Trophic status and lake depth play important roles in determining the nutrient-chlorophyll a relationship: Evidence from thousands of lakes globally

Decreasing denitrification rates poses a challenge to further decline of nitrogen concentration in Lake Taihu, China

Nitrogen (N) concentrations in many lakes have decreased substantially in recent years due to external load reduction to mitigate harmful algal blooms. However, little attention has been paid to the linkage between the lakes' nitrogen removal efficiency and improved water quality in lakes, especially the variation of denitrification rate (DNR) under decreasing N concentrations. To understand the efficiency of N removal under improving water quality and its influence on the N control targets in Lake Taihu, a denitrification model based on in situ experimental results was developed and long-term (from 2007 to 2022) water quality and meteorological observations were used to estimate DNR and relate it to the amount of N removal (ANR) from the lake. The concentration of total nitrogen (TN) in Lake Taihu decreased from 3.28 mg L-1 to 1.41 mg L-1 from 2007 to 2022 but the reduction showed spatial heterogeneity. The annual mean DNR decreased from 45.6 μmol m-2 h-1 to 4.2 μmol m-2 h-1, and ANR decreased from 11.85×103 t yr-1 to 1.17×103 t yr-1 during the study years. N budget analysis suggested that the amount of N removed by denitrification accounted for 23.3 % of the external load in 2007, but decreased to only 4.0 % in 2022. Thus, the contribution of N removal by internal N cycling decreased significantly as water quality improved. Notably, the proportion of ANR in winter to total ANR increased from 14 % in 2007 to 23 % in 2022 due to warming. This could potentially lead to N deficiencies in spring and summer, thus limiting the availability of N to phytoplankton. A TN concentration of less than 1.0 mg L-1 in the lake and 1.5 mg L-1 in the inflowing lake zones in spring contribute to local N-limitation in Lake Taihu for cyanobacteria control. Our study revealed a general pattern that N removal efficiency decreases with improved water quality, which is instructive for eutrophic lakes in nitrogen management.

Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (Rrs) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS Rrs, with R2, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 μg/L, 2.95 μg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R2 = 0.76, MAE = 2.12 μg/L, RMSE = 2.51 μg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

Impacts of oxbow lake evolution on sediment microbial community structure in the Yellow River source region

Oxbow lake formation and evolution have significant impacts on the fragile Yellow River Basin ecosystem. However, the effects of different oxbow lake evolutionary stages on sediment microbial community structure are not yet understood comprehensively. Therefore, microbial community structure in three stages of oxbow lake succession, namely, lotic lake (early stage), semi-lotic lake (middle stage), and lentic lake (late stage), was investigated in the present study in the Yellow River Basin on the Qinghai-Tibet Plateau. Amplicon sequencing was employed to reveal differences in microbial community diversity and composition. The bacterial and fungal communities in sediment were significantly different among the three succession stages and were driven by different environmental factors. In particular, bacterial community structure was influenced primarily by nitrate-nitrogen (N), microbial biomass phosphorus, and total carbon (C) and organic C in the early, middle, and late stages, respectively. Conversely, fungal community structure was influenced primarily by ammonium-N in the early stage and by moisture content in the middle and late stages. However, the predicted functions of the microbial communities did not exhibit significant differences across the three succession stages. Both bacteria and fungi were influenced significantly by stochastic factors. Homogeneous selection had a high relative contribution to bacteria community assembly in the middle stage, whereas the relative contributions of heterogeneous selection processes to fungal community assembly increased through the three stages. As succession time increased, the total number of keystone species increased gradually, and the late succession stage had high network complexity and the highest network stability. The findings could facilitate further elucidation of the evolution mechanisms of oxbow lake source area, high-altitude river evolution dynamics, in addition to aiding a deeper understanding of the long-term ecological evolution patterns of source river ecosystems.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Anoxia begets anoxia: A positive feedback to the deoxygenation of temperate lakes

Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep-water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (i.e., very low DO) during a given summer begets increasingly severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth, and subsequent phytoplankton decomposition can fuel heterotrophic respiration, resulting in increased spatial extent and duration of anoxia. However, while the individual relationships in this feedback are well established, to our knowledge, there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the mechanisms necessary to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed anoxia begets anoxia feedback. Lakes in the dataset span a broad range of surface area (1-126,909 ha), maximum depth (6-370 m), and morphometry, with a median time-series duration of 30 years at each lake. Using linear mixed models, we found support for each of the positive feedback relationships between anoxia, phosphorus concentrations, chlorophyll a concentrations, and oxygen demand across the 656-lake dataset. Likewise, we found further support for these relationships by analyzing time-series data from individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake-specific characteristics: For example, we found that surface phosphorus concentrations were more positively associated with chlorophyll a in high-phosphorus lakes, and oxygen demand had a stronger influence on the extent of anoxia in deep lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.

Relative impact of environmental variables on the lake trophic state highlights the complexity of eutrophication controls

For the effective management of lakes apart from defining and monitoring their current state it is crucial to identify environmental variables that are mostly responsible for the nutrient input. We used interpretative machine learning to investigate the environmental parameters that influence the lake's trophic state and recognize their patterns. We analysed the influence of the 25 environmental variables on the commonly used trophic state indicators values: total phosphorus (TP), Chlorophyll-a (Chl-a) and Secchi depth (SD) of 60 lakes located in the Central European Lowlands. We attempted to delineate the lakes into groups due to the influence of common prevailing environment variable/variables on the water trophic state reflected by each indicator. The results indicated that the relative impact of environmental variables on the lake trophic state has an individual hierarchy unique for each indicator. The most important are variables related to catchment impact on the lake, Ohle ratio (L. catchment area/L. area) for TP and Schindler ratio (L. area + L. catchment area)/L. volume for Chl-a and SD. There are also few variables strongly influential only for small sub-groups of lakes that stand out: lake maximum depth, catchment slope steepness expressed by the height standard deviation. The methods used in the study enabled the assessment of the character of the influence of the environmental variables on the indicator value and revealed that most essential variables (Ohle ratio for TP and Schindler ratio for Chl-a and SD) have bimodal distribution with a clear threshold value. These findings contribute to a better understanding of the drivers shaping the lake trophic status and have implication for planning effective management strategies.

Irreversible community difference between bacterioplankton generalists and specialists in response to lake dredging

Understanding response of bacterioplankton community responsible for maintaining ecological functions of aquatic ecosystems to environmental disturbance is an important subject. However, it remains largely unclear how bacterioplankton generalists and specialists respond to dredging disturbance. Illumina MiSeq sequencing and statistical analyses were used to evaluate landscape patterns, evolutionary potentials, environmental adaptability, and community assembly processes of generalists and specialists in response to dredging in eutrophic Lake Nanhu. The Proteobacteria and Actinobacteria dominated bacterioplankton communities of generalists and specialists, and abundances of Proteobacteria decreased and Actinobacteria increased after dredging. The generalists displayed higher phylogenetic distance, richness difference, speciation rate, extinction rate, and diversification rate as well as stronger environmental adaptation than that of specialists. In contrast, the specialists rather than generalists showed higher community diversity, taxonomic distance, and species replacement as well as closer phylogenetic clustering. Stochastic processes dominated community assemblies of generalists and specialists, and stochasticity exhibited a larger effect on community assembly of generalists rather than specialists. Our results emphasized that lake dredging could change landscape patterns of bacterioplankton generalists and specialists, whereas the short-term dredging conducted within one year was unable to reverse community difference between generalists and specialists. Our findings extend our understanding of how bacterioplankton generalists and specialists responding to dredging disturbance, and these findings might in turn call on long-term dredging for better ecological restoration of eutrophic lakes.