the accumulation of organic pollutants via air-to-leaf vs. water-to-leaf pathways. The presence of p, p'-dichlorodiphenyldichloroethylene (p, p'-DDE), hexachlorobenzene (HCB), 15 polycyclic aromatic hydrocarbons (PAHs), and 9 substituted PAHs (s-PAHs), including oxy-PAHs and sulfur-PAHs, in 10 submerged and emergent plants collected from Lake Dianchi was analyzed in this study. Relatively low concentrations of p, p'-DDE (ND to 2.22 ngig wet weight [ww]) and HCB (0.24-0.84 ng/g ww) and high levels of PAHs (46-244 ng/g ww) and s-PAHs (6.0-46.8 ng/g ww) were observed in the aquatic plants. Significantly higher concentrations of most of the compounds were detected in the leaves of the submerged plants than in those of the emergent plants. The percentages of concentration difference relative to the concentrations in the submerged plants were estimated at 55%, 40%, 10%-69% and 0.5% 79% for p, p'-DDE, HCB, PAHs, and s-PAHs, respectively. The percentages were found to increase significantly with an increase in log Kow, suggesting that the high level of phytoaccumulation of pollutants in aquatic plants is due to hydrophobicity-dependent diffusion via the water-to-leaf pathway and the mesophyll morphology of submerged plants. (C) 2018 Elsevier Ltd. All rights reserved.

Targeting nonpoint source (NPS) pollution hot spots is of vital importance for placement of best management practices (BMPs). Although physically-based watershed models have been widely used to estimate nutrient emissions, connections between nutrient abatement and compliance of water quality standards have been rarely considered in NPS hotspot ranking, which may lead to ineffective decision-making. It's critical to develop a strategy to identify priority management areas (PMAs) based on water quality response to nutrient load mitigation. A water quality constrained PMA identification framework was thereby proposed in this study, based on the simulation-optimization approach with ideal load reduction (ILR-SO). It integrates the physically-based Soil and Water Assessment Tool (SWAT) model and an optimization model under constraints of site-specific water quality standards. To our knowledge, it was the first effort to identify PMAs with simulation-based optimization. The SWAT model was established to simulate temporal and spatial nutrient loading and evaluate effectiveness of pollution mitigation. A metamodel was trained to establish a quantitative relationship between sources and water quality. Ranking of priority areas is based on required nutrient load reduction in each sub-watershed targeting to satisfy water quality standards in waterbodies, which was calculated with genetic algorithm (GA). The proposed approach was used for identification of PMAs on the basis of diffuse total phosphorus (TP) in Lake Dianchi Watershed, one of the three most eutrophic large lakes in China. The modeling results demonstrated that 85% of diffuse TP came from 30% of the watershed area. Compared with the two conventional targeting strategies based on overland nutrient loss and instream nutrient loading, the ILR-SO model identified distinct PMAs and narrowed down the coverage of management areas. This study addressed the urgent need to incorporate water quality response into PMA identification and showed that the ILR-SO approach is effective to guide watershed management for aquatic ecosystem restoration.

Ammonia oxidation, performed by both ammonia oxidizing bacteria (AOB) and archaea (AOA), is an important step for nitrogen removal in constructed wetlands (CWs). However, little is known about the distribution of these ammonia oxidizing organisms in CWs and the associated wetland environmental variables. Their relative importance to nitrification in CWs remains still controversial. The present study investigated the seasonal dynamics of AOA and AOB communities in a free water surface flow CW (FWSF-CW) used to ameliorate the quality of polluted river water. Strong seasonality effects on potential nitrification rate (PNR) and the abundance, richness, diversity and structure of AOA and AOB communities were observed in the river water treatment FWSF-CW. PNR was positively correlated to AOB abundance. AOB (6.76 x 10(5)-6.01 x 10(7) bacterial amoA gene copies per gram dry sed-iment/soil) tended to be much more abundant than AOA (from below quantitative PCR detection limit to 9.62 x 10(6) archaeal amoA gene copies per gram dry sediment/soil). Both AOA and AOB abundance were regulated by the levels of nitrogen, phosphorus and organic carbon. Different wetland environmental variables determined the diversity and structure of AOA and AOB communities. Wetland AOA communities were mainly composed of unknown species and Nitrosopumilus-like organisms, while AOB communities were mainly represented by both Nitrosospira and Nitrosomonas. (C) 2018 Elsevier B.V. All rights reserved.

Worldwide there has been deterioration of lakeshore habitat and increasing eutrophication. These stresses have impacted littoral macroinvertebrate communities. However, bioassessment and rehabilitation have been largely carried out offshore, and the inshore macroinvertebrates have received less attention especially in shallow plateau lakes. In this study, we compared inshore and offshore macroinvertebrate communities in a shallow plateau lake, Lake Dianchi, China. The environmental parameters determining the distribution of macroinvertebrates were analyzed with partial redundancy analysis. Our results showed that macroinvertebrate communities differed significantly between inshore and offshore. Taxonomic richness was much higher inshore than offshore, due to higher habitat heterogeneity. By contrast, both density and biomass inshore were significantly lower than those of offshore. Generally, vegetation and substrate type were the key environmental parameters shaping macroinvertebrate communities. Eutrophication exerted great effect on offshore communities, while its impacts on inshore communities varied spatially. Shoreline degradation and seasonal eutrophication effects resulted in the limited density and biomass of inshore communities. Our results emphasized the significance of inshore habitats for macroinvertebrates in Lake Dianchi, and provided important implications for bioassessment and ecological rehabilitation in shallow lakes.

China is an agricultural country with the largest population in the world. However, intensification of droughts and floods has substantial impacts on agricultural production. For effective agricultural disaster management, it is significant to understand and quantify the influence of droughts and floods on crop production. Compared with droughts, the influence of floods on crop production and a comprehensive evaluation of effects of droughts and floods are given relatively less attention. The impact of droughts and floods on crop production is therefore investigated in this study, considering spatial heterogeneity with disaster and yield datasets for 1949-2015 in China mainland. The empirical relationships between drought and flood intensity and yield fluctuation for grain, rice, wheat, maize and soybean are identified using a Bayesian hierarchical model. They are then used to explore what social-economic factors influenced the grain sensitivity to droughts and floods by the Pearson's coefficient and locally weighted regression (LOSEE) plots. The modeling results indicate that: (a) droughts significantly reduce grain yields in 28 of 31 provinces and obvious spatial variability in drought sensitivity exists, with Loess Plateau having highest probability of crop failure caused by droughts; (b) floods significantly reduce grain yield in 20 provinces, while show positive effect in the northwestern and southwestern China; (c) the spatial patterns of influence direction of droughts and floods on rice, maize and soybean are consistent with the grain's results; and (d) promoting capital investments and improving access to technical inputs (fertilizer, pesticide, and irrigation) can help effectively buffer grain yield lose from droughts.

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM. (C) 2018 Elsevier B.V. All rights reserved.

Habitat is of great importance in, determining the trophic transfer of pollutants in freshwater ecosystems; however, the major factors influencing chemical trophodynamics in pelagic and benthic food webs remain unclear. This study investigated the levels of p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), polycyclic aromatic hydrocarbons (PAHs), 3 and substituted PAHs (s-PAHs) in 2 plankton species, 6 invertebrate species, and 10 fish species collected from Lake Dianchi in southern China. Relatively high concentrations of PAHs and s-PAHs were detected with total concentrations of 11.4-1400 ng/g wet weight (ww) and 5.3-115 ng/g ww, respectively. Stable isotope analysis and stomach content analysis were applied to quantitatively determine the trophic level of individual organisms and discriminate between pelagic and benthic pathways, and the trophodynamics of the detected compounds in the two food webs were assessed. P,p'-DDE was found to exhibit relatively higher trophic magnification rate in the pelagic food web than in the benthic food web. In contrast, PAHs and s-PAHs exhibited greater dilution rates along the trophic levels in the pelagic food web. The lower species differences of pollutants accumulated in benthic organisms compared to pelagic organisms is attributable to extra uptake via ingested sediment in benthos. The average uptake proportions of PAHs and s-PAHs via ingested sediment in benthic biotas were estimated to be 31-77%, and that of p,p'-DDE was 46%. The uptake routes are of importance for assessing the trophic magnification potentials of organic pollutants, especially in eutrophic freshwater ecosystems.

The industrial sector is a major source of China's major CO2 emissions. In 2012, the emissions embedded in the final-use-induced intermediate CO2 flows contributed to approximately 91.2% of the total emissions. Hence, controlling CO2 emissions along supply chains could have a significant contribution in achieving the climate mitigation goal to which the Chinese government has been committed. In this study, we first extracted final-use induced CO2 transfer networks from input-output tables, and then applied the power-of-pull approach to the networks to identify the key sectors significantly affecting the CO2 emissions of each targeted sector's supply chains. Our results showed that each sector itself has significant power of pulling their emissions. Energy and raw material providers have played an essential role in pulling other sectors' emissions in the past years. The results of all sectors can easily construct a network reflecting the system's characteristics. And the power-of-pull approach could easily be integrated with the structural path analysis. Our proposed approach has the potential in helping policy making by offering a new perspective.

The distribution of archaeal community and the associated environmental variables in constructed wetland (CW), especially in free water surface flow CW (FWSF-CW), remain poorly understood. The present study explored the spatial and temporal dynamics of archaeal community in an FWSF-CWused for surface water treatment and evaluated the driving environmental variables. The archaeal density varied considerably among sites and seasons, ranging from 3.37 x 10(8) to 3.59 x 10(9) 16S rRNA gene copies per gram dry sediment/soil. The archaeal population density was adversely affected by high temperatures and tended to be lower during summer than during spring and winter. Moreover, considerable spatio-temporal variations of archaeal richness, diversity and community structure also occurred in the FWSF-CW. Higher nutrient contents correlated with a lower archaeal richness and diversity. Nitrate and carbon/nitrogen ratiowere found to play important roles in shaping the overall archaeal community structure. Euryarchaeota and Bathyarchaeota were the dominant archaeal phyla in wetland sediments, while Thaumarchaeota tended to be dominant in wetland soils. In addition, the wetland archaeal community was related to vegetation type. (C) 2017 Elsevier B.V. All rights reserved.

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood.characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses. (C) 2017 Elsevier B.V. All rights reserved.

Practical and optimal reduction of watershed loads under deep uncertainty requires sufficient search alternatives and direct evaluation of robustness. These requirements contribute to the understanding of the tradeoff between cost and robustness; while they are not well addressed in previous studies. This study thereby (a) uses preconditioning technique in Evolutionary Algorithm to reduce unnecessary search space, which enables a sufficient search; and (b) derives Robustness Index (RI) as a second-tier optimization objective function to achieve refined solutions (solved by GA) that address both robustness and optimality. Uncertainty-based Refined Risk Explicit Linear Interval Programming is used to generate alternatives (solved by Controlled elitist NSGA-II). The robustness calculation error is also quantified. Proposed approach is applied to Lake Dianchi, China. Results demonstrate obvious improvement in robustness after conducting sufficient search and negative robustness-optimality trade-offs, and provides a detailed characteristic of robustness that can serve as references for decision-making.

This study developed a framework for combining multi-regional input-output analysis and network indicators to assess the interregional CO2 flows in China. The interregional CO2 flows of eight regions were calculated and visualized based on a multiregional input-output (MRIO) model for China. The focus of the research was intermediate use. The results of the network indicators showed that refined petroleum, coke, nuclear fuel and chemical products (07), and basic metals and fabricated metal sectors (09) played key roles in the complex networks. and these sectors in most regions controlled a large share of CO2 transfer by functioning as key hubs and authorities. They along with commerce, transport, storage, and post (16) acted as agents that brokered the CO2 flows within and between regions. The roles of some other industrial sectors were also identified, e.g., construction (15) functioned as the largest authority. The results demonstrated the importance and effectiveness of network indicators for identifying the characteristics of CO2 emissions embedded in the domestic supply chain, and provided new information relevant to policy implementation.

Lake eutrophication is associated with excessive anthropogenic nutrients (mainly nitrogen (N) and phosphorus (P)) and unobserved internal nutrient cycling. Despite the advances in understanding the role of external loadings, the contribution of internal nutrient cycling is still an open question. A dynamic mass-balance model was developed to simulate and measure the contributions of internal cycling and external loading. It was based on the temporal Bayesian Hierarchical Framework (BHM), where we explored the seasonal patterns in the dynamics of nutrient cycling processes and the limitation of N and P on phytoplankton growth in hyper-eutrophic Lake Dianchi, China. The dynamic patterns of the five state variables (Chla, TP, ammonia, nitrate and organic N) were simulated based on the model. Five parameters (algae growth rate, sediment exchange rate of N and P, nitrification rate and denitrification rate) were estimated based on BHM. The model provided a good fit to observations. Our model results highlighted the role of internal cycling of N and P in Lake Dianchi. The internal cycling processes contributed more than external loading to the N and P changes in the water column. Further insights into the nutrient limitation analysis indicated that the sediment exchange of P determined the P limitation. Allowing for the contribution of denitrification to N removal, N was the more limiting nutrient in most of the time, however, P was the more important nutrient for eutrophication management. For Lake Dianchi, it would not be possible to recover solely by reducing the external watershed nutrient load; the mechanisms of internal cycling should also be considered as an approach to inhibit the release of sediments and to enhance denitrification. (C) 2017 Elsevier Ltd. All rights reserved.

A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system investment and probability of constraint satisfaction, which were selected from 23 sets of Pareto solutions out of 120 solution sets. Decision-makers can select optimal decisions from the solutions above in accordance with the actual conditions of different sub-watersheds under various engineering measures. The relationship between system investment and risk demonstrated that system investment increased rapidly when the probability level of constraint satisfaction was higher than 0.9, but it reduced significantly if appropriate risk was permitted. Evaluation of robustness of the optimal scheme indicated that the Pareto solution obtained from the model provided the ideal option, since the solutions were always on the Pareto frontier under various distributions and mean values of the random parameters. The application of the multi-objective chance-constrained programming to optimize the reduction of watershed nutrient loads in Lake Qilu indicated that it is also applicable to other environmental problems or study areas that contain uncertainties.

The present study investigated the abundance, richness, diversity, and community composition of denitrifiers (based on nirS and nosZ genes) in the stratified water columns and sediments in eutrophic Dianchi Lake and mesotrophic Erhai Lake using quantitative PCR assay and high-throughput sequencing analysis. Both nirS- and nosZ denitrifiers were detected in waters of these two lakes. Surface water showed higher nosZ gene density than bottom water, and Dianchi Lake waters had larger nirS gene abundance than Erhai Lake waters. The abundance of sediment nirS- and nosZ denitrifiers in Dianchi Lake was larger than that in Erhai Lake. nirS richness and diversity and nosZ richness tended to increase with increasing sediment layer depth in both lakes. The distinct structure difference of sediment nirS- and nosZ denitrifier communities was found between in Dianchi Lake and Erhai Lake. These two lakes also differed greatly in water denitrifier community structure. Moreover, phylogenetic analysis indicated the presence of several different groups of nirS- or nosZ denitrifiers in both lakes. The novel nirS denitrifiers were abundant in both Dianchi Lake and Erhai Lake, while most of the obtained nosZ sequences could be affiliated with known genera.

Nutrients loading reduction in watershed is essential for lake restoration from eutrophication. The efficient and optimal decision-making on loading reduction is generally based on water quality modeling and the quantitative identification of nutrient sources at the watershed scale. The modeling process is influenced inevitably by inherent uncertainties, especially by uncertain parameters due to equifinality. Therefore, the emerging question is: if there is parameter uncertainty, how to ensure the robustness of the optimal decisions? Based on simulation-optimization models, an integrated approach of pattern identification and analysis of robustness was proposed in this study that focuses on the impact of parameter uncertainty in water quality modeling. Here the pattern represents the discernable regularity of solutions for load reduction under multiple parameter sets. Pattern identification is achieved by using a hybrid clustering analysis (i.e., Ward-Hierarchical and K-means), which was flexible and efficient in analyzing Lake Bali near the Yangtze River in China. The results demonstrated that urban domestic nutrient load is the most potential source that should be reduced, and there are two patterns for Total Nitrogen (TN) reduction and three patterns for Total Phosphorus (TP) reduction. The patterns indicated different total reduction of nutrient loads, which reflect diverse decision preferences. The robust solution was identified by the highest accomplishment with the water quality at monitoring stations that were improved uniformly with this solution. We conducted a process analysis of robust decision-making that was based on pattern identification and uncertainty, which provides effective support for decision making with preference under uncertainty. (C) 2017 Elsevier B.V. All rights reserved.

Anaerobic ammonium-oxidizing (anammox) process can play an important role in freshwater nitrogen cycle. However, the distribution of anammox bacteria in freshwater lake and the associated environmental factors remain essentially unclear. The present study investigated the temporal and spatial dynamics of sediment anammox bacterial populations in eutrotrophic Dianchi Lake and mesotrophic Erhai Lake on the Yunnan Plateau (southwestern China). The remarkable spatial change of anammox bacterial abundance was found in Dianchi Lake, while the relatively slight spatial shift occurred in Erhai Lake. Dianchi Lake had greater anammox bacterial abundance than Erhai Lake. In both Dianchi Lake and Erhai Lake, anammox bacteria were much more abundant in summer than in spring. Anammox bacterial community richness, diversity, and structure in these two freshwater lakes were subjected to temporal and spatial variations. Sediment anammox bacterial communities in Dianchi Lake and Erhai Lake were dominated by Candidatus Brocadia and a novel phylotype followed by Candidatus Kuenenia; however, these two lakes had distinct anammox bacterial community structure. In addition, trophic status determined sediment anammox bacterial community structure.